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Ultra-Reliable Low Latency Communication (URLLC) is mostly used for core services in the fifth generation (5G) and beyond cellular networks. Due to the conflicting demands of low latency and extremely high reliability, the ongoing research on this subject remains in its nascent phase. The utilization of Device-to-Device (D2D) communication shows great potential in facilitating URLLC. The implementation of URLLC and its Quality of Service (QoS) requirements may encounter high power consumption in dynamic wireless channels, potentially conflicting with the system's power limitations. In this paper, a packet delivery mechanism is introduced, aiming to achieve energy efficiency by incorporating a Power-Aware QoS-centric Preemptive Dropping mechanism to minimize latency while ensuring the successful transmission of URLLC data. The method aims to optimize bandwidth and channel assignment to effectively reduce overall power consumption. The optimization problem is formulated which is non-convex, posing challenges in selecting an optimal solution. To address this problem, we have undertaken the task of transforming the original problem into two subproblems: bandwidth allocation and subchannel assignment. To achieve optimal resource allocation for the formulated problem, we propose the Optimal Bandwidth Allocation (OBA) and Optimal Sub-channel Assignment (OSA) algorithms. The simulation results validate the proposed method outperforms the benchmark schemes, demonstrating higher energy efficiency ranging from 6.96 to 31.6%, as well as a reduction in latency ranging from 5.7 to 25.67%. The proposed method exhibits broad applicability in various domains, including remote healthcare and Industrial Internet of Things (IIoT) applications. [ABSTRACT FROM AUTHOR]

Modeling IoT applications in distributed computing systems as workflows enables automating their procedure. There are different types of workflow-based applications in the literature. Executing IoT applications using device-to-device (D2D) communications in distributed computing systems especially edge paradigms requiring direct communication between devices in a network with a graph topology. This paper introduces a toolkit for simulating resource management of scientific workflows with different structures in distributed environments with graph topology called WIDESim. The proposed simulator enables dynamic resource management and scheduling. We have validated the performance of WIDESim in comparison to standard simulators, also evaluated its performance in real-world scenarios of distributed computing. The results indicate that WIDESim’s performance is close to existing standard simulators besides its improvements. Additionally, the findings demonstrate the satisfactory performance of the extended features incorporated within WIDESim. [ABSTRACT FROM AUTHOR]

Abstract Enhancement in both energy efficiency and spectral efficiency in cellular networks is made possible by means of an advanced technique called device-to device (D2D) communication. The enhancement of these efficiencies is done by utilizing the cellular user (CU) resources once again to make communication with nearby cellular devices in an effective manner by spectral means. As the D2D communication technology is capable of providing a direct communication link with nearby devices effectively with enhanced spectral efficacy, this approach is considered as an ideal solution for the futuristic cellular communication network. A flexible and reliable relay-assisted communication by means of D2D technology is required that acts as an intermediate relay when the attenuation between the channels across the D2D devices becomes high. The throughput of the system is increased by utilizing D2D communication technology as it uses direct data transmission within cellular devices. When the cellular user is far apart from one another, the data loss in the D2D communication system is minimized with the utilization of the relay. When the channels are good, then the relay nodes (RNs) serve the cellular user. However, it is noted that the D2D systems are affected by issues such as higher consumption of energy and spectral sharing. Also, the sum rate gets degraded as a result of mutual interference between resource-sharing cellular devices in the relay-assisted D2D communication system. The transmission of the data in a traditional relay-assisted D2D communication system is carried out between the D2D receiver (DR) and D2D transmitter (DT) only with the utilization of its own energy by the RN. The issues in relay selection and resource allocation in the conventional joint resource allocation schemes are tackled by executing a scheme for performing the task of optimal relay selection and joint resource allocation. The enhancement of the overall sum rate in the D2D communication is the main motive behind the implemented scheme. This goal is attained along with the minimization of the link rates in the cellular and D2D networks. The ideal selection of the relay and the execution of the joint resource allocation are done with the utilization of a new optimization scheme called the hybrid flow direction with the chameleon swarm algorithm (HFDCSA), in which the flow direction algorithm (FDA) is fused along with chameleon swarm algorithm. This optimal selection of the relays is assisted by considering constraints like the network’s sum rate and energy efficiency in the network to achieve high performance. The data obtained from distinct sources are given to the adaptive multi-layer perceptron (AMLP) in which optimal resource allocation and the relay selection are performed with the help of the suggested HFDCSA. The parameters in the MLP are tuned by the same HFDCSA. Finally, the performance validation is conducted in the stage to verify the working of the suggested approach.

