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Abstract Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time.

Hardware implementation of new neuron models or improved conventional neuron models has made a significant contribution to neuromorphic development. One of the important factors considered to improve the conventional neuron models is to explore the impact of electromagnetic energy on neurons. In this work the efficient FPGA implementation of memristive Wilson (MW) neuron model using two approximate MW model is presented. For the first approximate MW (AMW1) model in a hybrid method, piecewise linear (PWL) and CORDIC functions have been used to provide a multiplierless and accurate model. The PWL approximation method is used to provide the second approximate MW (AMW2) model. Results of the FPGA implementation for both the MW and AMW models illustrate that, the AMW1 model with an overall saving of 79%, and the AMW2 model with an overall saving of 69% are appropriate options for large scale implementations. The average NRMSE for the AMW1 model is 0.57%, while for the AMW2 model it is 1.23%. The maximum frequency of AMW2 model is 91.5% better than AMW1 model and realizes high frequency implementation.

This research presents a novel microstrip configuration for a lowpass-bandpass triplexer. To design this triplexer, first a lowpass filter (LPF) with some empty spaces is designed. Then, two microstrip sections are embedded inside the empty spaces to create bandpass (BP) channels. Therefore, this triplexer occupies a very small size of $0.003~\lambda \text{g}^{2}$ . A rigorous mathematical analysis and an optimization process are conducted to enhance the triplexer performance. The proposed triplexer features a lowpass (LP) channel cut-off frequency at 870 MHz with bandpass (BP) channels operating at 1.335 GHz and 2.055 GHz. The achieved insertion losses are 0.2 dB, 0.09 dB, and 0.04 dB for all LP and BP channels that are remarkably low, making our triplexer ideal for energy harvesting applications. All bands are flat with suitable group delays (GDs). Meanwhile, the BP channels are wide with 14.1% and 25.5% fractional bandwidths (FBWs) for the middle and upper channels respectively. Our triplexer suppresses the harmonics after the LP channel from the $1^{st}$ harmonic up to $3^{rd}$ harmonic. Fabrication and measurement of the proposed triplexer validate our designing method and the simulation results.

This work presents a very compact microstrip lowpass-bandpass (LP-BP) triplexer, which is designed and analyzed based on a novel structure. Due to its complex design process, this type of triplexer is rarely designed. Compared to the previous LP-BP triplexers it has the most compact size of $0.006~\lambda _{\mathrm {g}}^{2}$ , whereas an LP-BP triplexer with dimensions smaller than $0.01~\lambda _{\mathrm {g}}^{2}$ has not been designed yet. This triplexer is designed based on a perfect mathematical method and optimization simultaneously. Its lowpass band has a cut-off frequency of 0.67 GHz, suitable for low-band 5G applications. The resonance frequencies of its bandpass channels are located at 2.15 GHz and 3.19 GHz. These bandpass channels make the proposed triplexer appropriate for 5G mid-band applications. This triplexer can suppress the harmonics from the first up to 8th harmonic. The bandpass channels are flat and wide with two fractional bandwidths (FBW) of 15% and 11.97%. To prove the designing process and its simulation results, the presented novel LP-BP triplexer is fabricated and experimentally measured. The comparison results show that the experimental measurement confirms the simulation results. The close alignment between the measurements and simulation results demonstrates a high level of accuracy of our designing method.

In this paper, a new microstrip triplexer is designed to work at 2.5 GHz, 4.4 GHz and 6 GHz for mid-band 5G applications. All channels are flat with three low group delays (GDs) of 0.84 ns, 0.75 ns and 0.49 ns, respectively. Compared to the previously reported works, the proposed triplexer has the minimum group delay. The designed triplexer has 18.2%, 13.7%, 23.6% fractional bandwidths (FBW%) at 2.5 GHz, 4.4 GHz and 6 GHz, respectively. The obtained insertion losses (ILs) are low at all channels. These features are obtained without a noticeable increase in the overall size. A novel and simple resonator is used to design the proposed triplexer, which includes two pairs of coupled lines combined with a shunt stub. A perfect mathematical analysis is performed to find the resonator behavior and the layout optimization. The type of shunt stub is determined mathematically. Also, the smallness or largeness of some important physical dimensions is determined using the proposed mathematical analysis. Finally, the designed triplexer is fabricated and measured, where the measurement results verify the simulations.

