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Serverless computing is a new concept as cloud computing, which dynamically manages the networks and is applied in Serverless Wireless Sensor Networks (SWSN) to help the networks. These networks are becoming famous for monitoring various physical and environmental factors. Serverless computing also facilitates the networks by offering an extensive range of applications. Different applications have been designed for monitoring purposes where the sensor nodes sense the data and transmit it to the base station through single or multi-hop routing. However, existing routing protocols cannot manage the sensor nodes’ energy issues because of the complex routing processes and depleted their power before their time. Because of these limitations, the nodes close to BS continuously rely on the network for data forwarding. As a result, these nodes cause energy consumption and lead to a useless state. This paper proposes a serverless architecture and designs an Enhanced Centroid-based Energy Efficient Clustering (ECEEC) protocol for SWSN networks. The proposed serverless architecture provides automated scalability, cost-effective services, and stateless execution. In addition, the proposed protocol offers the cluster head selection and its rotation to maximize the energy efficiency in the network. Furthermore, gateway nodes are chosen in every cluster to overcome the load on the cluster head. Simulation results indicated the excellent performance of the proposed protocol as compared to the existing routing protocols concerning network lifetime and energy consumption. The proposed protocol shows better reliability with nodes failing at 650 rounds compared to 600 rounds, especially with 5 % and 10 % Cluster Heads. The proposed protocol exhibits superior energy efficiency consumption of SNs under varying CH percentages, indicating the protocol’s consistent performance across different scenarios.

Flying Ad Hoc Networks (FANET) is an emerging area of research due to its low cost, high coverage and fast transmission features. In these networks, the flying nodes are connected with ground stations and communicate wirelessly, especially when the networks are congested and complex. Due to mobility, and lack of predefined infrastructure, these networks have suffered from various security and trust issues. The traditional trust and security solutions are designed for ground networks and are not feasible for these networks. This paper proposes a trust and authentication model including Trust Establishment Mechanism for FANET (TEM-FANET) and authentication system by using Block-chain method. The trust is calculated to evaluate the node’s trust status and ensure the existence of the trustworthy nodes by using direct, indirect, and cumulative trust values. Whereas the authentication system is utilizing blockchain technology for nodes authentication and evaluate its feasibility. The proposed model is lightweight and able to monitor the node’s behavior and compute the trusted quality and broadcast the node status with neighbor nodes. The proposed model is also integrated with ground stations for record keeping and decision-making processes. The proposed model is evaluated in simulation with state-of-the-art trust solutions where the results show the better performance in terms of overhead, data delivery, node detection rate, and computational time.

Low Power Wide Area Network (LPWAN) technologies have been exponentially growing because of the tremendous growth of the Internet of Things (IoT) devices across the globe. Several LPWAN technologies have been utilized by the researchers to address certain issues like increased number of collisions, retransmissions, delay, and energy consumption. However, Long Range Wide Area Network (LoRaWAN) is the most suitable and attractive technology in terms of delay optimization, low cost and efficient energy consumption. The main issue which arises in LoRaWAN is because of its high packet drop rate due to collision. The reason behind this packet drop rate is the MAC scheme known as Pure Aloha used by LoRaWAN for the transmission of the frames. Long Range (LoRa) End Devices (EDs) initiate communication with Pure Aloha that leads to a high number of retransmissions. These retransmissions further enhance the delay in LoRa networks. This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). By using ASA with GMM, the retransmissions are reduced which optimizes the delay in LoRaWAN. The results show that in our approach, Packet Collision Rate (PCR) is reduced by 39% as compared to conventional LoRaWAN. In addition, the Packet Success Ratio (PSR) is also increased by 39% as compared to the conventional LoRaWAN and Dynamic Priority Scheduling Technique (PST). Further, the delay is optimized by 91% and 79%. This research could be effective for the environments where the critical data of patients need to be sent with optimised retransmissions and minimum delay towards gateways.