Enhancement in both energy efficiency and spectral efficiency in cellular networks is made possible by means of an advanced technique called device-to device (D2D) communication. The enhancement of these efficiencies is done by utilizing the cellular user (CU) resources once again to make communication with nearby cellular devices in an effective manner by spectral means. As the D2D communication technology is capable of providing a direct communication link with nearby devices effectively with enhanced spectral efficacy, this approach is considered as an ideal solution for the futuristic cellular communication network. A flexible and reliable relay-assisted communication by means of D2D technology is required that acts as an intermediate relay when the attenuation between the channels across the D2D devices becomes high. The throughput of the system is increased by utilizing D2D communication technology as it uses direct data transmission within cellular devices. When the cellular user is far apart from one another, the data loss in the D2D communication system is minimized with the utilization of the relay. When the channels are good, then the relay nodes (RNs) serve the cellular user. However, it is noted that the D2D systems are affected by issues such as higher consumption of energy and spectral sharing. Also, the sum rate gets degraded as a result of mutual interference between resource-sharing cellular devices in the relay-assisted D2D communication system. The transmission of the data in a traditional relay-assisted D2D communication system is carried out between the D2D receiver (DR) and D2D transmitter (DT) only with the utilization of its own energy by the RN. The issues in relay selection and resource allocation in the conventional joint resource allocation schemes are tackled by executing a scheme for performing the task of optimal relay selection and joint resource allocation. The enhancement of the overall sum rate in the D2D communication is the main motive behind the implemented scheme. This goal is attained along with the minimization of the link rates in the cellular and D2D networks. The ideal selection of the relay and the execution of the joint resource allocation are done with the utilization of a new optimization scheme called the hybrid flow direction with the chameleon swarm algorithm (HFDCSA), in which the flow direction algorithm (FDA) is fused along with chameleon swarm algorithm. This optimal selection of the relays is assisted by considering constraints like the network's sum rate and energy efficiency in the network to achieve high performance. The data obtained from distinct sources are given to the adaptive multi-layer perceptron (AMLP) in which optimal resource allocation and the relay selection are performed with the help of the suggested HFDCSA. The parameters in the MLP are tuned by the same HFDCSA. Finally, the performance validation is conducted in the stage to verify the working of the suggested approach. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication is an emerging technology for beyond fifth-generation (B5G) networks to support significant features, such as spectrum efficiency, high data rate, and reduced power consumption. Efficient spectrum allocation and optimum power control in D2D networks is one of the major issues that arise due to dynamic traffic arrivals. This article proposes a novel approach to maximize the spectral efficiency of the D2D network. The formulated problem is a mixed-integer nonlinear programming problem (MINLP). However, due to its complexity, the global optimal solution is difficult to solve directly. A two-stage optimization algorithm is presented: optimal resource allocation algorithm (ORAA) by utilizing the concept of queue dynamics and optimal power control algorithm by adapting the Lyapunov stability method. The proposed method achieves higher spectral efficiency up to 21.6% and latency is minimized up to 39.89% over the benchmark schemes. The proposed method shall find immense use in smart traffic management to support Intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication is proposed in cellular network as a vital technology to enhance spectral efficiency and system capacity, while reducing the energy consumption and the latency. Enabling D2D communications in multi-cell OFDMA systems poses two majors challenges: First, D2D equipements are characterizd by limited capacity of battery. Second, D2D devices inevitably suffer from co-channel interference (CCI) at cell edge. This paper addresses the mobility control (MC) for D2D in downlink multi-cell OFDMA system with cooperative relaying and Fractional Frequency Reuse (FFR) scheme. FFR and cooperative relaying schemes are proposed to improve energy efficiency (EE) and avoid CCI. In the edge region, according to a difference set and using the sectorization technique, the Frequency Reuse Factor (FRF) of 7/3 and 7/4 have been applied. The deployment of FFR with Amplify and Forward fixed relays improve significantly the system performance of FRF of 1 and 3. As shown in simulations results, the proposed cooperative MC scheme with FRF of 7/3 and 7/4 provides an improvement in term of EE nears 1 , 2. 10 6 bits/s/J and 1 , 3. 10 6 bits/s/J respectively in comparison with non-cooperative scheme with FRF=3. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is a new technology in cellular networks which enables direct communications between nearby devices. It could reuse the uplink spectrum of the cellular users (CUs) thus will introduce interference. Efficient resource allocation methods can coordinate interference and further improve network capacity. The difficulty of obtaining perfect channel state information (CSI) makes the resource allocation more complex. By using the statistical information of the unknown channel fading factors, we propose an optimal power control algorithm for any CU and any D2D pair sharing the same channel. Based on that, we formulate the resource allocation problem for multiple CUs and multiple D2D pairs with incomplete CSI as a maximum weight matching problem. We then solve the problem by combining the optimal power control algorithm and the maximum weight matching algorithm. Simulation results demonstrate the efficiency of our algorithms in achieving high D2D rates while guaranteeing the minimum rates required by CUs. [ABSTRACT FROM AUTHOR]

