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Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of asymmetric input and output data. The framework is applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of new energy companies under three input variables (R&D personnel input, R&D capital input, and comprehensive energy consumption input), two desirable output variables (green technology output and economic output), and one undesirable output variable (greenhouse gas emissions). Then, environmental factors and random factors are eliminated from the obtained input slack variables based on the SFA model, placing decision-making units in a homogeneous environment. The results demonstrate that TE, PTE, and SE are improved after eliminating environmental factors and random factors. Subsequently, based on the entropy method, this paper classifies companies' green innovation patterns into four categories and provides targeted solutions. The purpose of this paper is to provide an evaluation method for new energy companies to understand green innovation efficiency and assist decision makers in identifying the most optimal resource allocation approach. The proposed improved SBM model contributes to the literature and to industry practice by (1) providing a reliable evaluation of green innovation efficiency under asymmetric input and output data; (2) determining effective improvement actions based on a slack analysis of environmental variables and random variables that lead to improved process performance; and (3) making fuzzy innovation performance efficient to facilitate understanding and managing innovation resource allocation quality. [ABSTRACT FROM AUTHOR]

The recent advancements of mobile edge computing (MEC) technologies and unmanned aerial vehicles (UAVs) have provided resilient and flexible computation services for ground users beyond the coverage of terrestrial service. In this paper, we focus on a UAV-assisted MEC system in which the UAV equipped with MEC servers is used to assist user devices in computing their tasks. To minimize the weighted average energy consumption and delay in the UAV-assisted MEC system, a LQR-Lagrange-based DDPG (LLDDPG) algorithm, which jointly optimizes the user task offloading and the UAV trajectory design, is proposed. To be specific, the LLDDPG algorithm consists of three subproblems. The DDPG algorithm is used to address the issue of UAV desired trajectory planning, and subsequently, the LQR-based algorithm is employed to achieve the real-time tracking control of UAV desired trajectory. Finally, the Lagrange duality method is proposed to solve the optimization problem of computational resource allocation. Simulation results indicate that the proposed LLDDPG algorithm can effectively improve the system resource management and realize the real-time UAV trajectory design. [ABSTRACT FROM AUTHOR]

This paper discusses the operation of a massive multiple‐input multiple‐output (MIMO) system from a different perspective. The system is performed in the presence of an eavesdropper who is trying to disrupt the message being transmitted between the base station (BS) and the users. An attempt has been made to maximize the secure energy efficiency (EE) of the system by allocating appropriate power to each user. The channel state information (CSI) between users and the BS is considered imperfect and the employed precoding scheme is the zero forcing (ZF). The CSI related to the eavesdropper is considered to be perfect and its precoding is assumed to be the maximum ratio transmission (MRT). The problem of optimization that is for maximizing the EE has two constraints including the maximum transmission power and the minimum user data rate. To improve the performance of system, the cell division technique is applied, which results in approaching the performance of system to the without eavesdropper scenario. Five various scenarios have been investigated in this research, and it is proved that the proposed method improves the system performance in all cases. The numerical and simulation results obtained from implementation of the proposed algorithm confirm the claim. [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices has given rise to applications that demand real-time responses and minimal delay. Fog computing has emerged as a suitable platform for processing IoT applications, extending cloud computing services to the edge of the network. This enables more cost-effective and time-efficient processing at the network's edge. However, determining how to allocate tasks to fog nodes presents a fundamental challenge, involving factors like energy consumption and limited fog server capacity, impacting quality of service parameters such as delay. This paper introduces a mathematical formula for resource allocation to minimize delay and energy consumption while considering quality of service criteria. The subsequent step involves presenting a hybrid genetic algorithm (GA) and the gray wolf optimization (GWO), constituting an improved hybrid approach where the GA exhaustively explores the solution space to reduce the risk of converging to a locally optimal point. The combination of these algorithms produces multiple solutions. Despite incurring processing costs and computation delays, the implementation of these algorithms is crucial for enhancing the Quality of Service (QoS). In conclusion, the results indicate that the simultaneous use of positive aspects from both algorithms significantly improves execution time, final task completion time compared to the other methods. [ABSTRACT FROM AUTHOR]

The rapid growth of edge devices and mobile applications has driven the adoption of edge computing to handle computing tasks closer to end-users. However, the heterogeneity of edge devices and their limited computing resources raise challenges in the efficient allocation of computing resources to complete services with different characteristics and preferences. In this paper, we delve into an edge scenario comprising multiple Edge Computing Servers (ECSs), multiple Device-to-Device (D2D) Edge Nodes (ENs), and multiple edge devices. In order to address the resource allocation challenge among ECSs, ENs, and edge devices in high-workload environments, as well as the pricing of edge resources within the resource market framework, we propose a Risk Assessment Contract Algorithm (RACA) based on risk assessment theory. The RACA enables ECSs to assess risks associated with local users by estimating their future revenue potential and updating the contract autonomously at present and in the future. ENs acquire additional resources from ECSs to efficiently complete local users' tasks. Simultaneously, ENs can also negotiate reasonable resource requests and pricing with ECSs by a Stackelberg game algorithm. Furthermore, we prove the unique existence of Nash equilibrium in the established game, implying that equilibrium solutions can stably converge through computational methods in heterogeneous environments. Finally, through simulation experiments on the dataset, we demonstrate that risk assessment can better enhance the overall profit capability of the system. Moreover, through multiple experiments, we showcase the stability of the contract's autonomous update capability. The RACA exhibits better utility in terms of system profit capabilities, stability in high-workload environments, and energy consumption. This work provides a more dynamic and effective solution to the resource allocation problem in edge systems under high-workload environments. [ABSTRACT FROM AUTHOR]

Big data frameworks such as Storm, Spark and Hadoop are widely deployed in commercial and research applications, the energy consumption of cloud data centers that support big data processing platforms is becoming more and more prominent. However, job scheduling is a complex problem in the presence of various service level agreement (SLA) goals, such as cost reduction and job performance improvement. The highly heterogeneous nature of clusters and the variability of resource requirements acrossworkloads make energy-efficient scheduling on big data platforms extremely complex under SLA constraints. Existing performance-based models and heuristic scheduling methods rely excessively on historical data and are difficult to optimize or modify for changes in load and clusters. In this paper, we construct an energy consumption model based on resource utilization and a reinforcement learning model for energy-efficient scheduling under SLA constraints for Spark clusters, and design two Deep Reinforcement Learning (DRL) algorithms. The cluster scheduler designed and implemented based on this model can automatically capture different load characteristics and inherent cluster characteristics, find the appropriate executor creation policy for resource allocation, and reduce cluster energy consumption under the constraint of job execution time. Experimental results show that the DRL scheduler proposed in this paper saves a maximum energy of about 33% under different load characteristics. [ABSTRACT FROM AUTHOR]

