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.

A flexible endoscope introduces more dexterity to the image capturing in endoscopic surgery. However, manual control or automatic control based on instrument tracking does not handle the misorientation between the endoscopic video and the surgeon. We propose an automatic flexible endoscope control method that tracks the surgeon's head with respect to the object in the surgical scene. The robotic flexible endoscope is actuated so that it captures the surgical scene from the same perspective as the surgeon. The surgeon wears a head-mounted display to observe the endoscopic video. The frustum of the flexible endoscope is rendered as an augmented reality overlay to provide surgical guidance. We developed the prototype, FlexiVision, integrating a 6-DOF robotic flexible endoscope based on the da Vinci Research Kit and Microsoft HoloLens. We evaluated the proposed automatic control method via a lesion observation task, and evaluated the AR surgical guidance in a lesion targeting task. The multi-user study results demonstrated that, for both tasks, FlexiVision significantly reduced the completion time (by 59% and 58%), number of errors (by 75% and 95%) and subjective task load level. With FlexiVision, the flexible endoscope could act as the surgeon's eyes teleported into the abdominal cavity of the patient.