The combination of Non-Orthogonal Multiple Access (NOMA) technology and Device-to-Device (D2D) communication technology has outstanding advantages in achieving efficient spectrum utilization and large-scale access. Aiming at the problem of single channel allocation mode and the difficulty in obtaining optimal power allocation within D2D group in existing NOMA-D2D systems, this paper constructs a joint resource allocation algorithm with D2D group and rate as the optimization target. Firstly, in terms of sub-channel allocation, this problem is converted into a bilateral matching problem, and a D2D group channel allocation algorithm based on many-to-one scenarios is proposed, Then, the difference of two convex functions (DC) programming method based on successive convex approximation is used to find the power allocation value close to the optimal one. The simulation results show that the proposed channel matching algorithm in the many-to-one scenario is superior to the one in the one-to-one scenario in terms of sum rate, and the proposed power allocation algorithm is closer to the optimal power allocation compared to the dual iterative algorithm. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communications facilitate direct data exchange between two devices, bypassing the necessity of routing through a central base station. This proximity-based communication offers advantages such as high data rates, low latency, reduced power consumption, and efficient frequency reuse. In the cellular spectrum, D2D operations encompass overlay D2D and underlay D2D. Overlay D2D allocates orthogonal radio resources to D2D and cellular users, resulting in inefficient frequency reuse. On the other hand, underlay D2D shares the same radio resources among D2D and cellular users, necessitating centralized coordination and extensive exchanges of channel and traffic information for effective interference management. To address the limitations of overlay and underlay D2D, we introduce a scheme that allows D2D users to simultaneously and non-orthogonally transmit both D2D and cellular signals. This approach resembles underlay D2D in terms of high frequency reuse but eliminates the need for centralized interference management. In this paper, we investigate various system design aspects based on this scheme, including power allocation, multiple-antenna extension, scheduling constraints, and different interference cancellation techniques. Incorporating a coordinated scheduling method, the proposed scheme enhances the sum rate of a conventional multiple-antenna overlay D2D system by 2.14 times, with only a negligible impact on the cellular communication rate. Furthermore, the proposed scheme improves the sum rate by up to 87% within conventional underlay D2D systems, all without the need for additional centralized coordination.

This paper presents a communications framework to overcome the connectivity constraints due to the nonavailability of the line-of-sight transmissions in indoor optical communication systems. This nonavailability can arise for various reasons, such as blockages due to physical objects, unfavorable device orientations or large distances between the transmitter and the receiving devices. The proposed system utilizes multiple intelligent reflecting surface (IRS) arrays and device-to-device (D2D) communications. The D2D communication is realized using infrared (IR) light-emitting diodes (LEDs) with limited output power for eye safety. The performance of this system depends significantly on the assignment of the mirrors in the IRS arrays to the appropriate user links and a direct combinatorial assignment search is too complex to implement. The proposed approach identifies the assignment of each mirror in the IRS arrays as a multi-armed bandit (MAB) problem, and the assignment of all the mirrors together as a combinatorial MAB (CMAB) problem. Since a simultaneous movement of all the IRS mirrors during the implementation of the CMAB algorithm could cause frequent link disruptions, a CMAB algorithm with low disruptions (CMAB-LD) is proposed to obtain the best mirror assignment with low link disruptions. Simulation results demonstrate that the proposed algorithm can provide significant improvement in reward performance and the total reward increases by more than 100% over random mirror assignments when the channels are blocked with high probabilities. In small size problems, the proposed CMAB-LD is found to achieve the global optimal solution in just a few rounds of full arm explore operations.

With the exponential growth of mobile devices and high capabilities of intelligence, global communication network traffic is expected to experience remarkable growth in the next few years, as it leads us to poor network experience. The Device-to-Device (D2D) is a prominent solution to further expand the user experience and network performance. To improve it many nature-inspired computing algorithms are widely used. In line with this, we use swarm-based algorithms for network performance improvement. In this paper, we propose a swarm optimization-based resource allocation methodology for D2D communication, especially since we are focusing on the bee fly pattern to optimize the resource available resources within the network’s proximity area. Where all bees are working together to find the best optimal availability of the network’s services, hereafter mobile users get served with a respective set of resources. If the performance is not up to the pre-defined threshold value then the system adds another set of available network resources and relay nodes. It leads to relay-assisted D2D pair communication based on the bee fly pattern. It improves the D2D mobile pair’s user experience in terms of energy, delay, and mobility support. To validate our results we compare our outcome with state-of-the-art works and provide detailed observations subject to various listed parameters.