Purpose: Internet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm's success in complex problems, the authors used this algorithm to allocate resources in IoT. Design/methodology/approach: For the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm. Findings: Simulation results show that the proper performance of the proposed technique. The proposed method, in terms of "energy" and "delay," is better than other ones (GA, PSO and distance-based algorithm). Practical implications: The paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources. Originality/value: The proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business. [ABSTRACT FROM AUTHOR]

The significance of vehicle-to everything (V2X) communication in ensuring road safety is undeniable. In addition, real-time vehicle communication requires an ample amount of spectrum resources. However, the existing spectrum resources are seriously scarce, and the utilization rate is not high, leading to high delays in V2X communication and other unfavorable factors in the case of fast-moving vehicles, bringing great safety risks to driving. Load balancing is one of the most effective methods to improve spectrum utilization. However, the existing load balancing schemes merely focus on static conditions, with a lack of joint scheduling schemes, which cannot support the communication framework of dynamic V2X. To address both of these issues, in this paper, a new communication method is proposed. In addition, this paper studies a joint load balancing scheme of mobility vehicle-to-vehicle (V2V) and user association under incomplete channel state information (CSI) and realizes the load balancing management of a cross-cell V2X network. An algorithm combining power control and resource allocation mode selection is proposed. In particular, according to different coverage areas, different allocation algorithms are adopted to maximize the overall system efficiency. The simulation results show that this strategy can maintain low latency and effectively improve the system energy efficiency of vehicle users. [ABSTRACT FROM AUTHOR]

As users have higher and higher requirements for the quality of experience, traditional cloud computing is gradually unable to meet the needs of user equipments. Hence mobile edge computing networks mounted by unmanned aerial vehicles are introduced to improve user experience and reduce energy consumption. However, most current work is based on neural networks, which require large amounts of labeled data or long training times. Given these challenges, this paper proposes an energy-efficient multi-stage alternating optimization scheme to reduce the weighted energy consumption of the entire network. We analyze the energy consumption of each device and formulate a non-convex optimization problem. Considering the impact of task offloading, resource allocation, and path planning on network energy consumption, we transform the energy consumption problem into three subproblems. And use the coordinate descent algorithm, interior point method, and successive convex approximation method to optimize them alternately. The simulation results show that the proposed optimization scheme can significantly reduce the network's total energy consumption. [ABSTRACT FROM AUTHOR]

In order to enhance resource allocation in fog computing networks and establish an energy-aware service, this paper proposes a user satisfaction-based energy-saving computation offloading mechanism that jointly optimizes service decision, task offloading ratio, uplink bandwidth resource ratio, and computing resource ratio. Specifically, the proposed mechanism takes user satisfaction as a priority. It constructs a novel satisfaction function that considers the historical energy consumption distribution to capture the user's subjective perception of the service quality. Then, we develop a user satisfaction-based service decision (US-SD) algorithm to select unique service nodes for the users. Furthermore, to minimize the processing energy consumption, a subtask partition and resource allocation-based intelligent computation offloading (SPRA-ICO) algorithm is proposed. In such an algorithm, we design an innovative actor-critic network structure and add noise to the continuous output action to guarantee the randomness of deterministic policy exploration. Meanwhile, the experience replay buffer mechanism and parameter soft update operation are comprehensively employed to reduce the mutual guidance of training samples and improve the function convergence performance. Finally, the simulation results show that compared with other benchmark schemes, the proposed mechanism can realize good convergence speed and user retention rate while effectively mitigating the total energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges of mobile edge computing with low latency and energy consumption caused by the explosive growth of communication traffic and data generated by mobile devices. To address the issue of selecting edge servers, a multi-user-oriented edge server selection strategy is proposed. The strategy minimizes the weighted sum of network latency and total energy consumption and develops an improved SPEA2-based algorithm to choose the most suitable edge server. The resource allocation problem is also considered, and a resource allocation strategy is proposed that maximizes the energy efficiency ratio. A DQN-based resource allocation strategy is devised to find the best resource allocation strategy. Experimental results demonstrate that the proposed server selection strategy reduces system overhead in terms of energy consumption and latency while improving resource utilization. The resource allocation strategy improves the computational efficiency of edge servers while reducing latency and total energy consumption. [ABSTRACT FROM AUTHOR]