This research introduces a new designing process and analysis of an innovative Silicon-on-Insulator Metal-Semiconductor Field-Effect (SOI MESFET) structure that demonstrates improved DC and RF characteristics. The design incorporates several modifications to control and reduce the electric field concentration within the channel. These modifications include relocating the transistor channel to sub-regions near the source and drain, adjusting the position of the gate electrode closer to the source, introducing an aluminum layer beneath the channel, and integrating an oxide layer adjacent to the gate. The results show that the AlOx-MESFET configuration exhibits a remarkable increase of 128% in breakdown voltage and 156% in peak power. Furthermore, due to enhanced conductivity and a significant reduction in gate-drain capacitance, there is a notable improvement of 53% in the cut-off frequency and a 28% increase in the maximum oscillation frequency. Additionally, the current gain experiences a boost of 15%. The improved breakdown voltage and peak power make it suitable for applications requiring robust performance under high voltage and power conditions. The increased maximum oscillation frequency and cut-off frequency make it ideal for high-frequency applications where fast signal processing is crucial. Moreover, the enhanced current gain ensures efficient amplification of signals. The introduced SOI MESFET structure with its modifications offers significant improvements in various performance metrics. It provides high oscillation frequency, better breakdown voltage and good cut-off frequency, and current gain compared to the traditional designs. These enhancements make it a highly desirable choice for applications that demand high-frequency and high-power capabilities.

Smart devices in Industry 5.0, such as sensors and robots, are often limited by low battery life and finite computational resources, hindering their ability to perform complex tasks. By offloading computation-intensive tasks to Mobile Edge Cloud Computing (MEC) servers at the network’s edge, businesses can achieve real-time data processing and analysis, reducing communication latency, quicker response times, and improved system reliability. This work presents an integrated framework for MEC and Industry 5.0, aimed at enhancing the performance, efficiency, and flexibility of industrial processes. In particular, we propose a joint optimization problem that maximizes computational energy efficiency by optimally allocating resources, such as processing power and computational resources, as well as device association, in the most efficient manner possible. The problem is formulated as nonconvex/nonlinear, which is intractable and poses high complexity. To solve this challenging problem, we first transform and decouple the original optimization problem into a series of subproblems using the block coordinate descent method. Then, we iteratively obtain an efficient solution using convex optimization methods. In addition, our work sheds light on the fundamental trade-off between local computation and partial offloading schemes. The results show that for small data size requirements, the performance is comparable among different schemes. However, as data size increases, our proposed hybrid scheme, which includes a partial offloading scheme, outperforms others, highlighting the effectiveness of the proposed joint optimization scheme.

Wireless power transfer (WPT) has emerged as a promising solution for delivering services to low-power Internet of Things (IoT) devices in a demand-driven manner. In this work, we consider the Wireless power-enabled Hybrid Mobile Edge Cloud (WPHMEC) network, which utilizes a dynamic offloading strategy (partial and binary) to maximize computational efficiency while minimizing device energy consumption. To address this challenge, we formulate a convex optimization problem to maximize the number of computational bits and minimize the energy consumption of the IIoT devices in the WPT-based MEC system for Industry 5.0 applications. To achieve this goal, we employ a reformulation approach based on block coordinate descent (BCD) to formulate an optimization problem that addresses the nonconvexity of the problem and propose a solution approach using the Karush–Kuhn–Tucker (KKT) conditions. To validate the effectiveness of the proposed scheme, extensive simulations are carried out using the Matlab software to evaluate the system’s performance and components. The results demonstrate that optimal resource allocation maximizes energy efficiency and enhances computational resource utilization, making WPHMEEC an ideal choice for addressing the evolving demands of Industry 5.0 applications.

The Internet of Things (IoT) provides new opportunities for different IoT platforms connecting various devices together. The need to identify those devices is the foremost important to perform any kind of operation. Many organizations and standard bodies that provide specifications and frameworks for the IoT currently have their own identification mechanisms. Some existing industrial identification mechanisms can also be used in the IoT. There is no common Identification Scheme (IS) for the IoT as yet, because of the political and commercial differences amongst the standard bodies. The unavailability of a unified IS method makes the inter-working among IoT platforms challenging. This paper analyses and compares ISs used by several selected IoT platforms. This work will help in understanding the need for a common identification mechanism to provide inter-working among different IoT platforms.