The Internet of Things (IoT) is an emerging and groundbreaking technology in which devices, individuals, and processes seamlessly exchange substantial volume of data without causing disruption. This paradigm offers a wide range of services like healthcare, transportation, education, and smart home operations. However, owing to factors like deployment unpredictability, battery-operated nature, and the intricate nature of IoT networks, the energy levels of these diminutive sensor nodes are rapidly depleted, significantly curtailing the network’s overall lifespan. Moreover, energy utilization and reputation assessment are pivotal in establishing dependability, reliability, and collaboration among sensor nodes, thereby profoundly influencing the decision-making framework. In response to these challenges, this paper introduces a State-based Energy Calculation Scheme (SECS) that leverages nodes’ statuses and the functions encompassing direct and indirect energy utilization. The scheme identifies energy-depleted nodes, segregating them within the context of end-to-end reliable path selection, all while minimizing control broadcasts. Empirical results underscore the enhanced performance of the proposed scheme, as evidenced by augmented network throughput, elevated packet delivery ratios, diminished node energy utilization, and truncated end-to-end delays.

Movie recommendation algorithms play an important role in assisting consumers in identifying films that match their likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, has shown substantial promise in collecting sequential patterns to improve movie recommendations among the different techniques used for this purpose. Long Short-Term Memory-Inter Intra-metapath Aggregation (LSTM-IIMA) in movie recommendation systems is proposed in this study, with a specific focus on incorporating intra and inter-metapath analysis. The intra-metapath analysis investigates interactions within a single metapath, whereas the inter-metapath analysis investigates links between numerous metapaths. Intra and inter-metapath analyses are used in the LSTM-based movie recommendation system LSTM-IIMA to capitalise on these rich linkages. Each metapath sequence records the dependencies of a user’s interactions with films and other things. The LSTM architecture has been modified to handle these metapath sequences, processing them to record temporal dependencies and entity interactions. To optimize the parameters and minimize prediction errors, the model is trained using supervised learning techniques. To measure the quality and usefulness of the recommendations, the LSTM-IIMA evaluation incorporates metrics such as precision, recall, ablation analysis, time efficiency and Area Under the Curve (AUC). The performance of the system is compared to that of alternative recommendation techniques HAN and MAGNN. Overall, incorporating intra and inter-metapath analysis into the LSTM-IIMA improves its ability to capture complex linkages and dependencies between movies, users, and other things.

The concept of smart cities is unique where the intelligent information technologies have used to form a heterogeneous with the help of edge and fog networks. Edge Computing (EC) has gained attention due to its distributive nature that brings computing resources closer to user devices for fast data communications. These networks are vulnerable to a variety of cyber threats due open network design and architecture and a lack of trusted computing resources. The Denial of Service (DoS) and Sybil attacks are common in these networks and cause of services degradation. Whereas in a Sybil attack, a node illegitimately claims multiple identities and escalate and launch more attacks in the networks. Identification and detection of malicious nodes and evaluating their trustworthiness is a challenging task. This paper proposes a novel Assertive Trust-based Efficient Technique (ATBET) for edge-based smart city networks by calculating the direct, indirect, penalized and cumulative trust. The trust is evaluated by using packet drop rate and the penalized over the transmission paths. The proposed solution detects the attacks and eliminates the malicious or selfish nodes from edge-based networks. The experiment results are conducted in simulation and compared proposed solution with state or the art solutions in terms of trustworthiness level, latency, packet delivery ratio, and network life span.

High-speed networks are utilized to satisfy user demands by using cloud and edge services. Due to the heterogeneous nature of these networks, security, trust, and privacy are the significant challenges for these networks. These networks are vulnerable to various security threats, lack of trustworthy devices, and user privacy concerns due to architectural complexities, diversity of protocols, and dynamic network topologies. This Systematic Literature Review (SLR) explores these networks in terms of technologies, edge and cloud services, security, trust and privacy concerns. Torress-Carrion methodology is used as a guideline to conduct this review to find the recent security, trust and privacy concerns. The attacks related to each area and their solutions are discussed to evaluate their strengths and weaknesses. The various existing technologies explore like Blockchain, encryption, hashing, and fuzzy logic. In addition, the trust evaluation and user privacy provision techniques are also discuss. This SLR also explores the methods designed for different communication layers against the various multilingual passive/active attacks like Sybil, Denial of Service (DoS), Distributed Denial of Service (DDoS), and Man in the Middle (MITM). This SLR will provide a direction to new researchers in the field of security, trust, and privacy to design more efficient and effective solutions to deal with new security challenges.

