The industrial Internet of Things (IoT) allows real-time monitoring and operational efficiency by enabling automated processes in industrial environments. In this paper, we propose a centralized task assignment framework for industrial IoT scenarios, focusing on light cargo delivery to specific locations via Unmanned Aerial Vehicles (UAVs). The task assignment problem is formulated as a Capacitated Vehicle Routing Problem (CVRP) with the objective of minimizing the maximum route distance among all UAVs. To solve CVRP, we employ a learning-based approach using an Attention Model (AM), which utilizes a deep learning framework with an encoder-decoder architecture to generate optimized UAV routes while satisfying the capacity constraints of UAVs. The AM is trained using policy gradient reinforcement learning to ensure that the solutions are both efficient and scalable. Numerical results demonstrate the effectiveness of the AM-based framework in delivering solutions that minimize the maximum tour length for deliveries.

Internet of Medical Things (IoMT) connects different medical devices, health sensors and hospital records to data platforms using wireless communications. Federated Learning (FL) is an emerging collaborative learning technique that can be beneficial for IoMT due to reduced communication overhead and enhanced security. This paper provides an overview of different architectures used in FL and potential approaches for FL based IoMT. We also discuss how Physical Layer Security (PLS) can be used for efficient privacy preservation of data in FL based IoMT. We highlight the recent work in this area and major research challenges related to PLS assisted FL in IoMT. We also provide a case study demonstrating that clustering of IoMT devices (such that a single device in each cluster acts as a cluster head) enhances the secrecy rate of the FL based IoMT network as compared to its non-clustered counterpart. Finally, we also discuss future opportunities and open research questions related to PLS assisted FL in IoMT.

6G vehicular networks will require massive connectivity among vehicles and Road Side Units (RSUs) to enable number of safety and non-safety applications. Intelligent Reflecting Surface (IRS) will be a vital part of 6G vehicular networks, providing features such as enhanced line of sight communication, extended coverage range and high data rate transmission. In this paper, we consider a multiple IRSs assisted multi-vehicle Multiple-Input Multiple-Output (MIMO) communication system and optimize the transmit beamforming vectors at the transmitter and reflect beamforming (phase shifts) at each IRS to achieve max-min Quality of Service (QoS) in the form of weighted Signal-to-Interference-Plus-Noise Ratio (SINR). In particular, we divide the optimization problem into two Semidefinite Relaxation (SDR) sub-problems and solve them iteratively using the alternate optimization technique. Simulation results show that the proposed technique significantly increases the sum-rate and SINR of the vehicular network.

Fog computing is a promising technology that can provide storage and computational services to future 6G networks. To support the massive Internet-of-Things (IoT) applications in 6G, fog computing will play a vital role. IoT devices and fog nodes have energy limitations and hence, energy-efficient techniques are needed for storage and computation services. We present an overview of massive IoT and 6G-enabling technologies. We discuss different energy-related challenges that arise while using fog computing in 6G-enabled massive IoT. We categorize different energy-efficient fog computing solutions for IoT and describe the recent work done in these categories. Finally, we discuss future opportunities and open challenges in designing energy-efficient techniques for fog computing in the future 6G massive IoT network.

Cybertwin is the intelligent data analytics agent of the future that interacts with the Internet of Vehicles using 6G to make intelligent decisions for automotive application reliability. Intelligent reflecting surface (IRS) is a vital component of future cybertwin-driven 6G networks due to its ability to control unfavorable wireless propagation conditions and achieves higher data rates. In this article, we explain the four major advantages that can be gained by using IRS-enabled cybertwin 6G vehicular networks, including overcoming blockages, Doppler mitigation, enhanced localization, and enhanced security. We also provide a case study showing that IRSs can increase the percentage of vehicles with line-of-sight links by 25% in a high traffic density of 60 vehicles/km, compared to the scenario where no IRS is deployed. Finally, we discuss future opportunities and open challenges to realize IRS-enabled cybertwin 6G vehicular networks.

Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other intelligent transportation system entities. Nonorthogonal multiple access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this article, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multicell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex, which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.