Vehicular networks aim to support cooperative warning applications that involve the dissemination of warning messages to reach vehicles in a target area. Due to the high mobility of vehicles, imperative technologies such as software-defined network (SDN) and edge computing (EC) have been proposed for the next-generation vehicular networks. The SDN separates the control plane from data plane entities and executes the control plane software on general purpose hardware. On the other hand, EC aims to reduce the network latency and packet loss rate by pushing the computations to the edge of the network. Nevertheless, the current solutions that integrate SDN and EC could not satisfy the latency requirements for data dissemination of vehicle-to-everything (V2X) services. To bridge the gap between the two technologies, the conventional EC is enhanced to multi-access edge computing (MEC) by collocating the edge computing servers with the radio access networks. In order to improve the latency for V2X services, we propose in this paper, an SDN-based multi-access edge computing framework for the vehicular networks (SDMEV). In the proposed solution, two main algorithms are implemented. First, a fuzzy logic-based algorithm is used to select the head vehicle for each evolved node B (eNB) collocated with road-side unit (RSU) for the purpose of grouping vehicles based on their communication interfaces. Afterward, an OpenFlow algorithm is deployed to update flow tables of forwarding devices at forwarding layers. In addition, a case study is presented and evaluated using the object-oriented modular discrete event network (OMNeT++) simulation framework which includes the INET framework-based SDN. Simulation results depict that the data dissemination based-SDN supported by multi-access edge computing over SDMEV can improve the latency requirements for V2X services.

A Web attack protection system is extremely essential in today's information age. Classifier ensembles have been considered for anomaly-based intrusion detection in Web traffic. However, they suffer from an unsatisfactory performance due to a poor ensemble design. This paper proposes a stacked ensemble for anomaly-based intrusion detection systems in a Web application. Unlike a conventional stacking, where some single weak learners are prevalently used, the proposed stacked ensemble is an ensemble architecture, yet its base learners are other ensembles learners, i.e. random forest, gradient boosting machine, and XGBoost. To prove the generalizability of the proposed model, two datasets that are specifically used for attack detection in a Web application, i.e. CSIC-2010v2 and CICIDS-2017 are used in the experiment. Furthermore, the proposed model significantly surpasses existing Web attack detection techniques concerning the accuracy and false positive rate metrics. Validation result on the CICIDS-2017, NSL-KDD, and UNSW-NB15 dataset also ameliorate the ones obtained by some recent techniques. Finally, the performance of all classification algorithms in terms of a two-step statistical significance test is further discussed, providing a value-added contribution to the current literature.

In order to cater to the energy requirement in the form of steam at a reasonable cost, the process industries are relying on the waste incineration plants by engaging themselves through industry symbiosis. However, before the establishment of industrial symbiosis, it is very crucial to monitor and predict the operational performance of the boiler used in the waste incineration plants. The existing works focus on using Artificial Neural Networks (ANNs) for prediction of the performance of the boiler in terms of pressure, temperature, and mass flow rate of steam using the input parameters viz. feed water temperature, feed water pressure, incinerator exit temperature and conveyor speed. However, the problem with this approach is that shallow ANNs cannot model the complex mathematical non-linear relationships so precisely. In addition, ANNs are not interpretable which makes stakeholders apprehensive to use these networks in production. In this paper, we address these drawbacks of ANNs by modeling the complex relationship governing the boiler performance by using a set of machine learning and deep learning models. Also, the research paper introduces multiple techniques like feature importance, Partial Dependence Plots(PDP) plots etc. which interpret the reason behind the model's output to make it more reliable for the stakeholders. It has been empirically shown that the new Machine Learning and Deep Learning models performed better than the ANNs for predicting the boiler performance. The Random Forest model made a Mean Absolute Percentage Error (MAPE) of 1.12 and LSTMs had a MAPE of 1.14 in the prediction of steam temperature Co which is a significant improvement in comparison to the original ANN model which had a MAPE of 6.93. In the case of the predictions for steam pressure kgf /cm2 the MAPE for the Random Forest model and LSTM was 5.54 and 4.21 respectively as compared to ANNs MAPE of 1.49. Similarly for steam mass flow rate(t/h), the MAPE was improved to 15.6 and 9.63 by Random Forest Model and LSTM respectively, which was originally 18.77 for ANN based model. These results clearly show that LSTM based models outperformed ANNs and Random Forests in terms of prediction accuracy.

Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today's medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.

