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ARTICLE
The Review of Socionetwork Strategies
https://doi.org/10.1007/s12626-024-00175-1
Abstract
The Internet of Multimedia Things, or IoMT, has grown signicantly in the modern
era. Major IoMT applications, like video surveillance and image processing, are
used to monitor real-time settings in smart cities. A number of frameworks and
techniques were put forth to improve IoT solutions. IoMT services must comply
with user preferences and address security concerns, but security accuracy in IoT
image processing and video surveillance is still a problem. In this work, we pro-
vide the state-of-the-art and reviewed concerns related to security, QoE for IoMT
applications, and image processing and video surveillance in IoT systems. Lastly,
we talked about open research questions for further IoMT research as well as the
diculties and constraints of the current study.
Keywords Internet of multimedia things (IoMT) · Image processing · Quality of
experience (QoE) · Security
1 Introduction
IoT is a platform whereby embedded devices are connected to the internet in order
to exchange and collect data [1]. It gives gadgets the ability to communicate, cooper-
ate, connect, and learn from each other’s experiences much like people do. A trac
signal camera that relays information to the gateway about trac, accidents, speed
limits, and weather conditions is an example of the Internet of Things [2, 3]. If the
Municipal Corporation chose to x a certain road, it could result in heavy trac on
the highway. The data is sent to the system that checks trac throughout the entire
city. Given that this is a perceptive trac system, it can quickly identify and fore-
cast trac models through machine learning [4]. As a result, the intelligent system
may assess the circumstances, anticipate the source of the problem, and transfer the
data to other town areas that are adjacent to a relevant route via their own intelligent
Received: 10 May 2024 / Accepted: 24 September 2024
© Springer Nature Japan KK, part of Springer Nature 2024
Internet of multimedia things (IoMT): A review
Asif AliLaghari1· HangLi1· ShahidKarim2· WaheeduddinHyder3·
YinShoulin1· Abdullah AyubKhan4· Rashid AliLaghari5,6
Extended author information available on the last page of the article
etal.[full author details at the end of the article]
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The Review of Socionetwork Strategies
system. To prevent trac bottlenecks, the Trac Management System may analyze
acquired data and recommend routes throughout the work. Similarly, the framework
may use radio channels and smart devices to provide drivers with real-time regula-
tions. At that point, nearby city oces and schools can also be asked to modify their
schedules. This safety measure is linked to a free framework, which aects ongoing
supervision and is the only way to distinguish between Internet of Things apps.
The collection of protocols, interfaces, and media-related data representations
known as the “Multimedia Internet of Things (IoT)” enables advanced applications
and administrations that rely on human-to-device and device-to-device connectiv-
ity in both the physical and virtual environments [5, 6]. IoT is expanding quickly
across a number of industries, including data mining, computer vision, agriculture,
and communication. The stock market has demonstrated the signicance of IoMT
and its critical role in economic growth.
The Internet of Things (IoT) comes in a plethora of forms and categories. The
most popular ones include intelligent, brilliant houses, smart assembly, keen struc-
ture, transportation, instruction, and so forth. The newest type of watch is the keen
watch, which debuted in 2019 [9]. All throughout the world, interest is growing
yearly. Numerous large organizations have invested in this innovation and have taken
a commendable stance. For various reasons, including security, comfort, and per-
sonal fullment, smart homes are important applications [10]. These days, we use a
few IoT-enabled appliances in our houses, such as air conditioners, TVs, lights, and
cameras [11]. These items will lessen the disruptions caused by the dazzling homes
and usher in a new era of comfort and innovation. The brilliant assembling likewise
will upset the assembling rather than the old ones since it makes the works and the
representatives simpler and agreeable and expands the nature of items.
Additionally, the owners of the manufacturing lines will install sensors in the
machines to inspect them and alert the administrator when something is about to
happen in the device or when a certain percentage of the segments will be nished
[12]. By doing this, he will be able to predict the event in advance, saving time and
ensuring that the industrial facility runs continuously. IoT in the intelligent car will
help the police follow any car, monitor trac, and observe what’s going on in the city
so they can make a decision [13]. The understudy will benet from the IoT in training
by learning more and having fun while also simplifying and enhancing the learning
cycle. Furthermore, the understudies can divide their work among one another or
what we see in TTC tests, the educator shares a document with the understudies, then
the understudies open that record and step through the examination. Also, the under-
studies can grow their insight in a single dislike the days of yore you need to look
in books and libraries. Smallholders are using signicant bits of information from
the data to generate better prot from the venture—a detector for soil dampness and
supplements, controlling water consumption for the plant. IoMT (IoT-based Multi-
media) and IoT security have, up until now, generally gotten less consideration, albeit
an essential corpus of exploration has arisen [14]. In this manner, openings for extra
examination and progressions exist, especially as identied with trac video priori-
tization, virtualization of organization portability, components, and security. IoMT
utilization is presently quickly developing at dierent but generally high informa-
tion rates. IoMT frameworks also, innovations have numerous applications: medical
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The Review of Socionetwork Strategies
care, transportation, coordination, public well-being, security and surveillance, keen
matrices, shrewd urban communities, brilliant structures, and smartness energy/green
activities are among the initially targeted applications [14–16]. Some have character-
ized the IoMT applications into 5 categories: Connected Vehicles; Connected (Smart)
Cities; Connected Health; Associated Industry; and Connected Living and dealing.
Dierent types of IoT sensors and devices are used for data collection in various
applications such as Bluetooth, Low-power wide-area network (LoRaWAN), Nar-
rowband-IoT (NB-IoT), Sigfox, Wi-Fi, and Zigbee [123, 124]. The things in IoT such
as devices, beacons, access points, gateway, hubs, and sensors and these things meet
with digital world applications via an actuator, cyber-physical systems, contactless,
digital twins, geofencing, Geographic Information Systems (GIS), global positions
system (GPS), Global Navigation Satellite System (GNSS), haptics, Hardware-
Assisted Virtualization (HAV), Inertial Measurement Unit (IMU), Light Detection
and Ranging (LIDAR), mechatronics, Radio Detection and Ranging (RADAR) [125,
126]. Other hardware/software are used in IoT infrastructure such as Embedded SIM
(eSIM), Integrated Circuit Card Identier (ICCID), International Mobile Subscriber
Identity (IMSI), and Subscriber Identity Module (SOC) [127].
IoT attributes support interactive media interchanges; nevertheless, prospect and
valuable applications are data transmission hungry and delay delicate. The growth of
IoT sites requires high-speed network trac, which will improve the new plans to
meet its requirements. Multimedia in IoT needs high capacity transmission facilities
with high memory and high performance computational power to process the infor-
mation. Supportable view and sound applications incorporate crisis reaction frame-
works, trac checking, crime assessment, keen urban communities, smart homes,
healthcare, smart farming, observation frameworks, Internet of Bodies (IoB), and
Industrial IoT (IIoT) [20]. The applications Internet of Multimedia Things such as
multimedia, smart health, trac monitoring, industry, security, smart homes, trans-
portation, and smart agriculture are given in Fig. 1.
The paper will address the topic of IoMT, the state of the art of IoMT, how IoT
is used for video surveillance and image processing, how QoE is used in IoMT, and
how assessment of multimedia things will be done for accurate user feedback and
security and privacy. It also surveys challenges in merging IoMT in architectures
with their applications, such as system modication and analysis. This paper also
elaborates challenges and limitations of IoMT and open research issues are given for
future development for accurate implementation and operation of IoMT.
This paper is based on the IoMT survey and review in dierent areas and is divided
into 7 sections. Section two is based on image processing and video surveillance,
and section three is based on the Internet of Multimedia Things and QoE. Similarly,
Sect. 4 is based on the Security and Privacy of IoMT, and Sect. 5 is based on the
challenges and limitations of IoMT. Finally, in Sect. 6, we present the open research
questions in this eld.
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2 Methodology
This research work is based on the review of the Internet of Multimedia Things
(IoMT) in image processing and video surveillance, QoE assessment, security and
privacy, limitations, and open research. It depends on recent facts and gures in
IoMT. Developing and implementing IoMT technology has some limitations and
security concerns. Moreover, a System Mapping Study (SMS) and Systematic Lit-
erature Review (SLR) have focused on IoMT, and also subsections are given below.
2.1 Research questions
RQ.1 How image processing and surveillance can be done by using IoT and what are
the recent case studies?
Answer: Sect. 3 clearly explains the topic related to Image processing and Video
Surveillance in IoT.
Fig. 1 Applications of internet of multimedia things
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RQ.2 How the quality of experience (QoE) can be merged into the IoT, what are the
QoE based frameworks for IoT?
Answer: In Sect. 4, we describe QoE in IoT and compare the framework in the
table.
RQ.3 How can developers manage security and privacy issues in the IoMT?
Answer: details are given in the Sect. 5.
RQ.4 Does the developer face challenges and limitations in developing IoMT and its
applications?
Answer: details of challenges and limitations are given in the Sect. 6.
RQ.5 Are there any open research issues, and is the future of IoMT given?
Answer: open research issues and future work are discussed in Sect. 7.
2.2 Selection and non-selection criteria
Our selection depends on well-known conferences and scientic papers published
in journals. The selected papers were written in English and downloaded from the
Springer, Elsevier, and IEEE Explorer libraries. They were published from 2010 to
2024. Details of our selection and non-selection criteria are given in Table 1.
Selection criteria Non-se-
lection
criteria
During the selection of the papers, the key terms were
focused such as “IoT image processing,” “IoT video
Surveillance,” “QoE assessment in IoT” and “Security
and privacy in IoMT.” The related literature was taken
from recently published papers. We downloaded 400
papers, and later the screening method was completed,
where 25% were Elsevier, 25% were IEEE, 20% were
Springer, 15% Wiley, 5% Taylor and Francis, and the
other 10% were taken conferences and other journal
publishers. The total calculation of the papers we used in
this SLR is 127.