Cellular vehicle-to-everything (V2X) communication is emerging as a feasible and cost-effective solution to support applications such as vehicle platooning, blind spot detection, parking assistance, and traffic management. To support these features, an increasing number of sensors are being deployed along the road in the form of roadside objects. However, despite the hype surrounding cellular V2X networks, the practical realization of such networks is still hampered by under-developed physical security solutions. To solve the issue of wireless link security, we propose a deep Q-learning-based strategy to secure V2X links. Since one of the main responsibilities of the base station (BS) is to manage interference in the network, the link security is ensured without compromising the interference level in the network. The formulated problem considers both the power and interference constraints while maximizing the secrecy rate of the vehicles. Subsequently, we develop the reward function of the deep Q-learning network for performing efficient power allocation. The simulation results obtained demonstrate the effectiveness of our proposed learning approach. The results provided here will provide a strong basis for future research efforts in the domain of vehicular communications.

The integration of Ambient Backscatter Communication (AmBC) with Non-Orthogonal Multiple Access (NOMA) is expected to support connectivity of low-powered Internet-of-Vehicles (IoVs) in the upcoming Sixth-Generation (6G) transportation systems. This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated. A Dinkelbach approach is first adopted to transform the optimization problem and then the transformed problem is decoupled into two subproblems for optimal transmit power at RSUs and efficient reflection power at BackTags, respectively. To solve the problems efficiently, dual theory and Karush-Kuhn-Tucker conditions are exploited, where the Lagrangian dual variables are iteratively calculated using the subgradient method. To check the performance of the proposed framework, a benchmark optimization without AmBC is also provided. Numerical results demonstrate the superiority of the proposed AmBC-enabled NOMA IoV framework over the benchmark conventional IoV framework.

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework.

Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years. With the integration of blockchain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicles at any time. Specifically, the blockchain technology is expected to improve the security, immutability, and decentralization of cellular V2X communication through smart contract and distributed ledgers. Although blockchain-based cellular V2X networks hold promise, many challenges need to be addressed to enable the future interoperability and accessibility of such large-scale platforms. One such challenge is the offloading of mining tasks in cellular V2X networks. While transportation authorities may try to balance the network mining load, the vehicles may select the nearest mining clusters to offload a task. This may cause congestion and disproportionate use of vehicular network resources. To address this issue, we propose a game-theoretic approach for balancing the load at mining clusters while maintaining fairness among offloading vehicles. Keeping in mind the low-latency requirements of vehicles, we consider a finite channel blocklength transmission which is more practical compared to the use of infinite blocklength codes. The simulation results obtained with our proposed offloading framework show improved performance over the conventional nearest mining cluster selection technique.

A cognitive radio network can be employed in any wireless communication systems, including military communications, public safety, emergency networks, aeronautical communications, and wireless‐based Internet of Things, to enhance spectral efficiency. The performance of a cognitive radio network (CRN) can be enhanced through the use of cooperative relays with buffers; however, this incurs additional delays which can be reduced by using virtual duplex relaying that requires selection of a suitable relay pair. In a virtual duplex mode, we mimic full‐duplex links by using simultaneous two half‐duplex links, one transmitting and the other one receiving, in such a way that the overall effect of duplex mode is achieved. The relays are generally selected based on signal‐to‐interference‐plus‐noise ratio (SINR). However, other factors such as power consumption and buffer capacity can also have a significant impact on relay selection. In this work, a multiobjective relay selection scheme is proposed that simultaneously takes into account throughput, delay performance, battery power, and buffer status (i.e., both occupied and available) at the relay nodes while maintaining the required SINR. The proposed scheme involves the formulation of four objective functions to, respectively, maximize throughput and buffer space availability while minimizing the delay and battery power consumption. The weighted sum approach is then used to combine these objective functions to form the multiobjective optimization problem and an optimal solution is obtained. The assignments of weights to objectives have been done using the rank sum (RS) method, and several quality‐of‐service (QoS) profiles have been considered by varying the assignment of weights. The results gathered through simulations demonstrate that the proposed scheme efficiently determines the optimal solution for each application scenario and selects the best relay for the respective QoS profile. The results are further verified by using the genetic algorithm (GA) and particle swarm optimization (PSO) techniques. Both techniques gave identical solutions, thus validating our claim.