Consider the Diophantine equation yn=x+x(x+1)+⋯+x(x+1)⋯(x+k), where x, y, n, and k are integers. In 2016, a research article, entitled – ’power values of sums of products of consecutive integers’, primarily proved the inequality n= 19,736 to obtain all solutions (x,y,n) of the equation for the fixed positive integers k≤10. In this paper, we improve the bound as n≤ 10,000 for the same case k≤10, and for any fixed general positive integer k, we give an upper bound depending only on k for n.

In this study, we consider a perishable inventory system that has an (s, Q) ordering policy, along with a finite waiting hall. The single server, which provides an item to the customer after completing the required service performance for that item, only begins serving after N customers have arrived. Impatient demand is assumed in that the customers waiting to be served lose patience and leave the system if the server’s idle time overextends or if the arriving customers find the system to be full and will not enter the system. This article analyzes the impatient demands caused by the N-policy server to an inventory system. In the steadystate, we obtain the joint probability distribution of the level of inventory and the number of customers in the system. We analyze some measures of system performance and get the total expected cost rate in the steadystate. We present a beneficial cost function and confer the numerical illustration that describes the impact of impatient customers caused by N-policy on the inventory system’s total expected cost rate.

Gender, religion, and migration are perplexing issues, especially in this era of the COVID-19 pandemic in which gendered and religious dynamics are emerging within migrant communities across the world. The relations between these three concepts are explored within this bleak time that has exposed previously neglected dynamics present in migrant communities living in distant host countries in Asia, Europe, and the United States of America. In this paper, we discuss the intricacies within religion and gender among migrant communities and the gendered impacts that COVID-19 has had on the aforementioned migrant communities. Through a secondary desk review analysis of the diverse emerging literature, we show that there are gendered implications of the pandemic measures taken by governments as migrant communities occupy unique translocalities. Overall, the intersection of religion, gender, and migration underscores religion reproducing gender roles among the migrants. The reproduction of gender in religious institutions disadvantage women amidst the COVID-19 pandemic. The analysis confirmed the trivial fact that migrant women continue to suffer disproportionately due to increased unemployment and disease burden coupled with religious practices that continue to advance the upward mobility of male migrants. There is a need to recast the place of migrant women in this era, and lastly, religion plays a renewed role among migrant communities especially for women who have enhanced their social positions and organizational skills through it.

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

Cloud computing supports many unprecedented cloud-based vehicular applications. To improve connectivity and bandwidth through programmable networking architectures, Software- Defined (SD) Vehicular Network (SDVN) is introduced. SDVN architecture enables vehicles to be equipped with SDN OpenFlow switch on which the routing rules are updated from a SDN OpenFlow controller. From SDVN, new vehicular architectures are introduced, for instance SD Vehicular Cloud (SDVC). In SDVC, vehicles are SDN devices that host virtualization technology for enabling deployment of cloud-based vehicular applications. In addition, the migration of Virtual Machines (VM) over SDVC challenges the performance of cloud-based vehicular applications due the highly mobility of vehicles. However, the current literature that discusses VM migration in SDVC is very limited. In this paper, we first analyze the evolution of computation and networking technologies of SDVC with a focus on its architecture within the cloud-based vehicular environment. Then, we discuss the potential cloud-based vehicular applications assisted by the SDVC along with its ability to manage several VM migration scenarios. Lastly, we provide a detailed comparison of existing frameworks in SDVC that integrate the VM migration approach and different emulators or simulators network used to evaluate VM frameworks’ use cases.

In post-disaster scenarios, such as after floods, earthquakes, and in war zones, the cellular communication infrastructure may be destroyed or seriously disrupted. In such emergency scenarios, it becomes very important for first aid responders to communicate with other rescue teams in order to provide feedback to both the central office and the disaster survivors. To address this issue, rapidly deployable systems are required to re-establish connectivity and assist users and first responders in the region of incident. In this work, we describe the design, implementation, and evaluation of a rapidly deployable system for first response applications in post-disaster situations, named RDSP. The proposed system helps early rescue responders and victims by sharing their location information to remotely located servers by utilizing a novel routing scheme. This novel routing scheme consists of the Dynamic ID Assignment (DIA) algorithm and the Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all the available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. The RDSP contains three devices; the client device sends the victim’s location information to the server, the relay device relays information between client and server device, the server device receives messages from the client device to alert the rescue team. We deployed and evaluated our system in the outdoor environment of the university campus. The experimental results show that the RDSP system reduces the message delivery delay and improves the message delivery ratio with lower communication overhead.