With the rapid growth in cellular user quantity and quality of service demand, the resource allocation in device-to-device communication system significantly affects the overall efficiency and user experience. In this study, the resource allocation for large-scale device-to-device communication system is modelled as a constrained optimization problem with thousands of dimensionalities. Then, the variable-coupling relationship of the developed model is analysed and the mathematical proof is firstly provided, and a novel algorithm namely multi-modal mutation cooperatively coevolving particle swarm optimization is developed to optimize the ultra-high dimensional model. Finally, efficacy of the developed method is verified by a comprehensive set of case studies, some famous algorithms for the specialized literature are also employed for comparison. Experimental results shown that the developed algorithm can obtain accurate and robust optimization performance for different system scales. In addition, when the system scale increases to 1000 cellular users and 300 D2D-pair users, the developed method can still outperform the compared algorithms and output accurate resource allocation solution. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions. [ABSTRACT FROM AUTHOR]

An exponential growth of users demands ubiquitous connectivity, which requires the integration of new technology. Therefore, Device to Device (D2D) communication has been considered a promising technology that utilizes effective and efficient communication. Even though numerous studies have been conducted for establishing secure D2D communication, however, existing techniques face challenges like privacy, security threats, and poor generality. To overcome these issues a novel Deep-MAD model is proposed to preserve data privacy along with its access control in the D2D network and multiple attack detection in a fog environment. A Fully Homomorphic Elliptic Curve Cryptography (FHECC) is introduced to transmit data securely in a D2D network. The data owner uses FHECC algorithm to encrypt the plain text into cipher text before storing it on the fog. Whenever the user requests data from the fog, the fog service provider confirm the user's access control. Furthermore, the deep learning-based Bi-LSTM is used to differentiate the device as an authorized or unauthorized user. If the IP address is genuine then the inverse FHECC is used to decrypt the data for authorized users. Otherwise, the particular device is blocked and it is sent for further verification for classifying the types of attacks. The effectiveness of the proposed strategy is examined using several parameters, such as computational complexity, scalability, accuracy, and Execution time. The proposed technique improves the overall computational overhead of 31.77, 9.34, and 4.67 better than AKA protocol, lightweight cipher, and FHEEP respectively. [ABSTRACT FROM AUTHOR]

An exponential growth of users demands ubiquitous connectivity, which requires the integration of new technology. Therefore, Device to Device (D2D) communication has been considered a promising technology that utilizes effective and efficient communication. Even though numerous studies have been conducted for establishing secure D2D communication, however, existing techniques face challenges like privacy, security threats, and poor generality. To overcome these issues a novel Deep-MAD model is proposed to preserve data privacy along with its access control in the D2D network and multiple attack detection in a fog environment. A Fully Homomorphic Elliptic Curve Cryptography (FHECC) is introduced to transmit data securely in a D2D network. The data owner uses FHECC algorithm to encrypt the plain text into cipher text before storing it on the fog. Whenever the user requests data from the fog, the fog service provider confirm the user's access control. Furthermore, the deep learning-based Bi-LSTM is used to differentiate the device as an authorized or unauthorized user. If the IP address is genuine then the inverse FHECC is used to decrypt the data for authorized users. Otherwise, the particular device is blocked and it is sent for further verification for classifying the types of attacks. The effectiveness of the proposed strategy is examined using several parameters, such as computational complexity, scalability, accuracy, and Execution time. The proposed technique improves the overall computational overhead of 31.77, 9.34, and 4.67 better than AKA protocol, lightweight cipher, and FHEEP respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is expected to have a positive impact on various sectors, such as manufacturing, users, and operators. Due to the capabilities of smart objects to share their contents and interact with their social networks, the combined Social Internet of Educational Things (SIoET) for smart education is expected to provide users with more efficient services. The primary objective of this paper is to provide an efficient resource management by adopting edge intelligence technique for higher education system workloads on next-generation networks. As the number of tasks handled by these interconnected devices continues to rise, there is an escalating concern regarding resource competition and power consumption within the network. To mitigate these challenges, we propose an innovative edge intelligence-based resource allocation approach to intelligently allocate resources while minimizing latency and optimizing power consumption with increasing task volumes. The problem is dissected into two core sub-problems: computation resource optimization and power optimization. Leveraging the concept of Lagrangian and Bisection methods, our proposed algorithm, implemented within a Device-to-Device (D2D) -assisted edge intelligence network, demonstrates remarkable performance improvements. Network latency and energy consumption is reduced by up to 57.9 % and 82.3 % compared to the benchmark schemes. These findings hold promising implications for the integration of this work into higher education systems driven by SIoET. Furthermore, simulations indicate that the system maintains robust performance even in dynamic urban environments characterized by random device movement.

