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The efficient and timely execution of tasks is a fundamental challenge in the realm of future Internet of Things (IoT) networks. To address this challenge, fog devices are often deployed close to end devices to facilitate task processing on behalf of IoT nodes. One strategy for improving task computational delay is to employ parallel task offloading, in which tasks are subdivided into subtasks and sent to different fog devices for execution in parallel. However, allocating computational resources to fog nodes and mapping these resources to IoT subtasks is a key challenge in this area. This work models the parallel task offloading problem as a graph-matching problem and utilizes a many-to-many matching technique to achieve a stable mapping of IoT subtasks to fog node resources. Unfortunately, the proposed solution is subject to the problem of externalities due to the dynamic preference profiling of fog nodes. To address this issue, we employ an iterative algorithm to resolve any blocking pairs that may arise. Our results demonstrate that the proposed technique reduces the task latency by 29% as compared to other matching-based techniques available in the literature.

Abstract Robust wireless communication using relaying system and Non-Orthogonal Multiple Access (NOMA) will be extensively used for future IoT applications. In this paper, we consider a fall detection IoT application in which elderly patients are equipped with wearable motion sensors. Patient motion data is sent to fog data servers via a NOMA-based relaying system, thereby improving the communication reliability. We analyze the average signal-to-interference-plus-noise (SINR) performance of the NOMA-based relaying system, where the source node transmits two different symbols to the relay and destination node by employing superposition coding over Rayleigh fading channels. In the amplify-and-forward (AF) based relaying, the relay re-transmits the received signal after amplification, whereas, in the decode-and-forward (DF) based relaying, the relay only re-transmits the symbol having lower NOMA power coefficient. We derive closed-form average SINR expressions for AF and DF relaying systems using NOMA. The average SINR expressions for AF and DF relaying systems are derived in terms of computationally efficient functions, namely Tricomi confluent hypergeometric and Meijer’s G functions. Through simulations, it is shown that the average SINR values computed using the derived analytical expressions are in excellent agreement with the simulation-based average SINR results.

With the increase in the number of Internet of Things (IoT) applications, the reliance on robust networking, reliable communications, and efficient and secure data storage is increasing day by day. Many of these applications such as cellular communications, transportation networks and Internet of Medical Things generate huge volumes of data that need to be stored and analyzed to generate insights and make decisions related to applications. Security of data storage is a paramount concern, particularly in the current cyber security threat landscape. It is important to design novel techniques and algorithms to protect the data against these upcoming attacks. This paper provides a brief overview of security challenges faced by the data storage mechanisms. In comparison to the existing review papers in this area, the work in this paper identifies three major security techniques for data storage and conducts a comprehensive review of the recent advancements related to these techniques. These techniques include Blockchain algorithms, encryption mechanisms, and deduplication algorithms for data storage. Finally, the work highlights future opportunities and challenges related to secure data storage in future networks.

Security and reliability are the major requirements for the successful implementation of Internet of Things (IoT) applications in smart cities such as healthcare. IoT networks are subject to various types of network attacks that can put critical IoT data at risk. To mitigate these security issues, robust security algorithms based on Elliptic Curve Cryptography (ECC) may be utilized. However, using such cryptographic techniques requires additional delay for signing/encrypting and verification/decryption of the messages. Hence, there is a tradeoff between the security strength of the algorithm and packet delay. In this paper, we present a novel technique to adaptively select the key size of ECC-based algorithms for multi-hop IoT networks. The proposed technique uses k-means clustering to classify the secrecy rates of the potential relay nodes into k clusters. We allocate lower key sizes to the relay nodes that have the highest secrecy rates and thus have better physical layer security. The key sizes and security strengths are selected as per the recommendation of the National Institute of Standards and Technology (NIST). The second part of the work utilizes the Hungarian algorithm to select the best relay nodes for each source-destination transmission to minimize the total delay. The techniques are implemented in MATLAB and results show that the total delay of the proposed technique is improved by 57% as compared to other techniques in the literature.

Ubiquitous computing is a key component of future wireless communications and Internet of Things (IoT) applications. By using distributed fog computing, data obtained from sensors can be timely processed and different tasks can be computed efficiently. As a result, IoT applications can make timely decisions and improve application reliability. In this paper, we consider a Partial Offloading to Multiple Helper (POMH) scenario where IoT device divide their tasks into subtasks that can be computed in a parallel manner. We propose a modified many-to-many matching-based task offloading algorithm to reduce the task latency in a POMH scenario. To overcome the externalities due to the dynamic preference profile of fog nodes, an intelligent proposal-based scheme is also introduced. The performance analysis of the proposed techniques shows that they provide quicker task computation as compared to different techniques available in the literature.

The proliferation of IoT devices has introduced challenges, including high delay, increased energy consumption, and heavy load. This paper proposes a new energy-aware task offloading approach to improve energy efficiency and minimize latency in fog computing using the Gale Shapley Stable Marriage Matching technique. The proposed Gale Shapley Stable Marriage matching game focuses on one-to-one matching between four mobile devices and four fog nodes. By integrating energy availability and proximity into a single preference function, the algorithm can identify an ideal partner for task offloading that minimizes energy consumption and latency. The simulation results demonstrate that the proposed technique reduces energy usage and latency by 31.36% and 30%, respectively.