The availability of Software Defined Network’s (SDNs) flow rule entry in the flow table is considered a key factor in the timely delivery of a certain flow. The controller is approached for instructions on how to deal with the flow when the rule for such flow is missing. The controller then updates the flow table accordingly at the switch so that flow could be dealt with locally. It becomes problematic when no rule is defined yet at the controller by the application plane. In most of these cases, such a situation is handled by programming the controller with wildcard rules. However, handling many flows at once under wildcard rules severely hampers the network performance. Flow rules formation by the application plane is sometimes critical and time-consuming which increases the latency ratio by creating a bottleneck at the switch level. To avoid the bottlenecks due to rule absence, in this paper, rather than waiting for the application plane’s response and putting the pending traffic flows in the buffer, which may be dropped, the controller is programmed in a way that has the built-in mechanism of self-flow rule formation. This atomized mechanism is based on the previously available traces of the same flows when they were forwarded on the network using the wildcard rules. To assess the performance of the proposed work, it is emulated and benchmarked with the latest research. The results show considerable performance achievement.

New and advanced technologies have introduced amazing areas like the Internet of Vehicles (IoV) and Unmanned Aerial Vehicle (UAV). These two technologies are emerged to facilitate the ground users by providing more fast and convenient data communication services. Due to the open nature of these networks, security and user privacy is always a serious concern. Different types of security attacks have been noticed in these networks like jamming attacks, global positioning system jamming, signal jamming, and data jamming. The existing solutions have been designed to resolve and tackle security issues such as authenticity, insurance of data integrity, provision of correct message authentication, and removal of redundant authentication. However, existing schemes have some limitations in terms of computational costs and efficiency and do not provide security at all levels. This paper proposes an Efficient Authentication Scheme for Safety Applications for Internet of Vehicles (EASSAIV) for message authentication. The proposed scheme can gather, process, and verify the information delivered to roadside units, UAVs, or to the vehicles and authenticate the received messages. Proposed scheme achieved better performance as compared to state of the art scheme in terms of safety, packet loss, delay and computational cost.

The smart environmental management system proposed in this work offers a new approach to environmental monitoring by utilizing data from IoT stations and MODIS satellite imagery. The system is designed to be deployed in vast regions, such as the Mekong Delta, with low building and operating costs, making it a cost-effective solution for environmental monitoring. The system leverages telemetry data collected by IoT stations in combination with MODIS MOD09GA, MOD11A1, and MCD19A2 daily image products to develop computational models that calculate the values land surface temperature (LST), 2.5 and 10 (µm) particulate matter mass concentrations (PM2.5 and PM10) in areas without IoT stations. The MOD09GA product provides land surface spectral reflectance from visible to shortwave infrared wavelengths to determine land cover types. The MOD11A1 product provides thermal infrared emission from the land surface to compute LST. The MCD19A2 product provides aerosol optical depth values to detect the presence of atmospheric aerosols, e.g., PM2.5 and PM10. The collected data, including remote sensing images and telemetry sensor data, are preprocessed to eliminate redundancy and stored in cloud storage services for further processing. This allows for automatic retrieval and computation of the data by the smart data processing engine, which is designed to process various data types including images and videos from cameras and drones. The calculated values are then made available through a graphic user interface (GUI) that can be accessed through both desktop and mobile devices. The GUI provides real-time visualization of the monitoring values, as well as alerts to administrators based on predetermined rules and values of the data. This allows administrators to easily monitor the system, configure the system by setting alerting rules or calibrating the ground stations, and take appropriate action in response to alerts. Experimental results from the implementation of the system in Dong Thap Province in the Mekong Delta show that the linear regression models for PM2.5 and PM10 estimations from MCD19A2 AOD values have correlation coefficients of 0.81 and 0.68, and RMSEs of 4.11 and 5.74 µg/m3, respectively. Computed LST values from MOD09GA and MOD11A1 reflectance and emission data have a correlation coefficient of 0.82 with ground measurements of air temperature. These errors are comparable to other models reported in similar regions in the literature, demonstrating the effectiveness and accuracy of the proposed system.

