关键词： Resource management   Decoding   Task analysis   Signal processing algorithms   Time division multiple access   Performance evaluation   NOMA   Mobile edge computing   computation offloading   multiple access   resource allocation  

摘要： When multiple devices seek to offload computational tasks to their access point, the nature of the multiple access scheme plays a critical role in the system performance. For a system with heterogeneous tasks, we adopt a time-slotted signaling architecture in which different numbers of devices transmit in each slot, subject to individual power constraints. We consider the problem of jointly selecting the devices that will offload, along with optimizing their communication resources (their powers and rates in each time slot, and the time slot lengths) so as to minimize the a weighted sum of the energy expended by the devices. We employ a customized tree search algorithm for the offloading decisions in which a resource allocation problem is solved at each node. For time-division multiple access (TDMA) and "rate optimal" multiple access, we obtain reduced-dimension convex formulations of the resource allocation problem. For non-orthogonal multiple access (NOMA) with independent decoding (ID) or fixed-order sequential decoding (FOSD) we show that the resource allocation problem has a difference-of-convex structure and we develop a successive convex approximation algorithm with feasible point pursuit. Furthermore, for the FOSD scheme we obtain a closed-form expression that provides the optimal decoding order when it is feasible, and efficient algorithms for finding a good decoding order when it is not. Our results capture the inherent tradeoffs between the complexity of a multiple access scheme (and its resource allocation algorithm), and its performance in the computation offloading application.

关键词： Distributed Denial of Service   EtherCAT   Inter Planetary File System   Mobile Edge Computing   Netlink   NSLKDD dataset   SoftRouter   Software-Defined Networking  

摘要： The advent of Software-Defined Networking (SDN) has ushered in a new era in network architecture, providing unprecedented levels of flexibility and adaptability. However, this advanced flexibility exposes SDN to security risks, particularly Distributed Denial of Service (DDoS) attacks. Detecting and mitigating DDoS attacks in SDN environments poses a critical challenge. This study introduces an innovative DDoS detection approach leveraging Echo State Networks (ESN) tailored specifically for SDN. This approach is based on two core assumptions: firstly, routine network operations primarily exhibit normal behavior, and secondly, there are discernible differences in data characteristics between normal and abnormal network conditions. These assumptions hold true in the realm of everyday network dynamics. To validate the efficacy of the ESN algorithm, we augment this approach by incorporating flow features to enhance DDoS detection capabilities. This study underscores the effectiveness of ESN in identifying and mitigating Distributed Denial of Service (DDoS) attacks, DDoS threats, achieving an impressive average success rate of 97.78%. By harnessing the potential of Echo State Networks, this work makes a substantial contribution to ongoing efforts in fortifying network security, providing a proactive defense against disruptive DDoS attacks. © (2024), (Research Institute of Intelligent Computer Systems). All rights reserved.

关键词： Servers   Costs   Resource management   Fault tolerant systems   Fault tolerance   Energy consumption   Encoding   Mobile edge computing   5G   edge server placement   load balancing   hexagonal coverage  

摘要： Edge Servers (ESs) enhance the efficiency and reliability of modern Beyond Fifth-Generation (B5G) network systems by reducing latency, boosting responsiveness, and optimizing network usage. From an industry standpoint, the optimal positioning of ESs is critical to enhancing coverage reach while reducing potential conflicts with neighboring servers, guaranteeing consistent and reliable user access. Traditional circular coverage for ESs is suboptimal due to inherent constraints. The hexagonal model, known for symmetrical and consistent distribution, emerges as a superior choice. Implementing Fault Tolerance Edge Server Placement (FT-ESP) demands workload distribution across servers to prevent overloading and mitigate service failures. The complexity of assigning multiple Base Stations (BSs) to ESs categorizes FT-ESP as a Nondeterministic Polynomial Time (NP-hard) problem. To address this, the Subset Sum Encoding Scheme (S2ES) has been introduced, linking BSs to ESs. This research Fault Tolerance Subset Sum Encoding Scheme (FT-S2ES) examines various Edge Server Placement Problem (ESPP) strategies, highlighting the proposed method's efficacy compared to traditional techniques. The empirical findings indicate that, when tested on real-world datasets, the suggested strategy successfully achieves a balanced workload and utilization rate of 8.82% and 7.67%, respectively. Compared to the Grey Wolf Optimizer (GWO) algorithm, energy usage is significantly reduced by 32.23%. In addition, the proposed approach ensures a reduction in latency by 22.80% with an increasing number of BSs and 9.47% with an increasing number of ESs.