5G is a key enabler of Industrial Internet of Things (IIoT) that provides seamless connectivity between machines, sensors and computing servers. Security and privacy are major concerns for 5G enabled IIoT. Physical Layer Security (PLS) is a promising technique that can enhance the security of 5G enabled IIoT. In this paper, we present an IRS-assisted PLS scheme for 5G enabled IIoT that improves the weighted secrecy sum-rate (WSSR) of the industrial wireless network, where eavesdroppers are present around the facility. The key idea is to jointly optimize active and passive beamforming vectors to increase the secrecy rate at the user. To maximize WSSR, we use the stable matching algorithm that optimally assigns IRSs for secure data sharing between industrial units. Simulation results show that the proposed scheme enhances the WSSR performance by 40% and minimum secrecy rate by 25% as compared to the random and maximum weight matching schemes, respectively.

The SAS-CBRS framework is being tested to share the federally held spectrum with licensed users and opportunistic users to maximize the underutilized spectrum’s utility and overcome spectrum scarcity. In the SAS-CBRS framework, radio resources are assigned to the incumbent access (IA), primary access licensees (PAL), and general authorized access (GAA) users according to the given priority. The SAS-CBRS three-tier framework is different from the conventional cognitive radio networks (CRN) as it involves a central entity that acts as a server called a spectrum access system (SAS). The methods to assign the resources using the SAS are still in the research phase. Yet, no standard method is defined by the FCC for resource allocation. The current CRN methods cannot be directly applied because of the addition of the third tier and a central server. Moreover, strict rules are defined for using the 3.5 GHz spectrum band for communication. In this paper, a novel DDRQ-SAS algorithm integrated with the double auction (DA) algorithm is proposed that uses deep recurrent double Q-learning. The DDRQ-SAS is used by the SAS to hold a spectrum auction and create a spectrum pool to get information on PAL channels. PAL operators use the DA algorithm to generate the asking prices intelligently for their available idle channels and the GAA users will use the DA algorithm to intelligently bid for their preferred channels. The DDRQ-SAS-DA algorithm allows the GAA users to get the guaranteed QoS offered by the PAL operators in an auction. GAA users maintain the preference list of the PAL reserved idle channels and bid intelligently based on the available QoS. SAS completes the transaction by allocating the channels to the winning GAAs. The defined problem is also modeled using the double auction multi-winner multi-channel technique and the TDSA-PS algorithm. Numerical results show that the proposed DDRQ-SAS-DA algorithm provides up to 20% better QoS at higher loads for GAA users, generates 24% more revenue for PAL operators, and is 1.6 times more efficient in assigning 500 GAA users.

Ultra-reliable and low latency communications (URLLC) will be the backbone of the upcoming sixth-generation (6G) systems and will facilitate mission-critical scenarios. A design accounting for stringent reliability and latency requirements for URLLC systems poses a challenge for both industry and academia. Recently, unmanned aerial vehicles (UAV) have emerged as a potential candidate to support communications in futuristic wireless systems due to providing favourable channel gains thanks to Line-of-Sight (LoS) communications. However, usage of UAV in cellular infrastructure increases interference in aerial and terrestrial user equipment (UE) limiting the performance gain of UAV-assisted cellular systems. To resolve these issues, we propose low-complexity algorithms for intercell interference coordination (ICIC) using cognitive radio when single and multi-UAVs are deployed in a cellular environment to facilitate URLLC services. Moreover, we model BS-to-UAV (B2U) interference in downlink communication, whereas in uplink we model UAV-to-BS (U2B), UAV-to-UAV (U2U), and UE-to-UAV (UE2U) interference under perfect/imperfect channel state information (CSI). Results demonstrate that the proposed perfect ICIC accounts for fairness among UAV especially in downlink communications compared to conventional ICIC algorithms. Furthermore, in general, the proposed UAV-sensing assisted ICIC and perfect ICIC algorithms yield better performance when compared to conventional ICIC for both uplink and downlink for the single and multi-UAV frameworks.

The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One such challenge is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipment is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains.

Microbially influenced corrosion (MIC) is a potentially critical degradation mechanism for a wide range of materials exposed to environments that contain relevant microorganisms. The likelihood and rate of MIC are affected by microbiological, chemical, and metallurgical factors; hence, the understanding of the mechanisms involved, verification of the presence of MIC, and the development of mitigation methods require a multidisciplinary approach. Much of the recent focus in MIC research has been on the microbiological and chemical aspects, with less attention given to metallurgical attributes. Here, we address this knowledge gap by providing a critical synthesis of the literature on the metallurgical aspects of MIC of carbon steel, a material frequently associated with MIC failures and widely used in construction and infrastructure globally. The article begins by introducing the process of MIC, then progresses to explore the complexities of various metallurgical factors relevant to MIC in carbon steel. These factors include chemical composition, grain size, grain boundaries, microstructural phases, inclusions, and welds, highlighting their potential influence on MIC processes. This review systematically presents key discoveries, trends, and the limitations of prior research, offering some novel insights into the impact of metallurgical factors on MIC, particularly for the benefit of those already familiar with other aspects of MIC. The article concludes with recommendations for documenting metallurgical data in MIC research. An appreciation of relevant metallurgical attributes is essential for a critical assessment of a material’s vulnerability to MIC to advance research practices and to broaden the collective knowledge in this rapidly evolving area of study.