During
the re-
search,
we did
not
select
papers
pub-
lished
in fake
journals
or data
from
blogs
and
web-
sites.
Table 1 Describes the criteria
for paper selection and
non-selection
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2.3 Key terms searching method
The most important keywords were used during the search (“IoT image processing,”
“IoT video Surveillance,” “QoE assessment in IoT” “Security and privacy in IoMT.”
“Limitations in IoMT,” and “Open Research Issues related to IoMT”) for data extrac-
tion from google scholar and from reputed journals.
2.4 SLR steps
The SLR and SMS based on the authenticated published data:
1. During this SLR, we took data from IEEE, Springer, Elsevier, Wiley, Taylor &
Francis, and conferences.
2. Answers to critical questions are addressed.
3. Fake published work and unveried work are not included in SLR.
4. Finally, we cited those papers that referred to or data extracted from them that is
related to IoMT.
3 Image processing and video surveillance in IoT
Image processing and video surveillance have been used widely in IoT-based appli-
cations and systems. IoT, which is now becoming a new research topic among tech-
nologists to bring ease in daily life, involves the internetworking of things connecting
dierent devices that correspond to each other to perform various operations. IoT
architecture is usually based on several layers: the data center, service-oriented, and
sensor layers [21]. Each layer possesses its protocols and technologies. The sensor
and service layers involve applying technologies that use image processing tech-
niques. Image processing provides a range of camera-based sensors; the process-
ing of generated data by these cameras might direct to several types of applications
designed through IoT [21]. Video surveillance technology is an IoT-based application
system that utilizes the Internet for various purposes [22]. Image processing, video
surveillance, and IoT are all dierent technologies that may work on similar and
dierent domains individually, but combining all these now may produce more e-
cient outcomes in specic elds [23]. This video surveillance and image processing
based on IoT will improve security and security-related cycles in numerous spaces
and businesses by empowering quicker and more instant responses to any situation
[24]. Many researchers have been done on combining IoT with image processing and
video surveillance techniques in IoT systems [25, 26] as such techniques bring more
and more ease in daily life.
IoT advancements have eased medical services, but patient disease diagnosis accu-
racy is still a concern for health service providers. In this regard, Kayalvizhi et al.
developed an IoT-based model for disease detection [122]. Using IoT sensors, patient
data such as signals, text, and images were collected. Further image data was prepro-
cessed, and features were extracted using IoT-collected images using the Inception
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network and VGG16. By using a hybrid algorithm termed Mating Probability-based
Hybrid Strider Glowworm Swarm Optimization (MP-HSGSO) along with Radial
Basis Recurrent Neural Network (RBRNN) for image data with processed signals
and text for optimal feature selection and detected outcome. Simulations were carried
out by using deep learning outcomes show results with precision and accuracy were
achieved at 96% and 97%.
IoMT and image processing can be applied in the agricultural eld to get high-
quality products and reduce crop damage. It will help to overcome the damage to
crops by informing farmers about the environmental situations such as temperature,
humidity, sunlight intensity, and soil moisture crucial for crop growth as well as what
kind of eects of the fertilizer should be used [27, 28]. All this could be done by
continuously monitoring the condition of the crop by using IoT-based circuits that
may include Arduino (open source hardware that reads input and turns them into an
output), sensors for various environmental factors, and a camera that could capture
images of crops at daily intervals [29]. Images captured will then be processed to rec-
ognize and observe a range of morphological changes that usually occur because of
dierent environmental factors. And if some changes could correspond to the wors-
ening in the plants/ crops growth, then the farmer is informed immediately. All these
observations are done so that early diagnosis will help to take the necessary precau-
tions and actions to increase production and reduce the failure of crops. The follow-
ing block diagram illustrates how image processing and IoT work together, giving
more accurate results in Fig. 2.
The application of IoT, image processing and video surveillance in trac manage-
ment and trac density controls has a great and accurate outcome in managing trac
hazards. It is important to have IoT-based trac management to deal with massive
trac, mainly in the daytime. The number of video surveillance cameras amplies
in open places, resulting in rising analysis of video content done using automated
systems. These systems automatically recognize the list of trac rule violations. The
video works, and processes at pixels, object and these are observed to analyze. The
simple functions of video-based surveillance systems usually are tracking automo-
biles, studying their behaviors, predicting abnormal events, and anomaly detection
Fig. 2 ‘Block diagram of IoT and image processing’
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right before their prevalence. For this purpose, IR sensors have been installed, are
given in Fig. 3 [30].
This study pursues to broaden glyph-based devices for the ongoing video rep-
resentation covering an extensive arrangement of trac lms on complete time of
interstates. We needed to foster an IoT Based trac board and control framework
to beat the eect of trac thickness. The images have been taken and stored in the
server to contrast the real-time captured image through the camera to perceive the
density. The trac ow management with IoT is given in Fig. 4.
Image processing and video surveillance are not only applicable in trac manage-
ment, but it has much more applications based on IoT. One of the IoT-based image
processing applications is home security, using image processing algorithms and
video capturing techniques. This technique or system comprises a sensor and a com-
puterized camera alongside its dataset in the system and the cell phone. These sen-
sors are kept at the entrance of the door, which will make a camera alert to capture a
picture of an individual who tries to enter the home; it sends the picture to the data
set being put away in the system. The image is processed, and image analysis is done
to detect and recognize the person and then match that image with the data stored to
authenticate people or pets. If the captured image does not go with the image stored
in the dataset, then the system sends an alert message to the owner of the house [31].
Moreover, image processing and IoT are being applied in the toll collection sys-
tem. This system provides the fastest toll collection and vehicle management auto-
matically through image processing techniques and algorithms. The whole process
of capturing an image is done by installing CCTV at the toll plaza and capturing
every single movement to ensure that collection and vehicle trac can be adequately
managed. This system eliminates waiting for problems, automating the process [31].
IoT has made many changes in human life by bringing automation and remote
control devices. Still, image processing techniques and adding video surveillance
Fig. 3 IR sensor radiation
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methods have brought immense change in this era. It helps increase security in
homes, oces, and many public places and also brings a dierence in the eld agri-
cultural environment. Once there was time, humans recorded and analyzed videos
continuously. Still, video surveillance allows machines to do the same with more
eciency and accuracy, and all these are now possible due to the involvement of
IoT. IoT is no doubt reshaping the process of video surveillance. It not only analyzes
the data but provides deep insight security, including not only general information.
This video surveillance and image processing based on IoT will improve safety and
security-related cycles in numerous spaces and businesses by empowering quicker
and more instant responses to any situation.
The authors have proposed a data mining approach to analyze the enterprise
operations of IoMT. Initially, wavelet transform is employed as an image process-
ing approach to process the big data of stocks to get the average data. Later on, the
stability of stocks can be measured by enforcing the comparisons between leading
Fig. 4 Trac management ow diagram
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and individual stock uctuations [7]. Due to the increasing eectiveness of IoMT,
it is adopted for data acquisition at a low cost and to preserve condentiality. To do
so, the authors proposed random subsampling and chaotic convolution to get image
signals. Then, a larger image was achieved by an ensemble of the sampled images,
which the Arnold encrypts transform and diusion model. The computational cost of
these models is low and encrypted images can easily send to severs and decryption
services [8].
The researchers proposed several algorithms to measure the accuracy of visual
recognition for IoT-based smart city surveillance. The novel method proposed by Liu
et al. [32] is similar to collaborative representation-based classication (CRC), oper-
ated by checking the minimal residual between the collaborative representations with
selected and test samples. This method provides 93% accuracy of face recognition
on the ORL dataset and 83% on the extended Yale Face dataset, and 79% for Facial
Recognition Technology. The research work was provided in [33] by preserving the
appearance of the face on the changing of lights by using an ecient preprocess-
ing chain and adding principle components analysis (PCA) Kernel for incorporating
local appearance by feature extraction. Also, local ternary pattern (LTP) was intro-
duced with local texture descriptors such as local binary pattern (LBP) for less sen-
sitivity for noise in unchanging regions. The accuracy of 88.1% of face recognition
was achieved on the datasets such as Face Recognition Grand Challenge (FRGC)-
204, Expression, Accessories, and Lighting (CAS-PEAL-R1), and Extended Yale-B,
Chinese Academy of Sciences-Pose.
The novel face recognition was presented based on the local binary pattern (LBP);
the dataset used in this research is FERET, which provides an accuracy of more than
95%; this is a common complex framework and not used for real-time [34]. Gumus et
al. evaluated several face recognition methods; the feature extraction techniques used
Eigenfaces and wavelet decomposition based on the PCA [35]. Image classication
was done using vector machines (SVMs), and the classication accuracy was done
according to the increasing dimension of the training set. The 98.1% accuracy was
achieved on the ORL face dataset using the Wavelet-SVM approach with 240 images
training set. The comparison of the most accurate providing algorithm for IoT-based
smart city surveillance is given in Table 2 [36].
4 Internet of multimedia things and QoE
Multimedia is a way of communication. The eld is concerned with the PC-con-
trolled combination of text, illustrations, drawings, still and moving items/pictures or
video, movements, sound, and other media. Each sort of data that can be addressed,
put away, communicated, and measured carefully is known as Mixed media [37]. It
may be partitioned into two sorts, known as straight and non-direct classications.
Internet of Multimedia Things (IoMT) is dened as the interconnection of multi-
media devices with solid communication. It enables us to connect “things” to the
internet [38]. These things or items exchange information between them and transmit
the data to other devices and systems. Internet of multimedia things (IoMT) devices
have limited memory and processing capabilities [39]. The Internet of Multimedia
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thing (IoMT) applications are for educational institutes, security purposes, biomedi-
cal, defense, industry, hospitals, oces, and others.
There are several approaches to strengthen the IoMT, and network-on-chip
enhances the user QoE eciently [40, 41]. IoT holds multiple types of sensors and
devices, specically multimedia devices, depending upon massive data volume with
dierent requirements and characteristics compared to simple IoT [42, 43]. Simulta-
neously, real-time implementation scenarios move from intelligent trac monitoring
to hospitals with intelligent technologies. So, optimal decision-making and robust
IoMT data delivery are critical because those directly aect human lives [15, 44].
