Federated Learning (FL) effectively preserves data privacy but faces challenges such as the significant communication overhead from frequent model updates between user equipment (UE) and mobile edge computing servers (MECS), performance degradation under non-IID data distributions, and UE dropouts due to resource constraints. Since MECSs cannot mandate participation, effective incentives are crucial to secure sufficient training data. This paper proposes an incentive-driven secure Clustered FL (CFL) framework supporting operations across base stations (BSs). The framework incorporates dual privacy protection through end-to-end encryption for secure cross-BS model exchange and differential privacy during aggregation. To accurately evaluate contributions, we design a dual-component metric combining model distance with local accuracy. Furthermore, we formulate the incentive problem as a two-layer Stackelberg game that integrates virtual rewards with tangible bandwidth allocation, incorporating amplified rewards for cross-BS participation. In this game, MECSs act as leaders aiming to maximize their long-term utility, defined as the weighted improvement in global model accuracy minus incentive costs, while ensuring sustained UE engagement. UEs, as followers, maximize their net utility, calculated as incentives derived from local accuracy, model similarity, and cross-BS reward minus their computational and energy costs, under individual resource constraints. The optimal UE strategy is derived using the bisection method, and a deep reinforcement learning-based algorithm is developed for MECS decision-making under incomplete information. Experimental results demonstrate that our approach effectively screens out low-contributing UEs that hinder convergence while incentivizing high-performing participants, leading to efficient and reliable model training.

The popularity of maritime activities not only exacerbates the carbon footprint (CF) but also places higher demands on Maritime Internet of Things (MIoTs) to support heterogeneous MIoT devices (MIoTDs) with different prioritized tasks. High-priority tasks can be processed cooperatively via local computation, offloading to nearby MIoTDs (helpers), or offloading to edge servers to ensure their timely and successful completion. Due to the differences in energy availability and rechargeability, MIoTDs exhibit distinct energy states, impacting their operational behaviors. We propose the Energy Anxiety Level (EAL) to quantify these states: Higher EAL tends to lead to increased packet dropping and earlier shutdown. Although low-EAL MIoTDs seem preferable as helpers, their scarce residual computational resources after local task completion may cause offloaded high-priority tasks to drop or time out. Therefore, helper selection should jointly consider candidate MIoTDs’ EALs and loads to evaluate their unsuitability. This paper addresses the problem of differentiated task offloading and resource allocation in MIoTs by formulating it as a mixed integer nonlinear programming model. The objective is to minimize system-wide carbon footprint (CF), packet loss, helper unsuitability risk, and high-priority task latency. To solve this complex problem, we decompose it into two subproblems. We then design algorithms to determine optimal offloading patterns, task partitioning factors, MIoTD transmission powers, and computation resource allocation for MIoTDs and edge servers. Simulation results demonstrate that our proposal outperforms benchmarks in reducing CF and EAL, lowering high-priority task latency, and improving task completion ratio.

Mobile edge computing (MEC) powered by renewable energy, is promising to provide green computing for the Internet of Things (IoT). However, the unpredictable renewable energy and computing demands usually cause a mismatch between system requirements and energy supply, resulting in wasted surplus energy or energy supply shortage. Hence, it is crucial to improve energy efficiency and system performance, that is, “make the best use of generated energy” and “make the best use of system’s talents” simultaneously. In this article, we focus on some islands far from the mainland, with growing computation requirements for environmental monitoring and navigation safety, and propose a device-to-device (D2D) collaboration-based software-defined network-MEC framework in Island IoT employing tidal energy. Following that, we formulate a multiobjective energy scheduling system performance association (MESPA) problem to minimize the long-term average task execution loss (TEL), including energy consumption per bit executed, overall execution latency, and energy waste, caused by underutilization of tidal energy, with the constraints of energy queue stability, peak transmission power, and central process unit-cycle frequency. To address this challenging problem, we propose a Lyapunov-based multidimensional resource allocation and computation offloading (LMDRACO) algorithm and transform the original problem into several individual subproblems in each time slot. These subproblems are then solved using convex decomposition and submodular methods. Theoretical research shows that the LMDRACO algorithm can achieve a [$\mathcal {O}$(1/V),$\mathcal {O}$(V)] tradeoff between TEL and energy queue length. Numerical results show that the proposed algorithm significantly improves both system performance and energy efficiency compared to baseline schemes.