The integration of vast quantities of clean energy causes intense impact on the power grid reliability. Digital Twin can be used to handle the high‐growth data flow generated in the power grid, and thus accelerate its digitalization process to fulfill the clean energy consumption target. As the traditional central cloud network structure can no longer effectively support the operation of digital twin power grid, the mobile edge network can be used as a reasonable substitute. To support the information interaction process of the mobile edge network, considering total profit, time delay and energy consumption, this paper proposes the mobile edge network communication and computing resource joint allocation model. On the basis, the GA‐PSO algorithm is proposed to solve the resource allocation optimization function. The feasibility and effectiveness of the proposed model is verified by the related simulation experiment. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Big Data applications require more energy consumption to process a massive volume of data in a heterogeneous environment. Moreover, reducing energy consumption in Big Data applications is an important research topic. It is one of the challenging issues to conserve energy with a deadline constraint in a heterogeneous environment. In this paper, we formulate scheduling the MapReduce jobs as a minimization problem by considering the decision variables with a user-specified deadline constraint. Further, a Learning Automata-based MapReduce Scheduling (LA-MRS) algorithm has been proposed to identify the resource allocation and save energy consumption of MapReduce tasks in a heterogeneous environment. We perform experimentation on the proposed LA-MRS algorithm using Hibench benchmark workloads such as Enhanced DFSIO, Nutch Indexing, k-mean Clustering and Hive Join. The experimentation illustrates that the proposed LA-MRS algorithm schedules the MapReduce task by saving around 25% of less energy consumed when compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has emerged as a promising solution to support multiple devices on the same network resources, improving spectral efficiency and enabling massive connectivity required by ever-increasing Internet of Things devices. However, traditional NOMA schemes operate in a grant-based fashion and require channel-state information and power control, which hinders its implementation for massive machine-type communications. Accordingly, this paper proposes synchronous grant-free NOMA (GF-NOMA) frameworks that effectively integrate user equipment (UE) clustering and low-complexity power control to facilitate the power-reception disparity required by the power-domain NOMA. Although single-level GF-NOMA (SGF-NOMA) designates an identical transmit power for all UEs, multi-level GF-NOMA (MGF-NOMA) groups UEs into partitions based on the sounding reference signals strength and assigns partitions with different identical power levels. Based on the objective of interest (e.g., max–sum or max–min rate), the proposed UE clustering scheme iteratively admits UEs to form clusters whose size is dynamically determined based on the number of UEs and available resource blocks (RBs). Once the UEs are acknowledged with power levels and allocated RBs through random-access response (RAR) messages, UEs can transmit anytime without grant acquisition. Numerical results show that the proposed GF-NOMA frameworks can compute clusters in the order of milliseconds for hundreds of UEs. The MGF-NOMA can reach up to 96–99% of the optimal benchmark max–sum rate, and the SGF-NOMA reaches 87% of the optimal benchmark max–sum rate at the same power consumption. Since the MGF-NOMA and optimal benchmark enforce the strongest and weakest channel UEs to transmit at maximum and minimum transmit powers, respectively, the SGF-NOMA also offers a significantly higher energy consumption fairness and network lifetime as all UEs consume equal transmit powers. Although the MGF-NOMA delivers an inferior max–min rate performance, the SGF-NOMA is shown to reach 3e6 MbpJ energy efficiency compared to the 1e7 MbpJ benchmark. [ABSTRACT FROM AUTHOR]

Summary: Device‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication. [ABSTRACT FROM AUTHOR]

The sustainable development and intelligent transformation of the manufacturing industry have become inevitable trends. As a typical example of the intelligent transformation, the networked manufacturing mode has been widely applied through sharing and utilizing manufacturing resources all over the world. Existing research on energy-saving use of manufacturing equipment mainly focuses on internal workshop of enterprises. However, it is also important to consider how to improve the energy efficiency in a networked manufacturing environment. Compared to traditional manufacturing mode, the networked manufacturing mode faces energy-saving challenges such as machining devices are numerous and scattered, manufacturing services are driven by customer orders, and significant random uncertainty occurs in manufacturing processes. This paper proposes a framework to select and schedule equipment resources for energy-saving in a networked manufacturing environment for different stages of manufacturing. First, to increase energy efficiency in the customer orders demand stage, a fuzzy multi-objective decision-making model is developed. This model considers economy, energy consumption, technology, and inter-enterprise collaboration. Second, to balance the competitive interests between customer orders, and to reduce manufacturing energy consumption, costs, and delivery dates in the manufacturing tasks implementation stage, an energy-saving scheduling model for equipment resources based on non-cooperative game theory is established. Then, to deal with random disturbance in a networked manufacturing, a scheduling model based on random chance constraint programming is built to optimize energy, economy, and production efficiency at the active preventive stage. Finally, a cloud platform implementation method of the proposed framework is summarized. The proposed framework helps provide theoretical analysis that is significant to energy-saving allocation of the equipment resources in the networked manufacturing mode. [ABSTRACT FROM AUTHOR]

This paper focuses on energy-efficient resource allocation in reconfigurable intelligent surface (RIS)-assisted multiple-input-single-output (MISO) communication systems. Specifically, it revisits the solution to the energy efficiency (EE) problem using the alternating optimization (AO) approach. In each AO iteration, the RIS phase optimization is achieved using the gradient descent method, which unfortunately does not guarantee convergence. To overcome this limitation, we propose two alternatives: the Wolfe-based gradient-descent (GAW) EE maximization Algorithm and the trust region (TR)-based EE maximization algorithm. Additionally, we use Dinkelbach's algorithm to obtain the optimal transmit power allocation. Our results demonstrate that the proposed methods outperform the existing approach that uses sequential fractional programming (SFP) for phase optimization and the traditional relay-based method. [ABSTRACT FROM AUTHOR]

• Examines resources allocation issue for minimizing the solar power usage in fog computing-enabled wireless IoTs using NOMA. • A typical case is considered to support several IoT devices for offloading computations together to the similar FN. • As a way of achieving an optimal efficiency-complexity balance, a whale optimization algorithm (WOA) is introduced. • The suggested layout is more efficient than current methods in terms of throughput, latency, blackout likelihood, and power usage. Fog computing is used to handle applications that require a lot of calculations at the network's edges for wireless Internet of Things (IoT) networks that have source-limited equipment. In addition to reducing computational time and fronthaul traffic information, fog computing still poses challenges in allocating the existing computing and communication sources, while maintaining the strict quality of service (QoS) standards. The current study examines the issue regarding task management and heterogeneous resource allocation among equipment for wireless IoT networks. As a means of transferring the information to the fog computing nodes (FNs), IoT devices collecting large amounts of information require appropriate offloading decisions. This paper investigates how non-orthogonal multiple access (NOMA) can be deployed for IoT networks in order to enable many devices for transmitting information of the load and the market to a similar FN simultaneously, in a similar frequency range, and in the same code domain. Several IoT devices are optimized together based on their resource allocation and transmission power in this paper while meeting the QoS standards. Additionally, the optimization problem can be expressed in the form of a mixed-integer nonlinear programming problem with the goal of minimizing the system's power usage. The problem would be NP-hard so a whale optimization algorithm (WOA) has been proposed for solving. According to simulation outcomes, the suggested architecture performs well in terms of throughput, delays, blackout likelihood, and power usage. [ABSTRACT FROM AUTHOR]

Summary: By incorporating on‐demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay‐per‐use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment‐friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy‐efficient cloud is the main objective of this paper. The paper will comprehensively review several energy‐efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