关键词： Costs   Resource management   Training   Task analysis   Quality of service   Simulation   Data models   Resource allocation   reinforcement learning   cost of learning   continual learning   meta-learning   mobile edge computing  

摘要： In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks with time-varying statistics. However, the increased complexity of 5G and Beyond networks requires correspondingly more complex learning agents and the learning process itself might end up competing with users for communication and computational resources. This creates friction: on the one hand, the learning process needs resources to quickly converge to an effective strategy;on the other hand, the learning process needs to be efficient, i.e., take as few resources as possible from the user's data plane, so as not to throttle users' Quality of Service (QoS). In this paper, we investigate this trade-off, which we refer to as cost of learning, and propose a dynamic strategy to balance the resources assigned to the data plane and those reserved for learning. With the proposed approach, a learning agent can quickly converge to an efficient resource allocation strategy and adapt to changes in the environment as for the Continual Learning (CL) paradigm, while minimizing the impact on the users' QoS. Simulation results show that the proposed method outperforms static allocation methods with minimal learning overhead, almost reaching the performance of an ideal out-of-band CL solution.

关键词： Task analysis   Computational modeling   Stability analysis   Optimization   Matrix decomposition   Heuristic algorithms   Feature extraction   Computation rate   data offloading   deep reinforcement learning (DRL)   mobile edge computing   orthogonal non-negative matrix factorization (NMF)   queue stability  

摘要： Data-intensive applications coupled with limited mobile resources make opportunistic computation offloading imperative. Therefore, efficient and reliable offloading strategies are crucial for achieving optimal performance in terms of stabilizing data queues at various data rates, efficient task management and reduced waiting times for users. This paper proposes a novel algorithm, Matrix Factorization-Based Deep Reinforcement Learning Approach for Stable Online Offloading in Mobile Edge Networks (MFDROO), that combines Orthogonal Non-negative Matrix Factorization (ONMF) and a Deep Reinforcement Learning approach (DRL) to address the problem of stable online offloading in large networks. The proposed algorithm utilizes ONMF to model network resource heterogeneity by decomposing it into a set of features, capturing the underlying structure of data traffic flows by removing irrelevant information from data. This reduces computational overhead and improves performance. Additionally, MFDROO incorporates a DRL agent to learn optimal offloading decisions over time. By utilizing ONMF in conjunction with DRL, MFDROO overcomes online offloading challenges. It optimizes user computation rates and enhances system performance. Additionally, it maintains data-queue stability for large networks or higher data rates. Large-scale network simulations were extensively conducted to demonstrate MFDROO effectiveness. Maintaining stability while scaling up the users number is a challenge in network computation. Our results demonstrate that MFDROO tackles this challenge and ensures that even with a 3.3% increase in user numbers (up to 100 users), computation networks remain stable and outperform other existing algorithms in terms of utility maximization and stability. This improvement ensures optimal performance and provides scalability and efficiency for various applications.

关键词： cyber-physical-social systems   CPSS   Society 50   Industry 40   resource scheduling   mobile edge computing   MEC   metaverse   digital twin   6G   blockchain   effective throughput   energy expense   financial expense   service delivery delay  

摘要： To ensure better living for humans through different collaborative systems/services, a human-centred Society 5.0 has emerged. Society 5.0 can balance economic and social sustainability via the high integration of cyber-physical-social systems (CPSS). The literary works on CPSS-based task execution did not examine a suitable mission-driven/super-smart service orchestration strategy by taking both Society 5.0 and Industry 4.0 tasks, multi-task to resource mapping, task impact, appropriate worker and communication resource selection with minimum delivery delay, energy, financial expense, and service requirements, into account. To master the challenges, this paper presents a task impact-based service orchestration scheme that selects appropriate resources for Society 5.0 task execution over a CPSS-enhanced network by examining distinguished tasks, resources, requirements, and minimum latencies. The simulation outcome ensures that up to 44.32% service delivery delay and 39.79% social welfare gain are obtained in the proposed impact-based scheme over existing schemes.

关键词： Mobile edge computing  

摘要： The development of the internet of things (IoT) and 6G has given rise to numerous computation-intensive and latency-sensitive applications, which can be represented as directed acyclic graphs (DAGs). However, achieving these applications poses a huge challenge for user equipment (UE) that are constrained in computational power and battery capacity. In this paper, considering different requirements in various task scenarios, we aim to optimize the execution latency and energy consumption of the entire mobile edge computing (MEC) system. The system consists of single UE and multiple heterogeneous MEC servers to improve the execution efficiency of a DAG application. In addition, the execution reliability of a DAG application is viewed as a constraint. Based on the strong search capability and Pareto optimality theory of the cuckoo search (CS) algorithm and our previously proposed improved multiobjective cuckoo search (IMOCS) algorithm, we improve the initialization process and the update strategy of the external archive, and propose a reliability-constrained multiobjective cuckoo search (RCMOCS) algorithm. According to the simulation results, our proposed RCMOCS algorithm is able to obtain better Pareto frontiers and achieve satisfactory performance while ensuring execution reliability. © 2024 Liangbin Zhu et al.

