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Due to limited network resources in internet of vehicles (IoV), vehicle’s heterogenous data, communication and computing resources significantly impact the training delay and model accuracy of federated learning (FL). To enhance resource utilization in IoV and mitigate the impact of limited resources on FL, we propose a resource allocation strategy to optimize computing and communication resource distribution within IoV. Additionally, A vehicle selection strategy based on vehicle data, communication and computing resources is introduced to reduce the impact of resource and data heterogeneity on FL. To further enhance FL training efficiency, a hierarchical FL algorithm that integrates vehicle selection and resource allocation based on deep deterministic policy gradients is designed. The vehicle selection and resource allocation problems are modeled as a Markov decision process. Deep reinforcement learning is adopted to find the optimal strategy. Simulation results demonstrate that the proposed algorithm achieves rapid convergence. It significantly reduces FL training latency while ensuring training accuracy.

Online education has become a popular teaching method beyond classroom education. However, online teaching resources bring the software and hardware resources cost and security burden of cloud computing, which limits the willingness to share online resources. How to effectively motivate different schools to share teaching resources is a challenging issue. This paper proposes an excitation mechanism to promote high-quality online resource sharing. Firstly, under the resource constraints of the cloud platform, a utility oriented resource sharing model is established. Then, a game of choice between students and course resources is studied, which allows users to participate course strategy selection. Next, a particle swarm optimization algorithm was used to design an optimal cloud computing resource allocation scheme that maximizes revenue. It expects that high-quality course resources will be allocate more cloud computing resources. Finally, experiment simulation shows that appropriately excitation mechanisms can effectively promote the sharing of teaching resources, furthermore, the proposed resource allocation scheme is significantly better than the average allocation scheme.

The emergence of Industrial Internet of Things (IIoT) networks has sparked a demand for reliable and efficient communication systems. One promising strategy to overcome interference and coverage issues in IIoT setups is leveraging massive multiple-input multiple-output (MIMO) technology in a cell-free configuration. However, implementing large antenna arrays for such systems can be impractical and expensive, especially in remote or dynamic environments. Unmanned aerial vehicles (UAVs) have emerged as a versatile solution, including for communication relays. This study investigates the integration of UAV swarms into cell-free massive MIMO systems for IIoT networks. The proposed approach involves deploying a swarm of UAVs equipped with multiple antennas as aerial base stations to serve ground-based IIoT devices. It examines the benefits of employing UAV swarms, such as expanded coverage, better interference management, and increased flexibility. Additionally, it addresses challenges like UAV mobility, resource allocation, and power consumption. To address these challenges, an algorithm for resource allocation is proposed. This algorithm considers factors like UAV mobility, quality-of-service requirements for IIoT devices, and interference among UAVs. It aims to optimize transmit power and antenna selection for each UAV, ensuring QoS satisfaction and enhancing overall system capacity. Simulation results demonstrate the effectiveness of the algorithm, indicating improvements in system capacity, coverage, and interference management. Moreover, the findings highlight the trade-offs between system performance and power consumption within the UAV swarm. In essence, this study contributes to understanding the potential of utilizing UAV swarms in cell-free massive MIMO systems for IIoT networks. The proposed resource allocation algorithm offers insights for planning and enhancing such nodes, paving the way for further research and development in this promising field.

Unmanned aerial vehicles (UAVs) are used widely in wireless communications to enhance the service experience of mobile users on the ground. This study investigates a dynamic resource management problem in a large-scale UAV-aided wireless network, where multiple UAVs operate as aerial base stations to serve ground users. To enhance communication efficiency for all users in the presence of co-channel interference, we propose the use of an Ising model-based optimization method for fast and accurate UAV-user association and resource allocation. First, we formulate the dynamic user association and resource allocation problem as a combinatorial optimization problem. Subsequently, we transform the objective function in the formulated problem into the energy function of a quadratic unconstrained binary optimization (QUBO) model and applied coherent ising machine (CIM), which has been proven to be robust for solving dense Ising problems. We evaluate our proposed method through simulations and the real CIM for optimization. Additionally, we compared our method with simulated annealing, steepest descent, and exhaustive search. Performance evaluation results indicate that our proposed method is superior in terms of computational time and data rate.

To effectively address the heterogeneous service requirements of diverse users, the Fifth Generation (5G) has introduced network slicing technology, which partitions physical resources into multiple virtual end-to-end network slices, allowing for the dynamic allocation of these slices. However, integrating network slicing with the 5G New Radio (NR) frame structure and allocating resources to meet Quality of Service (QoS) requirements, while ensuring user fairness, remains a challenging task. To tackle this challenge, a double layer resource configuration framework is proposed based on the 5G NR standard. Subsequently, an optimization problem is formulated to maximize user satisfaction taking into account their distinctive QoS requirements. In the upper layer of the framework, we leverage the Dueling Double Deep Q Network (D3QN) algorithm to optimize resource allocation from the base station to the network slices. In the lower layer, we employ the parameter tuning method to optimize the resource allocation of the users within each slice. These two steps are combined to form the Double Layer Distribution-D3QN (DLD-D3QN) algorithm. The effectiveness and superiority of the proposed algorithm is demonstrated through extensive simulations.

With the rapid growth of the mobile computing techniques, a wide variety of mobile edge computing (MEC) applications have emerged recently, aiming to provide computationally intensive and delay-sensitive network services. Through MEC, various complex tasks of mobile devices can be offloaded to the edge of network system for execution by edge servers, which greatly reduces the local computing burden. However, how to effectively allocate computational and communication resources in edge-cloud remains a challenging task, especially when multiple mobile users and edge servers are involved. In this paper, we propose a decomposition-based multi-objective optimization algorithm based on estimation-of-distribution models (MOEA/D-EoD) to deal with the task offloading and resource allocation problem in MEC. Especially, considering the features of multi-user and multi-server cloud-edge-end collaboration wireless MEC system, we construct a joint optimization model of task offloading and resource allocation, where limited communication and computational resource constraints are considered. To deal with the optimization model, we design an efficient decomposition-based algorithm, which incorporates two novel estimation-of-distribution models to deal with discrete and continuous decision variables of the problem. Experimental results obtained from benchmark test suites DTLZ and ZDT demonstrate that the proposed method exhibits significantly superior performance compared to other comparative algorithms. The proposed model and algorithm are simulated and tested on different test instances, and experimental results show the effectiveness and efficiency of our proposed method.