Internet of Vehicle (IoV) is an important paradigm to realize the intelligent transportation system. However, with the increasing number of vehicular applications, how to satisfy the ubiquitous requirements of communication and computation is challenging. Fog computing provides the real-time transportation services to local users timely through close-proximity data processing, rather than routing data to a remote central data center in the cloud. More importantly, the fog computing will facilitate the autonomous driving (AD) revolutionarily. This paper investigates the problem of optimal deployment and dimensioning (ODD) of fog computing-based IoV infrastructure for AD. For the ODD problem, we present two diverse architecture modes, i.e., the coupling mode (CRF) and the decoupling mode (DRF), and formulate the ODD problem into two integer linear programming formulations with the objective of minimizing the deployment cost. A heuristic algorithm is also proposed to achieve the suboptimal deployment solution for large-scale fog computing-based IoV. Numerical results show that the DRF is more cost-effective and flexible than the CRF for deployment in practice.

C-RAN, i.e., Cloud-Radio Access Network, is a new cellular network architecture for the future mobile network infrastructure. It is proposed to provide a possible solution for operators to construct mobile access networks in a cost-effective manner. Different from traditional cellular network architectures that are built with many stand-alone base stations (BSs), C-RAN is now viewed as an architecture evolution based on distributed BSs. C-RAN has drawn extensive attentions from the operators due to its “4C” characteristics, i.e., Clean, Centralized processing, Collaborative radio, and real-time Cloud radio access network. In this paper, we focus on the Infrastructure Deployment and Layout Planning (IDLP) problem under the C-RAN architecture. The IDLP problem is formulated as a generic integer linear programming (ILP) model which can optimally: (i) minimize the network deploying cost, (ii) identify the locations of Remote Radio Units (RRUs) and Wavelength Division Multiplexers (WDMs), (iii) identify the association relations between RRUs and WDMs, (iv) satisfy the mobile coverage requirements so as to allow the mobile user access through RRU. We solve the model using Gurobi, which is the newest ILP solver by now. A series of case studies are conducted to validate the optimization framework and demonstrate the solvability and scalability of the ILP model. Computational results show the significant performance benefits of CoMP in C-RAN in terms of lower cost, larger capacity and higher reliability.

A unified approach to the steady-state mean square error (MSE) and tracking performance analyses for real and complex adaptive filtes with error nonlinearities is developed. Some general clofied-form analytical expressions for the steady-state performances are given. Our analyses are based on Taylor series expansion and and so-called complex Brandwood-form series expansion (BSE). Under these general explicit expressions, some well-known adaptive filters can be viewed as special cases. In addition, the closed-form analytical expressions for the steady-state performance for real and complex least-mean p-power (LMP) algorithm with different choices of parameter p are also given. A mass of simulations show the accuration of our analyses.

Based on series expansion, the steady-state mean-square error (MSE) analysis for real and complex least mean$p$-order (LMP) algorithm is developed, and some closed-form analytical expressions for the steady-state MSE and the corresponding restrictive conditions for step-size are given. Moreover, in Gaussian noise environments, its steady-state performance is also investigated. The analyses for some well-known algorithms and the computer simulation validate the accuracy of the results.

In hybrid vehicular ad hoc networks (VANETs), roadside units (RSUs) are more powerful and robust than onboard units (OBUs) equipped on vehicles, which can exchange information and synchronize with other RSUs quickly. Due to the fast mobility of vehicles, wireless links in VANETs are particularly vulnerable to failure. Therefore, it is necessary to provide redundancy in terms of provision multiple link-disjoint paths between source and destination. In addition, VANETs support multiple applications, such as road safety applications and commercial applications, which may require different reliabilities. Accordingly, it is important to discover different number of link-disjoint paths for different applications. In this paper, we propose two notions, virtual equivalent node and differentiated reliable path, and develop an on-demand differentiated reliable routing (DRR) protocol for hybrid VANETs. Extensive simulations show that DRR is beneficial to reduce blocking probability and to maintain lower control overhead while providing differentiated services.

Without appealing to the circularity assumption of the a priori estimation error in , two closed-form analytical expressions for the steady-state excess mean-square error (EMSE) in a noise-free environment are derived again based on Taylor series expansion for real and complex Bussgang algorithms, respectively; and the restrictive conditions of these two expressions for the steady-state EMSE are also given.

Connection reliability is one important service level agreement (SLA) parameter for a customer and should be carefully considered in survivable WDM networks. A sound scheme should guarantee customers' reliability and simultaneously benefit a service provider in resource efficiency. Under the SLA constraints and the assumption of shared risk link group (SRLG) failures, a novel dynamic differentiated shared path-protection algorithm (DDSP) in WDM mesh networks is proposed. Based on the basic ideas of the K-shortest path algorithm and partial SRLGdisjoint protection, DDSP can provide differentiated services for customers according to their SLA parameters while optimizing resource utilization. Simulation results show that DDSP not only can efficiently guarantee the specific SLA requirements of customers, but also can achieve significant performance gain and lead to remarkable reduction in blocking probability.