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.

In the past decades, Dynamic Internet of Things (D-IoT) networks have played a conspicuously more important role in many real-life areas, including disaster relief, environment monitoring, public safety, and so on, to rapidly collect information from the environment and help people to make the decision. Meanwhile, due to the widespread implementation of dynamic IoT networks, there exists an enormous demand on designing suitable models and efficient algorithms for fundamental operations in dynamic IoT networks, to achieve the high throughput and reliable low-latency communication demands in 6G networks. In this article, we first present a general dynamic model to comprehensively depict most of the dynamic phenomena in IoT networks. Then, based on the proposed dynamic model, a distributed scheduling algorithm is proposed to competitively optimize the Age-of-Information (AoI) problem in the context of a D-IoT network. We say our scheduling algorithm is competitive: the throughput of the base station approximates the optimal solution with constant competitive ratio; and, the latency for a packet received by the base station is only constant times larger than the optimal latency. Rigorous theoretical analysis and extensive simulations are presented to verify the high throughput and reliable low-latency communications in our proposed algorithm.

As one of the most powerful tools in game theory, double auction is widely utilized to tackle the spectrum allocation problem in a secondary market. In this paper, we propose a multi-unit double auction framework in which the conflict graph-based bidder group formation, the winner determination strategy, and the spectrum pricing are elaborately designed. Through an in-depth theoretical analysis, we prove that our auction scheme can achieve three critical properties including the individual rationality, the ex-post budget balance, and the truthfulness. Extensive simulation results validate that the proposed auction framework can significantly improve the user satisfaction degree.

In smart city services, information systems can provide efficient and effective support during an emergency, and an emergency management system can make use of any available infrastructure network such as the Internet-of-Things. However, ordinary communication infrastructures can be prone to disruptions or even failures during emergencies. Hence, it is necessary to present a fallback system in case of such failures. In this paper, we propose such a fallback design for emergency management that relies on short-range multi-hop wireless communications. Specifically, we model the crowd by a multi-hop ad hoc network consisting of nodes (i.e., civilians with smartphones or wearable devices) that are capable of short-range communications, and address the problem of how to “diffuse” the crowd in an efficient and distributed fashion. The problem is subdivided into crowd density computation and crowd diffusion. We treat the area as a grid which is divided into square cells. Crowd density computation is to compute the density of each cell, for which we present efficient distributed algorithms that compute the density of each grid cell exactly. With the computed densities, crowd diffusion is to design a load balancing strategy (to direct local movements of individual civilians) such that in a short time the nodes/civilians will become evenly distributed over the entire area. We present a distributed diffusion algorithm that has good performance. We conduct extensive simulations to evaluate the proposed algorithms, and the results corroborate our theoretical analyses.

Robustness, discrimination and security are essential characteristics of perceptual image hashing in cloud computing. In this paper, we propose two image hashing schemes based on block discrete wavelet transform (BDWT) to achieve these three characteristics. The basic scheme combines block discrete wavelet transform, singular value decomposition and statistics feature to extract the features of each overlapping image block. And chaotic cryptography is applied to encrypt these features. Furthermore, image correction is added to the basic scheme to realize a more comprehensive image hashing IC-BDWT, which can resist rotation attack. Meanwhile, we conduct experiments on 400 images from four public databases to analyze the proposed schemes. The receiver operating characteristic (ROC) curve of the experimental results demonstrates that the proposed hashing algorithms exhibit superior robustness and discrimination compared to the latest hashing algorithms under common content-preserving attacks. And the IC-BDWT hashing is far better than other algorithms in resisting rotation attack. Experimental results show that our schemes are secure and sensitive to secret keys.

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.