Device-to-device (D2D)-assisted 6G networks are expected to support the proliferation of ubiquitous mobile applications by enhancing system capacity and overall energy efficiency towards a connected-sustainable world. However, the stringent quality of service (QoS) requirements for ultra-massive connectivity, limited network resources, and interference management are the significant challenges to deploying multiple device-to-device pairs (DDPs) without disrupting cellular users. Hence, intelligent resource management and power control are indispensable for alleviating interference among DDPs to optimize overall system performance and global energy efficiency. Considering this, we present a Federated DRL-based method for energy-efficient resource management in a D2D-assisted heterogeneous network (HetNet). We formulate a joint optimization problem of power control and channel allocation to maximize the system’s energy efficiency under QoS constraints for cellular user equipment (CUEs) and DDPs. The proposed scheme employs federated learning for a decentralized training paradigm to address user privacy, and a double-deep Q-network (DDQN) is used for intelligent resource management. The proposed DDQN method uses two separate Q-networks for action selection and target estimation to rationalize the transmit power and dynamic channel selection in which DDPs as agents could reuse the uplink channels of CUEs. Simulation results depict that the proposed method improves the overall system energy efficiency by 41.52% and achieves a better sum rate of 11.65%, 24.78%, and 47.29% than multi-agent actor-critic (MAAC), distributed deep-deterministic policy gradient (D3PG), and deep Q network (DQN) scheduling, respectively. Moreover, the proposed scheme achieves a 5.88%, 15.79%, and 27.27% reduction in cellular outage probability compared to MAAC, D3PG, and DQN scheduling, respectively, which makes it a robust solution for energy-efficient resource allocation in D2D-assisted 6G networks.

The edge computing utilizes vehicles as resources to assist in computational offloading can shorten the distance between users and computing servers, thereby improving the reliability of communication between them. In this paper, we investigate a Vehicle-assisted Edge Computing (VEC) model by jointly considering wireless access and backhaul links, and formulate an optimization problem that combines computational offloading and resource allocation, aiming at minimizing system delay. Further, the formulated problem is decomposed into two subproblems, e.g., computation offloading and resource allocation. In particular, we propose a new computational offloading approach that models the offloading decision for joint wireless access and backhaul as a potential game. The Nash equilibrium is guaranteed by the rational design of potential function, and the corresponding solution is solved by a backward induction method. On the other hand, the resource allocation subproblem is transformed from a nonconvex to a convex optimization problem based on equivalent transformation with successive convex approximation methods and finally derives the optimal solution satisfying Karush-Kuhn-Tucker (KKT) conditions. Simulation results show that the proposed algorithm have near-optimal performance and is superior to the state-of-the-art over a wide range of parameter settings.

Fifth Generation (5G) mobile networks considers an expansive set of heterogeneous services with stringent Quality of Service (QoS) requirements, and traffic demand with inherent spatial-temporal distribution, which places the backhaul network deployment under potential strain. In this paper, we propose to harness network slicing, Integrated Access and Backhaul (IAB) technology coupled with satellite connectivity to build a dynamic wireless backhaul network that can provide additional backhaul capacity to the base stations on demand when the wired backhaul link is temporarily out of capacity. To construct the network design, Deep Reinforcement Learning (DRL) models are used to select, for each network slice of the congested base station, an appropriate backhaul link from the pool of available IAB and satellite links that meets the QoS requirements (i.e., throughput and latency) of the slice. Simulation results show that around 20 episodes are sufficient to train a Double Deep Q-Network (DDQN) agent, with one fully-connected hidden layer and Rectified Linear Unit (ReLU) activation function, that adjusts the topology of the backhaul network.

The concept of Mobile Edge Computing (MEC) has been recently highlighted as a key enabling technology for the deployment of sixth-generation (6G) wireless network services. On the other hand, the possibility of combining Unmanned Aerial Vehicles (UAV) with Reconfigurable Intelligent Surfaces (RIS) has also been recognized as a powerful communication paradigm able to provide improved propagation characteristics of wireless communication channels, as well as increased capacity and extended coverage. Then, the possibility of merging the characteristics of such a communication paradigm with the one provided through MEC represents a valid solution to fulfill the main requirements of 6G networks. In this paper, we consider the combination of computation offloading and resource allocation in an MEC-based system where the MEC server is hosted by a massive MIMO base station, which serves multiple macro-cells assisted by a UAV-equipped RIS. In this context, we focus on minimising the latency for executing tasks of all user equipment (UE) within the considered scenario. To tackle this problem, we formulate an optimisation problem that jointly optimises computation offloading from user equipment (UE) towards the MEC server, and communication resources in the underlying UAV-assisted and RIS-aided network. The extensive simulation results demonstrate how the proposed method outperforms in terms of providing reduced latency for the considered system when compared with other conventional schemes.

Container and microservices management platforms are currently one of the most important tools for cloud computing, but since the scope of these tools is homogeneous cloud architectures they have serious limitations in adapting to new computing paradigms. Therefore, using the default scheduler in heterogeneous node systems faces significant limitations when tasked with orchestrating workloads in a Continuum Computing environment, as the nodes have very different characteristics and restrictions. To solve this limitation we decided to use Kubernetes as it is the most popular Container management tool and we propose to replace the native scheduler with a reimplementation that gives us complete flexibility for the process of assigning pods to nodes, providing a framework to design algorithms that considers all the necessary parameters for the deployment of services in a Continuum. In addition, we address one of the most limiting aspects of the K8s scheduler, its pod-by-pod allocation approach, which makes it difficult to optimise the complete set of allocations. To test our proposal we design a use case and perform several tests on a real environment based on virtual machines, in which stress tests are conducted to measure the performance of each method. We then present a series of results to justify the benefits of our proposal, including the reduced performance provided by the pod-pod approach and how a batch-based approach greatly improves efficiency. The results show the usefulness of using batch-based approaches and how the Kubernetes scheduler extension points are not enough to support the requirements of the Continuum.

Digital twins technology is a key pillar to realizing the vision of Industrial Metaverse and enabling Industry 5.0 where industries become more sustainable, resilient, and smart, while being human-centric. In this paper we aim at developing a high fidelity digital twin model consistent with its physical system for better informed decisions in terms of failure avoidance or performance enhancements. To enable this, we harness the capabilities of an unmanned aerial vehicle (UAV) to collect measurement data from Industrial Internet of Things devices that monitor various independent physical entities in a given industrial plant. Each device sends its readings to the UAV that in turn performs computations to determine the status of the physical entities. The UAV relays the resulting status details to the digital twin running at the edge of the network in a timely manner. For a high fidelity digital twin, the processed status has to be kept as close as possible to the actual status of the physical entities through ensuring high accuracy and synchronization. To do so, we introduce the age of digital twin metric based on the age of information concept to represent the degree of synchronization between the digital and physical twins then formulate the problem as a mixed integer non-convex program. Due to its complexity, we decompose the problem into two subproblems, convexify their constraints and optimize the UAV trajectory, device scheduling, wireless and computing resource allocation, and data collection. We run extensive simulations for various system parameters and demonstrate the resulting accuracy of the digital twin model while ensuring high synchronization. Simulation results corroborate the superiority of our proposed solution as compared to different baseline approaches.

With the dense deployment of the satellite constellations, spectrum sharing among multiple satellite communication systems is a feasible way to alleviate the scarcity of spectrum resources. In order to improve the spectrum efficiency, we investigate the resource allocation in the spectrum sharing scenario of multiple beam-hopping (BH)-based satellite systems. The BH technology enhances the scheduling flexibility and the reusability of the transmission resources, but it also makes the inter-system interference more complicated. Under the interference constraint, the resource allocation problem is formulated to maximize the throughput of the satellite system. Based on the set of feasible beam allocation patterns, the optimization problem is reformulated as a mixed integer linear programming problem to obtain the optimal solution. A greedy suboptimal algorithm is also proposed to solve the problem with low complexity. To further accelerate the resource allocation process, the inter-beam distance threshold is introduced to reduce the number of feasible beam allocation patterns. Simulation results show that the proposed resource allocation algorithms achieve higher system throughput and better adaptability to the uneven distribution of traffic demands. It is also shown that the suboptimal algorithm achieves desirable performance with lower computational complexity, which is more attractive for the real-time resource allocation in the satellite systems.