With the increasing use of smart devices and sensors, enormous amounts of data are being generated continuously. The data is commonly stored in centralized cloud platforms and consumed by different services. The data is indeed a valuable resource for many service providers who provide advanced features and utilities to their subscribers. However, user data include personal and sensitive information which can be misused in many ways. There is no way for a subscriber to confirm that their service provider is compliant with data privacy regulations. The existing privacy enhancing techniques such as anonymization and differential privacy substantially reduce data usability while ensuring privacy. Therefore, it remains essential to provide a feasible solution that allows service providers to take advantage of user data while guaranteeing their privacy. In this paper, we present PETchain: a novel privacy enhancing technology using blockchain and smartcontract. In PETchain, data is stored securely in a distributed manner and processed in a user-selected trusted execution environment. Users deploy the smartcontract that allows them to decide whether and how their data can be exploited by service providers. The feasibility and performance of PETchain are presented by implementing PETchain over a consortium Ethereum blockchain.

The indoor connected environment has witnessed significant research and development attention from industries and academia due to the growing number of smaller smart indoor devices around us. Developing an effective and efficient wireless access standard is one of the challenging tasks to enable the next generation indoor connected environment. The technical characteristics of existing wireless access standards, including IEEE 802.11a, 802.11n, and 802.11ac, are considerably limited for realizing indoor connected environments, particularly with a growing number of smaller intelligent devices. Moreover, their backward compatibility and migration strategies are significant for developing the next-generation wireless access standard for the indoor Internet of Things environment. In this context, this paper presents an indoor environmental experimental study focusing on the backward compatibility and migration-centric performance analysis of existing wireless access standards. Three wireless access standards that operate in the 5 GHz frequency spectrum are evaluated considering the metrics, including throughput, range, efficiency, and backward compatibility in an indoor environment. The experimental results are also compared with the analytical path loss model to observe the attributes for next-generation wireless access between the observed and analytical models. The evaluation can attest to the suitable migration strategy for stable next-generation wireless access development and deployment for an indoor smart Internet of Things environment.

Abstract Cellular networks based on new generation standards are the major enabler for Internet of things (IoT) communication. Narrowband-IoT and Long Term Evolution for Machines are the newest wide area network-based cellular technologies for IoT applications. The deployment of unmanned aerial vehicles (UAVs) has gained the popularity in cellular networks by using temporary ubiquitous coverage in the areas where the infrastructure-based networks are either not available or have vanished due to some disasters. The major challenge in such networks is the efficient UAVs deployment that covers maximum users and area with the minimum number of UAVs. The performance and sustainability of UAVs is largely dependent upon the available residual energy especially in mission planning. Although energy harvesting techniques and efficient storage units are available, but these have their own constraints and the limited onboard energy still severely hinders the practical realization of UAVs. This paper employs neglected parameters of UAVs energy consumption in order to get actual status of available energy and proposed a solution that more accurately estimates the UAVs operational airtime. The proposed model is evaluated in test bed and simulation environment where the results show the consideration of such explicit usage parameters achieves significant improvement in airtime estimation.

New technologies and communication standards have changed traditional network processes. Internet of Things (IoT) is one of the emerging technologies where devices are connected to facilitate the users. When the networks are more congested due to a large number of users then the existing routing protocol and communication channels suffer from congestion, disconnection, overhead, and packet drop issues. Unmanned Aerial Vehicles (UAVs) are adopted to support the ground networks for more feasible data communication. These networks provide coverage facilities to IoT networks and provide smooth data dissemination services. Through the use of relay and cooperative communication technologies, UAVs could enlarge the communication space for IoT networks. Traditional network routing protocols have been adopted for data communication in these networks. However, the adopted protocols are not able to handle mobility and uncertain network conditions. This paper proposes a Decision-based Routing for Unmanned Aerial Vehicles and Internet of Things (DR-UAVIoT) network. The proposed protocol is useful for UAV-to-IoT and UAV-to-UAV data communication. The performance of the proposed solution is evaluated with the existing protocols in terms of data delivery, delay, and network overhead. The experimental results indicate the better performance of the proposed protocol in terms of less delay, less overhead, and better data delivery ratio as compared with existing routing protocols.

The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions.

