Vehicle edge computing can effectively ensure the quality of experience for User Vehicles (UVs), but Road Side Units (RSUs) with limited resources may not be able to handle intensive tasks under high traffic conditions. In this case, Worker Vehicles (WVs) with idle resources can share resources to alleviate the pressure on RSUs. However, selfish WVs may be reluctant to share idle computation resources without any rewards. In addition, the optimization problems in previous research are relatively simple and cannot be applied to complex scenarios. To address the above challenges, we propose an incentive-driven partial offloading framework aiming to maximize social welfare. In particular, the Computing Service Provider (CSP) managing RSUs first determines resource prices and offloading rates with UVs, while also determining contract terms with WVs. Then, it generates the optimal task scheduling strategy and notifies the UVs to offload tasks to the corresponding WVs. Considering that maximizing social welfare is a Mixed-Integer Nonlinear Programming (MINLP) problem, we design the HPPO-based task Offloading and Resource Allocation algorithm (HORA) with a hybrid action space to directly solve the original problem. Finally, extensive simulation results show that HORA outperforms other baseline methods across various scenarios, and the contract terms meet the constraints of Individual Rationality (IR) and Incentive Compatibility (IC).

5G is a promising technology for improving the Quality of Service (QoS) in Internet of Vehicles (IoV) applications, including Vehicular Edge Computing (VEC). However, 5G networks have a limited communication range due to their radio-frequency properties, which can be a challenge in dynamic IoV environments. To address this issue, we propose a VEC architecture based on heterogeneous cellular networks, in which vehicles can select the appropriate communication network by classifying tasks according to their maximum tolerable latency. In order to further enhance the overall performance of the VEC system, we developed an efficient scheme that optimizes task offloading decisions and computation resource allocation in the proposed architecture. We analyze and formulate the optimization problem and use the linear relaxation improved branch-and-bound algorithm to solve it. Through extensive simulations, we demonstrate that the proposed scheme is superior to other solutions in computing latency, energy consumption, and failure rate.

Barrier coverage problem is one of hot research topics in directional sensor networks (DSNs). Directional sensors could zoom their sensing ranges, within which the event detection probability decreases with the increase of the distance between the location of event and the sensor. Although the probability that an event is detected by a single sensor may be below the required criteria, the detection probability achieved jointly by two sensors can be above the required criteria. In this work, we study the barrier coverage problem of DSNs, taking into account the directional nodes' ability of adjusting their working directions and sensing ranges. We mainly propose a barrier construction scheme, which schedules the nodes to form multiple barriers by jointly determining which nodes jointly forming a barrier, and their respective working directions and sensing ranges. Comparing to existing works, the proposed scheme is able to form more barriers, leading to the increase of service lifetime.

Federated Learning (FL), as a new distributed learning approach, allows multiple heterogeneous clients to cooperatively train models without disclosing private data. However, selfish clients may be unwilling to participate in FL training without appropriate compensation. In addition, the characteristics of clients in mobile edge networks (e.g., limited available resources) may also reduce the efficiency of FL and increase training cost. To solve these challenges, this paper proposes a Cost-Aware FL framework with client incentive and model compression (CAFL), aiming to minimize the training cost while ensuring the accuracy of the global model. In CAFL, we employ the reverse auction for incentive design, where Base Station (BS) acts as the auctioneer to select clients, and determines the local training rounds and model compression rates. Meanwhile, clients act as the bidders to train local models and get payments. We model the process of client selection, local training, and model compression as Mixed-Integer Non-Linear programming. Accordingly, we propose an improved Soft Actor-Critic-based client selection and model compression algorithm to solve the optimization problem, and design a Vickrey-Clarke-Groves-based payment rule to compensate for clients’ cost. Finally, extensive simulation experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method outperforms other benchmarks in terms of BS’s cost under various scenarios.

The coexistence of multiple stable equilibria in recurrent neural networks is an important dynamic characteristic for associative memory and other applications. In this paper, the existence and local Mittag-Leffler stability of multiple equilibria are investigated for a class of fractional-order recurrent neural networks with discontinuous and nonmonotonic activation functions. By using Brouwer $^{\prime }\text{s}$ fixed point theory, several conditions are established to ensure the existence of $5^{n}$ equilibria, in which all the components of $4^{n}$ equilibria are located in the continuous intervals of the activation functions. and some of the components of $5^{n}-4^{n}$ equilibria are located at some discontinuous points of the activation functions. The introduction of discontinuous activation functions makes the neural networks have more equilibria than those with continuous activation functions. Furthermore, some criteria are proposed to ensure local Mittag-Leffler stability of $3^{n}$ equilibria. The introduction of nonmonotonic activation functions makes the neural networks have more stable equilibria than those with monotonic activation functions. Two examples are given to illustrate the effectiveness of the results.

κ-barrier coverage is one of the critical issues in directional sensor networks. In actual application, the sensing distance becomes small with the increase of sensing angle, and the event sensing probability is constantly changing as the distance between node and event changes. But so far, there is no high efficient distributed barrier construction scheme based on a directional probabilistic sensing model. This paper first creates a directional probabilistic sensing model with changing angle, based on which four target locations of barrier member are created in a distributed manner. In addition, the energy efficiency ratio (EER) is investigated to minimize the energy consumption. Based on these two models, a distributed probabilistic barrier construction scheme based on EER is proposed, which commences the initiator of the barrier on the west end boundary and selects suitable relay sensors from left to right until the east end boundary is reached. The target locations and corresponding orientations of barrier member are determined only by its ancestor. When there are many candidate relay sensors, the one with the largest EER is the actual relay sensor. Simulation results indicate our method could form κ-barrier coverage with smaller energy consumption than other methods.