Satellite networks can enhance global network coverage without geographical restrictions. By applying edge computing paradigm to satellite networks, they can provide communication and computation services for Internet of Remote Things (IoRT) mobile devices (IMDs) at any time. Thus, in 6G mobile systems, satellite networks serve as a complement to terrestrial networks. This paper places its emphasis on satellite-terrestrial integrated networks (STINs). Computation offloading and resource allocation (CORA) play a crucial role in STINs given the limited communication and computation resources of satellites. In this paper, we investigate the CORA to minimize system energy consumption while ensuring the latency tolerance via jointly optimizing the offloading decision and the allocation of radio and computation resources. Taking into account the dynamic characteristics of network conditions, the CORA problem is formulated as a Markov decision process (MDP). Subsequently, an algorithm based on twin delayed deep deterministic policy gradient (TD3) is designed to automatically determine the optimal decisions. Finally, the convergence and superiority of the proposed algorithm in terms of energy efficiency are evaluated through extensive simulation experiments.

Nowadays, the evolution of AI is noticed in supporting many life applications and manipulating different data types. It helps complete the tasks and get the required information efficiently and precisely. The deployment of AI techniques and machine learning models moves to limited-resources energy-constrained platforms, ranging from simple IoT devices to unmanned aerial vehicles (UAVs). Employing such models on limited-resource UAVs to support a wide range of applications is an inevitable duty and it is at the same time a challenging task. Additionally, obtaining high-accuracy outputs from a single AI-enabled UAV within the operating context of delay-sensitive applications faces a lot of obstacles, and may not be feasible. Accordingly, distributed operations and cooperation among a set of UAVs can provide the required level of accuracy within the time constraints for some applications. This work proposes a distributed computing architecture for networked UAVs based on collaborative learning and edge-of-things computing. Such architecture would help a suite of UAVs to train based on their local ML model and captured data and to collaborate with other UAVs in the same network to generate an aggregated ML model that improves the operation accuracy with acceptable performance speed. Using a networked UAV system and various application scenarios, numerical simulation studies have been presented. The performance analysis and results show how the proposed distributed computing architecture with collaborative learning outperforms the centralized computing architecture with edge and cloud computing paradigms.

Recent developments in cloud technology enable one to dynamically deploy heterogeneous resources as and when needed. This dynamic nature of the incoming workload causes fluctuations in the cloud environment, which is currently addressed using traditional reactive scaling techniques. Simple reactive approaches affect elastic system performance either by over-provisioning resources which significantly increases the cost, or by under-provisioning, which leads to starvation. Hence automated resource provisioning becomes an effective method to deal with such workload fluctuations. The aforementioned problems can also be resolved by using intelligent resource provisioning techniques by dynamically assigning required resources while adapting to the environment. In this paper, a reinforcement learning-based proactive resource allocation framework (RLPRAF) is proposed. This framework simultaneously learns the environment and distributes the resources. The proposed work presents a paradigm for the optimal allocation of resources by merging the notions of automatic computation, linear regression, and reinforcement learning. When tested with real-time workloads, the proposed RLPRAF method surpasses previous auto-scaling algorithms considering CPU usage, response time, and throughput. Finally, a set of tests demonstrate that the suggested strategy lowers overall expense by 30% and SLA violation by 77.7%. Furthermore, it converges at an optimum timing and demonstrates that it is feasible for a wide range of real-world service-based cloud applications.

To meet the extremely stringent but diverse requirements of Beyond Fifth-Generation (B5G) networks, traffic-aware adaptive utilization of network resources is becoming essential. To cope with that, a detailed traffic data analysis enables opportunities for mobile network operators to improve the Quality of Service (QoS) in the next-generation mobile communication systems. This paper presents a comprehensive analysis of the real world data collected from an operator’s 4G+ and 5G infrastructure during a seven-month campaign. Efficient Machine Learning (ML) based network traffic predictions are presented together with a statistical model to develop optimal resource allocation strategies by using the data gathered during the pandemic, an era when the data volume, as well as the bandwidth requirements and the end users’ expectations, were significantly elevated in terms of QoS, given the huge shift to the online world. Data analysis confirmed the assumption that there are traffic changes during the day and the whole week, which helped us to find new research directions regarding resource allocation optimization of next-generation mobile networks. Furthermore, we introduce the Predictive Energy Saver for Baseband Units (PESBiU) algorithm, which utilizes traffic prediction and power consumption analysis to manage the power states (sleep or active) of BBUs in a network. The PESBiU algorithm utilizes the results from ML predictions to effectively balance energy efficiency and network performance, demonstrating its potential for practical deployment in future mobile communication networks by transitioning BBUs to sleep mode during low-traffic periods, thereby achieving significant power savings.

Telco edge data centers (DCs) accommodate applications whose load fluctuates considerably during a day. This variability mandates swift and responsive resource adjustments to mitigate the risk of incurring unnecessary costs during off-peak periods, where a significant fraction of nodes may be under-utilized. Tackling this challenge is integral to optimizing operational efficiency and cost-effectiveness of telco edge DCs. To this end, this article aims to address the Dynamic Data Center Sizing (DDS) problem, which boils down to optimizing the number of active nodes as per current resource demand. The proposed DDS solution consists of two core modules, namely a forecasting module, which predicts resource demands, and a decision-making module, which acts upon predicted demands. The decision-making of DDS is implemented via the filtering and the rank-drain-observe (RDO) algorithms. Filtering is based on integer linear programming, and it computes the theoretically optimal state of the DC based on predicted resource demands. RDO is a heuristic that strives to realize the optimal DC state in an iterative and robust fashion. To expedite RDO in large-scale clusters, we further devise a Reinforcement Learning (RL)-enabled DDS variant (i.e., RL-DDS), in which RDO integrates an RL agent that computes optimized batches of nodes for concurrent deactivation. We propose an innovative solution based on the notion of Digital Twin to train the RL agent in emulation mode. DDS and RL-DDS are evaluated upon real-life testbeds resembling actual DCs. Results demonstrate significant cost reduction, ranging from 7% in conservative and relatively static scenarios, up to 38% in highly dynamic settings.

Open Radio Access Network (RAN) introduces a groundbreaking industry standard for Radio Access Networks, fostering vendor interoperability and network flexibility through open interfaces while leveraging network softwarization, Artificial, and Machine Learning Intelligence; however, it also poses significant security challenges due to its unique configuration, prompting stakeholders to cautiously approach its deployment and necessitating thorough analysis and implementation of security measures and standards. This paper systematically examines existing literature and case studies to underscore the indispensable role of Intrusion Detection Systems (IDS) in identifying and mitigating security breaches within Open RAN environments. We elucidate the distinct challenges that Open RAN’s disaggregated architecture introduced and classify them into technical and non-technical threats. Finally, we discussed a series of new advancements gaining momentum in the Open RAN security domain and provided insights for future research directions.