The exponential growth of the edge-based Internet-of-Things (IoT) services and its ecosystems has recently led to a new type of communication network, the Low Power Wide Area Network (LPWAN). This standard enables low-power, long-range, and low-data-rate communications. Long Range Wide Area Network (LoRaWAN) is a recent standard of LPWAN that incorporates LoRa wireless into a networked infrastructure. Consequently, the consumption of smart End Devices (EDs) is a major challenge due to the highly dense network environment characterised by limited battery life, spectrum coverage, and data collisions. Intelligent and efficient service provisioning is an urgent need of a network to streamline the networks and solve these problems. This paper proposes a Dynamic Reinforcement Learning Resource Allocation (DRLRA) approach to allocate efficient resources such as channel, Spreading Factor (SF), and Transmit Power (Tp) to EDs that ultimately improve the performance in terms of consumption and reliability. The proposed model is extensively simulated and evaluated with the currently implemented algorithms such as Adaptive Data Rate (ADR) and Adaptive Priority-aware Resource Allocation (APRA) using standard and advanced evaluation metrics. The proposed work is properly cross validated to show completely unbiased results.

Internet of Vehicle (IoV) is a sub class of vehicular ad hoc networks with more advanced cloud and Internet-enabled services. These networks offer various types of safety and infotainment services and provide comfortability and safety to passengers as well as to the drivers. Due to the high mobility of nodes, the nodes are out from its communication range and the information becomes outdated and causes link disconnections and packet dropping. Most feasible routing protocols are needed to provide in-time data communication, handle high mobility of nodes, dynamic topologies and unpredictable environments of these networks. In this paper, we proposed SACBR (Self-Assessment Cluster-based Routing) protocol in which the Cluster Heads (CHs) can communicate with other CHs. Every vehicle node initiates a self-assessment approach based on more appropriate routing metrics and elects the CH for every cluster and then collects the data from member nodes and further forward the data to other CHs. The CH is responsible to manage its own and member nodes' data forwarding process. The proposed protocol provides more stability and less overhead compared to the aggregation method where every node exchanges its data with a one-hop neighbor. Proposed protocol suites sparse and dense traffic scenarios where most of the time vehicle nodes are moving in platoons or snaking structures. The experimental results show the better performance of SACBR compared to state-of-the-art protocols.

Intelligent Transportation System (ITS) in sustainable smart cities are taking advantage of moving vehicle nodes for data communication. VANETs support many applications related to safety, infotainment, and accident detection. The routing protocols are using for data communication in the presence of high mobility nodes and dynamic topologies. Due to high mobility and unpredictable topologies, the data communication becomes unreliable which causes data loss, delay, and link disconnections among vehicle nodes. To address these routing limitations, various types of routing protocols have developed. In all existing routing protocols types, geographic routing protocols are one of the efficient types due to its low overhead processes. Geographical routing protocols are able to handle vehicular environment constraints. However, with many advantages, geographic routing protocols are not considering many constraints of the vehicular environment. Geographical routing protocols should have well-defined routing metrics to deal with high mobility and other data loss and link disconnection issues. This research designs a Beaconless Traffic-Aware Geographical Routing Protocol (BTA-GRP) by considering traffic density, distance and direction for next forwarder node and route selection. The protocol is feasible for urban dense and sparse traffic conditions and addresses delay, disconnection and packet dropping issues. The proposed protocol has simulated with state of the art routing protocols. The simulation results indicated that the proposed protocol has higher performance in VANETs.

Cyber-attacks are causing losses amounted to billions of dollars every year due to data breaches and Vulnerabilities. The existing tools for data leakage prevention and detection are often bypassed by using various different types of sophisticated techniques such as network steganography for stealing the data. This is due to several weaknesses which can be exploited by a threat actor in of existing detection systems. The weaknesses are high time and memory training complexities as well as large training datasets. These challenges become worse when the amount of generated data increasing in every second in many realms. In addition, the number of false positives is high which make them inaccurate. Finally, there is a lack of a framework catering the needs such as raising alerts as well as data monitoring and updating/adapting of a threshold value used for checking the data packets for covert data. In order to overcome these weaknesses, this paper proposes a novel framework that includes elements such as continuous data monitoring, threshold maintenance, and alert notification. This paper also proposes a model based on statistical measures to detects covert data leakages, especially for non-linear chaotic data. The main advantage of proposed model is its capability to provide results with tolerance/threshold values much more efficiently. Experiment are indicated that the proposed framework has low false positives and outperforms over various existing techniques in terms of accuracy and efficiency.