Nevertheless, IoMT is also involved in video streaming with QoE concerns. In [45],
the authors have discussed the interruption, engagement, and payload metrics and
also reported an overview of mobile users for network access, engagement, geoloca-
tion, and operating system.
IoMT is required to lead to massive bidirectional constant trac over the hazes
and fog that contain the developing organization foundation [17]. At last, the IoMT
frameworks are made out of heterogeneous fog devices and networks [18]. These
Used
procedures
Precision Simulated
databases
Real
Time
Convo-
lution
Sparse
representation
and optimal
coecient
vector [32]
83% achieved
on Extended
Yale Face
Dataset, 93%
is achieved
on Recogni-
tion accuracy
for face da-
tabase ORL,
79% Facial
Recognition
Technology
(FERET),
Aleix and
Robert (AR)
and Illumination
and Expression
(PIE) databases,
Carnegie Mel-
lon University
(CMU), ORL,
Extended Yale
B, Pose, FERET
No High
Local ter-
nary patterns
(LTP) [33]
face verica-
tion rate of
88.1% at
0.1% false
accept rate
achieved
Accessories, and
Lighting (CAS-
PEAL-R1) and
Face Recogni-
tion Grand
Challenge
(FRGC)-204
data set, Chi-
nese Academy
of Sciences-
Pose, Expres-
sion, Extended
Yale-B,
Yes Low
Local Binary
[34] Pattern
95% accuracy
was achieved
FERET
Database
No Low
SVM and
Karhunen–
Loeve trans-
form [35]
97.5%,
97.5%, 91.5%
and 90.6%
Accuracy was
achieved
ORL database No High
Table 2 Comparison of dierent
algorithms with respect to IoT
based smart city surveillance
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heterogeneous devices typically give dierent picture goals, computational inves-
tigation, and distinctive radio handset abilities involving exible recurrence range
data transfer capacities and assorted force utilization. The device and administration
variety requires IoMT frameworks to be updated with adaptable asset distribution
structures and new basic conventions to oblige the mixed media Quality of experi-
ence (QoE) [19].
The IoMT is an expansion to the IoT, where one of the prime goals is to empower
video web-based as a feature of the acknowledgment of IoT [46]. In IoMT, assets
require minimal expense low-power heterogeneous media devices can connect and
be universally available by exceptional IP addresses with a similar atmosphere as the
PCs and other systems administration devices associated with the web. The dicul-
ties presented by IoMT are like IoT, like managing a lot of data, inquiries, furthermore
computations just as some necessities [47]. In IoMT-based remote interactive media
networks, the sight and sound devices should be little-measured items prepared with
a restricted measure of force assets, which they must use eectively to expand the
network lifetime [48]. Subsequently, energy-productive techniques should have been
conceived for network regulatory methodology. Essentially, sight and sound devices
should be inserted with the application, furthermore setting mindful knowledge, with
the goal that the media content from the actual climate is just obtained when required,
subsequently limiting excess data procurement.
According to Qualinet’s white paper [49], QoE is dened as “QoE is the level of
enjoyment or disturbance of the client of an application or product. It results from
the satisfaction of their assumptions concerning the utility and delight in the appli-
cation or product in the light of the client’s character and present status.” QoE is
the assessment of satisfaction and annoyance of the customer’s experience from the
goods and services, and it is also the calculation of delight and dissatisfaction of
customers or users by watching multimedia in IoMT [50–52]. In this advance and
fast development of products and services, people now use multimedia content and
interact with advanced devices. So, in this advanced world, the challenges for provid-
ers and operators are rising. Here the problem arises of how we provide a good qual-
ity experience to our users because a good experience can connect the user with the
provider [53, 54]. Multimedia communication faces the problem of slow networking
due to bandwidth constraints that delay the network and impact multimedia quality
[55]. Many users connect, edit, store, and share multimedia data online. It becomes a
problem for cloud users when a delay in the network occurs [56]. The customers of
cloud providers are dissatised with the services. Therefore, cloud users need good
battery life and high-speed internet connection for providing multimedia content [57,
58]. To deliver good quality multimedia, the user should focus on the context they
use, like images, videos, etc. It is known as user-generated content (UGC). The QoE
is associated with UGC [59]. The purpose should be to give eective or innovative
multimedia content to maximize the users’ QoE [60].
There are many challenges of QoE in IoMT, which take more work to measure
and quantify the user’s perceptions [61]. You cannot directly observe hard to reduce
the system’s parameter space like network factors where delay, loss, and jitter prob-
lem occurs. Transmission factors where redundancy and compression problems arise
[62]. Codec factors where a lot of Codec-depending parameter problem occurs [63].
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Hard to measure and quantify the environment which may aect the user’s experi-
ence (ambient noise, quality of headset, and distance from where to display).
Researchers for multimedia services in IoT environments proposed several QoE-
based frameworks. Pal et al. [44] proposed a model to assess the relationship between
quality perception and human experience in relation smart wearable section. The
variables used quality of information (QoI), which is based on useful information
extracted from the raw data of connected wearables with smartphones, and quality
of data (QoD) is based on the precision and accuracy of captured data of smart wear-
ables. For creating the QoE model, an experiment was conducted, and 40 subjects
were invited to use ve wearable devices in free-living conditions. While proposing
the QoE model, four dierent methods for weight dimension were given: priority-
based, hybrid distribution, correlation-based distribution, and balanced weight distri-
bution. The author claims this model provides better results and correlates best to the
subjective QoE compared to previous methods.
Floris and Atzori proposed the method for assessing QoE for IoT applications
and which QoE factor has more inuence to consider for appropriate application
scenarios; the layered IoT architecture was analyzed [64]. A layered model with mul-
timedia of IoT (MIoT) was introduced to assess overall QoE. This model aims to
assess and merge the contributions of each impact factor to calculate overall QoE
in MIoT applications. Finally, the subjective QoE was conducted on the Vehicular
MIoT application to analyze the performance of the proposed method.
Karaadi et al. proposed a new concept of Quality of Things (QoT) and IoT archi-
tecture based on the QoT for multimedia communication [65]. This framework is
based on the application, network, environment, and device factors to assess the QoT.
Shin [66] proposed a conceptual model of QoE for IoT; this model is based on subjec-
tive QoE and QoS evaluations. The proposed model summarizes QoE to subjective
informatics and shows an association with other factors. The model categorizes IoT
services via experimental quality measurement tools from a user-centered percep-
tion. The proposed model will help the development of QoE based IoT framework
for service provision for wearable technologies.
Ikeda et al. proposed QoE enable IoT framework for applications; this framework
is based on the huge quantity of quality metrics in IoT architecture [67]. Physical
metrics were introduced based on the four layers: user interface, computing, network,
and device. The metaphysical metrics were also added to the framework for sum-
marizing IoT applications on request. Suryanegara et al. [68] proposed a ve-step
framework for the QoE assessment of IoT services based on the absolute category
rating with hidden references (ACR-HR) scale. IoT users provide their evaluation of
services according to the ACR-HR scale before and after implementation. The com-
parison of QoE frameworks for IoT is given in Table 3.
5 Security and privacy of IoMT
The rapid growth of the IoMT has led to major changes in the market, which is target-
ing the 3rd wave of the information market globally after the era of computers and
the Internet. This great opportunity also poses many major challenges. This study
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The Review of Socionetwork Strategies
presents IoMT problems faced due to security and privacy, water signature scenarios,
current technologies, targets, new researchers’ guidelines, & considerations. They
also subdivide methods into privacy, security, and watermarking categories in the
documentation. New technologies and new information in the IoT have created new
challenges requiring researchers’ attention.
IoMT technology is a new management network based on the Internet and linked
by various high-performance multimedia devices and sensors. With the support of
the IoT, people and devices with devices can be linked together to achieve a digital,
networked, information-based, and intelligent management system. The applications
of IoMT are equivalent to building a smart society and resources with the help of
software and hardware facilities. They are reasonably congured to provide high-
quality activities and multimedia equipment management platforms. Several security
concerns need to be considered, as shown in Fig. 2, which can be overcome and
optimized in various ways. The privacy and security issues and their architectures
have been briey discussed with several unusual complications [69]. IoMT incor-
porates dierent data types; therefore, privacy and security could be improved. The
main concerns in IoMT security are privacy, authentication, data preserving, verica-
tion, packet loss, transmission delay, weak networks, and protocols, etc., and most
solutions for these issues have been proposed based on blockchain, machine, and
deep learning technologies [14, 18, 69, 70]. In [71], the authors proposed an interac-
tion that considers the proximity of people, digital devices, and non-digital devices
and denes ve dimensions: distance, direction, mobility, identity, and location. Any
change in any size will trigger the interaction. In [72], a public interactive white-
board is proposed, which can collaborate to complete a self-organized meeting. It
uses proximity sensors to manage and display interactions with people.
IoT-based security solution provided by Chee [121] by proposing IoT Security
Simulator (IoTSecSim) for creating network topology of IoT, analyzing the attacks
and defense. This case study experiment was performed with Mirai malware and its
variants to track the behavior and impact on the IoT networks. The authors conducted
experimental simulations and carefully compared results with previous research to
Table 3 Comparison of QoE frameworks for IoT
QoE Based IoT
Frameworks
Pal [44]Floris [64]Karaadi [65]Shin [66]Ikeda
[67]
Sury-
aneg-
ara
[68]
Parameters Quality of
Information/Data
NQoS/QoD NQoS/AQoE NQoS/
AQoE
AQoE NQoS
Analysis
Support
Quantitative Qualitative Qualitative Quantitative/
Qualitative
Quali-
tative
Quan-
tita-
tive/
Qual-
itative
Monitoring
Support
Yes Yes Yes Yes Yes Yes
Remarks Subjective QoE Subjective QoE Objective QoE/
QoS
Subjective/
objective
QoE
Sub-
jec-
tive
QoE
Sub-
jec-
tive
QoE
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The Review of Socionetwork Strategies
solutions and proposed framework to provide better accuracy. The proposed frame-
work was also tested on the four combinations of cyber-attack patterns and this
proves that these have an inuence on the IoT malware propagation in diverse condi-
tions. The IoTseSim is a better framework, which provides an extendable simulation
facility for users to build models to analyze cyber-attacks IoT networks.