With the fifth generation (5G), there are several mobiles devices may connect to the network from any location. Device-to-device (D2D) communication is a crucial component of 5G wireless networks since it increases performance. D2D communication in the 5G networks is the subject of extensive study. However, the existing D2D communication techniques did not show an improvement in energy economy or a reduction in latency. The Jarvis-Patrick-Clusterative African Buffalo Optimised Deep Learning (JPCABODL) Model was created to overcome the problems. The JPCABODL Model's main goal is to improve D2D communication performance in 5G networks using a deep learning classifier with improved energy efficiency and lower latency. Four layers make up the JPCABODL Model: one output layer, two hidden levels, and one layer for device-to-device communication. A layer of input is the total number of mobile devices. After that, each mobile device's received signal strength (RSS) and residual energy are taken into account when the Jarvis-Patrick-Clustering procedure is carried out at hidden layer 1. After that, each cluster's cluster head is selected from mobile devices with the highest residual energy, bandwidth, and RSS. The cluster is then transmitted to hidden layer 2 together with information about the cluster leader. African Buffalo Optimisation is used in that layer to choose the ideal cluster head. In 5G networks, effective device-to-device communication is accomplished with the aid of an ideal cluster head. The JPCABODL Model and current techniques are used for simulation, and several metrics such as energy efficiency, data delivery ratio, packet loss rate, throughput, and latency are measured. When compared to traditional approaches, the experimental evaluation of the JPCABODL Model enhances EE, DDR, and minimizes latency. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of resource allocation in wireless networks, given the increasing usage of mobile devices and sensors. To achieve energy efficiency, we propose a novel method called Hybrid Random Forest Ensemble Support Vector Machine (RFESVM) with Chaotic Cloud Quantum Bat Algorithm (CCQBA). The proposed method identifies the best power allocation for each user in Device-to-Device (D2D) communication, which can significantly improve coverage and lower data rates and latency. To overcome the shortcomings of existing methods, we use the chaotic cloud quantum bat algorithm that combines randomness, ergodicity of chaotic mapping, and stability inclination to enhance the convergence speed. To address the issue of class imbalance in the dataset, we combine SVM with RF and establish an ensemble technique called RFESVM. Our proposed method achieves higher energy efficiency compared to other techniques, demonstrating the effectiveness of optimal power allocation in D2D communication. Overall, the proposed method can significantly improve the systems' energy efficiency while ensuring high network performance in wireless networks. [ABSTRACT FROM AUTHOR]

Abstract With the rapid growth in cellular user quantity and quality of service demand, the resource allocation in device-to-device communication system significantly affects the overall efficiency and user experience. In this study, the resource allocation for large-scale device-to-device communication system is modelled as a constrained optimization problem with thousands of dimensionalities. Then, the variable-coupling relationship of the developed model is analysed and the mathematical proof is firstly provided, and a novel algorithm namely multi-modal mutation cooperatively coevolving particle swarm optimization is developed to optimize the ultra-high dimensional model. Finally, efficacy of the developed method is verified by a comprehensive set of case studies, some famous algorithms for the specialized literature are also employed for comparison. Experimental results shown that the developed algorithm can obtain accurate and robust optimization performance for different system scales. In addition, when the system scale increases to 1000 cellular users and 300 D2D-pair users, the developed method can still outperform the compared algorithms and output accurate resource allocation solution.