Recently, titania nanotubes (TNTs) have been extensively studied because both their functional properties and highly controllable morphology make them important building blocks for understanding nanoscale phenomena and realizing nanoscale devices. Compared with sol–gel and template-assisted methods, electrochemical anodization is a simple, cost-effective, and low-temperature technique offering additional advantages such as straightforward processing and ease of scale-up. This review focuses on the process modalities and underlying mechanism of electrochemical anodization to achieve a different set of TNTs for a variety of applications. Finally, important applications of TNTs are highlighted including biomedical devices, water purification, and solar cells.

Fog computing is a key component of future intelligent transportation systems (ITSs) that can support the high computation and large storage requirements needed for autonomous driving applications. A major challenge in such fog-enabled ITS networks is the design of algorithms that can reduce the computation times of different tasks by efficiently utilizing available computational resources. In this paper, we propose a data-enabled cooperative technique that offloads some parts of a task to the nearest fog roadside unit (RSU), depending on the current channel quality indicator (CQI). The rest of the task is offloaded to a nearby cooperative computing vehicle with available computing resources. We developed a cooperative computing vehicle selection technique using an artificial neural network (ANN)-based prediction model that predicts both the computing availability once the task is offloaded to the potential computing vehicle and the link connectivity when the task result is to be transmitted back to the source vehicle. Using detailed simulation results in MATLAB 2020a software, we show the accuracy of our proposed prediction model. Furthermore, we also show that the proposed technique reduces total task delay by 37% compared to other techniques reported in the literature.

The efficient collection of municipal solid waste poses a significant challenge for the prospective development of smart cities. Using Internet of Things (IoT) technology enables the detection of various kinds of waste-related information, facilitating the implementation of a comprehensive plan for efficient waste collection. In this paper, an innovative waste collection mechanism leveraging the IoT sensors and widely recognized Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a robust multi-criteria decision analysis method is introduced. Recognized for its ability to yield optimal solutions amidst multiple influencing parameters, TOPSIS proves particularly advantageous when confronted with diverse factors affecting decision making. A TOPSIS-based algorithm for optimal route selection is developed to collect waste using metrics such as toxicity, volume, and time duration of the waste while minimizing the route distance. To demonstrate the efficiency of our proposed algorithm, a comparative analysis with existing algorithms that are dependent on and optimized for single parameters is conducted. Through rigorous evaluation, it is shown that our proposed algorithm reduces the total distance traveled to collect the waste from all the bins by 14% by effectively considering multiple criteria and optimizing waste collection routes.

Fog computing is a key technology that supports timely and efficient computation of different tasks in IoT networks. By using the nearby fog nodes for quick task computation, application related decisions by IoT devices can be taken within the delay requirements. Resource allocation in terms of task placement on the free computing resources of the fog nodes is a major challenge in IoT networks. In this paper, we consider task offloading from IoT devices to the logically partitioned fog computing resources known as Virtual Resource Units (VRUs) to reduce the number of task outages and energy consumption of the IoT and fog nodes. We propose a two phased task offloading algorithm to minimize the number of task outages. In the first phase, we utilize the task deadline to compute the minimum number of resources required for a task from the fog nodes. To meet the heterogeneous task computing requirements, we introduce the concept of variable sized VRUs in the fog nodes. Moreover, we propose a modified Deferred Acceptance Algorithm (DAA) for stable matching between IoT tasks and variable sized VRUs. In the second phase of the algorithm, the unmatched fog node resources are distributed among the previously matched IoT tasks. Simulation results show that the proposed algorithm outperforms available techniques in the literature in terms of task outages and energy efficiency.

Spectrum access system (SAS) is a three-tier layered spectrum sharing architecture proposed by the Federal Communications Commission (FCC) for Citizens Broadband Radio Service (CBRS) 3.5 GHz band. The available 150 MHz spectrum is dynamically shared among Incumbent Access (IA), Primary Access Licensees (PAL) and General Authorized Access (GAA) users. IA users are the highest priority federal military users, PAL users are the licensed users and the GAA users are the least priority unlicensed users. In this scenario, PAL operators are willing to give access to their idle spectrum to GAA users to generate extra revenue. SAS will ensure to protect IA users and PAL users from interference caused by lower-tier users. It is the responsibility of SAS to allocate resources to GAA users but the method to do so is left open. In this article, a novel auction algorithm based on Q-learning for dynamic spectrum access (SAS-QLA) is proposed. In SAS-QLA, multiple GAA users dynamically and intelligently bid using Q-learning to access PAL reserved idle channels. SAS will decide to allocate the channels to GAA users with maximum bidding offers. GAA users have their own quality of service (QoS) demands i.e., transmission rate, packet loss, bidding efficiency, and maintain the preference of available PAL reserved idle channels based on Q-learning considering the available QoS. The proposed scenario is also modeled as a knapsack NP-hard problem and solved using dynamic programming and distributed relaxation method. Numerical results demonstrate the effectiveness of the SAS-QLA algorithm in improving the bidding efficiency, maximizing the data rate per unit cost and spectrum utilization.