The current security challenges such as Data integrity, encryption capabilities, pri-
vacy issues, common framework, automation, updating, authentication, and security
in IoMT are given in Fig. 5 also details description is given in further subsections.
5.1 Privacy and security challenges
Nowadays, around 23 Billion IoT devices are the world idly connected. At the end
of the year 2020, it will raise more and expected it will reach 30 billion, and after the
year, it will increase more because this era is the era of IT. As we know, the more we
close to the technologies, the more security challenges we phase. The security oppo-
sitions to IoMT are as follows.
Fig. 5 Some security challenges in IoT which need to be xed
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5.2 Data connectivity
Data Connectivity is one of the main problems of the IoMT when no one can change
the data at any cost at any given time. Digital watermarks and blockchains must be
implemented to ensure Data Connectivity [73, 74].
5.3 Problem faces due to security and privacy
Variety of devices of IoMT that can communicate on dierent platforms. IoT devices
interchange and collect data for various reasons, including decision-making, good
services, and greater quality. Therefore, it is imperative to secure data endpoints fully.
5.4 Common framework
There is no common framework for the IoMT, so complete production and maintain
security and privacy at the risk of their own [75]. Once the platform standards are in
place, security concerns will be solved again.
5.5 Updating
Most devices of IoT software are updated automatically, but some devices need to
be manually updated [76]. The other devices’ production provides short-time updates
and stops them. Managing updates for the millions of IoT-connected devices is chal-
lenging. Not all devices support automatic updates and require manual updates. It is
time-consuming and creates security loopholes in the event of an error.
5.6 Auto creation
In our daily lives, IoT plays an invasion to continue and targets many internet devices.
Managing large amounts of user data takes a lot of work. The fact that an error in the
algorithm leads to the collapse of the entire infrastructure is undeniable.
5.7 Limitation of IoMT devices
The main two problems with the capacity of the battery of the IoT devices and the
other is processing energy [77]. Few devices of IoT are planted in a non-rechargeable
or non-rechargeable environment, which requires the machine to perform functions
designed for limited power, and strict safety regulations consume little power. There
are three possible methods for solving this problem. The rst is to minimize safety
requirements; the second is to increase the battery capacity. It seems impossible due
to most IoT devices’ small size and lightweight design. However, there is no extra
space in large batteries.
The 3rd and most important method is to obtain power from sources of nature
such as hotness, glow, air, and shaking. Still, renewable energy sources require this
technology and add to the cost due to the limited memory space of the devices of IoT,
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The Review of Socionetwork Strategies
which is not possible to store or manage the computing needs of special algorithms.
The devices of IoT are intelligent and must meet al.l of these requirements [78].
5.8 Digital watermarking
Digital tokens contain hidden information and protect against copyright problems,
authentication and validation [79]. When hiding information on social media, they
rarely talk about writing watermarks. Protecting numeric values, especially simple
notation, is a long process. The hidden information is also categorized into stegan-
ography, encryption, and digital watermarks. The hidden letter is encapsulated in
numeric values that do not aect the actual text to validate the property. The scenario
of digital watermarking is given in Fig. 6.
6 Challenges and limitation of iot multimedia
In today’s life, IoMT has penetrated all aspects of human life. The quality of human
life has taken a qualitative leap inuenced by the IoT [80, 81]. In the current social
development process, IoMT plays a vital role in scientic research, and reasonable
use of it can eectively promote society’s overall development and progress. IoMT
is a product developed based on the internet combined with intelligent technology
and self-organizing networks [82]. IoT is widely used in college teaching, agricul-
tural production, computer technology development, and other elds [83–85]. People
and things can communicate, and the development trend of IoT is to achieve global
intelligence, which is also the inevitable direction of human development [86–88].
IoT improves people’s lives and makes daily life more convenient and comfortable.
The traditional multimedia laboratory management method is usually manual man-
agement, which is high in cost, low in eciency, and wastes human and material
Fig. 6 Digital Watermarking
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resources. It is easy to occur that the information needs to be updated in time, and
the supervision and maintenance cannot consider the overall situation, leading to the
loss of equipment in the multimedia laboratory [89]. To control the cost due to the
high price of traditional management, the reduction of manpower and equipment will
often cause security risks. From the teaching perspective, the conventional multi-
media laboratory management method needs help to comprehend resource sharing.
There will be more borrowing procedures, and the work process could be more con-
venient when high-precision equipment is transferred and used in laboratories of the
same nature [90].
Now if we look at the benets it gives from a business point of view, IoMT enables
them to gather vast quantities of customer and product data. For instance, it’s now
easy to tell how products are used or how they can be improved to enhance the cus-
tomer experience. From a business perspective, IoMT improves the ability a lot. It
provides data by which an enterprise can see a clear, real-time overview of inventory
and its use of resources. IoT devices give us automated alerts like IoT lighting that
send messages via its control app to remind the user to turn o extra lights that are
not needed at that time.
Another example that came to my mind is a printer ink that automatically reminds
me to replace it before it runs out. Not only has this but also in security concerns,
IoT has given a lot of enhancements like IoT-enabled access control and CCTV tech-
nologies [91]. Now companies are using IoMT access control technology to monitor
attendance and even manage car parking with video surveillance [92].
6.1 Challenges
●Internet of Multimedia Nano-Things (IoMNT)
The challenges include novel medium access control techniques, addressing schemes,
neighbour discovery and routing schemes, and security solutions [93].
●Vehicular Networks
Vehicles capable of supporting real-time acquisition and transmission of multimedia
trac that is generated by built-in IoT devices [94].
●Architecture performances
Dynamic networks, dicult kinds of information, and the delay between the IoMT
smart objects are some of the challenges for IoMT.
●Complexity in all the encoding audio/video IoMT.
●Complex algorithms for real-time IoMT computing.
●Issues with Big Data analysis techniques in IoMT.
●How to control quality over complexity for each media thing?
●Designing of a hardware structure having low power and real time service.
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●And of course, the synchronization for IoMT.
●Personal information protection and security schemes.
6.2 Limitations
Aiming at the network delay problem in the emergency data communication of the
IoT, a non-uniform clustering resource scheduling scheme based on the priority
of data change rate is proposed, which can comprehend the priority scheduling of
emergency data without setting the data priority in advance. However, to facilitate
resource scheduling of the data monitoring system in IoT, only temperature data of
bearings in the metallurgical production process are monitored. In the later system
upgrade, multi-functional sensor equipment should be introduced to detect multiple
emergency data simultaneously. Given the energy hole phenomenon caused by the
node near the base station undertaking more data forwarding in the large-scale multi-
hop communication of IoT, the current studies only discuss the situation of xed
base stations and use the non-uniform clustering strategy to alleviate the energy hole
problem. However, if the base station supports mobile, it can also lessen the energy
hole problem of nodes near it for IoT application scenarios where the base station
is far from the data collection area. To improve the eciency of data collection and
transmission, an optimal routing node that combines the clustered data collection
constructs a heterogeneous IoT and uses dierent resource scheduling schemes for
nodes with other functions should be developed [95, 96].
IoT early adaptors have their rst negative experience because, at the start of the
industry 4.0 wave, most companies still need machines ready to gather data. For
more, there was a shortage of experts, and the upfront investment cost was high. It is
a fairly common reason why IoT projects fail today.
If we talk about the limitation of the experts, the IoMT environment is not easy to
build and maintain; if it’s easy, it’s expensive. A lot of skills are required to make a
successful IoT product. An Android app or additional app developer is required, too,
to connect the sensor to the device. A backend developer is also needed for uploading
the backend server in the cloud and their management stu. Front-end developers
and data scientists are also part of the expertise that these work needs.
Device manufacturers only look for software support once users come to them-
selves to replace it. Also, in the current situation where most of us are doing remote
jobs, a minor IoT failure can cause a hacker attack then aects the network. Numer-
ous IoT network options are one of the reasons why it’s more overwhelming than
helpful. As we know, rich and well-equipped hardware and software and data storage
infrastructure are required for IoMT. The IoMT always surrounds security issues.
Hence the devices must share data with a great encryption technique to avoid data
leakage. This approach takes a lot of time to execute.
6.3 Solutions for IoMT architectures
The IoT scalar data is so dierent from the multimedia QoS data. Multimedia IoT
requires higher bandwidth, computing power and massive memory resources and
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processing. These multimedia applications require a lot of data transmission through
multipoint-to-point communication or multipoint-to-multipoint. Dynamic networks,
dicult kinds of information, and the delay between the IoMT smart objects are
some of the challenges for IoMT.
Sabri et al. presented an intelligent management system for less network trac
introduced for the IoT video surveillance system based on AI Software. It guarantees
not having a delayed loss rate and jitter [97]. It classies the data and estimates the
resources.
Ibrahim proposed Context-Aware Fog Cloud Hybrid-based architecture that aims
to optimize energy resource uses and reduce the end-to-end delay for the heavy
crowd in a smart city [98]. There is 3 tier architecture: Client, Fog (Wi-Fi, 5G, etc.,
can be used for communication), and the Remote Cloud tier (forms an IP-based big
data architecture).
Patel et al. proposed a Big Data layered Architecture; six layers in it are aggrega-
tion, computation, and the extraction of multimedia data [99]. Three problems exist
in computing huge amounts of data and then detecting and extracting useful info.
Also, the limits of Big Data processing for multimedia.
