Energy efficiency is a key performance metric for ultra-dense wireless sensor networks. In this letter, an unsupervised learning approach for topology control is proposed to prolong the lifetime of ultra-dense wireless sensor networks by balancing energy consumption. By encoding sensors as genes according to the network clusters, the proposed genetic-based algorithm learns an optimum chromosome to construct a close-to-optimum network topology using unsupervised learning in probability. Moreover, it schedules some of the cluster members to sleep to conserve the node energy using geographically adaptive fidelity. Simulation results demonstrate the superior performance of the proposed algorithm by improving energy efficiency in comparison with state-of-the-art algorithms at an acceptable computational complexity.

Connected and Autonomous Vehicles (CAVs) combine technologies of autonomous vehicles (AVs) and connected vehicles (CVs) to develop quicker, more reliable, and safer traffic. Artificial Intelligence (AI)-based CAV solutions play significant roles in sustainable cities. The convergence imposes stringent security requirements for CAV safety and reliability. In practice, vehicles are developed with increased automation and connectivity. Increased automation increases the reliance on the sensor-based technologies and decreases the reliance on the driver; increased connectivity increases the exposures of vehicles’ vulnerability and increases the risk for an adversary to implement a cyber-attack. Much work has been dedicated to identifying the security vulnerabilities and recommending mitigation techniques associated with different sensors, controllers, and connection mechanisms, respectively. However, there is an absence of comprehensive and in-depth studies to identify how the cyber-attacks exploit the vehicles’ vulnerabilities to negatively impact the performance and operations of CAVs. In this survey, we set out to thoroughly review the security issues introduced by AV and CV technologies, analyze how the cyber-attacks impact the performance of CAVs, and summarize the solutions correspondingly. The impact of cyber-attacks on the performance of CAVs is elaborated from both viewpoints of intra-vehicle systems and inter-vehicle systems. We pointed out that securing the perception and operations of CAVs would be the top requirement to enable CAVs to be applied safely and reliably in practice. Additionally, we suggested to utilize cloud and new AI methods to defend against smart cyber-attacks on CAVs.

Different from conventional wireless sensor networks (WSNs), ultra-reliable and low-latency WSNs (uRLLWSNs), being an important application of 5G networks, must meet more stringent performance requirements. In this paper, we propose a novel algorithm to improve uRLLWSNs performance by applying machine learning techniques and genetic algorithms. Using the K-means clustering algorithm to construct a 2-tier network topology, the proposed algorithm designs the fetal dataset, denoted by the population, and develops a clustering method of energy conversion to prevent overloaded cluster heads. A multi-objective optimization model is formulated to simultaneously satisfy multiple optimization objectives including the longest network lifetime and the highest network connectivity and reliability. Under this model, the principal component analysis algorithm is adopted to eliminate the various optimization objectives’ dependencies and rank their importance levels. Considering the NP-hardness of wireless network scheduling, the genetic algorithm is used to identify the optimal chromosome for designing a near-optimal clustering network topology. Moreover, we prove the convergence of the proposed algorithm both locally and globally. Simulation results are presented to demonstrate the viability of the proposed algorithm compared to state-of-the-art algorithms at an acceptable computational complexity.

In large field of view for open country, the real-time detection and identification of moving objects with high accuracy is a very challenging work due to the excessive amount of data. This paper proposes a novel framework that consists of a coarse-grained detection as well as a fine-grained detection. To solve the problem of noise-induced object fracture during the coarse-grained detection process, we present a low-complexity connected region detection algorithm to extract moving regions. Furthermore, in the finegrained detection, Deep Convolution Neural Networks are leveraged to detect more precise coordinates and identify the category of objects. To the best of our knowledge, this is the first work that proposes a coarse-tofine grained framework to detect moving objects on high-resolution scenes. Experimental results show that the proposed framework can robustly work on the high resolution video frames (1920*1080p) with complex situations more fastly and accurately over existing methods.

As a crucial issue for routing algorithms in wireless sensor networks (WSNs), balancing energy consumption is a vital strategy to extend the network lifetime. Considering the node energy property or the node distance property, some conventional WSNs’ routing algorithms unevenly distribute traffic over the nodes, which are not optimal to maximize the network lifetime. In this letter, a distributed joint optimization routing algorithm based on the analytic hierarchy process (DORAHP) is proposed to extend WSNs lifetime. The analytic hierarchy process is applied at the transmitter side to select the next hop node by defining three key decision criteria including the residual energy of the node, the distance from the node to the base station, and the degree of the node, which aims to extend WSNs lifetime through balancing energy consumption. At length, extensive simulation experiments for DORAHP are performed and compared with the LEACH, HEED, EERC, and GASONeC algorithms. The results indicate that DORAHP has superior performance on balancing energy consumption and on extending WSNs lifetime.

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum–minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.