The integration of Mobile Edge Computing (MEC) and microservice architecture drives the implementation of the sustainable Internet of Vehicles (IoV). The microservice architecture enables the decomposition of a service into multiple independent, fine-grained microservices working independently. With MEC, microservices can be placed on Edge Service Providers (ESPs) dynamically, responding quickly and reducing service latency and resource consumption. However, the burgeoning of IoV leads to high computation and resource overheads, making service resource requirements an imminent issue. What’s more, due to the limited computation power of ESPs, they can only host a few services. Therefore, ESPs should judiciously decide which services to host. In this paper, we propose a Microservice-oriented Service Placement (MOSP) mechanism for MEC-enabled IoV to shorten service latency, reduce high resource consumption levels and guarantee long-term sustainability. Specifically, we formulate the service placement as an integer linear programming program, where service placement decisions are collaboratively optimized among ESPs, aiming to address spatial demand coupling, service heterogeneity, and decentralized coordination in MEC systems. MOSP comprises an upper layer to map the service requests to ESPs and a lower layer to adjust the service placement of ESPs. Evaluation results show that the microservice-oriented service deployment mechanism offers dramatic improvements in terms of resource savings, latency reduction, and service speed.

Grey wolf optimizer (GWO) is an efficient swarm intelligence algorithm for kinds of optimization problems. However, GWO tends to be trapped in local optimum when solving large-scale problems. Social hierarchy is one of the main characteristics of GWO which affect the searching efficiency. Thus, an improved algorithm called hierarchy strengthened GWO (HSGWO) is proposed in this paper. Firstly, the pack of wolves is roughly divided into two categories: dominant wolves and omega wolves. Secondly, the enhanced elite learning strategy is performed for dominant wolves to prevent the misguidance of low-ranking wolves and improve the collective efficiency. Then, the hybrid GWO and differential evolution (DE) strategy is executed for omega wolves to avoid falling into local optimum. In addition, a new hybrid one-dimensional and total-dimensional selection strategy is designed for omega wolves to balance the exploration and the exploitation during optimization. Finally, a perturbed operator is used to maintain the diversity of the population and further improve the exploration. To make a complete evaluation, the proposed HSGWO is first compared with six representative GWO variants for 50-dimensional problems based on CEC2014 benchmarks. The scalability of HSGWO is further tested by comparing it with eight state-of-the-art non-GWO algorithms for large-scale optimization problems with 100 decision variables. In addition, feature selection problem is used for testing the effectiveness of HSGWO on real-world applications. Experimental results demonstrated that the proposed algorithm outperforms other algorithms in terms of solution quality and convergence rate in most of the experiments.

Thermal interface material (TIMs) pads/sheets with both high elasticity and low thermal resistance are indispensable components for thermal management. In this paper, we propose a facile strategy for the fabrication of highly thermal conductive liquid metal (LM) nano/micro-channels embedded in an elastomeric matrix (polydimethylsiloxane, PDMS). The isolated LM micro-droplets were reshaped and became thermally connected upon applying stress to form conductive LM nano-channels, while improving the thermal conductivity (κ) up to 8.3 W m⁻¹ K⁻¹, which is greater than that of most commercial thermal silicone pads (<6 W m⁻¹ K⁻¹). In addition, a series of experiments on a commercial smartphone were performed to evaluate the heat dissipation performance. The results proved that LM thermal pads could not only reduce the temperature of the CPU and the back cover, but could also save energy and enhance the battery running time by as much as 31%.

The flow fluctuations in the highly dynamic Internet of Vehicles (IoV) make the IoV difficult to provide reliable and scalable wireless network services for the emerging applications in the 5 G and beyond era. The software-defined networks (SDN) could feasibly manage and optimize the network according to the network load. Controller placement is a critical problem in SDN to achieve its robustness and flexibility with the changes of network status. Motivated by the advantages of SDN and Mobile-edge computing (MEC), this paper aims at enhancing the performance of IoV with the assistance of these two. Specifically, we consider a three-layer hierarchical SDN control plane for the IoV, where the SDN controllers are placed at the edge of networks. Under this framework, we investigate a multi-objective optimization problem on controller placement problem including delay, load balancing, and path reliability. To efficiently solve the formulated NP-hard problems, we develop an algorithm based on multi-agent deep Q-learning networks (MADQN) because of its advantages for large-scale combinatorial optimization. At last, we use multi-process technology to accelerate the operation of the algorithm, so as to complete the algorithm iteration faster. Numerical results show that the proposed methods achieve better performances than three baselines.

The Internet of Things (IoT) mainly consists of a large number of Internet-connected devices. The proliferation of untrusted third-party IoT applications has led to an increase in IoT-based malware attacks. In addition, it is infeasible for the IoT devices to support the sophisticated detection systems due to the restricted resources. Edge computing is considered to be promising. It provides solutions to the data security and privacy leakage brought by untrusted third-party IoT applications. In this article, an intelligent trusted and secure edge computing (ITEC) system is proposed for IoT malware detection. In this system, a signature-based preidentification mechanism is built for matching and identifying the malicious behaviors of untrusted third-party IoT applications. A delay strategy is then embedded into the risk detection engine in order to “buy time” for threat analysis and rate-limit the impact of suspicious third-party IoT applications in the system. We conduct extensive experiments to verify the effectiveness of the ITEC system and show that we can achieve accuracies of up to 98.52%.