7 Open research issues
IoMT is a growing eld due to sensing and imaging of real-world applications and
video surveillance, so important to give a review of IoMT areas and research chal-
lenges in future research direction for further development to make this technology
more accurately implemented. In the current section, we will discuss problems and
future research directions according to the topics we discussed earlier.
Image Processing in IoT IoT image processing is commonly used in smart cities
and agriculture applications [100]. To measure the growth of plants and diseases,
images are captured on continuous bases by IoT devices; there is a problem in man-
aging storage for a huge amount of data and high-performance computing required
to process [101, 102]. So, there is still a need for precise algorithms for an accurate
time interval of image capturing and ltration on fog level to avoid unnecessary
data stored in the cloud. Object detection via IoT systems such as number plates
[103], vision-based data readers for industry [104], and wildlife monitoring [105],
re detection in cities [106], baby monitoring [107], home security [108] needs more
accuracy to dierentiate similar shape objects in a real-time environment with similar
colors and also in shadow or night mode.
IoT Video Surveillance There is a misconception about IoT video surveillance
that people referred cameras and sensors directly connected to traditional security
systems IoT surveillance [109]. IoT surveillance is a system based on cameras and
sensors to cloud data analytics and middle fog-level ltration of data [110]. Every
device senses the environment, such as objects, light, motion, change in temperature,
smoke, and moisture level, and also itself for failure and working. So still need cor-
rect information and implementation of IoT video surveillance systems that provide
security, sense the environment, and provide data analysis [111, 112]. Future research
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and development of a complete framework of IoT video surveillance at the organiza-
tional level are required, which provides a complete solution for consumers.
QoE in IoMT It is challenging to manage IoT services according to user needs
because there still needs to be a concrete QoE-based IoT architecture provided
by academia or industry. The proposed architectures are a lake of support of both
subjective and objective QoE to dierentiate between users’ positive and negative
feedback [113]. It is necessary that accurate QoE for service upgradation in an IoT
environment. No organization like ITU provided any QoE assessment metrics for
IoMT applications; still, old ITU standards are used for assessment, which does not
entirely t in the IoMT environment [114]. So research needs to propose new metrics
for the QoE assessment of IoMT applications, which accurately measure the QoE in
IoMT applications. IoMT applications operate automatically, and there is no subject
involved, so we need to add subjective QoE features in IoMT applications that will
help get users’ perceptions. The proper denition of QoE for IoMT applications will
be introduced to dene QoE in an IoT environment accurately. There is a need to
explore which factors inuence the QoE of IoMT applications regarding computation
and network impact at dierent stages.
Security and Privacy of IoMT There is no denite cyber security for IoMT and
an ongoing organization process due to the launch of new hacking attacks and vul-
nerabilities [115]. IoT rmware devices are contained security vulnerabilities, and
vendors rely on third-party rmware and integrate them without testing, so licensed
and authenticated organizations will make rmware and integrate it into devices after
testing [116, 117]. IoT systems are based on heterogeneous devices, so there is a
need for a framework that integrates and handles all devices for proper functionality
with security measures. New emerging technologies such as FL and blockchain can
be used for multimedia data protection in IoT, and solutions will be proposed for
better security [118]. For better security of IoMT applications, researchers need to
merge new technologies for solutions such as Fog/Edge and AI [119]. The AI algo-
rithm causes high energy consumption and high computation on low-resource IoT
devices, but, Fog/edge can be utilized for faster IoT devices. The IoT devices have
low power and computation; however, multimedia applications need high computa-
tion and power, so future development of IoT device consideration of computation
and energy consumption for IoMT applications [120].
8 Conclusion
This paper presents image processing and video surveillance, IoMT-based QoE
frameworks, and the problems faced by IoMT due to security and privacy, water
signature scenarios, current technologies, future guidelines, and considerations. This
research subdivides methods into privacy, security, and watermarking categories in
the documentation. New technologies and new information in the IoMT have added
new challenges that require the attention of researchers. Further, we provide limi-
tations of previous research such as proper IoMT architecture, which supports all
protocols. Still, limitations of the security model for IoMT are given by any stan-
dard organizations and approved by academia, and open research issues for future
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The Review of Socionetwork Strategies
research such as the development of new IoMT models, which provide complete
security, which will lead to better solutions for IoMT applications of image process-
ing/video surveillance, the merging of the QoE domain, and security and privacy.
Acknowledgements N/A.
Funding Not available.
Data Availability No data available related to this article.
Declarations
Ethical Approval N/A.
Conict of Interest Authors did not have any conict of interest.
References
1. Yu, Y., Li, Y., Tian, J., & Liu, J. (2018). Blockchain-based solutions to security and privacy issues in
the internet of things. IEEE Wireless Communications, 25(6), 12–18.
2. Al-Turjman, F. (2022). Smart‐city medium access for smart mobility applications in internet of
things. Transactions on Emerging Telecommunications Technologies, 33, 8.
3. Vermesan, O., Bahr, R., Falcitelli, M., Brevi, D., Bosi, I., Dekusar, A., Velizhev, A., Alaya, M. B.,
Firmani, C., Simeon, J. -F., Tcheumadjeu, L. T., Solmaz, G., Bisconti, F., Di Mauro, L., Noto, S.,
Pagano, P., Ferrera, E., Gavilanes Castillo, G. A., Bonetto, E., Di Massa, V., & Schreiner, F. (2022).
IoT technologies for connected and automated driving applications. In Internet of things–the call of
the edge (IoT Technologies for Connected and Automated Driving Applications, 1st ed., pp. 255–
306). River Publishers. https://www.taylorfrancis.com/chapters/oa-edit/10.1201/9781003338611-6/
iot-technologies-connected-automated-driving-applications-ovidiu-vermesan-roy-bahr-mariano-
falcitelli-daniele-brevi-ilaria-bosi-anton-dekusar-alexander-velizhev-mahdi-ben-alaya-carlotta-
rmani-jean-francois-simeon-louis-touko-tcheumadjeu-g%C3%BCurkan-solmaz-francesco-bis-
conti-luca-di-mauro-sandro-noto-paolo-pagano-enrico-ferrera-guido-alejandro-gavilanes-castillo-
edoardo-bonetto-vincenzo-di-massa-xurxo-legaspi-marcos-cabeza-diego-bernardez-francisco-san-
chez-robert-kaul-bram-van-den-ende-antoine-schmeitz-johan-scholliers-georgios-karagiannis-jos-
den-ouden-sven-jansen-herv%C3%A9-marcasuzaa-oriane-schreiner
4. Rabby, M. K., & Monir (2019). Muhammad Mobaidul Islam, and Salman Monowar Imon. A review
of IoT application in a smart trac management system. In 5th International Conference on Advances
in Electrical Engineering (ICAEE), pp. 280–285. IEEE, 2019.
5. Saha, A., Lee, Y. W., Hwang, Y. S., & Psannis, K. E. (2018). Context-aware block-based motion esti-
mation algorithm for multimedia internet of things (IoT) platform. Personal and Ubiquitous Comput-
ing, 22(1), 163–172.
6. Curry, E., Salwala, D., Dhingra, P., Pontes, F. A., & Yadav, P. (2022). Multimodal event processing:
A neural-symbolic paradigm for the internet of multimedia things. IEEE Internet of Things Journal,
9(15), 13705–13724.
7. Yang, J., Li, J., & Liu, S. (2020). A new algorithm of stock data mining in internet of Multimedia
things. The Journal of Supercomputing, 76(4), 2374–2389.
8. Zhang, Y., He, Q., Xiang, Y., Zhang, L. Y., Liu, B., Chen, J., & Yiyuan Xie. (2017). Low-cost and
condentiality-preserving data acquisition for internet of multimedia things. IEEE Internet of Things
Journal, 5(5), 3442–3451.
9. Khanna, A., & Kaur, S. (2020). Internet of things (IoT), applications and challenges: A comprehen-
sive review. Wireless Personal Communications, 11 4(2), 1687–1762.
1 3
The Review of Socionetwork Strategies
10. Tayyaba, S., Khan, S. A., Ashraf, M. W., & Balas, V. E. (2020). Home automation using IoT. In V. E.
Balas, R. Kumar, & R. Srivastava (Eds.), Recent trends and advances in articial intelligence and inter-
net of things (pp. 343–388). Springer. https://link.springer.com/book/10.1007/978-3-030-32644-9
11. Omran, M. A., Wasan, K., Saad, B. J., Hamza, Ahmed, F., & Al-Baghdadi (1973). Designing and
Manufacturing of Home Automation Monitoring System Using Internet of Things Technology. In
Journal of Physics: Conference Series, vol. no. 1, p. 012081. IOP Publishing, 2021.
12. Teoh, Y. K., Gill, S. S., & Parlikad, A. K. (2021). IoT and fog-computing-based predictive mainte-
nance model for eective asset management in Industry 4.0 using machine learning. IEEE Internet of
Things Journal, 10(3), 2087–2094.
13. Agarwal, Y., Jain, K., & Karabasoglu, O. (2018). Smart vehicle monitoring and assistance using
cloud computing in vehicular ad Hoc networks. International Journal of Transportation Science and
Technology, 7(1), 60–73.
14. Lv, Z., Qiao, L., & Song, H. (2020). Analysis of the security of internet of multimedia things. ACM
Transactions on Multimedia Computing Communications and Applications (TOMM), 16(3s), 1–16.
15. Zikria, Y., Bin, M. K., & Afzal (2020). Internet of multimedia things (IoMT): Opportunities, chal-
lenges and solutions. Sensors (Basel, Switzerland), 20(8), 2334.
16. Kumar, D., Verma, C., Dahiya, S., & Singh, P. K. (2021). Maria Simona Raboaca, Zoltán Illés,
and Brijesh Bakariya. Cardiac diagnostic feature and demographic identication (CDF-DI): an IoT
enabled healthcare framework using machine learning. Sensors, 21(19), 6584.