Future Internet of Things (IoT) will be a connected network of sensors enabling applications such as industrial automation and autonomous driving. To manage such a large number of applications, efficient computing techniques using fog nodes will be required. A major challenge in such IoT networks is to manage the resource allocation of fog computing nodes considering security and system efficiency. A secure selection of fog nodes will be needed for forwarding the tasks without interception by the eavesdropper and minimizing the task delay. However, challenges such as the secure selection of fog nodes for forwarding the tasks without interception by the eavesdropper and minimizing the task delay are critical in IoT-based fog computing. In this paper, an efficient technique is proposed that solves the formulated problem of allocation of the tasks to the fog node resources using a stable matching algorithm. The proposed technique develops preference profiles for both IoT and fog nodes based on factors such as delay and secrecy rate. Finally, Gale–Shapley matching is used for task offloading. Detailed simulation results show that the performance of the proposed technique is significantly higher than the recent techniques in the literature.

In this paper, we analyze the performance of an intelligent reflecting surface (IRS)-aided terahertz (THz) wireless communication system with pointing errors. Specifically, we derive closed-form analytical expressions for the upper bounded ergodic capacity and approximate expression of the outage probability. We adopt an α-μ fading channel model for our analysis that is experimentally demonstrated to be a good fit for THz small-scale fading statistics, especially in indoor communication scenarios. In the proposed analysis, the statistical distribution of the α-μ fading channel is used to derive analytical expressions for the ergodic capacity and outage probability. Our proposed analysis considers not only the IRS reflected channels, but also the direct channel between the communication nodes. The results of the derived analytical expressions are validated through Monte Carlo simulations. Through simulations, it has been noticed that pointing errors degrade the performance of the IRS-assisted THz wireless communication system which can be compensated by deploying an IRS having a large number of reflecting elements.

Smart health care will be a major application in future smart cities. Timely and precise delivery of patients’ data to their medical consultant, to allow the necessary actions, is one of the basic needs in health care systems. Blockchain technology, with the provisioning of recording and tracking of data blocks, guarantees secure and error-free data delivery. The vital sign data from patients’ sensors are placed in different data blocks. To become a part of the blockchain, the block must contain a valid key, based on a hash function. Mining nodes with high processing capabilities generate the required key using a 32-bit number, known as a nonce, which is changed for every new block. Finding a nonce that meets the hash function requirements is a time-intensive process in blockchain technology and is performed by several fog mining nodes. However, an efficient resource allocation that results in the fair placement of data in these fog mining nodes, while maintaining the priority and sensitivity of patients’ data, is a challenge. This work proposes two algorithms for the resource allocation of mining nodes. The first algorithm uses a load balancing technique to distribute the load of nonce computing tasks. The second algorithm utilizes the knapsack algorithm to allocate the caching space of the mining nodes. The simulation results highlighted that the proposed resource allocation techniques outperformed the existing techniques, in terms of quick mining of the most sensitive patient data blocks.

In this contribution, we investigate the performance of an untrusted relaying system when Cooperative Jammers (CJs) are available. We propose two scenarios, Untrusted Relaying-aided Multiple Cooperative-Jammers-based Simultaneous Transmission (UR-MCJST) and an Untrusted Relaying-aided Multiple Cooperative Jammers-based Time-Division Transmission (UR-MCJTDT). The performances of both UR-MCJST and UR-MCJTDT schemes are investigated. The source node is the primary user (PU) that has access to a transmission bandwidth. An untrusted relay is employed for improving the reliability of the PU transmission, while CJs are invoked for improving the secrecy rate of the PU transmission. The transmission strategy for the scheme proposed in this paper is split into three unique phases. The first phase is called the broadcast-and-jamming phase, while the second phase is called the relaying phase, and the final phase is called CJ’s secondary phase, which is used for transmitting the secondary user message. More explicitly, CJs transmit noise for impairing the untrusted relay node’s ability to decode the message during the broadcast-and-jamming phase. In compensation for their help, CJs are allocated part of the available spectrum by the PU for their transmission during the secondary transmission phase. A Stackelberg leader–follower game was considered between the PU and the CJs, while a power control game that is based on the well-known Nash equilibrium is employed by the CJs. Furthermore, an investigation into the effect of invoking simultaneous transmission and time-division-based transmission scenarios during the CJs’ secondary transmission phase was carried out. Finally, we evaluated the achievable secrecy rate of the PU and the utility rate of the CJs in our proposed scheme.

Integration of Device-to-Device (D2D) communication into the cellular network can greatly enhance the spectrum utilization as well as creating additional communication opportunities. D2D can be combined with cognitive radio to further enhance spectrum utilization and cellular network performance. Unlike the traditional D2D communication approach which employs only cellular spectrum, cognitive D2D can enable sensing and utilizing non-cellular spectrum opportunistically as well, thus allowing to offload cellular base station traffic to non-cellular spectrum such as WiFi, Bluetooth, or TV white-spaces. Cognitive D2D users (cDUs) must vacate channel for primary users and hand-off to another secondary channel which renders it quite challenging to meet Quality of Service (QoS) requirements of multi-class cDUs in the presence of higher primary network load. In this work, an enhanced hybrid spectrum access scheme has been developed based on non-switching spectrum hand-off for multi-class DUs, utilizing both interweave and hybrid interweave underlay spectrum access strategies. Further, lower priority cDUs with non-real-time traffic remains in the system and wait for channels to become available, rather than being dropped from the system due to lack of secondary channels. A Continuous-Time Markov Chain (CTMC) has been developed to analyze the performance of the proposed scheme. For comparison, several cases, ranging from the simple cellular network to complex cellular-cognitive-D2D with hybrid-spectrum-access, have been analyzed. The main focus of the analysis is to compare the efficacy of enhanced hybrid spectrum access scheme with individual interweave and hybrid interweave underlay spectrum access strategies in terms of QoS provisioning for multi-class cDUs. The results depict improvement in throughput, spectrum utilization, and extended data delivery time for the proposed scheme and validate the suitability of the proposed scheme to meet QoS requirements for both delay-sensitive and delay-tolerant users of the multi-class cognitive D2D communication system.