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Full-duplex (FD), Device-to-Device (D2D) and non-orthogonal multiple access (NOMA) are promising wireless communication techniques to improve the utilization of spectrum resources. Meanwhile, introducing FD, D2D and NOMA in cellular networks is very challenging due to the complex interference problem. To deal with the complex interference of FD D2D underlaying NOMA cellular networks, power allocation (PA) is extensively studied as an efficient interference management technique. However, most of the previous research works on PA to optimize energy efficiency only consider the system framework of partially joint combining techniques of FD, D2D and NOMA, and the constraints of optimization problem are very different. In this paper, in order to further improve the energy efficiency of a system, a dual-layer iteration power allocation algorithm is proposed to eliminate the complex interference. The outer-layer iteration is to solve the non-linear fractional objective function based on Dinkelbach, and the inner-layer iteration is to solve the non-convex optimization problem based on D.C. programming. Then, the non-convex and non-linear fractional objective function is transformed into a convex function to solve the optimal power allocation. In this approach, FD D2D users reuse the spectrum with downlink NOMA cellular users. Imperfect self-interference (SI) cancellation at the FD D2D users and the successive interference cancellation (SIC) at the strong NOMA user are considered in the system framework. The optimization problem is constructed to maximize the system's energy efficiency with the constraints of successful SIC, QoS requirements, the maximum transmit power of BS and FD D2D users. Numerical results demonstrate that the proposed algorithm outperforms the traditional orthogonal multiple access (OMA) in terms of energy efficiency with a higher system sum rate. [ABSTRACT FROM AUTHOR]

Bees, like other insects, indirectly contribute to job creation, food security, and poverty reduction. However, across many parts of the world, bee populations are in decline, affecting crop yields due to reduced pollination and ultimately impacting human nutrition. Technology holds promise for countering the impacts of human activities and climatic change on bees' survival and honey production. However, considering that smart beekeeping activities mostly operate in remote areas where the use of grid power is inaccessible and the use of batteries to power is not feasible, there is thus a need for such systems to be energy efficient. This work explores the integration of device-to-device communication with 5G technology as a solution to overcome the energy and throughput concerns in smart beekeeping technology. Mobile-based device-to-device communication facilitates devices to communicate directly without the need of immediate infrastructure. This type of communication offers advantages in terms of delay reduction, increased throughput, and reduced energy consumption. The faster data transmission capabilities and low-power modes of 5G networks would significantly enhance the energy efficiency during the system's idle or standby states. Additionally, the paper analyzes the application of both the discovery and communication services offered by 5G in device-to-device-based smart bee farming. A novel, energy-efficient algorithm for smart beekeeping was developed using data integration and data scheduling and its performance was compared to existing algorithms. The simulation results demonstrated that the proposed smart beekeeping device-to-device communication with data integration guarantees a good quality of service while enhancing energy efficiency. [ABSTRACT FROM AUTHOR]

In a cellular network, direct Device-to-Device (D2D) communication enhances Quality of Service (QoS) in terms of coverage, throughput and amount of power consumed. Since theD2D pairs involve cellular resources for communication, the chances of interference are high. D2D communications demand minimum interference along with maximum throughput and sum rate which can be achieved by employing optimal resources and efficient power allocation procedures. In this research, a hybrid optimization model called Genetic Algorithm-Adaptive Bat Optimization (GA-ABO) algorithm is proposed for efficient resource allocation in a cellular network with D2D communication. Simulation analysis demonstrates that the proposed model involves reduced interference with maximum sum rate and throughput. The performance of the proposed model is compared with the existing Ant Colony Optimization-based resource exchange and GAME (ACO-GAME) theory models, Trader-assistedResource EXchange mechanism-RadioAccess Network (TREX-RAN) and De-centralized Radio Access Network (TREXDRAN), and greedy CYcle-Complete preferences (CYC) models. The proposed model offers a maximum sum rate of 83 kB/s, which is much better than the existing techniques. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The fifth-generation wireless communication is expected to provide a huge amount of capacity to cater to the need of an increasing number of mobile consumers, which can be satisfied by device-to-device (D2D) communication. Reusing the cellular user's resources in an efficient manner helps to increase the spectrum efficiency of the network but it leads to severe interference. The important point in reusing cellular user resources is that D2D communication should not affect the cellular user's efficiency. After achieving this requirement, the focus is now turned toward the allocation of resources to D2D communication. This resource allocation strategy is to be designed in such a way that it will not affect communication among the cellular user (CU). This scheme improves various performance objectives. This paper aims at designing a proportional fair resource allocation algorithm based on the bipartite graph which maintains the quality of service (QoS) of CUs while providing D2D communication. This algorithm can be merged with any other scheme of resource allocation for improving QoS and adopting changing channels. In this scheme, a D2D pair can be allocated with one or more than one resource blocks. The MATLAB simulations analyze the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Device-to-device communication attracts the research community due to its diversifying range of applications. Device-to-device communication occurs between two devices without any aid from the base station. Thus, it helps in providing connectivity in low connectivity areas. In this article, authors proposed a new technique named as Clustering Based Opportunistic Traffic Offloading (CBOT), for Device-to-device communication. The proposed CBOT technique divided the network into small clusters and used a hybrid scheme for data transmission. To improve the energy consumption and throughput of the system, authors have proposed cluster formation, cluster head selection, and rotation techniques. The simulation results show that CBOT improves network lifetime by achieving high energy efficiency and increased system performance by achieving higher throughput. Further, the performance of CBOT tested under opportunistic networks scenario and simulation results demonstrate that the proposed approach improves energy consumption and throughput in a better way than the existing approaches. [ABSTRACT FROM AUTHOR]