17. Wang, Q., Zhao, Y., Wang, W., Minoli, D., Sohraby, K., Zhu, H., & Occhiogrosso, B. (2017, June).
Multimedia IoT systems and applications. In 2017 Global Internet of Things Summit (GIoTS) (pp.
1–6). IEEE. https://ieeexplore.ieee.org/abstract/document/8016221
18. Jan, M., Ahmad, J., Cai, X. C., Gao, F., Khan, S., Mastorakis, M., & Usman (2021). Mamoun Alazab,
and Paul Watters. Security and blockchain convergence with internet of Multimedia things: Current
trends, research challenges and future directions. Journal of Network and Computer Applications,
175, 102918.
19. AlAslani, M., & Basem Shihada. (2019). and. Analyzing latency and dropping in Today’s Internet of
multimedia things. In 2019 16th IEEE Annual Consumer Communications & Networking Conference
(CCNC), pp. 1–4. IEEE.
20. Laghari, A., Ali, H., Li, Y., Shoulin, S., Karim, A. A., Khan, & Muhammad Ibrar. (2023). Blockchain
applications for internet of things (IoT): A review. Multiagent and Grid Systems, 19(4), 363–379.
21. Singha, A. (2018). Image Processing and IOT based applications. International Journal of Innovative
Science and Research Technology, 3, 11.
22. Rego, A., Canovas, A., & Jiménez, J. M. (2018). An intelligent system for video surveillance in IoT
environments. Ieee Access : Practical Innovations, Open Solutions, 6, 31580–31598.
23. Sanil, Nischal, V., Rakesh, R., Mallapur, & Mohammed Riyaz Ahmed. (2020). and. Deep learning
techniques for obstacle detection and avoidance in driverless cars. In 2020 International Conference
on Articial Intelligence and Signal Processing (AISP), pp. 1–4. IEEE.
24. Saponara, S., Elhanashi, A., & Gagliardi, A. (2021). Real-time video re/smoke detection based on
CNN in antire surveillance systems. Journal of Real-Time Image Processing, 18(3), 889–900.
25. Gulve, S. P., Khoje, S. A., & Pardeshi, P. (2017). Implementation of IoT-based smart video surveil-
lance system. In H. S. Behera & D. P. Mohapatra (Eds.), Computational intelligence in data mining:
proceedings of the international conference on CIDM (pp. 771–780). Springer. https://link.springer.
com/chapter/10.1007/978-981-10-3874-7_73
26. Sultana, T., Khan, A., & Wahid (2019). IoT-guard: Event-driven fog-based video surveillance sys-
tem for real-time security management. Ieee Access : Practical Innovations, Open Solutions, 7,
134881–134894.
27. Mourikis, A., Ioannis, R., Kalamatianos, I., Karydis, & Avlonitis, M. (2021). A survey on the use
of the internet of multimedia things for precision agriculture and the agrifood sector. Engineering
Proceedings, 9(1), 32.
28. AlZu’bi, S., Hawashin, B., Mujahed, M., Jararweh, Y., & Brij, B. (2019). Gupta. An ecient employ-
ment of internet of multimedia things in smart and future agriculture. Multimedia Tools and Applica-
tions, 78(20), 29581–29605.
29. Kapoor, A., Bhat, S. I., Shidnal, S., & Mehra, A. (2016). Implementation of IoT (Internet of Things)
and Image processing in smart agriculture. In 2016 International Conference on Computation System
and Information Technology for Sustainable Solutions (CSITSS), pp. 21–26. IEEE.
1 3
The Review of Socionetwork Strategies
30. Rani, L., Paul Jasmine, M., Khoushik Kumar, K. S., Naresh, & Vignesh, S. (2017). Dynamic trac
management system using infrared (IR) and Internet of Things (IoT). In Third International Confer-
ence on Science Technology Engineering & Management (ICONSTEM), pp. 353–357. IEEE, 2017.
31. Yang, A., Zhang, C., Chen, Y., Zhuansun, Y., & Huixiang Liu. (2019). Security and privacy of smart
home systems based on the internet of things and stereo matching algorithms. IEEE Internet of
Things Journal, 7(4), 2521–2530.
32. Liu, S., Li, L., Jin, M., Hou, S., & Yali Peng. (2019). Optimized coecient vector and sparse repre-
sentation-based classication method for face recognition. Ieee Access : Practical Innovations, Open
Solutions, 8, 8668–8674.
33. Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under dicult
lighting conditions. IEEE Transactions on Image Processing, 19(6), 1635–1650.
34. Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Appli-
cation to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12),
2037–2041.
35. Gumus, E., Kilic, N., Sertbas, A., Osman, N., & Ucan (2010). Evaluation of face recognition tech-
niques using PCA, wavelets and SVM. Expert Systems with Applications, 37(9), 6404–6408.
36. Kumar, M., Raju, K. S., Kumar, D., Goyal, N., Verma, S., & Singh, A. (2021). An ecient framework
using visual recognition for IoT based smart city surveillance. Multimedia Tools and Applications,
80(20), 31277–31295.
37. Barakabitze, A., Alex, N., Barman, A., Ahmad, S., Zadtootaghaj, L., Sun, M. G., Martini, & Luigi
Atzori. (2019). QoE management of multimedia streaming services in future networks: A tutorial and
survey. IEEE Communications Surveys & Tutorials, 22(1), 526–565.
38. Khan, S., Abbas, N., Nasir, M., Haseeb, K., Saba, T., Rehman, A., & Mehmood, Z. (2021). Steg-
anography-assisted secure localization of smart devices in internet of multimedia things (IoMT).
Multimedia Tools and Applications, 80(11), 17045–17065.
39. Singh, D., Maurya, A. K., Dewang, R. K., & Keshari, N. (2022). A review on internet of multimedia
things (IoMT) routing protocols and quality of service. In S. Shukla, A. K. Singh, G. Srivastava, & F.
Xhafa, (Eds.), Internet of Multimedia Things (IoMT) (pp. 1–29).
40. Ibrahim, M., Baloch, N. K., Anjum, S., & Zikria, Y. B. (2021). An energy ecient and low overhead
fault mitigation technique for internet of thing edge devices reliable on-chip communication. Soft-
ware: Practice and Experience, 51(12), 2393–2410.
41. Shaque, M., Akmal, N. K., Baloch, M. I., Baig, F., & Hussain (2020). Yousaf Bin Zikria, and Sung
Won Kim. NoCGuard: A reliable network-on-chip router architecture. Electronics, 9(2), 342.
42. Jha, D., Nandan, K., Alwasel, A., Alshoshan, X., Huang, R. K., Naha, S. K., Battula, S., Garg, et al.
(2020). IoTSim-Edge: A simulation framework for modeling the behavior of internet of things and
edge computing environments. Software: Practice and Experience, 50(6), 844–867.
43. Siddiqi, M., Ahmed, H. Y., & Joung, J. (2019). 5G ultra-reliable low-latency communication imple-
mentation challenges and operational issues with IoT devices. Electronics, 8(9), 981.
44. Pal, D., Vanijja, V., Arpnikanondt, C., Zhang, X., & Papasratorn, B. (2019). A quantitative approach
for evaluating the quality of experience of smart-wearables from the quality of data and quality
of information: An end user perspective. Ieee Access : Practical Innovations, Open Solutions, 7,
64266–64278.
45. da Silva, Daniel, V. C., Antonio, A., de Rocha, A., & Pedro, B. (2021). Velloso. Mobile vs. Non-
Mobile Live-Streaming: A Comparative Analysis of Users Engagement and Interruption Using Big
Data from a Large CDN Perspective. Sensors, 21(16), 5616.
46. Al-Shammari, N., Khalaf, T. H., Syed, & Syed, M. B. (2021). An Edge–IoT framework and prototype
based on blockchain for smart healthcare applications. Engineering Technology & Applied Science
Research, 11(4), 7326–7331.
47. Tanwar, S., Tyagi, S., & Kumar, N. (Eds.). (2019). Multimedia big data computing for IoT applica-
tions: Concepts, paradigms and solutions (Vol. 163). Springer.
48. Ganesh Babu, R., K. Elangovan, Maurya, S., & Karthika, P. (2021). Multimedia security and privacy
on real-time behavioral monitoring in machine learning IoT application using big data analytics.
In R. Kumar, R. Sharma, & P. K. Pattnaik (Eds.), Multimedia technologies in the internet of things
environment (pp. 137–156). Springer. https://link.springer.com/book/10.1007/978-981-15-7965-3
49. Brunnström, K., Beker, S. A., De Moor, K., Dooms, A., Egger, S., Garcia, M.-N., T. Hossfeld, et al.
(2013). Qualinet white paper on denitions of quality of experience.
1 3
The Review of Socionetwork Strategies
50. Laghari, A., Ali, H., He, A., Khan, R. A., Laghari, S., Yin, & Wang, J. (2022). Crowdsourcing plat-
form for QoE evaluation for cloud multimedia services. Computer Science and Information Systems,
00, 38–38.
51. Laghari, A., Ali, K. A., Memon, M. B., & Soomro (2020). Rashid Ali Laghari, and Vishal Kumar.
Quality of experience (QoE) assessment of games on workstations and mobile. Entertainment Com-
puting, 34, 100362.
52. Kettouche, S., Maimour, M., & Derdouri, L. (2021). Bandwidth provision through disjoint multipath
RPL in the IoMT. In 2nd International Conference on Computer Science’s Complex Systems and
their Applications, ICCSA 2021.
53. Laghari, A., Ali, H., He, A., Khan, N., Kumar, & Kharel, R. (2018). Quality of experience framework
for cloud computing (QoC). Ieee Access : Practical Innovations, Open Solutions, 6, 64876–64890.
54. Jorquera Valero, J., Maria, P. M. S., & Sanchez (2018). Lorenzo Fernández Maimó, Alberto Huertas
Celdran, Marcos Arjona Fernandez, Sergio De Los Santos Vílchez, and Gregorio Martínez Pérez.
Improving the security and QoE in mobile devices through an intelligent and adaptive continuous
authentication system. Sensors, 18(11), 3769.