Wireless sensor network (WSN) is an integral part of Internet of Things (IoT). The sensor nodes in WSN generate large sensing data which is disseminated to intelligent servers using multiple wireless networks. This large data is prone to attacks from malicious nodes which become part of the network, and it is difficult to find these adversaries. The work in this paper presents a mechanism to detect adversaries for the IEEE 802.15.4 standard which is a central medium access protocol used in WSN‐based IoT applications. The collisions and exhaustion attacks are detected based on a soft decision‐based algorithm. In case the QoS of the network is compromised due to large data traffic, the proposed protocol adaptively varies the duty cycle of the IEEE 802.15.4. Simulation results show that the proposed intrusion detection and adaptive duty cycle algorithm improves the energy efficiency of a WSN with a reduced network delay.

In this paper, we propose an innovative self‐organizing medium access control mechanism for a distributed cognitive radio network (CRN) in which utilization is maximized by minimizing the collisions and missed opportunities. This is achieved by organizing the users of the CRN in a queue through a timer and user ID and providing channel access in an orderly fashion. To efficiently organize the users in a distributed, ad hoc network with less overhead, we reduce the sensing period through parallel sensing wherein the users are divided into different groups and each group is assigned a different portion of the primary spectrum band. This consequently augments the number of discovered spectrum holes which then are maximally utilized through the self‐organizing access scheme. The combination of two schemes augments the effective utilization of primary holes to above 95%, even in impasse situations due to heavy primary network loading, thereby achieving higher network throughput than that achieved when each of the two approaches are used in isolation. By efficiently combining parallel sensing with the self‐organizing MAC (PSO‐MAC), a synergy has been achieved that affords the gains which are more than the sum of the gains achieved through each one of these techniques individually. In an experimental scenario with 50% primary load, the network throughput achieved with combined parallel sensing and self‐organizing MAC is 50% higher compared to that of parallel sensing and 37% better than that of self‐organizing MAC. These results clearly demonstrate the efficacy of the combined approach in achieving optimum performance in a CRN.

Cooperative vehicular networks will play a vital role in the coming years to implement various intelligent transportation related applications. Both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications will be needed to reliably disseminate information in a vehicular network. In this regard, a roadside unit (RSU) equipped with multiple antennas can improve the network capacity. While the traditional approaches assume antennas to experience independent fading, we consider a more practical uplink scenario where antennas at the RSU experience correlated fading. In particular, we evaluate the packet error probability for two renowned antenna correlation models, i.e., constant correlation (CC) and exponential correlation (EC). We also consider intermediate cooperative vehicles for reliable communication between the source vehicle and the RSU. Here, we derive closed-form expressions for packet error probability, which help to quantify the performance variations due to fading parameter, correlation coefficients, and the number of intermediate helper vehicles. To evaluate the optimal transmit power in this network scenario, we formulate a Stackelberg game, wherein, the source vehicle is treated as a buyer and the helper vehicles are the sellers. The optimal solutions for the asking price and the transmit power are devised which maximize the utility functions of helper vehicles and the source vehicle, respectively. We verify our mathematical derivations by extensive simulations in MATLAB.

The Internet-of-Things (IoT) is the future of the Internet, where everything will be connected. Studies have revealed that fog/edge computing-based services will play a major role in extending the cloud by carrying out intermediary services at the edge of the network. Fog/edge computing-based IoT's (FECIoT) distributed architecture enhances service provisioning along the Cloud-to-Things continuum, thereby making it suitable for mission-critical applications. Furthermore, the proximity of fog/edge devices to where the data is produced makes it stand-out in terms of resource allocation, service delivery, and privacy. From the business perspective, FECIoT will lead to a boom and spring up of small-to-medium-sized enterprises, thereby encouraging inclusion for all. To this end, we present a comprehensive survey on state-of-the-art IoT literature over the period 2008-2018 and propose the FECIoT framework which covers the enabling technologies, services, and open research issues. A tutorial approach is employed, progressing from basic to more advanced concepts within the IoT domain. Lastly, we show how FECIoT can be deployed in real-life cyber-physical systems, such as the intelligent transportation system, smart grid, smart health-care, smart homes, and smart environment.