Basic safety message (BSM) are messages that contain core elements of a vehicle such as vehicle’s size, position, speed, acceleration and others. BSM are lightweight messages that can be regularly broadcast by the vehicles to enable a variety of applications. On the other hand, event-driven message (EDM) are messages generated at the time of occurrence such as accidents or roads sliding and can contain much more heavy elements including pictures, audio or videos. Security, architecture and communication solutions for BSM use cases have been largely documented on in the literature contrary to EDM due to several concerns such as the variant size of EDM, the appropriate architecture along with latency, privacy and security. In this paper, we propose a secure and blockchain based EDM protocol for 5G enabled vehicular edge computing. To offer scalability and latency for the proposed scenario, we adopt a 5G cellular architecture due to its projected features compared to 4G tong-term evaluation (LTE) for vehicular communications. We consider edge computing to provide local processing of EDM that can improve the response time of public agencies (ambulances or rescue teams) that may intervene to the scene. We make use of lightweight multi-receiver signcryption scheme without pairing that offers low time consuming operations, security, privacy and access control. EDM records need to be kept into a distributed system which can guarantee reliability and auditability of EDM. To achieve this, we construct a private blockchain based on the edge nodes to store EDM records. The performance analysis of the proposed protocol confirms its efficiency.

Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.

There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could be avoided by adopting Cellular Vehicle-to-Everything (C-V2X) technology with the possibility to re-use the existing mobile network infrastructure. More and more, with the emergence of Software Defined Networks, the flexibility and the programmability of the network have not only impacted the design of new vehicular network architectures but also the implementation of V2X services in future intelligent transportation systems. In this paper, we define the concepts that help evaluate software-defined-based vehicular network systems in the literature based on their modeling and implementation schemes. We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. We then present the different architectures and their underlying system models for LTE-V2X communications. We later describe the key ideas of software-defined networks and their concepts for V2X services. Lastly, we provide a comparative analysis of existing SDN-based vehicular network system grouped according to their modeling and simulation concepts. We provide a discussion and highlight vehicular ad-hoc networks’ challenges handled by SDN-based vehicular networks.

In the age of the Internet of Things, connected devices are changing the delivery system in the healthcare communication environment. With the integration of IoT in healthcare, there is a huge potential for improvement of the quality, safety, and efficiency of health care in addition to promising technological, economical, and social prospects. Nevertheless, this integration comes with security risks such as data breach that might be caused by credential-stealing malware. In addition, the patient valuable data can be disclosed when the perspective devices are compromised since they are connected to the internet. Hence, security has become an essential part of today’s computing world regarding the ubiquitous nature of the IoT entities in general and IoT-based healthcare in particular. In this paper, research on the algorithm for anonymizing sensitive information about health data set exchanged in the IoT environment using a wireless communication system has been presented. To preserve the security and privacy, during the data session from the users interacting online, the algorithm defines records that cannot be revealed by providing protection to user’s privacy. Moreover, the proposed algorithm includes a secure encryption process that enables health data anonymity. Furthermore, we have provided an analysis using mathematical functions to valid the algorithm’s anonymity function. The results show that the anonymization algorithm guarantees safety features for the considered IoT system applied in context of the healthcare communication systems.

In the vehicular networks, the real-time video reporting service is used to send the recorded videos in the vehicle to the cloud. However, when facilitating the real-time video reporting service in the vehicular networks, the usage of the fourth generation (4G) long term evolution (LTE) was proved to suffer from latency while the IEEE 802.11p standard does not offer sufficient scalability for a such congested environment. To overcome those drawbacks, the fifth-generation (5G)-enabled vehicular network is considered as a promising technology for empowering the real-time video reporting service. In this paper, we note that security and privacy related issues should also be carefully addressed to boost the early adoption of 5G-enabled vehicular networks. There exist a few research works for secure video reporting service in 5G-enabled vehicular networks. However, their usage is limited because of public key certificates and expensive pairing operations. Thus, we propose a secure and lightweight protocol for cloud-assisted video reporting service in 5G-enabled vehicular networks. Compared to the conventional public key certificates, the proposed protocol achieves entities’ authorization through anonymous credential. Also, by using lightweight security primitives instead of expensive bilinear pairing operations, the proposed protocol minimizes the computational overhead. From the evaluation results, we show that the proposed protocol takes the smaller computation and communication time for the cryptographic primitives than that of the well-known Eiza-Ni-Shi protocol.