Blind-spots, where wireless signals do not reach within the coverage range, often emerge in a dynamic environment due to obstacles, geographical location or mobility of cellular users (CUs). Thus greatly reducing the overall system performance in terms of coverage and throughput. Relay-aided cognitive Device to Device (cD2D) communication system underlying the 5G cellular network can help mitigate blind-spots. Cognitive capability helps D2D users to acquire the spectrum opportunistically for proximity communication and establish a semi-independent network underlying the 5G network, which not only offloads 5G-New Radio (NR) base station but also enhances the overall system performance. In this work, we have developed a relay-aided cognitive D2D network that helps CUs falling into the blind-spots to retain access to the 5G network and increase wireless coverage. Relay selection requires mutual consent between the relay and the device in the blind-spot. The in-coverage devices are tempted to act as relays through incentive-based mechanism. For enhanced system performance a suitable match among the devices in blind-spots and the relays is required. cD2D enabled relay selection algorithm (cDERSA) is proposed in this work, in which a cognitive D2D user (cDU), which is a CU falling in the blind-spot, establishes a relayed cD2D link to access 5G-NR gNodeB. All cDUs as well as the tempted relays, i.e. cognitive D2D relays (cDRs), first scan their surroundings for devices capable of D2D communication and based on multi-criteria objective functions, build a priority table. A stable marriage problem is formulated and solved using a unique, stable, distributed, and efficient matching algorithm based on the Gale-Shapley algorithm. A new incentive mechanism is also developed to keep relays motivated to share their resources. Simulation is performed and their results show improvement in throughput and average user satisfaction, which validates our proposed cDERSA.

Cellular V2X (C-V2X) is an emerging standard that will enable the employment of the 6G Internet of Vehicles (IoV). It is envisaged to provide seamless connectivity, long-range communications, higher data rate transmissions, and centralized and ad hoc modes of communications for vehicular applications. In particular, service applications that require data transmission between vehicles and infrastructure stations known as Road Side Units (RSUs) can greatly benefit from C-V2X. In this paper, we propose a Network-Assisted Service Message Transmission (NA-SMT) protocol that uses Uu and PC5 links of C-V2X to efficiently disseminate service messages from RSUs to the vehicles. In the first phase of the NA-SMT protocol, RSUs select downlink relay vehicles for each service message with the highest Channel Quality Indicator ( $CQI$ ) within a defined reliable range of the destination vehicle. The second phase of the NA-SMT protocol schedules V2V transmission by cooperative sub-channel allocation by the RSUs to reliably disseminate service messages to the destination. Simulation results show that NA-SMT protocol improves packet reception ratio and end-to-end delay of service messages at different channel conditions and service message generation rates.

The speedy advancement in wireless communication technologies provides considerable development to enable smart cities with applications such as Intelligent Transport Systems (ITS) and the Internet of Medical Things (IoMT). Blockchain is an emerging technology that provides a secure and distributed data storage mechanism useful for smart city applications. The full nodes in the Blockchain contain a record of all the transactions and data blocks of the Blockchain users. As the number of full nodes is less and the number of Blockchain users is high, there is a huge load on the full nodes for accessing and verifying the data by the Blockchain users. Efficient hardware caching techniques are needed to decrease the data access delay. In this paper, we implement different caching techniques on the Field-Programmable Gate Array (FPGA) Network Interface Card (NIC) and analyze their performance for the key-value store caching in the Blockchain. We design the 2-way and 4-way caching techniques on Block Random-Access Memory (BRAM) and compare them with the conventional direct-mapped caching technique in terms of cache hits and cache misses. The improvements in the hit ratio of the 2-way set-associative cache technique with respect to the direct-mapped cache technique for 10 K, 25 K, and 50 K addresses are 0.8%, 0.77%, and 1.67%, respectively. On the other hand, for the same sets of addresses, the hit rate improvement of the 4-way set-associative cache technique with respect to the direct-mapped cache technique is 0.92%, 2.01%, and 2.4%, respectively. The improvements in hit rate for large data sets show that 2-way and 4-way set-associative cache techniques perform better than the direct-mapped cache technique for caching systems.