关键词： Sensors   Internet of Things   Optimization   Interference   Array signal processing   Reconfigurable intelligent surfaces   Computational efficiency   Stochastic processes   Servers   Hands   Mobile edge computing   RIS   deep reinforcement learning   cognitive radio   Markov decision process  

摘要： Nowadays, running applications on devices is restricted by limited computational capability and battery capacity, which needs the aid of mobile edge computing (MEC). This article aims to enhance MEC systems by employing revolutionary techniques: 1) adjusting wireless environment using reconfigurable intelligent surface (RIS), 2) exploiting opportunistically licensed spectrum using cognitive radio (CR), and 3) scavenging renewable energy using energy harvesting. We consider a RIS-assisted CR-MEC network where a secondary user (SU) powered by solar energy attempts to optimize computation. We study the computation rate maximization of SU, where it opportunistically either performs local computation or offloads data to the MEC server on a primary channel. A Markov decision process problem is formulated and then is firstly solved by a proposed deep policy gradient scheme, in which the system directly learns the policy from gradients of actions. To obtain higher stability, we subsequently propose a deep Q-learning scheme to derive a proper solution by maximizing the state-action value function. By taking the advantages of both policy-based and value-based methods, we further develop a deep actor-critic scheme, where an actor selects actions and a critic evaluates actions to acquire an optimal policy.

关键词： Mobile edge computing   multi-user collaboration   wireless power transfer   time and data allocation   deep learning  

摘要： The combination of mobile edge computing (MEC) and radio frequency-based wireless power transfer (WPT) presents a promising technique for providing sustainable energy supply and computing services at the network edge. This study considers a wireless-powered mobile edge computing system that includes a hybrid access point (HAP) equipped with a computing unit and multiple Internet of Things (IoT) devices. In particular, we propose a novel multi-user cooperation scheme to improve computation performance, where collaborative clusters are dynamically formed. Each collaborative cluster comprises a source device (SD) and an auxiliary device (AD), where the SD can partition the computation task into various segments for local processing, offloading to the HAP, and remote execution by the AD with the assistance of the HAP. Specifically, we aims to maximize the weighted sum computation rate (WSCR) of all the IoT devices in the network. This involves jointly optimizing collaboration, time and data allocation among multiple IoT devices and the HAP, while considering the energy causality property and the minimum data processing requirement of each device. Initially, an optimization algorithm based on the interior-point method is designed for time and data allocation. Subsequently, a priority-based iterative algorithm is developed to search for a near-optimal solution to the multi-user collaboration scheme. Finally, a deep learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.

关键词： Autonomous aerial vehicles   Servers   Signal to noise ratio   Computational modeling   Real-time systems   Internet of Things   Resource management   Processor scheduling   Data models   Trajectory planning   Mobile edge computing   UAV   deep deterministic policy gradient   task offloading   trajectory planning  

摘要： The proliferation of Internet of Things (IoT) devices has resulted in a massive increase in data generation, necessitating robust solutions for real-time data processing and analysis. The integration of Unmanned Aerial Vehicles (UAVs) with MEC systems presents a promising enhancement, providing dynamic, mobile edge computing capabilities that can adapt to changing conditions and demands. However, efficiently managing the task offloading and trajectory planning of UAVs in such scenarios poses significant challenges, particularly in maximizing coverage while minimizing time and energy consumption. In this context, this paper proposes a novel UAV-based MEC model utilizing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm to optimize task scheduling and UAV trajectory in real-time. Our model enables UAVs to function as mobile edge servers, dynamically processing and routing tasks between terminal devices and edge servers based on real-time device demands. By incorporating a sophisticated reward function that prioritizes the minimization of system costs-including energy, time, and throughput maximization-our approach not only enhances the operational efficiency of UAV-assisted MEC systems but also improves the quality and responsiveness of edge computing services. After training the MADDPG model in the simulated environment, the experiments show that our approach significantly improved the performance of UAV-assisted MEC systems in real-time scenarios, converging after 50 learning episodes and achieving 90% task completion rate.