This survey paper provides a comprehensive overview of integrating Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surfaces (IRS) in wireless communication systems. IRS is known as reconfigurable metasurfaces, have emerged as a transformative technology to enhance wireless communication performance by manipulating the propagation environment. This work delves into the fundamental concepts of MIMO and IRS technologies, exploring their benefits and applications. It subsequently investigates the synergies of resource allocation and energy efficiency that emerge when these technologies are combined, elucidating the IRS improved in MIMO systems through signal manipulation and beamforming. Through an in-depth analysis of various techniques and cutting-edge algorithms in resource allocation and energy efficiency can explore the key research areas such as optimization techniques, beamforming strategies and practical implementation consideration. Furthermore, it provides open research directions, individually addressing topics such as limitations of resource allocation and energy efficiency in the MIMO IRS system. This paper offers insights into MIMO-enabled IRS systems challenges and future trends. Through presenting a consolidated view of the current state-of-the-art, this survey underscores their potential to revolutionize wireless communication paradigms, ushering in an era of enhanced connectivity, spectral efficiency and improved coverage.

The application of UAV-aided MEC well-suited for the execution of the data-intensive and latency-sensitive tasks in the infrastructure-deprived regions. However, the growing number of UAVs and smart devices causing a major difficulty in the devising an effective scheme for the task offloading and resource allocation in multi-UAV-aided MEC networks. Furthermore, the resource deficient environments unable to sustain prolonged resource-intensive activities, additional complexities are posed on the optimum utilization of the resources. In this paper, we introduced a multi-agent deep reinforcement learning scheme for the task offloading in the multi-UAV-assisted networks (MUAVDRL). In this configuration, the mobile users fetch computational resources from the UAVs with the goal of minimizing the computation cost which incorporates both the energy consumption and the computation delay. Initially, we start with the optimization problem which is defined as the minimizing the computational costs. Through modelling it as MDP, we aim to reduce the computational costs for mobile users. Leveraging the dynamic and high-dimensional nature of the challenge, the MUAVDRL algorithm solves this problem efficiently. Comprehensive simulation results exhibit the efficacy and superiority of our projected framework when compared to existing state-of-the-art methods, illustrating its potential in the practice.

This paper investigates the interaction structure and decision-making process of swarm robots. It analyses the impact of communication links on the robots’ gains when they interact in local meta-structures and resolves the decision dynamic mechanisms of swarm robot systems. The collaboration efficiency of swarm robots is highly dependent on their local interactions. Complex networks are leveraged to portray the interaction structure between individuals, while classical games are employed to depict the decision-making paradigm of individuals. The study focuses on two typical local network meta-structures of distributed swarm robots: the cross-shaped grid world and the nine-lattice grid world. Within these meta-structures, the robots are categorized as resource-providing or resource-consuming. The information interaction and decision-making process between the central robot and its neighboring robots are mapped into resource-allocating group-interacting network games. The payoff of the central robot within the meta-structure is determined by the strategies of its neighbors and its neighbors’ neighbors, establishing the interaction strategies between robots, namely the yield game strategy and the price game strategy. A game model is constructed for meta-structures consisting of various classes and numbers of robots, which is subsequently solved. The results demonstrate that robots in network structures with simpler configurations and fewer communication links achieve higher gains in the group interaction network game. This economic perspective supports the notion that simpler network structures are more efficient and preferable to select.

Stateless cloud computing presents remarkable scalability and cost-effectiveness by offering dynamically adjustable resources tailored to fluctuating demands, eliminating the constraints of stateful architectures. However, the challenges presented by dynamic workload are substantial in the context of system health monitoring, frequently leading to service interruptions owing to insufficient resources. It underscores the need for the development of more efficient monitoring systems. Our study introduces cSysGuard, a novel framework designed to enhance monitoring capabilities within cloud environments. The methodology employs an ensemble regression model with a stacking strategy to forecast dynamic performance metrics. The algorithm also leverages a classification model to assess the system’s health based on forecasted metrics, effectively identifying potential failures in the future. Under the configuration utilized, our evaluations demonstrated increased predictive performance with cSysGuard in forecasting various metrics compared to traditional models. The results showed an improvement of up to a remarkable 2.28-fold increase, varying significantly based on the specific metric under consideration. In addition, the effectiveness of health assessment was achieved through Decision Trees with hyperparameter tuning, resulting in a macro-averaged F1 score of 89.79%. This research contributes to both the theoretical and practical aspects of server monitoring, presenting a solution that assesses system performance metrics and health to tackle dynamic challenges in cloud infrastructure.

Network virtualization, as a key driver for revenue enhancement, is under deep exploration by the communication society. Elastic optical network is a growing inter-data center communication solution whose flexible nature facilitates commercial implementation of virtualization concepts. Flexibility and virtuality are two main pillars of efficient networking which meet in a virtualized inter-data center elastic optical network and their interwoven management can significantly reduce the power consumption of data center networks, as a significant energy-consuming contributor. An efficient resource allocation is required to harvest the expected gain of a virtualized network. We propose a power-efficient joint dynamic resource allocation scheme, which optimally allocates computation and communication resources in an inter-data center elastic optical network. The scheme incorporates the benefits of conventional offline and online resource allocation schemes and reduces the power consumption by 19% compared to its traditional counterparts while keeping the delay and service outage below their desired thresholds. The proposed scheme not only supports traffic streams with distinguished service requirements but also exploits the involved service differentiation to further improve power efficiency. Strikingly, in a typical scenario where delay-sensitive and delay-tolerant services co-exist, the reduction in power consumption can be enhanced up to 50%. The efficiency is further improved for a higher number of delay-tolerant services with more relaxed delay requirement. The proposed scheme provides an intelligent compromise between service outage and service requirements under tough networking conditions such that the service requirements of the delay-sensitive services are maintained for the least number of dropped bits in the delay-tolerant services.

With the advent of the Internet of Things (IoT) paradigm and the prolific growth in technology, the volume of data generated by intelligent devices has increased tremendously. Cloud computing provides unlimited processing and storage capabilities to process and store the generated data. However, the cloud computing paradigm is associated with high transmission latency, high energy consumption, and a lack of location awareness. On the other hand, the data generated by the intelligent devices is delay-sensitive and needs to be processed on the fly. Thus, cloud computing isn’t suitable for the execution of this delay-sensitive data. To curtail the issues associated with the cloud paradigm, the fog paradigm, which allows data to be processed at the proximity of IoT devices, was introduced. One common feature of the fog paradigm is its limitations in capabilities, which make it unsuitable for processing large volumes of data. To ensure the smooth execution of delay-sensitive application tasks and the large volume of data generated, there is a need for the fog paradigm to collaborate with the cloud paradigm to achieve a common goal. In this paper, an efficient resource allocation framework is proposed to efficiently and effectively utilise the fog and cloud resources for executing delay-sensitive tasks and the huge volume of data generated by end users. The allocation of resources to tasks is done in two stages. Firstly, the tasks in the arrival queue are classified based on the task guarantee ratio on the cloud and fog layers and allocated to suitable resources in the layers of their respective classes. Secondly, we apply Bayes’ classifier to previous allocation history data to classify newly arrived tasks and allocate suitable resources to the tasks for execution in the layers of their respective classes. A Crayfish Optimization Algorithm (COA) is used to generate an optimal resource allocation in both the fog and cloud layers that reduces the delay and execution time of the system. The proposed method is implemented using the iFogSim simulator toolkit, and the execution results prove more promising in comparison with the state-of-the-art methods.