Smart cities are emerging rapidly due to the provisioning of comfort in the human lifestyle. The healthcare system is an important segment of the smart city. The timely delivery of critical human vital signs data to emergency health centers without delay can save human lives. Blockchain is a secure technology that provides the immutable record-keeping of data. Secure data transmission by avoiding erroneous data delivery also demands blockchain technology in healthcare systems of smart cities where patients’ health history is required for their necessary treatments. The health parameter data of each patient are embedded in a separate block in blockchain technology with SHA-256-based cryptography hash values. Mining computing nodes are responsible to find a 32-bit nonce (number only used once) value for each data block to compute a valid SHA-256-based hash value in blockchain technology. Computing nonce for valid hash values is a time-taking process that may cause life losses in the healthcare system. Increasing the mining nodes reduces this delay; however, the uniform distribution of mining data blocks to these nodes by considering the priority data is a challenging task. In this work, an efficient scheme is proposed for scheduling nonce computing tasks at the mining nodes to ensure the timely execution of these tasks. The proposed scheme consists of two parts, the first one provides a load balancing scheme to distribute the nonce execution tasks among the mining nodes such that makespan is minimized and the second part prioritizes more sensitive patient data for quick execution. The results show that the proposed load balancing scheme effectively allocates data blocks in different mining nodes as compared to round-robin and greedy algorithms and computes hash values of most of the higher-risk patients’ data blocks in a reduced amount of time.

Intelligent Transport Systems (ITS) require accurate information to be shared among vehicles and infrastructure nodes for applications including accident information or pre-crash warnings, to name a few. Due to its sensitive nature, ITS applications are vulnerable against data integrity attacks where nodes transmit false information that results in wrong decision making by the applications. A characteristic of such attacks is that the false transmitted information is significantly different than the actual information. In this paper, we propose an Outlier Detection, Prioritization and Verification (ODPV) protocol that efficiently isolates false data and improves traffic management decisions. ODPV uses the isolation forest algorithm to detect outliers, fuzzy logic to prioritize outliers and C-V2X communications to verify the outliers. Extensive simulation results verify the effectiveness of the proposed protocol to isolate the outliers.

Unmanned aerial vehicle (UAV) assisted cell-free communications hold promise for enhancing the coverage and capacity of heterogeneous cellular networks. However, the network interference in such scenarios must be accurately modeled for efficient system design. The spatial characteristics of the desired and interfering signals can be jointly modeled by considering the characteristics of the signal-to-interference ratio (SIR). This work proposes a generalized framework for modeling the spatial statistics of the SIR encountered in 3-D volumetric inter-vehicular communication channels. Though the novel paradigm of UAV-assisted cell-free vehicular communications is analyzed in particular, the proposed framework is more general in that it incorporates 3-D mobility at both link ends. Also, this framework is shown to include as its special cases, several notable 2-D propagation models of network interference including those for terrestrial vehicle-to-vehicle and fixed-to-vehicle scenarios. Analytical expressions are derived for the SIR level-crossing-rate (LCR), average-fade-duration (AFD), spatial auto-covariance (SAC), and coherence distance (CD). Both single- and multi-cluster scattering environments are analyzed and the impact of channel parameters such as the direction and velocity of mobile nodes as well as the altitudes of the UAV and scattering cluster(s) on the SIR fading statistics is investigated. Finally, some future extensions of this work are also discussed such as the integration of intelligent reflective surfaces in the propagation scenario to generate favorable channel conditions.

Motivated by the synergistic integration of soft computing paradigms this paper introduces a fully adaptive multiple-input-multiple-output NeuroFuzzy control for multi-type Flexible AC Transmission Systems (FACTS) to damp low frequency oscillations. The novel control strategy integrates the complementary features of locally controllable fuzzy Bspline membership functions and robust wavelet neural networks in NeuroFuzzy structure. The gradient decent based back-propagation mechanism used for parameters update has been optimized using online Adaptive Learning Rates (ALRs). The stability of the proposed algorithm has been ensured by deriving an upper bound on ALRs using Lyapunov stability criteria. The application of this controller to provide damping signals to various FACTS controllers like Static Synchronous Series Compensator (SSSC) and Static Synchronous Compensator (STATCOM) can effectively enhance the dynamic stability of the system. A benchmark multi-machine power system has been used for performance validation of the controller by applying various faults under different loading scenarios. Conventional Lead-Lag and NeuroFuzzy controls have been considered for comparative evaluation using nonlinear time and frequency domain techniques to reveal that the proposed control performs better in different operating regions. Furthermore, the graphical results obtained from time and frequency domain simulations have been quantified numerically using different performance indices and Energy Spectral Density (ESD), respectively. The temporal, spectral and numerical analysis confirms the superior performance of the proposed control scheme.

Efficient data dissemination is a key challenge for IoT applications. In this paper, we present an ergodic achievable rate analysis of the non-orthogonal multiple access (NOMA)-based cooperative transmission scheme in spectrum-sharing cognitive radio networks (CRNs). The transmission scheme consists of two phases, where the NOMA transmission strategy is employed at the secondary transmitter (ST) in the second phase to serve the primary receiver (PR) and secondary receivers (SRs). This cooperative spectrum-sharing transmission is favourable when the ST-PR link is strong and the ST faces a spectrum scarcity issue. To evaluate the performance of the network over Nakagami-m channels, ergodic achievable rates at PR and SRs are derived in closed forms, which can be used for any integer or non-integer value of fading index m. Finally, we compare the results of the analytical expressions with simulated results for validation.