55. Laghari, A., & Ali (2021). Quality of experience assessment of calling services in social network. ICT
Express, 7(2), 158–161.
56. Laghari, A., Laghari, R., Wagan, A., & Umrani, A. (2019). Eect of packet loss and reorder on qual-
ity of audio streaming. EAI Endorsed Transactions on Scalable Information Systems, 7, 24.
57. Schatz, R., Egger, S., & Platzer, A. (2011). Poor, good enough or even better? bridging the gap
between acceptability and qoe of mobile broadband data services. In 2011 IEEE International Con-
ference on Communications (ICC), pp. 1–6. IEEE.
58. Laghari, A., Ali, H., He, S., & Karim, H. A. (2017). Shah, and Nabin Kumar Karn. Quality of experi-
ence assessment of video quality in social clouds. Wireless Communications and Mobile Computing
2017.
59. Nguyen, D., Tran, H., & Truong Cong, T. (2021). An Ensemble Learning-Based No Reference Qoe
Model For User Generated Contents. In 2021 IEEE International Conference on Multimedia & Expo
Workshops (ICMEW), pp. 1–6. IEEE.
60. Laghari, A., Ali, H., He, K. A., Memon, R. A., Laghari, I. A., Halepoto, & Khan, A. (2019). Quality of
experience (QoE) in cloud gaming models: A review. Multiagent and grid Systems, 15(3), 289–304.
61. Rani, S., Ahmed, S. H., Talwar, R., Malhotra, J., & Song, H. (2017). IoMT: A reliable cross layer
protocol for internet of multimedia things. IEEE Internet of Things Journal, 4(3), 832–839.
62. Shifa, A., Asghar, M. N., Noor, S., Gohar, N., & Fleury, M. (2019). Lightweight cipher for H. 264
videos in the Internet of multimedia things with encryption space ratio diagnostics. Sensors, 19(5),
1228.
63. Alari, A., Sankar, S., Altameem, T., & Jithin, K. C. (2020). Mohammed Amoon, and Walid El-
Shafai. A novel hybrid cryptosystem for secure streaming of high eciency H. 265 compressed
videos in IoT multimedia applications. Ieee Access : Practical Innovations, Open Solutions, 8,
128548–128573.
64. Floris, A. (2015). and Luigi Atzori. Quality of Experience in the Multimedia Internet of Things: De-
nition and practical use-cases. In 2015 IEEE International Conference on Communication Workshop
(ICCW), pp. 1747–1752. IEEE.
65. Karaadi, A., Lingfen Sun, and, & Is-Haka, M. (2017). Multimedia communications in internet of
things QoT or QoE? In IEEE International Conference on Internet of Things (iThings) and IEEE
Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Comput-
ing (CPSCom) and IEEE Smart Data (SmartData), pp. 23–29. IEEE, 2017.
66. Shin, D. H. (2016). A User-based Model for the Quality of Experience of the Internet of Things.
Information & Management (2017).
67. Ikeda, Y., Kouno, S., Shiozu, A., & Noritake, K. (2016, December). A framework of scalable QoE
modeling for application explosion in the Internet of Things. In IEEE 3rd world forum on internet of
things (WF-IoT) (pp. 425–429). IEEE. https://ieeexplore.ieee.org/xpl/conhome/7831562/proceeding
68. Suryanegara, M., Prasetyo, D. A., Andriyanto, F., & Hayati, N. (2019). A 5-step framework for mea-
suring the quality of experience (qoe) of internet of things (iot) services. Ieee Access : Practical
Innovations, Open Solutions, 7, 175779–175792.
69. Malhotra, P., Singh, Y., Anand, P., & Bangotra, D. K. (2021). Pradeep Kumar Singh, and Wei-Chiang
Hong. Internet of things: Evolution, concerns and security challenges. Sensors, 21(5), 1809.
70. Yu, J., Xue, H., Liu, B., Wang, Y., Zhu, S., & Ding, M. (2020). Gan-based dierential private image
privacy protection framework for the internet of multimedia things. Sensors, 21(1), 58.
1 3
The Review of Socionetwork Strategies
71. Marquardt, N., Diaz-Marino, R., Boring, S., & Saul Greenberg. (2011). and. The proximity toolkit:
prototyping proxemic interactions in ubiquitous computing ecologies. In Proceedings of the 24th
annual ACM symposium on User interface software and technology, pp. 315–326.
72. Ju, W., Lee, B. A., Scott, R., & Klemmer (2008). Range: exploring implicit interaction through elec-
tronic whiteboard design. In Proceedings of the 2008 ACM conference on Computer supported coop-
erative work, pp. 17–26.
73. Perwej, Y., Ahamad, F., Khan, M. Z., & Akhtar, N. (2021). An empirical study on the current state of
internet of multimedia things (IoMT). International Journal of Engineering Research in Computer
Science and Engineering. https://ijercse.com/editorial-board.php
74. Ren, Y., Zhu, F., Zhu, K., Sharma, P. K., & Wang, J. (2021). Blockchain-based trust establish-
ment mechanism in the internet of multimedia things. Multimedia Tools and Applications, 80(20),
30653–30676.
75. Usman, M., Jan, M. A., He, X., & Chen, J. (2019). P2DCA: A privacy-preserving-based data collec-
tion and analysis framework for IoMT applications. IEEE Journal on Selected Areas in Communica-
tions, 37(6), 1222–1230.
76. Zuo, C., Wen, H., Lin, Z., & Zhang, Y. (2019). Automatic ngerprinting of vulnerable ble iot devices
with static uuids from mobile apps. In Proceedings of the 2019 ACM SIGSAC Conference on Com-
puter and Communications Security (pp. 1469–1483).
77. Imteaj, A., Mamun Ahmed, K., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022). Federated
learning for resource-constrained iot devices: Panoramas and state of the art. In R. Razavi-Far, B.
Wang, M. E. Taylor, & Q. Yang (Eds.), Federated and Transfer Learning (pp. 7–27). https://link.
springer.com/book/10.1007/978-3-031-11748-0
78. Rehman, M., Javaid, N., Awais, M., Imran, M., & Naseer, N. (2019). Cloud based secure service pro-
viding for IoTs using blockchain. In 2019 IEEE Global Communications Conference (GLOBECOM)
(pp. 1–7). IEEE.
79. Nath, M., Prasad, S., Bibhuprada, B., & Priyadarshini (2022). Mitrabinda Ray, and Debapriya Sou-
myesh Das. An overview of multimedia technologies in current era of internet of things (IoT). Mul-
timedia Technologies in the Internet of Things Environment, 2, 1–23.
80. Li, W. (2021). Design of smart campus management system based on internet of things technology.
Journal of Intelligent & Fuzzy Systems, 40(2), 3159–3168.
81. Gao, J., Li, P., Laghari, A. A., Srivastava, G., Gadekallu, T. R., Abbas, S., & Zhang, J. (2023). Incom-
plete Multiview Clustering via Semidiscrete Optimal Transport for Multimedia Data Mining in IoT.
ACM Transactions on Multimedia Computing, Communications and Applications
82. Qi, Lei, and Jing Guo (2019). Development of smart city community service integrated management
platform. International Journal of Distributed Sensor Networks 15, no. 6: 1550147719851975.
83. Alvi, S. A., Afzal, B., & Shah, G. A. (2015). Luigi Atzori, and Waqar Mahmood. Internet of multime-
dia things: Vision and challenges. Ad Hoc Networks, 33, 87–111.
84. Mukherjee, A., De, D., Soumya, K., & Ghosh (2020). FogIoHT: A weighted majority game theory
based energy-ecient delay-sensitive fog network for internet of health things. Internet of Things,
11, 100181.
85. Santos, M. A. G., Roberto Munoz, R., Olivares, Pedro, P., Rebouças Filho, J. D., Ser, & de Victor
Hugo, C. (2020). Albuquerque. Online heart monitoring systems on the internet of health things
environments: A survey, a reference model and an outlook. Information Fusion, 53, 222–239.
86. Swan, M. (2012). Sensor mania! The internet of things, wearable computing, objective metrics, and
the quantied self 2.0. Journal of Sensor and Actuator Networks, 1(3), 217–253.
87. Palattella, M., Rita, M., Dohler, A., Grieco, G., Rizzo, J., Torsner, T., Engel, & Ladid, L. (2016).
Internet of things in the 5G era: Enablers, architecture, and business models. IEEE Journal on
Selected Areas in Communications, 34(3), 510–527.
88. Borgia, E. (2014). The internet of things vision: Key features, applications and open issues. Com-
puter Communications, 54, 1–31.
89. Li, J., Maiti, A., Springer, M., & Gray, T. (2020). Blockchain for supply chain quality management:
Challenges and opportunities in context of open manufacturing and industrial internet of things.
International Journal of Computer Integrated Manufacturing, 33(12), 1321–1355.
90. Misra, S., Roy, C., Sauter, T., Mukherjee, A., & Maiti, J. (2022). Industrial Internet of things for
Safety Management Applications: A Survey. Ieee Access : Practical Innovations, Open Solutions, 10,
83415–83439.
91. Zhong, R. Y., Xu, X., & Wang, L. (2017). IoT-enabled smart factory visibility and traceability using
laser-scanners. Procedia Manufacturing, 10, 1–14.
1 3
The Review of Socionetwork Strategies
92. Zheng, W., Yang, Z., Feng, L., Fu, P., & Shi, J. (2019). APP Design of Energy Monitoring in Smart
Campus Based on Android System. International Journal of Online & Biomedical Engineering, 15,
5.
93. Maphathe, B., Francis, P., Thakur, G., Singh, & Hashimu, E. (2022). Iddi. The Terahertz Channel
Modeling in internet of Multimedia Design In-Body antenna. International Journal of E-Health and
Medical Communications (IJEHMC), 13(4), 1–17.