As the complexity of wireless mobile networks increases significantly, artificial intelligence (AI) and machine learning (ML) have become key enablers for radio resource management and orchestration. In this paper, we propose a multi-agent reinforcement learning (RL) method for allocating radio resources to mobile users under random traffic arrivals, in which Ultra-Reliable Low-Latency Communications (URLLC) and enhanced Mobile Broad-Band (eMBB) services are jointly considered in the same radio access network (RAN). The proposed system includes hierarchically placed RL agents, where the main-agent residing on the upper hierarchy performs inter-slice resource allocation between the URLLC and eMBB slices. The URLLC and eMBB sub-agents are responsible for the resource allocation within their own slice, where the objective is to maximize the eMBB throughput while satisfying the latency requirements of the URLLC slice. In the RL algorithm, the state space includes the queue occupancy and the channel quality information of mobile users while the action space specifies the resource allocation to the users. For a computationally efficient RL training, the state space is significantly reduced by quantizing the queue occupancy and grouping the users according to their channel qualities. The numerical results for the URLLC show that the proposed RL-based approach provides the average delay results of lower than 1 ms for all experiments while the worst case eMBB throughput degradation is limited to 4%.

In modern emergency responses, unmanned aerial vehicles (UAVs) play a crucial role in redefining disaster management through diverse task execution (e.g., object detection). However, UAVs are usually resource-constrained. To address this issue, UAV mother-ships with edge servers (UMECs) can be placed near UAV scouts for remote task processing. UMECs can have a larger scale of energy consumption and battery capacity than UAV scouts. Therefore, minimizing the total or maximum energy consumption may result in ignoring the energy consumption in UAV scouts and focusing on UMECs, consequently reducing the network’s lifetime. Furthermore, UMECs might lack the capacity to process all types of tasks owing to memory or software restrictions, and faster task execution often necessitates both central processing unit (CPU) and graphics processing unit (GPU), which are rarely considered by existing works. Therefore, this paper studies a task offloading and resource (computation capacity and power) allocation (TORA) problem with the goal of minimizing the maximum energy consumption ratio among UMECs and UAV scouts alongside task execution latency ratio and total energy consumption, where tasks are executed using both CPU and GPU, each UMEC can execute a specific set of tasks, and devices have limited resources. We then formulate the problem as a non-convex mixed-integer nonlinear programming problem and decompose it into multiple sub-problems. Finally, a soft-actor-critic (SAC) based TORA algorithm (SATORA) is proposed to address the problem, which can adapt to the time-varying environment scenario. Numerical simulation results show that SATORA outperforms other baseline algorithms.

Intelligent transportation systems (ITSs) have witnessed a rising interest from researchers because of their promising features. These features include lane change assistance, infotainment, and collision avoidance, among others. To effectively operate ITSs for these functions, there is a need for edge computing. One can install edge computing servers at the roadside units (RSUs). There must be seamless communication between the edge servers and the cars. Additionally, there will be some cars that experience higher delays and thus, are not preferable because they will highly degrade the performance. Therefore, in this work, we consider a vehicular network scenario and define a cost function that takes into account the latency that is determined by the car’s computing frequency, association, and resource allocation while considering fairness constraints. Our cost function is to minimize the total latency (i.e., both local computing latency and transmission latency). The cost of the optimization problem is minimized by optimizing the car’s local frequency allocation, resource allocation, and association. The problem is separable, therefore, we first compute the local frequencies of the cars using a convex optimizer. Next, we split the core problem into two separate problems: (a) the distribution of resources and (b) association, because the last defined problem (joint association and resource allocation) is NP-hard. We then suggest an iterative solution. In the end, we offer numerical findings to support the suggested solution.

A disaggregated data center (DDC) can efficiently use resources such as CPU and memory. In a DDC, because each resource is independent and connected by a network, communication between resources is required for task execution. Communication delays can be an overhead for task execution, causing performance degradation. Because communication delays depend on the correspondence between resources on the network and the paths over which they communicate, an efficient resource allocation method is required to determine this relationship. Herein, we propose a resource allocation method called RA-CNP to execute many tasks simultaneously while satisfying performance requirements. This method models the impact of the network on the performance of tasks for the provided service. Furthermore, this method defines a resource allocation problem to avoid the allocation of resources that will be requested in the future. We evaluated the effectiveness of our method by simulating various DDC networks, assuming a DDC at the edge. The results demonstrated that RA-CNP could execute more tasks than conventional methods could, without violating performance requirements, based only on current network information in both networks configured by circuit and packet switches. RA-CNP could allocate resources in less than 10 s, even in a relatively large network configured with 64 switches; this capability demonstrates its practicality.

The advancement of Internet of Everything (IoE) propels the fast growth of next-generation, such as 6G networks, leading to a new era of coverage, connectivity, and technological innovation, which calls for novel approaches to address rising energy consumption and maximize resource use. The proposed article presents a robust hybrid algorithm that combines leader-based optimization and Adaptive Differential Evolution (DE) in the framework of the Energy Efficient Hybrid Evolutionary Algorithm (EEHEA), which is specifically designed for the complex environment of IoE-enabled 6G networks. The scheme EEHEA combines the efficacy of leader-based optimization (LBO) for an effective decision-making process and adaptive differential evolutionary optimization (ADE)’s dynamic network-parameters adaptation, enhanced convergence, and global searching ability, persistently fine-tuning optimization strategies based on the dynamics of the network. Combining these components into the scheme EEHEA allows it to balance local exploitation and global exploration effectively. This implies better resource allocation and improved energy efficiency in ecosystems with IoE-driven 6G (IoE-6G). The outcomes report that the scheme EEHEA can address the rising energy consumption issues and enhance the efficiency of IoE-6G. Based on simulation experiments, the proposed scheme EEHEA can demonstrate faster convergence times, higher accuracy, and superior flexibility concerning changing network conditions. Its capability to handle energy-related challenges and navigate complex network environments with resilience shows the ability to enhance the performance of IoE-6G. The EEHEA scheme reports its efficacy over state-of-the-art schemes regarding localization, latency, coverage, and energy expenditure performance metrics.