Green communications have witnessed significant attention being paid to the next generation of wireless systems research and development. This is due to growing use of sensor- and battery-oriented smart wireless devices. The related literature in green communications for next-generation wireless systems majorly relies on transmission and sensing power management, but lacks a fault-tolerant centric approach. In this context, this paper presents a fault-tolerant and reliable green communications framework for next-generation wireless systems (FRGNWS). Firstly, maximum node-disjoint routes from all source nodes to the base station are identified based on the hybrid adapted grey wolf sine cosine optimizer. Secondly, a fault-tolerant and reliable route is selected from the maximum disjoint routes for each sensor node to the base station based on the hybrid adapted grey wolf whale optimizer. The performance of our proposed green communications framework is assessed by simulation experiments considering a realistic implementation scenario and different metrics. Simulation results clearly validate the efficacy of the proposed green communications framework as compared to the state-of-the-art techniques.

Internet availability and its integration with smart technologies have favored everyday objects and things and offered new areas, such as the Internet of Things (IoT). IoT refers to a concept where smart devices or things are connected and create a network. This new area has suffered from big data handling and security issues. There is a need to design a data analytics model by using new 5G technologies, architecture, and a security model. Reliable data communication in the presence of legitimate nodes is always one of the challenges in these networks. Malicious nodes are generating inaccurate information and breach the user’s security. In this paper, a data analytics model and self-organizing architecture for IoT networks are proposed to understand the different layers of technologies and processes. The proposed model is designed for smart environmental monitoring systems. This paper also proposes a security model based on an authentication, detection, and prediction mechanism for IoT networks. The proposed model enhances security and protects the network from DoS and DDoS attacks. The proposed model evaluates in terms of accuracy, sensitivity, and specificity by using machine learning algorithms.

Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection.

Wireless body area network is a promising technology that brings healthcare to a new level of personalization. The applications of wireless body area network are not limited to healthcare monitoring applications but vastly used in entertainment applications. The applications are emerging at a fast pace and attract the attention of researchers. IEEE 802.15.6 provides a communication standard which specifies the physical layer and media access control layer operations for wireless body area networks. A fixed superframe structure is used for handling of heterogeneous traffics of wireless body area networks through pre-defined user priorities. This leads to inefficient use of superframe time duration because of fixed time phases for different types of data traffic. In this article, a novel group-based classification of traffic is introduced to avoid contention and inefficient use of superframe duration. A group-based media access control is developed to adjust the superframe duration according to high priority traffic whereas the rest of the traffic is controlled using node-based buffering. The experimental results showed that the proposed media access control outperformed adaptive beaconing medium access control and priority media access control, in terms of stability period, delay, throughput, transmission loss, and residual energy.

In this paper, we have investigated some of the most recent energy efficient routing protocols for wireless body area networks. This technology has seen advancements in recent times where wireless sensors are injected in the human body to sense and measure body parameters like temperature, heartbeat and glucose level. These tiny wireless sensors gather body data information and send it over a wireless network to the base station. The data measurements are examined by the doctor or physician and the suitable cure is suggested. The whole communication is done through routing protocols in a network environment. Routing protocol consumes energy while helping non-stop communication in a wireless environment. Because of the very tiny size, energy consumption matters a lot. We have focused on the protocols, which provide energy efficiency and improve network lifetime, through put and minimal path loss. We will be discussing some of the most recent energy efficient routing protocols in this paper; later their comparison with an appropriate table is presented. In addition, recent challenges, possible application and perspectives are discussed.