Wireless energy harvesting (WEH) enabled Device-to-Device (D2D) communication emerges as an effective technique to improve spectral and energy efficiencies. However, D2D users are usually power-constrained devices that may be monitored or attacked by adversaries easily. To confuse randomly distributed adversaries in WEH-enabled D2D underlaying cellular networks, power beacons (PBs) can be used to send jamming signals in idle time slots. A time slot is divided into two sub-slots, i.e., WEH sub-slot and covert transmission sub-slot. The energy collected by a D2D transmitter in the first sub-slot is used to support possible covert transmission in the second sub-slot. The performance of the proposed scheme is measured by covert throughput, which is defined as D2D communication rate with several constraints, e.g., wireless energy harvesting, covertness, and cellular link communication requirements. The closed-form expressions of energy outage probability, covert probability, and desired link connection outage probabilities are derived. The minimum covert probability is used to measure covertness. Moreover, an alternating optimization algorithm is adopted to maximize covert throughput. The analytical results are compared with Monte-Carlo simulation results to verify the analytical approach. In addition, the impacts of different parameters (e.g., density of PBs and covert signal transmit power) on covert performance are evaluated. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) serves as a crucial element in interconnecting diverse devices within the realm of smart technology. However, the energy consumption of IoT technology has become a notable challenge and an area of interest for researchers. With the aim of achieving an IoT with low power consumption, green IoT has been introduced. The use of unmanned aerial vehicles (UAVs) represents a highly innovative approach for creating a sustainable green IoT network. UAVs offer advantages in terms of flexibility, mobility, and cost. Moreover, device-to-device (D2D) communication is essential in emergency communications, due to its ability to support direct communication between devices. The intelligent reflecting surface (IRS) is also a hopeful technology which reconstructs the radio propagation environment and provides a possible solution to reduce co-channel interference resulting from spectrum sharing for D2D communications. The investigation in this paper hence focuses on energy-efficient UAV-IRS-assisted D2D communications for green IoT. In particular, a problem of optimization aimed at maximizing the system’s average energy efficiency (EE) is formulated, firstly, by simultaneously optimizing the power coefficients of all D2D transmitters, the UAV’s trajectory, and the base station (BS)’s active beamforming, along with the IRS’s phase shifts. Second, to address the problem, we develop a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based scheme to find a near-optimal solution, where D2D transmitters, the BS, and the UAV cooperatively learn to improve EE and suppress the interference. To conclude, numerical simulations are conducted to assess the availability of the proposed scheme, and the simulation results demonstrate that the proposed scheme surpasses the baseline approaches in both convergence speed and EE performance.

Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions.

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Industrial Internet of Things (IIoT) gateways are affected by many cybersecurity threats, compromising their security and dependability. These gateways usually represent single points of failure on the IIoT infrastructure. When compromised, they can disrupt the entire system, including the security of the IIoT devices and the confidentiality and privacy of the data. This paper introduces a Secure IIoT Gateway Architecture that encompasses Trusted Execution Environment concepts and consolidated security algorithms to achieve a secure IIoT environment. Sensitive procedures of the IIoT, like device admission, bootstrapping, key management, authentication, and data exchange among operational technology (OT) and information technology (IT) are handled by the gateway inside the secure execution domain. The bootstrapping does not require devices to have any pre-stored secret or a pre-established secure channel to any trusted third party. Moreover, our architecture includes mechanisms for IIoT devices to safely interact with the Cloud without assuming the integrity of the gateways between them, enabling continuous verification of gateway integrity. A formal proof of the proposed solution security is provided. Finally, the security of the proposed architecture is discussed according to the specified requirements. [ABSTRACT FROM AUTHOR]