In this work, we investigate content caching problem in unmanned aerial vehicle (UAV)-enabled networks where UAVs are allowed to store user requested content so as to enhance the content transmission performance of users. To predict user content request, we propose a bidirectional long short-term memory-based algorithm. Then, the content transmission delay of users is examined and the UAV deployment, content caching and resource allocation problem is formulated as an overall content fetching delay minimization problem. As the formulated problem is difficult to solve, we transform it into two subproblems which are solved iteratively. To tackle the UAV deployment and content caching subproblem, we first design a modified K-means-based clustering scheme and then propose a UAV deployment strategy by using quadratic transformation. To solve subcarrier and power allocation subproblem, we apply Lagrangian dual method to determine power allocation strategy and propose a Kuhn-Munkres algorithm-based subcarrier allocation strategy. Simulation results demonstrate the effectiveness of the proposed algorithms.

Device-to-device (D2D) communications underlaying cellular networks enhance system capacity and bandwidth utilization. In this approach, secondary users (D2D devices) share the radio resources allocated to primary users (cellular UE), achieving increased system capacity and spectrum efficiency. However, the allocation of inappropriate resources and transmission power to devices introduces excessive co-channel interference, which could impair primary user communication. The proposed research addresses this challenge by developing a Multi-Value Bipartite Matching (MBM) Algorithm that jointly allocates resources and transmission power for both primary and secondary users maintaining individual data rate constraints. Numerical analyses show that this approach outperforms existing algorithms in determining appropriate power levels while meeting specified constraints, ultimately improving system capacity and reducing interference.

The rapid surge in internet-driven smart devices and bandwidth-hungry multimedia applications demands high-capacity internet services and low latencies during connectivity. Cloud radio access networks (C-RANs) are considered the prominent solution to meet the stringent requirements of fifth-generation (5G) and beyond networks by deploying the fronthaul transport links between baseband units (BBUs) and remote radio heads (RRHs). High-capacity optical links could be conventional mainstream technology for deploying the fronthaul in C-RANs. But densification of optical links significantly increases the cost and imposes several design challenges on fronthaul architecture which makes them impractical. Contrary, Ethernet-based fronthaul links can be lucrative solutions for connecting the BBUs and RRHs but are inadequate to meet the rigorous end-to-end delays, jitter, and bandwidth requirements of fronthaul networks. This is because of the inefficient resource allocation and congestion control schemes for the capacity constraint Ethernet-based fronthaul links. In this research, a novel reinforcement learning-based optimal resource allocation scheme has been proposed which eradicates the congestion and improves the latencies to make the capacity-constraints low-cost Ethernet a suitable solution for the fronthaul networks. The experiment results verified a notable 50% improvement in reducing delay and jitter as compared to the existing schemes. Furthermore, the proposed scheme demonstrated an enhancement of up to 70% in addressing conflicting time slots and minimizing packet loss ratios. Hence, the proposed scheme outperforms the existing state-of-the-art resource allocation techniques to satisfy the stringent performance demands of fronthaul networks.

With the increasing prevalence of multi-user, multi-service, and heterogeneous multi-device environments, there is a need to address the imperative for efficient resource allocation in contemporary wireless networks, such as those involving unmanned aerial vehicles (UAVs) or drones. In this regard, this work addresses the challenges within a five-dimensional heterogeneous wireless network model, focusing on diverse services such as Big Data Analytics, Video Rendering, and Computer-Aided Design, and the allocation of resources among heterogeneous devices, including UAVs, tethered balloons, and multi-rotors. The resource allocation is facilitated through multiple interfaces like LTE, Wifi, LoRa, and Sigfox, catering to the diverse needs of users operating in aerial Networks. Additionally, this work introduces a novel Intelligent Relaxation using the Penalty Function (IRPF) approach for resource allocation, treating it as an integer programming problem to balance user needs while ensuring affordability. A comparative analysis is conducted between the proposed approach and the traditional branch-and-bound algorithm. In scenarios requiring resource allocation for numerous services based on user demand and device capabilities, the proposed work presents a penalty-based integrality gap solution adept at managing fractional values. The resulting optimization framework is meticulously designed to minimize activation and operating costs while optimizing utility. Additionally, the computing efficiency of the proposed approach is demonstrated by extensive simulations that prove its superiority over the traditional algorithm. Consequently, this research emphasizes the essential role of the proposed model in navigating the intricate challenges of resource allocation in modern drone-centric wireless networks.

Emergency events in a city cause considerable economic loss to individuals, their families, and the community. Accurate and timely prediction of events can help the emergency fire and rescue services in preparing for and mitigating the consequences of emergency events. In this paper, we present a systematic development of predictive models for various types of emergency events in the City of Edmonton, Canada. We present methods for (i) data collection and dataset development; (ii) descriptive analysis of each event type and its characteristics at different spatiotemporal levels; (iii) feature analysis and selection based on correlation coefficient analysis and feature importance analysis; and (iv) development of prediction models for the likelihood of occurrence of each event type at different temporal and spatial resolutions. We analyze the association of event types with socioeconomic and demographic data at the neighborhood level, identify a set of predictors for each event type, and develop predictive models with negative binomial regression. We conduct evaluations at neighborhood and fire station service area levels. Our results show that the models perform well for most of the event types with acceptable prediction errors for weekly and monthly periods. The evaluation shows that the prediction accuracy is consistent at the level of the fire station, so the predictions can be used in management by fire rescue service departments for planning resource allocation for these time periods. We also examine the impact of the COVID-19 pandemic on the occurrence of events and on the accuracy of event predictor models. Our findings show that COVID-19 had a significant impact on the performance of the event prediction models.

Internet of Things (IoT) is a promising technology for realizing massive interconnection, especially when it is enabled by Unmanned Aerial Vehicles (UAVs) in terms of flexibility and operability. However, limited cache resources and channel resources limit the implementation of high throughput and low-loss communication systems. Furthermore, in order to better user experience and system resource utilization, it is imperative for different services to acquire individualized resource configurations. This paper proposes a Service-Oriented Resource Allocation (SoRA) algorithm based on a scenario involving multiple users, a relay, and sink. Firstly, SoRA uses a fuzzy complementary matrix to evaluate different types of service. Secondly, the storage and communication resources are allocated jointly to maximize system utility. Finally, the deep Q-learning strategy is adopted to allocate the resources dynamically according to the network conditions. The simulation results show that the proposed SoRA algorithm reduces the delay by 43.49%, reduces the average packet loss by 14.28%, and decreases the average power consumption by 25.36%.

Fog computing is a promising and challenging paradigm that enhances cloud computing by enabling efficient data processing and storage closer to data sources and users. This paper introduces a game-theoretic approach called GTRADPMFC (Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing) to address resource allocation and dynamic pricing challenges in fog computing environments with limited resources. The proposed model features non-cooperative competition among fog nodes for resources and dynamic pricing mechanisms to encourage efficient resource utilization. Theoretical analysis and simulations demonstrate that GTRADPMFC improves resource efficiency and overall fog computing system performance. Additionally, the paper discusses how to handle situations with insufficient samples and provide flexibility for users unable to meet completion time requirements. GTRADPMFC effectively manages resource allocation by establishing pricing in fog computing, considering potential delays in completion time. This is achieved through research, simulations, convergence analysis, complexity evaluation, and optimization guarantees.

