Advancements in the Internet of Medical Things (IoMT) have made remote patient monitoring increasingly viable. However, challenges persist in safeguarding sensitive data, optimizing resources, and addressing the energy constraints of patient devices. This paper presents a health monitoring framework integrating Mobile Edge Computing (MEC) and sixth-generation (6G) technologies, structured into internal Medical Body Area Networks (int-MBANs) and external communications beyond MBANs (ext-MBANs). For int-MBANs, the proposed OptiBand algorithm optimizes energy consumption, extends device standby time, and considers message timeliness and medical criticality. A key innovation of OptiBand is its incorporation of patient’s device standby time into the resource allocation strategy to address real-world patient needs. For ext-MBANs, the DynaMEC algorithm dynamically balances energy efficiency, privacy protection, latency, and fairness, even under varying patient scales. A latency-aware scheduling mechanism also be introduced to guarantee timely completion of emergency tasks. Theoretical analysis and experimental results confirm the feasibility, convergence, and optimality of both algorithms. These characteristics and advantages of the proposed system make remote patient monitoring through IoMT more feasible and effective.

Edge computing is a computational paradigm that brings resources closer to the network edge, such as base stations or gateways, in order to provide quick and efficient computing services for mobile devices while relieving pressure on the core network. However, the current computing power of edge servers are insufficient to handle the high number of tasks generated by access devices. Additionally, some mobile devices may not fully utilize their computing resources. To maximize the use of resources, we propose a novel edge computing system architecture consisting of a resource-constrained edge server and three computing groups. Tasks from each group can be offloaded to either the edge server or the corresponding computing group for execution. We focus on optimizing the computation offloading of devices to minimize the maximum overall task processing latency in the system. This problem is proved to be NP-hard. To solve it, we propose a DQN-based resource utilization task scheduling (DQNRTS) algorithm that has two desirable characteristics: 1) it effectively utilizes the computing resources in the system and 2) it uses deep reinforcement learning to make intelligent scheduling decisions based on system state information. Experimental results demonstrate that the DQNRTS algorithm is capable of reducing the processing latency of the system by converging to optimal solutions.

The Linux kernel is regularly updated to enhance security, improve performance, and introduce new functionalities. Traditional updating methods typically require rebooting, leading to service disruptions and potential data loss. Live-patching technology dynamically updates the kernel modules without rebooting, ensuring continuous service availability. However, this technique has its drawbacks. Since live-patching alters the original structure of data types, it can no longer utilize base offsets to access the members, imposing considerable overheads. This paper proposes LPAH (Live Patching with Alignment Holes), a live patching system that leverages the fragmented space generated by compile-time alignment for data types, to enable effective live patching updates for security vulnerability fixes, feature enhancements, and user-defined patching tasks. LPAH capitalizes on the relationship between these alignment holes and data objects. This approach ensures efficient access to extended data members while preserving the original data's integrity. This approach allows other functions to remain unaffected by updates and replacements through explicit type casts. Extensive experimental results show that LPAH offers valid and robust live patching for multiple real vulnerabilities in the Linux kernel, without degrading performance. Our method provides an efficient way to install security patches in the Linux kernel, and thus reenforces kernel security.

Training machine learning models in a decentralized way has attracted tremendous attention on intelligent Internet of Vehicles (IIoV). However, it is highly dynamic and asymmetric for the connections between vehicles in IIoV due to the mobility of vehicles and the complex communication environment, which poses great challenges on designing efficient distributed learning algorithms. To address this problem, we focus on the basic stochastic gradient descent (SGD) algorithm and propose a decentralized parallel SGD algorithm (DPSGD-WB) for the complex IIoV. The algorithm is based on weight-balancing to overcome the difficulty caused by the dynamic and asymmetric connectivity in IIoV. With rigorous analysis, we show that DPSGD-WB converges on the optimal rate of$O(1/\sqrt {Kn})$, where$n$is the number of vehicle terminals and$K$is the number of iterations. To the best of our knowledge, our proposed algorithm is the first known decentralized parallel SGD algorithm that can be implemented in asymmetric and dynamic intelligent IoV systems. Finally, extensive experiments demonstrate the efficacy of our algorithm.

Currently, communications in the vehicular ad hoc network (VANET) can be established via both Dedicated Short Range Communication (DSRC) and mobile cellular networks. To make use of existing Long Term Evolution (LTE) network in data transmissions, many methods are proposed to manage VANETs. Grouping the vehicles into clusters and organizing the network by clusters are one of the most universal and most efficacious ways. Since the high mobility of vehicles makes VANETs different from other mobile ad hoc networks (MANETs), the previous cluster-based methods for MANETs may have trouble for VANETs. In this paper, we introduce a center-based clustering algorithm to help self-organized VANETs forming stable clusters and decrease the status change frequency of vehicles on highways and two metrics. A novel Cluster Head (CH) selection algorithm is also proposed to reduce the impact of vehicle motion differences. We also introduce two metrics to improve the security of VANETs. A simulation is conducted to compare our mechanism to some other mechanisms. The results show that our mechanism obtains high stability and lower packet loss rate.