Nonorthogonal multiple access (NOMA), one of the favorable candidates of next-generation wireless networks combined with group device-to-device (D2D) networks, can sufficiently increase a system's spectral efficiency. In fact, in a cooperative scenario, successive interference cancellation (SIC) is used in NOMA receivers to reduce the complexity of relaying, as each user has to decode high-order user data. This work presents a quality of service (QoS)-based cooperative NOMA-aided group D2D system (Q-CNOMA). The Q-CNOMA system not only reduces the burden on the group transmitter by relaying the signal to a receiver in neighboring cells but also improves the overall system performance. In order to model the major components in a D2D scenario such as receivers clustering around a transmitter, the spatial distribution of D2D transmitters is modeled using a Gaussian–Poisson process (GPP). A closed-form expression of outage probability is calculated and benchmarked against conventional systems to prove the superiority of the proposed Q-CNOMA system. [ABSTRACT FROM AUTHOR]

D2D communication technology has been more and more widely used as a mobile communication technology that can perform specific services in specific areas. This paper introduces D2D technology into the offshore ships' communication system and proposes a channel resource allocation scheme for interference control in the system, so as to increase the number of devices in the network. This paper first establishes the model of the offshore ships' communication system and then applies the Hungarian algorithm based on the maximization of the average position. Finally, the comparative simulation experiments of the algorithms are proposed, which could show that the Hungarian algorithm based on model application can effectively control interference, and reduce the impact after introducing D2D communication devices into the network.

Device-to-Device (D2D) communication-enabled Heterogeneous Cellular Networks (HCNs) have been a promising technology for satisfying the growing demands of smart mobile devices in fifth-generation mobile networks. The introduction of Millimeter Wave (mm-wave) communications into D2D-enabled HCNs allows higher system capacity and user data rates to be achieved. However, interference among cellular and D2D links remains severe due to spectrum sharing. In this paper, to guarantee user Quality of Service (QoS) requirements and effectively manage the interference among users, we focus on investigating the joint optimization problem of mode selection and channel allocation in D2D-enabled HCNs with mm-wave and cellular bands. The optimization problem is formulated as the maximization of the system sum-rate under QoS constraints of both cellular and D2D users in HCNs. To solve it, a distributed multiagent deep Q-network algorithm is proposed, where the reward function is redefined according to the optimization objective. In addition, to reduce signaling overhead, a partial information sharing strategy that does not observe global information is proposed for D2D agents to select the optimal mode and channel through learning. Simulation results illustrate that the proposed joint optimization algorithm possesses good convergence and achieves better system performance compared with other existing schemes.

Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication has become a key complementary technology in emerging next-generation communication paradigm. It offers a number potential use cases including peer to peer communication, cellular offloading, device-relaying and content sharing etc. Efficient resource management is one of the key factor for boosting overall network data rate. This paper provides an effective resource management approach for D2D networks that not only maximises the network's overall throughput but also maximises the number of admitted users while adhering to power and interference limits. The optimization problem considered in this work is NP-complete and belongs to the category of mixed integer non-linear programming problems. The complexity of problem is proportional to the number of users, exhaustive search of solution is not feasible for large network size. In this work, the resource management problem solved using Binary Particle Swarm Optimization (BPSO) algorithm with eight different types of transfer functions which facilitate for calculation of probability values from velocities of the particles. The performance results are presented for different networks scenarios and compared with classical evolutionary algorithm. The simulation results show that the proposed solution achieves a better sum rate and faster convergence as compared to other solution. [ABSTRACT FROM AUTHOR]

Cirrus cloud contributes significantly to earth's radiation budget and the greenhouse effect. The Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite lacks a 1.37 μm band, sensitive to monitoring cirrus clouds. This article proposed a conditional generative adversarial network-based data-to-data translation (D2D) model to generate a hypothetical AHI 1.37 μm band. For training and testing the D2D model, the Geo-Kompsat-2A Advanced Meteorological Imager (AMI) 1.37 μm bands and other highly correlated bands to cirrus from July 24, 2019 to July 31, 2020, were used. The D2D model exhibited a high level of agreement (mean of statistics: correlation coefficient (CC) = 0.9827, bias = 0.0004, and root-mean-square error (RMSE) = 0.0086 in albedo units) between the observed and D2D-generated AMI 1.37 μm bands from validation datasets. The application of the D2D model to the AHI sensor showed that the D2D-generated AHI 1.37 μm band was qualitatively analogous to the observed AMI 1.37 μm band (average of statistics: bias = 0.0026, RMSE = 0.0191 in albedo units, and CC = 0.9158) on the 1st, 15th, and 28th of each month of 2020 in the common observing regions between Korea and Japan. The validation results with the CALIPSO data also showed that the D2D-generated AHI 1.37 μm band performed similarly to the observed AMI 1.37 µm band. Consequently, this article can significantly contribute to cirrus detection and its application to climatology. [ABSTRACT FROM AUTHOR]