The deployment of 5G has exposed capacity constraints in realizing the key vision of the Internet of Everything (IoE). Therefore, the researchers are exploring potentials of Digital Twin Network (DTN) in wireless networks. DTN is a novel technology to create virtual replicas of physical environment for testing, optimizing, and managing wireless networks. The integration of Artificial Intelligence (AI) and DTN appears to be a promising approach to address communication systems by providing an efficient environment for testing and improving AI models before deployment in real networks for effective network management, optimal resource allocation, and precise behavior prediction. Therefore, AI-enabled DTN in 6G represents a compelling avenue to address multifaceted challenges faced by wireless networks. In this comprehensive work, we offer a holistic survey that delves into the state-of-the-art approaches for AI-enabled DTNs in 6G. Firstly, we discuss the evolution of wireless networks and concept of AI-enabled DTN in 6G. Secondly, we discuss the role of AI-enabled DTN in 6G and driving advancements in fundamental components of 6G including resource allocation, caching, data offloading, and data security. Thirdly, we conduct a detailed discussion on key enabling technologies for realizing the capabilities of AI-enabled DTN in 6G. Fourthly, several applications of AI-enabled DTN in 6G are discussed for the practical relevance and significance in various industries such as smart cities, healthcare, and transportation etc. Finally, we provide lessons learned and highlight existing challenges and research directions to embark on further research efforts in the realm of AI-enabled DTN in 6G.

Wireless networks operate in a resource-constrained setting, of which spectrum is an important example. Here, we attack the scarcity problem by developing a market for virtual resources based on a common ownership self-assessed tax. We propose a utilization-based penalty mechanism to incentivize efficient use of resources and discourage hoarding. Our analysis and simulations show that the resulting market behaviour under various scenarios achieves this goal.

In urban settings, the highly complex communication environment leads to a frequent occurrence blockage in the link between ground users and unmanned aerial vehicle (UAV), which renders the quality of communication poor. To address this issue, an UAV edge computing system based on reconfigurable intelligent surface (RIS) is proposed in this paper. The goal of this system is to minimize energy consumption by considering various factors such as user transmission power, intelligent reflector phase shift matrix, UAV trajectory, computational resource allocation and the stability of task queues. By applying the Lyapunov optimization theory, the original non-convex problem is decomposed into a series of optimization-related sub-problems. Additionally, it is possible to obtain the closed solution to the original problem through Lagrange dual, Karush-Kuhn-Tucker (KKT) conditions, and mixed differential genetic evolution algorithm (MDGEA). As indicated by the simulation results, the proposed RIS-assisted UAV edge computing system is more effective in reducing energy consumption for the system than traditional UAV mobile edge computing on the basis of maintaining queue stability.

Zero-Touch Deterministic Industrial Machine-to-Machine (ZT-DI-M2M) serves as a customized communication solution designed to meet the specific needs of industrial settings. Though 5G is the promising solution for ZT-DI-M2M, optimized scheduling in 5G remains challenging to design over aspects such as network overload and congestion control, especially in the uplink transmission of ZT-DI-M2M. Effective allocation of resources for Machine-Type Communication (MTC) devices stands out as a pivotal hurdle within the domain of 5G networks. This challenge directly influences the longevity of battery-operated devices and the Quality of Service (QoS) experienced by applications. This paper aims to develop a Group-Based and Energy Aware (GBEA) resource allocation algorithm for M2M communication in the 5G networks. The GBEA algorithm solves the resource allocation problem by initially clustering the active nodes concerning their delays. Consequently, inter and intra-cluster resource distribution occurs through the Gaussian Mixture Model Expectation Maximization (GMM-EM) algorithm. The GBEA algorithm optimizes the resource allocation of M2M devices by factoring delay, energy, proximity, and fairness into the allocation process. The simulation outcomes unveil the superiority of the GBEA scheduling algorithm over established state-of-the-art resource allocation methods. It showcases remarkable enhancements in throughput, delay sensitivity, and energy efficiency, boasting nearly 1.5-fold, 1.75-fold, and 6.5-fold respective improvements.

Mutual interference between NOMA cellular users (CUs) and D2D links poses a challenge in extending NOMA’s resource allocation schemes to NOMA-D2D models. In this paper, we develop a novel resource allocation algorithm for NOMA-D2D systems and utilize it for comparing NOMA-D2D and OFDMA-D2D models. The proposed scheme aims to optimize the system sum rate and ensure a minimal rate for CUs. We formulate this as a mixed integer non-linear programming (MINLP) problem encompassing resource block (RB) allocation and power control. We propose a heuristic sub-optimal solution to lower the computational complexity. Our solution has two stages. In the first stage, D2D pairs and half of the CUs are admitted to the system to maximize the system sum rate. The rest of the CUs join the system in the next stage, while the upper bound for their achievable signal-to-interference-plus-noise-ratio (SINR) is maximized. Our first stage involves resource allocation for an OFDMA-D2D system. Employing an equal power allocation policy, we decompose the resource allocation problem for D2D pairs and CUs into two assignment problems. In the next step, the RB allocation scheme for CUs, which complies with the NOMA principle, is formulated as another assignment problem, and the power control strategy is updated. This two-stage heuristic solution allows a comparison of NOMA-D2D and OFDMA-D2D performances with identical power budgets, while the number of cellular connections in the NOMA-D2D system is twice that of the OFDMA-D2D system. Our results indicate that OFDMA-D2D achieves a higher sum rate and individual rates over NOMA-D2D.

The Internet of Things (IoT) is an essential part of 5G, Beyond-5G (B5G), and 6G systems; it has several applications in two of the principal 5G use cases, namely ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC), and in their successors within B5G and 6G: extreme ultra-reliable low-latency communication (eURLLC) and ultra-massive machine-type communication (umMTC). IoT systems, which are characterized by narrow bandwidths, have stringent requirements owing to the specific nature of their applications and use cases. The purpose of this study is to investigate and jointly optimize the energy efficiency (EE) and latency through resource allocation for IoT cellular systems. With regard to the contributions, in this study we investigated the optimization of EE in narrowband IoT systems, compared resource unit configurations (RUCs), jointly formulated the optimization of EE and latency, and introduced a suboptimal but efficient algorithm. More precisely, as the EE performance of various resource unit configurations has not been exhaustively investigated in the current state of the art, we analyzed and compared the EE of RUCs. The results show vast differences in performance between RUCs. For example, in terms of EE, the best RUC has an EE more than 80 times higher than the worst, which illustrates the importance of this investigation. We then proposed a scheduler based on the shortest job first (SJF) for minimum latency allocation, and another scheduler based on a joint evaluation of EE and latency. With respect to conventional techniques, these schedulers achieve a better trade-off between latency reduction and gain in terms of EE for a wider range of parameter configurations in multi-cellular layouts. The study demonstrates that in the presence of repetitions, algorithms that achieve high EE will mostly achieve low latency.