IEEE 802.15.4 standard is widely used as a communication protocol by low-powered devices, including Internet of Medical Things–enabled technologies. These low-powered technologies have the ability to integrate several communicating and interacting devices for the purpose of wide range applications such as Smart homes and healthcare applications. However, the fixed superframe structure of IEEE 802.15.4 results in the unequal resource utilization among these low-powered heterogeneous Internet of Things devices. The low resource utilization degrades the network performance. In order to improve the network performance and optimum resource utilization, dynamic superframe approach is recommended. This article presents a content-based dynamic superframe adaptation algorithm for the low-powered Internet of Medical Things devices to address the resource utilization challenges. In the content-based dynamic superframe adaptation, the network coordinator dynamically adjusts the superframe along with the backoff exponent. It uses five Quality of Service metrics simultaneously: the application defined data traffic, receive ratio, Personal Area Network source nodes in numbers, number of collisions, and observed delay to achieve the optimal solution. A detailed analysis of the simulations shows that the content-based dynamic superframe adaptation is able to behave more intelligently to adjust superframe dynamic allocations according to the application’s content requirements. Moreover, it outperforms the other existing schemes in achieving the better resource utilization in IEEE 802.15.4 framework.

The growing use of the internet has resulted in an exponential rise in the use of web applications. Businesses, industries, financial and educational institutions, and the general populace depend on web applications. This mammoth rise in their usage has also resulted in many security issues that make these web applications vulnerable, thereby affecting the confidentiality, integrity, and availability of associated information systems. It has, therefore, become necessary to find vulnerabilities in these information system resources to guarantee information security. A publicly available web application vulnerability scanner is a computer program that assesses web application security by employing automated penetration testing techniques that reduce the time, cost, and resources required for web application penetration testing and eliminates test engineers’ dependency on human knowledge. However, these security scanners possess various weaknesses of not scanning complete web applications and generating wrong test results. Moreover, intensive research has been carried out to quantitatively enumerate web application security scanners’ results to inspect their effectiveness and limitations. However, the findings show no well-defined method or criteria available for assessing their results. In this research, we have evaluated the performance of web application vulnerability scanners by testing intentionally defined vulnerable applications and the level of their respective precision and accuracy. This was achieved by classifying the analyzed tools using the most common parameters. The evaluation is based on an extracted list of vulnerabilities from OWASP (Open Web Application Security Project).

The rapid advancement in the area of the Internet of Vehicles (IoV) has provided numerous comforts to users due to its capability to support vehicles with wireless data communication. The exchange of information among vehicle nodes is critical due to the rapid and changing topologies, high mobility of nodes, and unpredictable network conditions. Finding a single trusted entity to store and distribute messages among vehicle nodes is also a challenging task. IoV is exposed to various security and privacy threats such as hijacking and unauthorized location tracking of smart vehicles. Traceability is an increasingly important aspect of vehicular communication to detect and penalize malicious nodes. Moreover, achieving both privacy and traceability can also be a challenging task. To address these challenges, this paper presents a blockchain-based efficient, secure, and anonymous conditional privacy-preserving and authentication mechanism for IoV networks. This solution is based on blockchain to allow vehicle nodes with mechanisms to become anonymous and take control of their data during the data communication and voting process. The proposed secure scheme provides conditional privacy to the users and the vehicles. To ensure anonymity, traceability, and unlinkability of data sharing among vehicles, we utilize Hyperledger Fabric to establish the blockchain. The proposed scheme fulfills the requirement to analyze different algorithms and schemes which are adopted for blockchain technology for a decentralized, secure, efficient, private, and traceable system. The proposed scheme examines and evaluates different consensus algorithms used in the blockchain and anonymization techniques to preserve privacy. This study also proposes a reputation-based voting system for Hyperledger Fabric to ensure a secure and reliable leader selection process in its consensus algorithm. The proposed scheme is evaluated with the existing state-of-the-art schemes and achieves better results.

The Internet of Things gateways with multi-radio facilities in wireless networks can simultaneously communicate using multiple available channels. This feature enhances the carrying capacity of wireless links and thus increases the overall network throughput. However, designing an efficient resource allocation strategy is a complex task due to the decisive behavior of interference. There is only a limited number of available channels; therefore, the resource allocation requires careful planning to mitigate the effect of interference. This research proposes a backtracking search-based resource allocation scheme that maps resource allocation to the constraint satisfaction problem. Some of the resource allocation constraints are applied as soft constraints which are relaxed to find a feasible solution, provided the perfect allocation of limited resources is not possible. The proposed approach has been benchmarked through simulations and the results prove the effectiveness of the proposed approach especially in dense multi-hop network deployments.

