Spiking neural networks (SNNs) have attracted considerable attention as third-generation artificial neural networks, known for their powerful, intelligent features and energy-efficiency advantages. These characteristics render them ideally suited for edge computing scenarios. Nevertheless, the current mapping schemes for deploying SNNs onto neuromorphic hardware face limitations such as extended execution times, low throughput, and insufficient consideration of energy consumption and connectivity, which undermine their suitability for edge computing applications. To address these challenges, we introduce EdgeMap, an optimized mapping toolchain specifically designed for deploying SNNs onto edge devices without compromising performance. EdgeMap consists of two main stages. The first stage involves partitioning the SNN graph into small neuron clusters based on the streaming graph partition algorithm, with the sizes of neuron clusters limited by the physical neuron cores. In the subsequent mapping stage, we adopt a multi-objective optimization algorithm specifically geared towards mitigating energy costs and communication costs for efficient deployment. EdgeMap—evaluated across four typical SNN applications—substantially outperforms other state-of-the-art mapping schemes. The performance improvements include a reduction in average latency by up to 19.8%, energy consumption by 57%, and communication cost by 58%. Moreover, EdgeMap exhibits an impressive enhancement in execution time by a factor of 1225.44×, alongside a throughput increase of up to 4.02×. These results highlight EdgeMap’s efficiency and effectiveness, emphasizing its utility for deploying SNN applications in edge computing scenarios.

Vehicular mobile edge computing (vMEC) and nonorthogonal multiple access (NOMA) have emerged as promising technologies for enabling low-latency and high-throughput applications in vehicular networks. In this article, we propose a novel multiagent deep deterministic policy gradient (MADDPG) approach for resource allocation in NOMA-based vMEC systems. Our approach leverages deep reinforcement learning (DRL) to enable vehicles to offload computation-intensive tasks to nearby edge servers, optimizing resource allocation decisions while ensuring low-latency communication. We introduce an attention mechanism within the MADDPG model to dynamically focus on relevant information from the input state and joint actions, enhancing the model’s predictive accuracy. Additionally, we propose an attention-based experience replay method to expedite network convergence. The simulation results highlight the effectiveness of multiagent reinforcement learning (MARL) algorithms, such as MADDPG with attention, in achieving better convergence and performance in various scenarios. The influence of different model parameters, such as input data volumes, task load levels, and resource configurations, on optimization results is also evident. The decision making processes of agents are dynamic and depend on factors specific to the task and environment.

With the proliferation of Internet of Things (IoT) devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile edge computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This article introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable intelligent surfaces (RIS), uncrewed aerial vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multiagent twin delayed deep deterministic policy gradient (MATD3) reinforcement learning algorithm, this article further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-convex procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm’s high energy efficiency and effectiveness.

With the convergence of fixed and mobile networks, heterogeneous networks are becoming ubiquitous. Internet giants are seeing the plight of identity authentication. To address this issue, unified access management (UAM) was conceived. This paper provides a novel unified access management scheme, named SGX-UAM, with one-time passwords (OTPs) based on Intel software guard extensions (SGX). SGX-UAM outperforms generic UAM for providing resistance to most client attacks, man-in-the-middle (MITM) attacks, phishing attacks, most replay attacks and most denial of service (DoS) attacks to which generic UAM implementaions are vulnerable. Specifically, client attacks are prevented by ensuring input security and memory security, where the former is achieved through shuffle mapping and “periodic hooking” strategy, the latter is mainly guaranteed by Intel SGX; MITM attacks are prevented by transferring ciphertext rather than plaintext; phishing attacks are avoided by authorization control; replay attacks cannot succeed because we adopts OTPs, which contain time-related dynamic factors that expire in a few seconds; as for DoS attack, we blunted its edge by blocking-invocation for identical user connection. SGX-UAM also differs from generic UAM in that it relieves the security concerns of sevice providers (SPs) and protects users’ privacy at little cost of performance. An exceptional value of SGX-UAM is that it brings a lightweight OTP solution that eliminates the need of additional hardware devices, thus reducing the costs. The experimental results show that SGX-UAM consumes almost the same time with OpenID and OAuth2.0 for one login request and performs steadily when handling sequential login requests. Furthermore, the resource usage for SGX-UAM is acceptable.

Wireless sensor network deployment should be optimized to maximize network coverage. The D-S evidence theory is an effective means of information fusion that can handle not only uncertainty and inconsistency, but also ambiguity and instability. This work develops a node sensing probability model based on D-S evidence. When there are major evidence disputes, the priority factor is introduced to reassign the sensing probability, with the purpose of addressing the issue of the traditional D-S evidence theory aggregation rule not conforming to the actual scenario and producing an erroneous result. For optimizing node deployment, a virtual force-directed particle swarm optimization approach is proposed, and the optimization goal is to maximize network coverage. The approach employs the virtual force algorithm, whose virtual forces are fine-tuned by the sensing probability. The sensing probability is fused by D-S evidence to drive particle swarm evolution and accelerate convergence. The simulation results show that the virtual force-directed particle swarm optimization approach improves network coverage while taking less time.

Wireless sensor networks are attractive, in large part because they do not require wired infrastructure. However, this very characteristic renders them energy-constrained. Duty cycle scheduling (DCS) is thought to contribute to the energy efficiency of sensing. We develop a novel paradigm for modeling wireless sensor networks, and a DCS strategy based on the additive increase/multiplicative decrease (AIMD) rule. The scheduling problem is framed as an optimization problem with the optimization objectives of lowering energy consumption and enhancing detection ability. The classic GA converges slowly in late evolution phases for using fixed crossover and mutation probability. An adaptable genetic algorithm (AGA) eliminates the above concern by allowing modifying crossover and mutation probability as per fitness values, but unfortunately, it often fails to select the best individuals. We suggest a quartile-directed adaptive genetic algorithm (Q-AGA) in which individuals with fitness above the first quantile of the last generation were selected. The inter-quartile range (IQR) is utilized to weight crossover and mutation probability. If IQR is larger, the fitness distribution is more concentrated, then we can diminish the crossover and mutation probability, allowing favorable traits to be retained to the greatest extent feasible. Simulation results show that Q-AGA has a faster convergence speed and a stronger global search ability. Furthermore, we change the hypothesis of the system model by considering that the two objectives can be weighted, thereby broadening the application scenarios.