Blockchain is an emerging computing platform that provides recording and tracking facilities to substantially increase the security issues in healthcare systems. The evolution of wireless body area networks requires the continuous monitoring of the health parameters of traveling patients while traveling on road. The health parameter data of each patient are sent to the Road Side Units (RSUs) for generating the blocks by computing the required hash functions. A major challenge in such a network is to efficiently exchange the data blocks between mining RSUs and vehicles using a medium access protocol with a reduced number of collisions. The medium access problem becomes more challenging due to the vehicle mobility, high vehicle density and the varying nature of the data generated by the vehicles. In this work, a TDMA-based MAC protocol to meet an Adaptive Patients Data traffic for Vehicular Network (TAPDVN) is proposed. TAPDVN is specifically designed for patients in a vehicular network by considering the frequent entry and exit of vehicles in a mining node’s coverage area. It allows mining nodes to adjust time slots according to the sensitive patient’s data and allows the maximum number of patient vehicular nodes by considering their sensitivity to send their data in a session to compute their hash values accordingly. Simulation results verify that the proposed scheme accommodates the maximum number of high-risk patient data and improves bandwidth utilization by 20%.

Fog computing is one of the major components of future 6G networks. It can provide fast computing of different application-related tasks and improve system reliability due to better decision-making. Parallel offloading, in which a task is split into several sub-tasks and transmitted to different fog nodes for parallel computation, is a promising concept in task offloading. Parallel offloading suffers from challenges such as sub-task splitting and mapping of sub-tasks to the fog nodes. In this paper, we propose a novel many-to-one matching-based algorithm for the allocation of sub-tasks to fog nodes. We develop preference profiles for IoT nodes and fog nodes to reduce the task computation delay. We also propose a technique to address the externalities problem in the matching algorithm that is caused by the dynamic preference profiles. Furthermore, a detailed evaluation of the proposed technique is presented to show the benefits of each feature of the algorithm. Simulation results show that the proposed matching-based offloading technique outperforms other available techniques from the literature and improves task latency by 52% at high task loads.

IEEE 802.15.4 standard is specifically designed for a low-rate and low-processing Internet of things (IoT) applications and offers guaranteed time slots. A beacon-enabled IEEE 802.15.4 consists of a superframe structure that comprises of the contention access period and contention-free period. During contention-free period, nodes transfer their data using guaranteed time slots without any collision. The coordinator node receives data transmission requests in one cycle and allocates guaranteed time slots to the nodes in the next cycle. This allocation process may cause large delay that may not be acceptable for few applications. In this work, a novel superframe structure is proposed that significantly reduces guaranteed time slots allocation delay for the nodes with data requests. The proposed superframe structure comprises of two contention access periods and one contention-free period, where contention-free period precedes both contention access periods with reduced slot size. In addition, the knapsack algorithm is modified for better guaranteed time slots allocation by allowing more guaranteed time slots requesting nodes to send their data as compared to the IEEE 802.15.4 standard. The simulation and analytical results show that the proposed superframe structure reduces the network delay by up to 80%, increases contention-free period utilization up to 50%, and allocates guaranteed time slots up to 16 nodes in a single superframe duration.

Traffic information exchange between vehicles and city-wide traffic command center will enable various traffic management applications in future smart cities. These applications require a secure and reliable communication framework that ensures real-time data exchange. In this paper, we propose a Fog-Assisted Cooperative Protocol (FACP) that efficiently transmits uplink and downlink traffic messages with the help of fog Road Side Units (RSUs). FACP divides the road into clusters and computes cluster head vehicles to facilitate transmission between vehicles and traffic command center or fog RSUs. Using a combination of IEEE 802.11p and C-V2X wireless technologies, FACP minimizes the time required by a vehicle to retrieve traffic information. Furthermore, FACP also utilizes cooperative transmissions to improve the reliability of traffic messages. Simulations results show that FACP improves the reception rate and end-to-end delay of traffic messages.

Future Internet of Things (IoT) will utilize IEEE 802.15.4 based low data rate communication for various applications. In the IEEE 802.15.4 standard, nodes send data to their Personal Area Network (PAN) coordinator using the Guaranteed Time Slot (GTS). The standard does not meet the adaptive data requirements of GTS requesting nodes in an efficient manner. If requesting GTSs in an active period are more or less than the available limit, either the requested nodes will not be entertained or GTSs remain underutilized. Consequently, it may cause unnecessary delay or poor GTS utilization. In this paper, an Optimal GTS allocation Mechanism for Adaptive Duty cycle (OGMAD) is proposed that adapts the active period of the superframe in accordance with the requested data. OGMAD also reduces GTS size to improve link utilization as well as accommodate more GTS requesting nodes. Simulation results verify that OGMAD improves link utilization, reduces network delay and offers more nodes to transmit their data as compared to the standard.

Connected vehicles in vehicular networks will lead to a smart and autonomous transportation system. These vehicles have a large number of applications that require wireless connectivity by using cellular vehicle-to-everything (C-V2X). The infrastructure of C-V2X comprises multiple roadside units (RSUs) that provide direct connectivity with the on-road vehicles. Vehicular traffic applications are mainly categorized into three major groups such as emergency response traffic, traffic management and infotainment traffic. Vehicles have limited processing capabilities and are unable to process all tasks simultaneously. To process these offloaded tasks in a short time, fog servers are placed near the RSUs. However, it is sometimes not possible for the fog computing server to process all offloaded tasks. In this work, a utility function for the RSU to process these offloaded tasks is designed. In addition, a knapsack-based task scheduling algorithm is proposed to optimally process the offloaded tasks. The results show that the proposed scheme helps fog nodes to optimally scrutinize the high-priority offloaded tasks for task execution resulting in more than 98% of emergency tasks beingprocessed by fog computing nodes.

The paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550–3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels’ attributes and the diversification of GAA users’ business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale–Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.

The reliable transmission of multimedia information that is coded through highly compression efficient encoders is a challenging task. This article presents the iterative convergence performance of IrRegular Convolutional Codes (IRCCs) with the aid of the multidimensional Sphere Packing (SP) modulation assisted Differential Space Time Spreading Codes (IRCC-SP-DSTS) scheme for the transmission of H.264/Advanced Video Coding (AVC) compressed video coded stream. In this article, three different regular and irregular error protection schemes are presented. In the presented Regular Error Protection (REP) scheme, all of the partitions of the video sequence are regular error protected with a rate of 3/4 IRCC. In Irregular Error Protection scheme-1 (IREP-1) the H.264/AVC partitions are prioritized as A, B & C, respectively. Whereas, in Irregular Error Protection scheme-2 (IREP-2), the H.264/AVC partitions are prioritized as B, A, and C, respectively. The performance of the iterative paradigm of an inner IRCC and outer Rate-1 Precoder is analyzed by the EXtrinsic Information Transfer (EXIT) Chart and the Quality of Experience (QoE) performance of the proposed mechanism is evaluated using the Bit Error Rate (BER) metric and Peak Signal to Noise Ratio (PSNR)-based objective quality metric. More specifically, it is concluded that the proposed IREP-2 scheme exhibits a gain of 1 dB Eb/N0 with reference to the IREP-1 and Eb/N0 gain of 0.6 dB with reference to the REP scheme over the PSNR degradation of 1 dB.

Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Medium Access Control (MAC) techniques are vital to meet various application requirements. Most of the IoT applications need low data rate and low powered wireless transmissions and IEEE 802.15.4 standard is mostly used in this regard which offers superframe structure at the MAC layer. However, for IoT applications where nodes have adaptive data traffic, the standard has some limitations such as bandwidth wastage and latency. In this paper, a new superframe structure is proposed that is backward compatible with the existing parameters of the standard. The proposed superframe overcomes limitations of the standard by fine-tuning its superframe structure and squeezing the size of its contention-free slots. Thus, the proposed superframe adjusts its duty cycle according to the traffic requirements and accommodates more nodes in a superframe structure. The analytical results show that our proposed superframe structure has almost 50% less delay, accommodate more nodes and has better link utilization in a superframe as compared to the IEEE 802.15.4 standard.

The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users. Before applying matching theory, two objective functions were defined from the secondary user’s perspective as well as from the channel’s perspective. The objective function of secondary users is the weighted sum of the data rate of the secondary users and the probability of reappearance of the primary user on the channel. Whereas, the objective function of the channel is the maximum utilization of the channel. The weight factors included in the objective functions allow for diverse service classes of secondary users (SUs) or varying channel quality characteristics. The objective functions were used in developing the preference lists for the secondary users and the idle channels. The preference lists were then used by the Gale Shapely matching algorithm to determine the most suitably matched SU-channel pairs. The performance of the proposed scheme was evaluated using Monte–Carlo simulations. The results show significant improvement in the overall satisfaction of the secondary users with the proposed scheme in comparison to other contemporary techniques. Further, the impact of changing the weight factors in the objective functions on the secondary user’s satisfaction and channel utilization was also analyzed.

Internet of Things (IoT)-based devices consist of wireless sensor nodes that are battery-powered; thus, energy efficiency is a major issue. IEEE 802.15.4-compliant IoT devices operate in the unlicensed Industrial, Scientific, and Medical (ISM) band of 2.4 GHz and are subject to interference caused by high-powered IEEE 802.11-compliant Wireless Local Area Network (WLAN) users. This interference causes frequent packet drop and energy loss for IoT users. In this work, we propose a WLAN Aware Cognitive Medium Access Control (WAC-MAC) protocol for IoT users that uses techniques, such as energy detection based sensing, adaptive wake-up scheduling, and adaptive backoff, to reduce interference with the WSN and improve network lifetime of the IoT users. Results show that the proposed WAC-MAC achieves a higher packet reception rate and reduces the energy consumption of IoT nodes.

Connecting vehicles securely and reliably is pivotal to the implementation of next generation ITS applications of smart cities. With continuously growing security threats, vehicles could be exposed to a number of service attacks that could put their safety at stake. To address this concern, both US and European ITS standards have selected Elliptic Curve Cryptography (ECC) algorithms to secure vehicular communications. However, there is still a lack of benchmarking studies on existing security standards in real-world settings. In this paper, we first analyze the security architecture of the ETSI ITS standard. We then implement the ECC based digital signature and encryption procedures using an experimental test-bed and conduct an extensive benchmark study to assess their performance which depends on factors such as payload size, processor speed and security levels. Using network simulation models, we further evaluate the impact of standard compliant security procedures in dense and realistic smart cities scenarios. Obtained results suggest that existing security solutions directly impact the achieved quality of service (QoS) and safety awareness of vehicular applications, in terms of increased packet inter-arrival delays, packet and cryptographic losses, and reduced safety awareness in safety applications. Finally, we summarize the insights gained from the simulation results and discuss open research challenges for efficient working of security in ITS applications of smart cities.