This paper presents a detailed study of uplink scheduling in narrowband internet of things (NB-IoT) networks. As NB-IoT devices need a long battery lifetime, we aim to maximize energy efficiency while satisfying the main requirements for NB-IoT devices. Also, as the NB-IoT scheduling problem is divided into link adaptation problem and resource allocation problem, this paper investigates the correlation between these two problems. Accordingly, we propose two scheduling schemes: the joint scheduling scheme, where the two problems are combined as one optimization problem, and the successive scheduling scheme that manages each problem separately but successively. Each scheme aims to maximize energy efficiency while achieving reliable transmission, satisfying delay requirements, and guaranteeing resource allocation specifications. Also, we investigate the impact of the selected devices to be served on the total energy efficiency. Accordingly, we propose two device selection techniques to maximize the total energy efficiency. The first technique exhaustively searches for the optimal devices, while the second sorts the devices based on a proposed priority score. The simulation results compare the successive and the joint scheduling schemes. The results show that the joint scheme outperforms the successive scheme in terms of energy efficiency and the number of served devices but with higher complexity. Also, the results highlight the impact of each proposed selection technique on the scheduling schemes' performance. [ABSTRACT FROM AUTHOR]

The symmetric wireless network has been expected to be a revolutionary technology for mobile communications. Due to the limited resources of the microbase stations in the wireless network, the way to jointly optimize resource allocation, traffic throughput, latency, and other key performances is a hot research issue. In this paper, we introduce a joint optimization algorithm for improving the performance and balancing the traffic load of the wireless network. For the optimal traffic routing scheme, we transfer the problem to a mixed mathematical programming model. The model contains multiple traffic constraints and a single joint objective; the objective of the joint optimization are data transmission latency, energy consumption of wireless microbase stations, and throughput of links. Moreover, in order to approximately solve the optimization problem, we propose an efficient heuristic traffic transmission and migration scheme with load balancing, called an adaptive data traffic control scheme. The main idea of the proposed scheme is to split the traffic of overloaded microbase stations and links in the symmetric wireless network, so as to achieve load balancing and reduce the energy consumption of microbase stations. At last, the evaluations and simulations verify the proposed algorithm can efficiently optimize the energy allocation of microbase stations, and the network lifetime is increased to 210 rounds. Meanwhile, the network latency is reduced to 2–3 ms, and the network throughput is increased to 1000 Mb in our simulation environment. The constructed traffic control system for the traffic engineering-based wireless network in this paper can serve the intelligent system in the future. [ABSTRACT FROM AUTHOR]

This paper proposes an approach for pool mining in public blockchain systems based on the employment of a recently reported consensus protocol with the puzzle based on a symmetric encryption that provides an energy–space trade-off and reduces energy consumption. The proposed architecture employs a pseudo-symmetric allocation of the resources for the blockchain consensus protocol and provides protection against certain malicious actions of the pool members, as well as a miner's opportunity for selecting the resources required for participation in the consensus protocol. Given that the considered consensus protocol employs two resources, the proposed architecture uses this two-dimensional nature to provide resistance against block withholding and selfish mining attacks, as well as a reduction in energy spending as a trade-off with the employment of certain memory resources. The high resistance of the proposed pool mining approach against the considered attacks appears to be a consequence of the success probability of the pool in comparison with the success probability of malicious miners. Assuming appropriate selection of the puzzle hardness, the probability that malicious miners can solve the puzzle without the support of the pool manager can be arbitrarily small. Implementation of the proposed approach on a modified Ethereum platform and experimental evaluation issues have also been reported. The conceptual novelty of the proposed pool mining approach is the following: Instead of separation of the blockchain consensus protocol and control of pool miners honest work, this paper proposes an approach where honest work of miners and pool managers is provided by a dedicated application of the considered consensus protocol. Advantages of the proposal in comparison with the previously reported ones include the following: (i) high resistance against block withholding and selfish mining attacks without an additional security procedure; (ii) reduction in the energy required, and at the same time preservationthe security of the consensus protocol; (iii) flexibility of the pool miners regarding selection of the resources that should be employed providing a trade-off between required energy and memory resources. The proposed architecture was implemented employing a dedicated modification of the Ethereum platform and the performed experiments confirmed the feasibility and effectiveness of the proposal. [ABSTRACT FROM AUTHOR]

As mobile devices are widely used and various applications emerge, users have higher demands on data rates and computing power. Software Defined Network (SDN) can configure and manage various devices in the network through a centralized control controller, making the network more flexible. In an SDN-enabled edge computing environment, dense multiple access devices make mobile devices handover frequently, and mobile devices handover between different access points becomes an inevitable problem. To address this problem, we propose an Access Point (AP) handover strategy based on the signal strength and traffic load. The scheme uses the global view and centralized control capability of the SDN controller to obtain, manage, and analyze information, then calculate the weights and compare them, and finally develop the handover policy. On the other hand, to improve system resource utilization and meet the performance demands of different applications, MEC systems need to allocate computing and communication resources appropriately to keep users' Quality-of-Service (QoS) experience. We propose a joint optimization strategy for computing and communication resources based on the Lagrange multiplier method. The policy calculates and analyzes the task execution latency and energy consumption of edge servers and local terminals, and develops an optimization scheme for sub-channel allocation and resource allocation. It aims to reduce latency and energy consumption as much as possible. The results of the experiments in this paper illustrate that the proposed AP handover scheme which is on the basis of received signal strength indicator (RSSI) and traffic load can effectively improve the task completion time and energy consumption performance. The proposed joint optimization strategy of computing and communication resources based on the Lagrange multiplier method can effectively improve energy consumption and delay performance. [ABSTRACT FROM AUTHOR]