Covert timing channels provide a mechanism to transmit unauthorized information across different processes. Applications that generate large datasets allow this information to be easily hidden within the big data, making it difficult to detect. In this paper, we introduce the application of big data analysis techniques, specifically MapReduce, in the process of speeding up the performance of covert time channels detection. The hierarchal entropy algorithm (HEA) is utilized to reveal a “needle” of covert timing channels from a huge “haystack” of inter-arrival times. A real indexed inter-arrival dataset of approximately 1.4 gigabyte is generated between two different machines and injected by 615 bytes of covert timing message. The HEA with MapReduce was able to uncover around 7*10−6of hidden covert message from this huge amount of data in a significantly shorter time as compared to the classical sequential HEA.

Cooperative intelligent transport system (C-ITS) is an emerging technology that enables secure and safe road travel using wireless communications. Vehicles regularly share their mobility information with the neighborhood road traffic and infrastructure-based road side units using cooperative awareness messages (CAMs) to develop a local dynamic map for safety applications. Since the applications provided by C-ITS are related to human safety, reliable as well as secure communications are required. To protect the vehicular network from untrusted data of malicious users that could cause network congestion and reduce vehicle safety, ITS standards have proposed various security procedures. In this paper, we analyze the interrelation between security, quality of service (QoS), and safety awareness of vehicles in C-ITS. To formulate an accurate measure of vehicle safety awareness, we first propose novel vehicle and infrastructure centric metrics that use the number of received CAMs, their accuracy, safety importance, and vehicle heading. We then implement the standard CAM signature and verification procedure in the ITS standard. Using simulation results and our proposed metrics, we show the impact of security signature and verification speed on the level of vehicle awareness and, hence, QoS in different road traffic conditions.

Vehicular ad hoc network facilitates cooperative awareness applications by periodically sharing basic safety messages (BSMs) with the neighborhood vehicles. As these applications impact human safety, authenticity of BSMs is a key requirement, which however is a challenging task in a dense traffic density where many BSMs are queued up simultaneously for signature verification. As a result, many important BSMs from nearby vehicles may experience significantly long signature verification delays. To overcome this problem, we propose a channel aware signature verification scheme that prioritizes the BSMs from the closer neighbors with the help of a multi-level security queue. The BSM to security queue mapping is done by a k-means clustering based classification algorithm that takes into account the received signal strengths and application dependent safety areas. Simulation results verify the performance enhancement achieved by the proposed framework in terms of several safety awareness metrics as compared with the existing schemes.

Cooperative Intelligent Transportation Systems (C-ITS) are key component of the future road traffic management system. To evaluate and analyze the reliability of various applications supported by C-ITS, realistic performance metrics are required. Such metrics serve as a performance indicator and help in tuning the wireless transmission parameters to maintain required quality of service. The essential purpose of a C-ITS is to enhance the vehicle safety awareness and hence, provide an accurate information of vehicles in the vicinity through regular sharing of cooperative awareness messages (CAMs). The existing metrics such as packet delivery ratio provide quantitative measure of vehicle safety awareness but do not consider the accuracy of received information. In this paper, we propose two novel safety awareness metrics (SAM) to enhance the measure of vehicle awareness by considering factors such as age and weight of a received CAM in the perspective of C-ITS. Using simulation results in NS-3, we show that the current metrics provide an optimistic view of vehicle safety awareness and proposed metrics serve as a valuable gauge to analyze the reliability of applications in C-ITS.

Internet of Vehicles (IoV) is an important constituent of next generation smart cities that enables city wide connectivity of vehicles for traffic management applications. A secure and reliable communications is an important ingredient of safety applications in IoV. While the use of a more robust security algorithm makes communications for safety applications secure, it could reduce application QoS due to increased packet overhead and security processing delays. Particularly, in high density scenarios where vehicles receive large number of safety packets from neighborhood, timely signature verification of these packets could not be guaranteed. As a result, critical safety packets remain unverified resulting in cryptographic loss. In this paper, we propose two security mechanisms that aim to reduce cryptographic loss rate. The first mechanism is random transmitter security level section whereas the second one is adaptive scheme that iteratively selects the best possible security level at the transmitter depending on the current cryptographic loss rate. Simulation results show the effectiveness of the proposed mechanisms in comparison with the static security technique recommended by the ETSI standard.