Exposure to Electromagnetic Fields (EMF) is one of the main concerns on current mobile wireless deployments, due to the use of active antenna systems and the increase of users connected simultaneously to these systems. In this paper, we evaluate the achievable rate resulting from simultaneous connections from multiple users, along with the signal-to-noise ratio (i.e., SNR), considering existing regulatory constraints on EMF, which is the subject of limitations in several countries. Our approach considers a random deployment of several base stations, according to a Poisson point process, and focuses on exploring the convexity of the achievable rate, as it will increase together with the number of antennas, and decrease with the overhead when a higher number of antennas is used. This article also studies the effects of the placement of new base stations on the overall EMF exposure, ensuring compliance with the current EMF limitations. Results show that due to the narrowness of the beam, the blockage can considerably affect the SNR received. Although several configurations are available from an operator perspective, some flexibility is allowed and the EMF exposure does not affect the average performance dramatically.

Multi-camera systems are now widely employed across numerous domains. The exponential growth of deep learning has simplified the implementation of advanced video analytics applications. While current systems strive to enhance live video analytics from several aspects, they overlook the potential degradation in performance resulting from dynamic content changes, such as the variation in the quantity and types of objects of interest over a period and across different cameras. The authors introduce Workload and Model Adaptation (WMA), a two-stage resource allocation strategy for a three-tiered, cross-camera video analytics system. This system not only supports model fine-tuning but also ensures workload balance. Notably, both the system architecture and control workflow fully comply with the IEEE 1935 edge standard. This paper delves into the GPU utilization performance of a vehicle re-identification application and examines the workload dynamics spanning multiple cameras. Furthermore, the challenges related to multi-process execution are explored. The system is evaluated using a commonly employed dataset and a popular open-source project. The results demonstrate that the proposed design surpasses the baseline and enhances the overall throughput and latency across cameras within the system.

This paper investigates a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In the system, a UAV acts as a hybrid access point (HAP) to charge users in the downlink (DL) and receive messages in the uplink (UL). In particular, the RIS is exploited to significantly enhance the efficiency of both the DL and UL transmission. Our objective is to enhance the minimum throughput among all ground users by jointly optimizing the horizontal location of UAVs, the transmit power of users, transmission time allocation, and passive beamforming vectors at the RIS. To address this problem, we present an alternating optimization-based algorithm with low complexity to decompose the problem into four subproblems and solve them sequentially. In particular, we derive a lower bound of the composite channel gain to tighten the constraints and employ successive convex approximation (SCA) to optimize the horizontal location of the UAV. The transmit power closed-form optimum solutions are then obtained, and the problem of time allocation is reformulated as a linear programming problem. Finally, we optimize the passive beamforming vectors by adopting semi-definite relaxation (SDR). The effectiveness of the algorithm is supported by numerical results, which also demonstrate that the RIS-assisted UAV-enabled WPCN outperforms the traditional WPCN in terms of the minimum throughput.

The energy sector faces numerous challenges, including rising electricity costs and inconsistent services due to network overload, often requiring the involvement of a central network operator to address these issues. However, a user-centric approach that prioritizes demand-side management, exemplified by decentralized Community Energy Systems (dCES), presents a promising solution to energy distribution and supply network challenges. dCES can be conceptualized as a small-scale, dynamic distribution network seamlessly integrated into the broader framework of the Smart Grid. In this paradigm, prosumers play an active role, as they must contribute to and draw from a shared energy resource pool, with the overarching goal of avoiding depletion. Specifically, various individuals with different energy consumption patterns and preferences work together to solve collective action problems, i.e., blackouts. Motivated firstly by fair resource allocation, and secondly by the idea that trust is a crucial factor for successful collective action among diverse individuals, we developed a suitable Multi-Agent System (MAS) for dCES to prevent resource depletion. Our experimental results show that introducing trust into dCES can lead to successful collective action, resulting in stable energy networks.

Edge computing plays a crucial role in the field of the Internet of Vehicles (IoV), meeting the resource and latency requirements of time-sensitive vehicle applications. However, the emergence of numerous compute-intensive and latency-sensitive applications, such as augmented reality and autonomous driving, has led to a situation where traditional edge computing architectures cannot meet the increasing application demands of the IoV. This paper extends the paradigm of vehicular edge computing to a collaborative cloud-edge cluster resource provisioning framework. Integrating compute resources from multiple Edge Service Providers (ESP) and the cloud enables horizontal and vertical collaborative computation offloading among service nodes. To facilitate resource sharing among different ESPs, we introduce a dynamic pricing model and utilize software-defined networking (SDN) to tackle this scenario’s complex resource management challenges. Furthermore, with the optimization objectives of minimizing task computation latency and maximizing the profits of ESPs, we establish a mathematical model. Before resource allocation, we employ a clustering algorithm to determine initial offloading decisions, reducing the dimensionality of the action space. Subsequently, we employ the Double Deep Q-Network (DDQN) algorithm to achieve a rational allocation of compute resources. Simulation results demonstrate that compared to the Deep Q-Network (DQN) algorithm and greedy strategy, the proposed approach reduces latency by 18.18% and 34.85%, respectively, while increasing the profits of edge service providers by 16.25% and 33.33%, respectively.

This paper tackles the complex problem of optimizing resource configuration for microservice management in heterogeneous cloud environments. To address this challenge, an enhanced framework, the multi-objective microservice allocation (MOMA) algorithm, is developed to formulate the efficient resource management of cloud microservice resources as a constrained optimization problem, guided by resource utilization and network communication overhead, which are two important factors in microservice resource allocation. The proposed framework simplifies the deployment of cloud services and streamlines workload monitoring and analysis within a diverse cloud system. A comprehensive comparison is made between the effectiveness of the proposed algorithm and existing algorithms on real-world datasets, with a focus on resource balancing, network overhead, and network reliability. Experimental results reveal that the proposed algorithm significantly enhances resource utilization, reduces network transmission overhead, and improves reliability.

For an extended period, a technological architecture known as cloud IoT links IoT devices to servers located in cloud data centers. Real-time data analytic are made possible by this, enabling better, data-driven decision making, optimization, and risk reduction. Since cloud systems are often located at a considerable distance from IoT devices, the rise of time-sensitive IoT applications has driven the requirement to extend cloud architecture for timely delivery of critical services. Balancing the allocation of IoT services to appropriate edge nodes while guaranteeing low latency and efficient resource utilization remains a challenging task. Since edge nodes have lower resource capabilities than the cloud. The primary drawback of current methods in this situation is that they only tackle the scheduling issue from one side. Task scheduling plays a pivotal role in various domains, including cloud computing, operating systems, and parallel processing, enabling effective management of computational resources. In this research, we provide a multiple-factor autonomous IoT-Edge scheduling method based on game theory to solve this issue. Our strategy involves two distinct scenarios. In the first scenario, we introduced an algorithm containing choices for the IoT and edge nodes, allowing them to evaluate each other using factors such as delay and resource usage. The second scenario involves both a centralized and a distributed scheduling approach, leveraging the matching concept and considering each other. In addition, we also introduced a preference-based stable mechanism (PBSM) algorithm for resource allocation. In terms of the execution time for IoT services and the effectiveness of resource consolidation for edge nodes, the technique we use achieves better results compared with the two commonly used Min-Min and Max-Min scheduling algorithms.