Optimization of the energy efficiency, fairness, and rates of the system is a vital part of communication systems. Multiple access techniques have a huge potential to enhance such performance parameters. This paper studies the performance of NOMA and OMA systems in a singular cell environment, where the cellular users are distributed randomly, and cooperative relays are considered for better system reliability. The relay nodes forward the signals to the cell-edge users. This paper considers a practical scenario where all the relay equipment is distributed with non-uniform battery power levels. The performance of OMA and NOMA schemes is compared based on the key performance indicators: sum rate, fairness, and energy efficiency. The fairness factor determines fairness in the allocation of resources to all the system's users. The performance of the two schemes is assessed in three deployment scenarios: urban, suburban, and rural scenarios. Through numerical results, it is proved that the performance of the NOMA dominates the OMA scheme. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing can be considered an emerging technology that can share resources with users. Because cloud computing is on-demand, efficient use of resources such as memory, processors, bandwidth, etc., is a big challenge. Despite the advantages of cloud computing, sometimes it is not a proper choice due to its delay in responding appropriately to existing requests, which led to the need for another technology called fog computing. Fog computing reduces traffic and time lags by expanding cloud services to the network and closer to users. It can schedule resources with higher efficiency and utilize them to impact the user's experience dramatically. This paper aims to survey some studies that have been done in the field of scheduling in fog/cloud computing environments. The focus of this survey is on published studies between 2015 and 2021 in journals or conferences. We selected 71 studies in a systematic literature review (SLR) from four major scientific databases based on their relation to our paper. We classified these studies into five categories based on their traced parameters and their focus area. This classification comprises 1—performance 2—energy efficiency, 3—resource utilization, 4—performance and energy efficiency, and 5—performance and resource utilization simultaneously. 42.3% of the studies focused on performance, 9.9% on energy efficiency, 7.0% on resource utilization, 21.1% on both performance and energy efficiency, and 19.7% on both performance and resource utilization. Finally, we present challenges and open issues in the resource scheduling methods in fog/cloud computing environments. [ABSTRACT FROM AUTHOR]

Because the traditional computing model can no longer meet the particularity of Internet of Vehicles tasks, aiming at its characteristics of high bandwidth, low latency and high reliability, this paper proposes a resource allocation strategy for Internet of Vehicles using reinforcement learning in edge cloud computing environment. First, a multi-layer resource allocation model for Internet of Vehicles is proposed, which uses the cooperation mode of edge cloud computing servers and roadside units to dynamically coordinate edge computing and content caching. Then, based on the construction of communication model, calculation model and cache model, make full use of idle resources in Internet of Vehicles to minimize network delay under the condition of limited energy consumption. Finally, the optimization goal is solved by two-layer deep Q network model, and the best resource allocation plan is obtained. The simulation results based on the Internet of Vehicles model show that the computational energy consumption and system delay of proposed strategy do not exceed 400 J and 600 ms, respectively. Besides, the overall effect of resource allocation is better than other comparison strategies and it has certain application prospects. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) in the communication industry is the general trend of green communication development, and the base station sleep (BSS) strategy is crucial to improving EE. However, how to maximize the cell's EE and optimize the performance of users in the sleeping area after the BSS is a challenging problem. The sub‐6GHz and millimeter‐wave (mmWave) integrated networks are considered in this paper. The users' backhaul of the cell is jointly provided by the sub‐6 GHz band and the mmWave band. User reaccess algorithm based on multiple parameters access criterion is adopted when their severed base stations (BSs) are shut down. The authors also consider a resource allocation algorithm based on the semi‐dynamic Markov approximation (MA) algorithm, which is proposed to quickly allocate sub‐channels to reaccessed users and ensure the maximum throughput. The simulation results show that the proposed algorithm can improve the EE of the heterogeneous network and promptly meet the users' needs when some BSs are shut down. [ABSTRACT FROM AUTHOR]

In cooperative cognitive radio networks (CCRN), Primary Users (PUs) share their unused spectrum resources with Secondary Users in order to facilitate cooperative communication in cognitive radio networks (SUs). The novel hybrid Giza Pyramids construction-based Complex valued Satin Bower Optimization (GPC-CSBO) algorithm is proposed in this paper to tackle the multiobjective resource allocation problem in CCRN. To operate the cooperative intermediate nodes and increase their throughput, a variety of constraints are taken into consideration, including the amount of power transmitted, QoS constraints, channel capacity, energy consumption, the priority of resource requests from secondary users (SUs), throughput, fairness evaluation constraints, etc. By constructing a cluster of SUs with the help of the SU base station, the GPC algorithm is used for effective load balancing. By providing 11 different constraints as an input to the hybrid GPC-CSBO algorithm, the SUs message request is prioritized and queued. The hybrid GPC-CSBO algorithm selects an optimal path between the SU to the PU base station based on fairness. The efficiency of the proposed algorithm is evaluated in an IEEE 802.11 WLAN area by comparing the proposed methodology with existing techniques. Throughput, energy efficiency, fairness index, delay, packet delivery, and network lifetime are taken into consideration when determining how effective the proposed approach is. [ABSTRACT FROM AUTHOR]

With the emergence of more and more computing-intensive and latency-sensitive applications, insufficient computing power and energy of user devices has become a common phenomenon. Mobile edge computing (MEC) is an effective solution to this phenomenon. MEC improves task execution efficiency by offloading some tasks to edge servers for execution. In this paper, we consider a device-to-device technology (D2D)-enabled MEC network communication model, and study the subtask offloading strategy and the transmitting power allocation strategy of users. The objective function is to minimize the weighted sum of the average completion delay and average energy consumption of users, which is a mixed integer nonlinear problem. We first propose an enhanced particle swarm optimization algorithm (EPSO) to optimize the transmit power allocation strategy. Then, we utilize the Genetic Algorithm (GA) to optimize the subtask offloading strategy. Finally, we propose an alternate optimization algorithm (EPSO-GA) to jointly optimize the transmit power allocation strategy and the subtask offloading strategy. The simulation results show that the EPSO-GA outperforms other comparative algorithms in terms of the average completion delay, average energy consumption, and average cost. In addition, no matter how the weight coefficients of delay and energy consumption change, the average cost of the EPSO-GA is the least. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising technique to support the emerging delay-sensitive and compute-intensive applications for user equipment (UE) by means of computation offloading. However, designing a computation offloading algorithm for the MEC network to meet the restrictive requirements towards system latency and energy consumption remains challenging. In this paper, we propose a joint user-association, task-partition, and resource-allocation (JUTAR) algorithm to solve the computation offloading problem. In particular, we first build an optimization function for the computation offloading problem. Then, we utilize the user association and smooth approximation to simplify the objective function. Finally, we employ the particle swarm algorithm (PSA) to find the optimal solution. The proposed JUTAR algorithm achieves a better system performance compared with the state-of-the-art (SOA) computation offloading algorithm due to the joint optimization of the user association, task partition, and resource allocation for computation offloading. Numerical results show that, compared with the SOA algorithm, the proposed JUTAR achieves about 21 % system performance gain in the MEC network with 100 pieces of UE. [ABSTRACT FROM AUTHOR]