A wireless body area network is a collection of Internet of Things–based wearable heterogeneous computing devices primarily used in healthcare monitoring applications. A lot of research is in process to reduce the cost and increase efficiency in medical industry. Low power sensor nodes are often attached to high-risk patients for real-time remote monitoring. These sensors have limited resources such as storage capacity, battery life, computational power, and channel bandwidth. The current work proposes a multi-hop Priority-based Congestion-avoidance Routing Protocol using IoT based heterogeneous sensors for energy efficiency in wireless body area networks. The objective is to devise a routing protocol among sensor nodes such that it has minimum delay and higher throughput for emergency packets using IoT based sensor nodes, optimal energy consumption for longer network lifetime, and efficient scarce resource utilization. In our proposed work, data traffic is categorized into normal and emergency or life-critical data. For normal data traffic, next-hop selection will be selected based upon three parameters; residual energy, congestion on forwarder node, and signal-to-noise ratio of the path between source and forwarder node. We use the data aggregation and filtration technique to reduce the network traffic load and energy consumption. A priority-based routing scheme is also proposed for life-critical data to have less delay and greater throughput in emergency situations. Performance of the proposed protocol is evaluated with two cutting-edge routing techniques iM-SIMPLE and Optimized Cost Effective and Energy Efficient Routing. The proposed model outperforms in terms of network throughput, traffic load, energy consumption, and lifespan.

The tremendous advancement in new wireless technologies has led to a renewed interest in vehicular ad hoc networks. However, due to the high mobility and dynamic nature of vehicular networks, great challenges exist for data delivery. In order to design efficient and smart routing strategies for stable communication, several different types of routing protocols have been integrated. One efficient type is the road-aware routing protocol which is based on road statistics (e.g., traffic density, intersections, and road segments). The main objective of the survey conducted in this paper is to discuss the recent road-aware routing protocols and categorize them according to different aspects. In addition, this review investigates the protocols in terms of their forwarding processes, routing metrics, recovery mechanisms, and performance. Moreover, routing challenges and comparisons are discussed.

Vehicular ad hoc networks (VANETs) are going to be an emerging multihop communication exploit among vehicles to deliver data packets. The special characteristics of vehicular network make the communication link between vehicles unreliable. To handle high mobility and environmental obstacles, most of geographical routing protocols do not consider stable links during packet transmission which lead to higher delay and packet dropping in network. In this paper, we propose road perception based geographical routing protocol named RPGR for VANET. The proposed routing protocol incorporates relative distance, direction, and midrange forwarder node with traffic density to forward the data toward destination in order to improve geographical forwarding between and at the intersections. Simulation results show that the proposed routing protocol performs better as compared to existing solutions.

Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters of the patient. These sensors are equipped with limited resources in terms of computation, storage, and battery power. The data communication in WBANs is a resource hungry process, especially in terms of energy. One of the most significant challenges in this network is to design energy efficient next-hop node selection framework. Therefore, this paper presents a green communication framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. The comparative performance evaluation attests the energy-oriented benefit of the proposed framework as compared to the state-of-the-art techniques. It reveals a significant enhancement in body area networking in terms of various energy-oriented metrics under medical environments.

In Wireless Body Area Network (WBAN), various biomedical sensors (BMSs) are deployed to monitor various vital signs of a patient for detecting the abnormality of the vital signs. These BMSs inform the medical staff in advance before the patient’s life goes into a threatening situation. In WBAN, routing layer has the same challenges as generally seen in WSN, but the unique requirements of WBANs need to be addressed by the novel routing mechanisms quite differently from the routing mechanism in Wireless Sensor Networks (WSNs). The slots allocation to emergency and nonemergency patient’s data is one of the challenging issues in IEEE 802.15.4 and IEEE 802.15.6 MAC Superframe structures. In the similar way, IEEE 802.15.4 and IEEE 802.15.6 PHY layers have also unique constraints to modulate the various vital signs of patient data into continuous and discrete forms. Numerous research contributions have been made for addressing these issues of the aforementioned three layers in WBAN. Therefore, this paper presents a cross-layer design structure of WBAN with various issues and challenges. Moreover, it also presents a detail review of the existing cross-layer protocols in the WBAN domain by discussing their strengths and weaknesses.