Emergency warning applications in a vehicular ad hoc network (VANET) requires successful dissemination of warning notification within a geographical area spanning multiple hops. Multimedia transmission provides an accurate overview of the emergency event in the form of an image, an audio or a video. However, the reliable transmission of emergency multimedia messages using multi-hop broadcast techniques suffer from broadcast storm and severe interference from the existing periodic single-hop safety messages. In this paper, we propose an efficient time-slotted multi-hop broadcast protocol for emergency multimedia transmission. To avoid interference with the safety messages already in place, we propose to allocate separate multi-hop time slots for the multimedia messages, and devise a signaling mechanism that ensures a reliable delivery of these multimedia messages. The proposed mechanism can also handle the scenario of lost acknowledgment (ACK) and thus effectively reduce the unnecessary retransmissions of the multimedia messages. Simulation results confirm that the developed protocol substantially outperforms existing schemes in terms of both the number of required multi-hop transmissions and the dissemination delay, while maintaining a high reception rate and a low end-to-end delay for the single-hop safety messages.

To notify all vehicles within a geographical area of an urgent situation, multi-hop communication is employed in a vehicular ad hoc network (VANET) to propagate the emergency warning messages. While being interfered by the existing periodic single-hop safety messages, the transmission of warning messages can also be strongly affected by the broadcast storm problem and the collisions due to hidden nodes. This paper develops an efficient tone-based time-slotted multi-hop protocol that significantly improves the performance of the emergency warning message transmissions. Specifically, we assign the responsibility of message forwarding to only a subset of vehicles on the road, known as ``segment leader," as part of our broadcast storm suppression strategy. To avoid the potential interference caused by the safety messages, we employ separate multi-hop time slots for the warning messages, which are reserved by a newly-proposed out-of-band long range CLEAR tone. Such a tone-based reservation mechanism allows the continuous transmission of the safety messages in the absence of a CLEAR tone, giving rise to a much higher network utilization. Our extensive simulation results confirm the timely and reliable delivery of the emergency warning messages while the required number of multi-hop transmissions is significantly reduced. At the same time, a high packet reception rate and a low end-to-end delay are maintained for the existing single-hop safety messages.

In a vehicular ad hoc network (VANET), basic safety messages (BSM) are exchanged among vehicles to provide cooperative awareness and support safety applications. While the periodic generation and transmission of such messages already place a heavy burden on the network load, the hidden nodes further complicate the network situation with packet collisions being undetectable. In this paper, we propose a space-division multiple access (SDMA) technique combined with an adaptive rate control mechanism to improve the efficiency of BSM transmissions. With SDMA to reduce the interference among the vehicles and address the hidden-node problem, only vehicles located sufficiently far apart are allowed to reuse the same time slot to send their BSMs. The developed SDMA approach permits multiple transmissions per road segment and uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in each segment, giving rise to a much better use of both space and bandwidth. To guarantee that the actual number of BSM transmissions does not exceed the maximum allowable, we devise an adaptive scheme that optimally adjusts the individual packet generation rates in accordance to the vehicle density. Toward this end, we find an optimal bandwidth utilization that maximizes the newly-defined metric, termed as “safety throughput” per vehicle. Such an optimal utilization allows a large number of BSM packets to be generated while maintaining a high reception probability. Our comprehensive simulation results confirm the clear advantages of our proposal over the existing techniques, in terms of safety range, BSM packet inter-arrival time, and safety throughput.

Safety applications in VANETs relies on various type of information exchanges among vehicles of which Cooperative Awareness Message (CAM) has the largest share in the network load due to their periodic broadcast nature. As the vehicle density grows, the load generated by the CAMs could increase beyond the transmission capacity of a VANET resulting in the loss of neighborhood information and a higher utilization of the control channel. In this paper, we present the concept of a safety zone to adaptively control the transmit power of CAMs to restrict the network load without compromising the safety features of VANET applications. Moreover, we also introduce a new cooperative information sharing technique to increase the vehicle's awareness beyond the transmission range. The simulation results show that the proposed technique could significantly reduce the packet losses and channel utilization for a range of vehicle densities.