The traditional cloud computing technology provides services to a plethora of applications by providing resources. These services support numerous industries for computational purposes and data storage. However, the obstruction of the cloud computing framework is its inadequate flexibility and problem to accommodate the diverse requirements generated from an IoT-based environment. Cloud computing is emerging with the latest paradigms to ensure that the connected heterogeneous system can achieve high-performance computing (HPC). Furthermore, many of today's requirements prefer diverse geographic distribution of resources and near to the end device location. Hence, the new fog computing paradigm provides some innovative solutions for real-time applications. The fog computing framework's prime agenda is to support latency-sensitive applications by utilizing all available resources. In this paper, a novel approach is designed for resource allocation and management. TRAM , a technique for resource allocation and management, is proposed to ensure resource utilization at the fog layer. This approach is used to track the intensity level of existing tasks using expectation maximization (EM) algorithm and calculate the current status of resources. All the available resources manage by using a wireless system. This paper provides a scheduling algorithm for the resource grading process in the fog computing environment. The performance of this approach is tested on the iFogSim simulator and compared the results with SJF, FCFS and MPSO. The experimental results demonstrated that TRAM effectively minimizes execution time, network consumption, energy consumption and average loop delay of tasks. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) emerged as a promising network paradigm that provides computation, storage and networking features within the edge of the pervasive mobile radio access network. This paper jointly considers computation offloading and resource allocation problem in device-to-device (D2D)-assisted and non-orthogonal multiple access (NOMA)-empowered MEC systems, where each mobile device (MD) is allowed to execute its task in one of the three ways, i.e., local computing, MEC offloading or D2D offloading. We invoke orthogonal multiple access (OMA) and NOMA schemes for MDs that select D2D offloading mode, allowing them to assign tasks to their peers using OMA or NOMA. The original problem is formulated as an overall energy consumption minimization problem, which proves to be NP-hard, making it intractable to solve optimally. We start from a simple case, OMA case and transform the original problem into two sub-problems, i.e., resource allocation sub-problem and computation offloading sub-problem and propose two heuristic algorithms to obtain the sub-optimal solutions of both sub-problems. Then, for the MDs selecting D2D offloading mode, we conduct user pairing and apply the NOMA scheme. Finally, simulation results demonstrate the efficiency of the proposed scheme when compared with the related schemes. [ABSTRACT FROM AUTHOR]

Mobile network processing and the Edge Computing paradigm can be integrated as a unit called mobile fog computing in fifth-generation networks. Because mobile devices have less computing capacity such as limited CPU power, storage capacity, memory constraints, and limited battery life, therefore, computationally intensive tasks migrate from MDs to MFC. In this paper, we formulate an optimization scheme based on the Greedy Knapsack Offloading Algorithm (GKOA) to minimize the energy consumption of the MDs and save the capacity of limited resources. For resource allocation and dynamic scheduling, we present a dynamic scheduling algorithm based on the priority queue. We design two queues where the tasks with high execution times have the high priority in high time queue and the other, tasks with low execution times have the high priority in low time queue. These two priority queues work together and call as High Low Priority Scheduling (HLPS) model. Numerical results demonstrate the GKOA scheme improves energy efficiency by 19 % , system overhead by 13.87 % , and average delay by 8.5 % on the MD side than local computing. Also, our proposed scheduling algorithm performs optimal results than several benchmark algorithms in terms of waiting time, delay, service level, average response time and the number of scheduled tasks on the MFC side. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah, known as Wi-Fi HaLow, is envisioned for long-range and low-power communication. It is sub-1 GHz technology designed for massive Internet of Things (IoT) and machine-to-machine devices. It aims to overcome the IoT challenges, such as providing connectivity to massive power-constrained devices distributed over a large geographical area. To accomplish this objective, IEEE 802.11ah introduces several unique physical and medium access control layer (MAC) features. In recent years, the MAC features of IEEE 802.11ah, including restricted access window, authentication (e.g., centralized and distributed) and association, relay and sectorization, target wake-up time, and traffic indication map, have been intensively investigated from various aspects to improve resource allocation and enhance the network performance in terms of device association time, throughput, delay, and energy consumption. This survey paper presents an in-depth assessment and analysis of these MAC features along with current solutions, their potentials, and key challenges, exposing how to use these novel features to meet the rigorous IoT standards. [ABSTRACT FROM AUTHOR]

Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism. [ABSTRACT FROM AUTHOR]

Mutualistic cooperative ambient backscatter communications (AmBC) have been proposed to improve the spectrum and energy efficiencies of Internet-of-Things (IoT) systems, where a primary link (from a primary transmitter to a primary receiver) and an AmBC link (from an IoT device to the same primary receiver) form a mutualism relationship. We note that hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we study a mutualistic cooperative AmBC network with HIs at all the active transceivers and a non-linear energy harvesting circuit at each IoT device. We derive closed-form rate expressions for both the AmBC and primary links and theoretically prove that the mutualism relationship between the AmBC and primary links is maintained under HIs, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. To maximize the weighted sum rate of all links in a cooperative AmBC network under HIs, we propose two resource allocation schemes for two scenarios with a single link and multiple AmBC links, respectively. For the single AmBC link case, we derive the optimal transmit power of the primary transmitter and the optimal power reflection coefficient of the IoT device in closed forms. For the scenario with multiple AmBC links, the weighted-sum-rate maximization problem is transformed into a convex one and solved with convex optimization tools. Computer simulations validate our theoretical results and that our proposed schemes outperform the benchmark schemes in terms of the weighted sum rate. [ABSTRACT FROM AUTHOR]

This paper studies a multi‐unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system, where multiple UAVs equipped with MEC server are deployed to provide computing offloading services for ground users with limited local computing resources. Specifically, each user divides its computing tasks into two parts: one part is offloaded to the associated UAV for calculation, and the remaining part is calculated locally. It is aimed to minimize the weighted energy consumption of all UAVs and all users by jointly optimizing the UAV trajectory, user scheduling, central processing unit (CPU) calculation frequency allocation, and offloading time allocation. However, the formulated problem is a mixed integer non‐convex problem, which is challenging to obtain an optimal solution. In order to effectively solve this, an efficient two‐stage iterative algorithm based on alternative optimization is proposed by decomposing the original problem into two sub‐problems to obtain a suboptimal solution. The simulation results show that the proposed scheme is superior to other benchmark schemes in reducing system energy consumption. [ABSTRACT FROM AUTHOR]