94. Anedda, M., Fadda, M., & Girau, R. (2023). Giovanni Pau, and Daniele Giusto. A social smart city
for public and private mobility: A real case study. Computer Networks, 220, 109464.
95. Bai, H., Zhang, X., Xie, Y., Gong, H., Li, Z., & Shilong Liu. (2022 (2022)). and. Resource Scheduling
Based on Unequal Clustering in Internet of Things. Mobile Information Systems.
96. Wang, K., Zhou, W., & Mao, S. (2017). On joint BBU/RRH resource allocation in heterogeneous
cloud-RANs. IEEE Internet of Things Journal, 4(3), 749–759.
97. Sabri, Naseer, M. S., Salim, S., Fouad, S., Alwee Aljunid, F. T., & AL-Dhief (2018). and C. B. M.
Rashidi. Design and implementation of an embedded smart intruder surveillance system. In MATEC
web of conferences, vol. 150, p. 06019. EDP Sciences.
98. Ibrahim, M. H. (2016). OCTOPUS: An edge-fog mutual authentication scheme. Int J Netw Secur,
18(6), 1089–1101.
99. Patel, P., Dave, J., Dalal, S., Patel, P., & Chaudhary, S. (2017). A Testbed for Experimenting Internet
of Things Applications. arXiv preprint arXiv:1705.07848.
100. Dagar, R., Som, S., & Sunil Kumar, K. (2018). Smart farming–IoT in agriculture. In 2018 Inter-
national Conference on Inventive Research in Computing Applications (ICIRCA), pp. 1052–1056.
IEEE.
101. Gao, D., Quan Sun, Bin Hu, and, & Zhang, S. (2020). A framework for agricultural pest and disease
monitoring based on internet-of-things and unmanned aerial vehicles. Sensors, 20(5), 1487.
102. Gopi, R., Sathiyamoorthi, V., Selvakumar, S., Manikandan, R., Chatterjee, P., Jhanjhi, N. Z., & Ash-
ish Kumar Luhach. (2022). Enhanced method of ANN based model for detection of DDoS attacks on
multimedia internet of things. Multimedia Tools and Applications, 81(19), 26739–26757.
103. Haziq, M. I., & Abdulla, R. (2022). Smart IoT-based security system for residence. Journal of Applied
Technology and Innovation, 6(1), 18.
104. Hsu, T. C., Tsai, Y. H., & Chang, D. M. (2022). The vision-based data reader in IoT system for smart
factory. Applied Sciences, 12(13), 6586. https://www.mdpi.com/2076-3417/12/13/6586
105. Elias, A., Rosales, N., Golubovic, C., Krintz, & Wolski, R. (2017). Where’s the bear?-automating
wildlife image processing using iot and edge cloud systems. In 2017 IEEE/ACM Second Interna-
tional Conference on Internet-of-Things Design and Implementation (IoTDI), pp. 247–258. IEEE.
106. Sharma, A., Singh, P. K., & Kumar, Y. (2020). An integrated re detection system using IoT and
image processing technique for smart cities. Sustainable Cities and Society, 61, 102332.
107. Dubey, Y. K. (2019). Baby monitoring system using image processing and IoT. International Journal
of Engineering and Advanced Technology, 8(6), 4961–4964.
108. Beatrice Dorothy, A., Britto Ramesh Kumar, S., & Jerlin Sharmila, J. (2017). IoT based home secu-
rity through digital image processing algorithms. In World congress on computing and communica-
tion technologies (WCCCT) (pp. 20–23). IEEE. https://ieeexplore.ieee.org/xpl/conhome/8063674/
proceeding
109. Memos, V. A., Kostas, E., Psannis, Y., Ishibashi, B. G., Kim, & Brij, B. (2018). Gupta. An ecient
algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework. Future Gen-
eration Computer Systems, 83, 619–628.
110. Markakis, E. K., Karras, K., Zotos, N., Sideris, A., Moysiadis, T., Corsaro, A., Alexiou, G., et al.
(2017). EXEGESIS: Extreme edge resource harvesting for a virtualized fog environment. IEEE
Communications Magazine, 55(7), 173–179.
111. Malche, T., Maheshwary, P., & Kumar, R. (2019). Environmental monitoring system for smart city
based on secure internet of things (IoT) architecture. Wireless Personal Communications, 107(4),
2143–2172.
112. Sultana, T., Khan, A., & Wahid (2019). Choice of application layer protocols for next generation
video surveillance using internet of video things. Ieee Access : Practical Innovations, Open Solu-
tions, 7, 41607–41624.
113. Fizza, K., Banerjee, A., Mitra, K., Jayaraman, P. P., & Ranjan, R. (2021). Pankesh Patel, and Dimi-
trios Georgakopoulos. QoE in IoT: A vision, survey and future directions. Discover Internet of
Things, 1(1), 1–14.
1 3
The Review of Socionetwork Strategies
114. Bouacheria, I., Bidai, Z., Kechar, B., & Francoise Sailhan. (2021). Leveraging multi-instance RPL
routing protocol to enhance the video trac delivery in IoMT. Wireless Personal Communications,
116 (4), 2933–2962.
115. Manhas, J., & Kotwal, S. (2021). Implementation of intrusion detection system for internet of things
using machine learning techniques. In K. J. Giri, S. Ahmad Parah, R. Bashir, K. Muhammad (Eds.),
Multimedia security: Algorithm development, analysis and applications (pp. 217–237). Springer.
https://link.springer.com/book/10.1007/978-981-15-8711-5
116. Waqas, M., Kumar, K., Laghari, A. A., Saeed, U., Rind, M. M., Shaikh, A. A., & Hussain, F. (2022).
Athaul Rai, and Abdul Qayoom Qazi. Botnet attack detection in internet of things devices over cloud
environment via machine learning. Concurrency and Computation: Practice and Experience, 34(4),
e6662.
117. Wen, H., & Lin, Z. (2020). and Yinqian Zhang. Firmxray: Detecting Bluetooth link layer vulnerabili-
ties from bare-metal rmware. In Proceedings of the 2020 ACM SIGSAC Conference on Computer
and Communications Security, pp. 167–180.
118. Khan, A. A., Laghari, A. A., Shaikh, Z. A., Dacko-Pikiewicz, Z., & Kot, S. (2022). Internet of
Things (IoT) security with blockchain technology: A state-of-the-art review. IEEE Access, 10,
122679–122695.
119. Laghari, A., Ali, A. K., & Jumani (2021). Review and state of art of fog computing. Archives of Com-
putational Methods in Engineering, 28(5), 3631–3643.
120. Li, X., Zhao, L., & Yu, K. (2021). Moayad Aloqaily, and Yaser Jararweh. A cooperative resource
allocation model for IoT applications in mobile edge computing. Computer Communications, 173,
183–191.
121. Chee, K., Onn, M., Ge, G., Bai, & Dan Dongseong Kim (2024). IoTSecSim: A framework for model-
ling and simulation of security in internet of things. Computers & Security, 136, 103534.
122. Kayalvizhi, S., Nagarajan, S., Deepa, J., & Hemapriya, K. (2023). Multi-modal IoT-based medical
data processing for disease diagnosis using Heuristic-derived deep learning. Biomedical Signal Pro-
cessing and Control, 85, 104889.
123. Yin, S., Li, H., Laghari, A. A., Gadekallu, T. R., Sampedro, G. A., & Almadhor, A. (2024). An anom-
aly detection model based on deep auto-encoder and capsule graph convolution via sparrow search
algorithm in 6G internet-of-everything. IEEE Internet of Things Journal, 11(18).
124. Chilamkurthy, N., Srinivasarao, O. J., Pandey, A., Ghosh, L. R., & Cenkeramaddi (2022). Low-
power wide-area networks: A broad overview of its dierent aspects. Ieee Access : Practical Innova-
tions, Open Solutions, 10, 81926–81959.
125. Kannan, B., Azhagu Jaisudhan Pazhani, A., Gunasekaran, P., & Rameshbabu, A. (2025). Smart
sensor systems. In N. N. Chiplunkar, K. V. S. S. S. S. Sairam, R. R. Gatti, & C. Singh (Eds.),
Self-powered AIoT systems (pp. 1–25). Apple Academic Press. https://www.amazon.com/
Self-Powered-AIoT-Systems-Niranjan-Chiplunkar/dp/1774915243
126. Huang, H., Kang, J., Pham, Q. V., & Jiao, Y. (2024). Intelligent device-free sensing for future internet
of things: emerging trends and challenges. Computer Communications. https://www.sciencedirect.
com/special-issue/10QT0L10G85
127. Hon, K. W. (2024). Mobile wireless/cellular communications networks. In W. Kuan Hon (Ed.), Tech-
nology and security for lawyers and other professionals (pp. 416–439). Edward Elgar Publishing.
https://www.elgaronline.com/monobook/book/9781803923918/9781803923918.xml
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Authors and Aliations
Asif AliLaghari1· HangLi1· ShahidKarim2· WaheeduddinHyder3·
YinShoulin1· Abdullah AyubKhan4· Rashid AliLaghari5,6
Asif Ali Laghari
asiaghari@synu.edu.cn
Hang Li
lihang@synu.edu.cn
Shahid Karim
shahidhit@yahoo.com
Waheeduddin Hyder
fhyder152@gmail.com
Yin Shoulin
yslin@synu.edu.cn
Abdullah Ayub Khan
abdullah.khan00763@gmail.com
Rashid Ali Laghari
rashidalilaghari@gmail.com
1 Software College, Shenyang Normal University, Shenyang, China
2 Faculty of Science and Technology, ILMA University, Karachi, Pakistan
3 Millennium Institute of Technology and Entrepreneurship, Karachi, Pakistan
4 Benazir Bhutto Shaheed University Lyari, Karachi, Sindh 75660, Pakistan
5 King Fahd University of Petroleum and Minerals, Dharan, Saudi Arabia
6 Department of Mechanical Engineering Technology, Sindh Institute of Management and
Technology, Karachi, Pakistan
1 3