The demand of mobile networks and quality of service have recently increased to higher Spectral Efficiency (SE) or Energy Efficiency (EE) and massive connectivity for 5G wireless communications. The concept of beamforming Multiple‐Input Multiple‐Output (MIMO) is capable of significantly reducing the amount of required Radio Frequency Chains (RFCs) used by massive MIMO systems without remarkable performance loss. However, in existing beamformed MIMO, the amount of supported devices cannot be higher than the amount of RFCs using the same time‐frequency resources, and it is the basic limit for these systems. To address this issue, non‐orthogonal multiple access (NOMA) has been recently recommended, which can accommodate higher covered using via non‐orthogonal resource allocation. Nevertheless, power allocation is a core factor of the NOMA scheme. However, maximizing the sum rate problem based on power allocation in Massive MIMO‐NOMA scenarios is non‐convex and non‐linear, which creates a very challenging situation to acquire the closed‐form approach. Thus, in this paper, an approach to this difficulty is outlined for Massive MIMO‐NOMA systems to maximize the sum rate as a convex and linear problem. Simulation results of the suggested Beamformed MIMO‐NOMA (BMN) algorithm show that a higher achievable sum rate is achieved compared with the usual beamformed MIMO. [ABSTRACT FROM AUTHOR]

In future wireless communication, a large number of devices equipped with several different types of sensors will require access networks with diverse quality-of-service constraints. In cellular network evolution, the long term evolution advanced (LTE-A) networks has standardized Machine-to-Machine (M2M) features. Such M2M technology can provide a promising infrastructure for Internet of things (IoT) sensing applications, which usually require real-time data reporting. However, LTE-A is not designed for directly supporting such low-data-rate devices with optimized energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. This paper investigate the maximum energy efficient data packets M2M transmission with uplink channels in LTE-A network. We formulate it into a joint problem of Modulation-and-Coding Scheme (MCS) assignment, resource allocation and power control, which can be expressed as a non-deterministic polynomial hard (NP-hard) mixed-integer linear fractional programming problem. Then we propose a global optimization scheme with Charnes-Cooper transformation and Glover linearization. The numerical experiment results show that with limited resource blocks, our algorithm can maintain low data packets dropping ratios while achieving optimal energy efficiency for a large number of M2M nodes, comparing with other typical counterparts. [ABSTRACT FROM AUTHOR]

Summary: Device‐to‐device (D2D) is a fifth‐generation (5G) network's key technology which allows devices in proximity to communicate one‐to‐one without a base station (BS). Moreover, it ameliorates channel gain, communication latency, energy efficiency, and spectral efficiency. However, mitigating interference (D2D and cellular mobile users [CMUs]) and eavesdropper effect (at D2D links) is a prime concern in D2D communication. These issues can be resolved with an efficient allocation of radio resources. Cognitive radio (CR) and non‐orthogonal multiple access (NOMA) are viable solutions to resolve the aforementioned issues. Motivated by the aforementioned discussion, in this paper, we propose a joint CR and NOMA‐based scheme, that is, SARAS for secure resource allocation in D2D communication. It aims to maximize the overall sum rate and secrecy capacity of D2D users with assured quality of service (QoS) and quality of experience (QoE). CR is used for pairing between strong and weak D2D users (i.e., D2D pair) and NOMA's successive interference cancelation (SIC) technique helps to reduce the interference effect between cellular and D2D users. We used a coalition game for efficient resource utilization, which transfers D2D users from one coalition to another based on the preference order and channel conditions. This landed D2D users in the best coalition having favorable channel conditions. We have executed the proposed SARAS scheme over the MATLAB simulation tool, considering a varying number of D2D and CMUs. Simulation results show that the proposed scheme outperforms the traditional OFDMA, random, and nearest approaches concerning parameters such as sum rate and secrecy capacity. [ABSTRACT FROM AUTHOR]

In the era of the Internet of Things (IoT), the volume of data is increasing immensely causing rapid growth in network data communication and data congestion. Computational offloading thus becomes a crucial and imperative action in terms of delay-sensitive task completion and data processing for the resource constraint end-users. Nowadays fog computing, as a complement of cloud computing, has emerged a well-known concept in terms of enhancing data processing capability as well as energy conservation in low-powered networks. In this paper, we consider a heterogeneous fog-cloud network architecture where the data processing is performed on the local or remote computing device by adopting a binary offloading policy. Based on the proposed system model, we calculate the total delay and energy consumption of data processing throughout the network and formulate a mixed-integer optimization problem to jointly optimize the offloading decision and bandwidth allocation. In order to solve such an NP-hard problem, we have proposed a deep-learning-based binary offloading strategy that employs multiple parallel deep neural networks (DNNs) to make offloading decisions. Such offloading decisions are subsequently placed in a relay memory system to train and test all DNNs. Simulation results show a near-optimal performance of the proposed offloading strategy while remarkably maintaining the quality of service by decreasing overall delay and energy consumption. [ABSTRACT FROM AUTHOR]

Plane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) has a significant importance with respect to resource utilization in cloud data centers. Indeed, the optimized management of machine placement usually results in a significant reduction in energy consumption. VMP is a bin packing problem generalization, which is a well known hard combinatorial optimization problem. Besides being NP-hard, VMP is characterized by conflicting objectives and a noisy search space. Meta-heuristics, such as genetic algorithms, particle swarm optimization (PSO), cuckoo search (CS), tabu search and simulated annealing (SA) have been shown to be effective for this category of problems. This paper reports a performance comparison between SA, CS and PSO meta-heuristics to solve the VMP problem. In contrast to reported research work in this area, we study the performance behavior of these three meta-heuristics with respect to, not only the quality of solutions, but also the quality of the explored solution sub-space, in addition to the convergence speed towards reported solutions and the speed with which each meta-heuristic evolves towards the best reported optimized solution. Extensive simulations on randomly generated tests with sizes varying between 200 and 1000 virtual machine demands show that PSO achieves the best performance behavior with respect to all criteria. Moreover, for all tests, PSO produces a reduction of as much as 17% of the number of physical machines, 15% of the energy cost and 21% of the resource utilization of physical machines. [ABSTRACT FROM AUTHOR]

Vehicular networks are envisioned to deliver data transmission services ubiquitously, especially in the upcoming autonomous driving era. Accordingly, the high data traffic load poses a heavy burden to the terrestrial network infrastructure. Unmanned Aerial Vehicles (UAVs) show enormous potential to assist vehicular networks in providing services. In a dense UAV-assisted vehicular network with a large number of users, spectrum sharing is leveraged for alleviating the spectrum scarcity. However, the increasing data traffic still leads to the UAV energy consumption problem. This paper considers a specific network scenario where a UAV transmits its cached content files to vehicular users over UAV-to-vehicle (U2V) links while vehicle-to-vehicle (V2V) links reuse the U2V spectrum for safety-critical message exchanges. To improve the UAV's energy efficiency while guaranteeing users’ quality of service (QoS), we jointly optimize content placement, spectrum allocation, co-channel link pairing, and power control, which are the key factors affecting energy efficiency and QoS. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved by combining Hungarian and DDQN. We perform system performance evaluations, demonstrating that our approach can not only improve the UAV's energy efficiency while satisfying the users’ QoS requirements but also increase the timeliness of making decisions. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient resource allocation problem in device-to-device (D2D) communications underlaying unmanned aerial vehicle (UAV)-enabled networks. The UAV is deployed as a flying base station to communicate with wireless users in the presence of an eavesdropper in the cell. We consider two types of users: the ground users (GUs) served by the UAV and the D2D users that communicate directly with one another. Our aim is to maximize the total energy efficiency (TEE) of all D2D pairs while guaranteeing the quality of service (QoS) requirements and secrecy rates of all GUs and D2D users via joint power control and channel allocation. The considered TEE maximization problem is a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve. Therefore, we propose a method that consists of outer and inner loops. In the outer loop, Dinkelbach's algorithm is utilized to transform the original fractional programming problem into a subtractive form. In the inner loop, we employ the alternating optimization method and divide the equivalent optimization problem into two sub-problems: power allocation and channel allocation. Solving the two sub-problems directly using standard convex optimization software may have high complexity. Therefore, we also propose a low-complexity algorithm using the Lagrangian dual and Kuhn—Munkres algorithm to obtain the optimal power allocation in closed-form and the optimal channel allocation, respectively. Simulation results show that the proposed algorithm converges in a small number of iterations. Furthermore, the proposed approach shows its superior performance compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

Open radio access network (O-RAN) is one of the promising candidates for fulfilling flexible and cost-effective goals by considering openness and intelligence in its architecture. In the O-RAN architecture, a central unit (O-CU) and a distributed unit (O-DU) are virtualized and executed on processing pools of general-purpose processors that can be placed at different locations. Therefore, it is challenging to choose a proper location for executing network functions (NFs) over these entities by considering propagation delay and computational capacity. In this paper, we propose a Soft Actor–Critic Energy-Aware Dynamic DU Selection algorithm (SA2C-EADDUS) by integrating two nested actor–critic agents in the O-RAN architecture. In addition, we formulate an optimization model that minimizes delay and energy consumption. Then, we solve that problem with an MILP solver and use that solution as a lower bound comparison for our SA2C-EADDUS algorithm. Moreover, we compare that algorithm with recent works, including RL- and DRL-based resource allocation algorithms and a heuristic method. We show that by collaborating A2C agents in different layers and by dynamic relocation of NFs, based on service requirements, our schemes improve the energy efficiency by 50% with respect to other schemes. Moreover, we reduce the mean delay by a significant amount with our novel SA2C-EADDUS approach. [ABSTRACT FROM AUTHOR]

Summary: In heterogeneous networks (HetNets), sharply increasing data traffic exacerbates the rapid growth in the network energy consumption and at the same time brings tremendous pressure on the backhaul links. Aiming at improving the network energy efficiency (EE) and relieving backhaul data volume traffic pressure, in this paper, we investigate energy efficient resource allocation which restricts small cell data traffic by imposing different kinds of backhaul capacity constraints (BCCs) on small cells, including small cell BCC (SBCC) and macrocell BCC (MBCC). We formulate the joint resource block (RB) and power allocation problem as a fractional programming problem. By using variable relaxation and replacement, we convert this nonconvex optimization problem into a concave–convex fractional programming problem, which can further be converted into a parametric convex problem and solved by convex optimization theory. Based on the Dinkelbach and dual decomposition methods, we propose iterative resource allocation algorithms under different BCCs, where RB and power are jointly allocated among users in each cell. Numerical results reveal that the proposed algorithms have good convergence performance and also evaluates the impact of both SBCC and MBCC on network performance. [ABSTRACT FROM AUTHOR]

EDI is a hot topic in the research of multimodal transportation informatization, which determines the exchange level of intermodal transportation information. However, its high cost, large system coupling degree and low performance threshold cannot adapt to mass data exchange in high concurrent environment. Therefore, a decentralized, scalable, distributed and efficient data exchange system is formed. It plays a key role in realizing the comprehensive sharing of interdepartmental intermodal information in the cloud environment. In order to solve the problem of mismatching between application load and computing resource capacity and realize on-demand resource allocation and low carbon emission, this paper proposes to build an Extensible EDI system (XEDI) based on MSA and studies the scaling mechanism in container environment. Based on the resource scheduling characteristics of container cloud and considering the distribution and heterogeneity of intermodal cloud computing platform from the perspective of resource allocation, the automatic scaling mechanism of XEDI is established, the scaling model is established, and the automatic scaling algorithm is proposed. For Dominant Resource Fairness for XEDI (XDRF) resource allocation algorithm and Dominant Resource Fairness for XEDI (CXDRF) based on carbon considering energy consumption, the CXDRF algorithm is proved by quantitative experiments to achieve system performance optimization on the basis of ensuring system reliability and effectively reducing energy consumption. XEDI can not only meet the demand of dynamic load and improve service quality, but also reduce resource occupation and save energy by releasing virtual resources when resource utilization rate is low. It has great research significance and practical value for mass data application under low energy consumption conditions. [ABSTRACT FROM AUTHOR]