In this paper, we proposed a novel method to solve the coverage control problem of sensor networks in Internet of Things (IoT). The coverage control is an important index to evaluate the performance of network services. Ensuring the quality of network services, it is mainly to maximize the coverage of the network and minimize the energy consumption at the same time for the purpose of extending the network life cycle effect. Because of the overlay redundancy, it adopts the sleeping scheduling mechanism of nodes. The optimal solution is obtained after utilizing the coverage rate and the node sleep rate as the optimization objective function. Particle swarm optimization (PSO) is a group intelligent optimization algorithm. In practical applications, PSO often convergence in the local optimal solution prematurely. In order to balance the global search ability and convergence speed of PSO, We have improved the PSO based on the resampling technique, named resampled particle swarm optimization (RPSO). The RPSO can not only maintain the diversity of the population, which can avoid premature convergence of the algorithm to some extent, but also ensure that each particle is active, reducing the calculation of redundancy, thereby improving the efficiency of the algorithm. The experimental results show that the RPSO can deal with complex multi-peak optimization problem efficiently and reliably. Then the RPSO is used to solve the coverage control problem of sensor networks in IoT and has a great performance.

With the increasing need for public security and intelligent life and the development of Internet of Things (IoT), the structure and application of vision sensor network are becoming more and more complex. It is no longer a system with simple static monitoring, but a complex system that can be used for intelligent processing, such as target localization, identification, tracking and so on. In order to accomplish various tasks efficiently, it is important to determine the deployment plan of camera network in advance. Many researches discretize the optimal camera placement problem into a binary integer programming (BIP) problem, which is NP-hard, and put forward some approximate solutions including greedy heuristics, semi-definite programming, simulated annealing, etc. In practice, however, camera parameters include both continuous values (location and orientation) and discrete values (camera type). To get a much more accurate result, we do not discretize the continuous camera parameters any more, on the contrary, we handle the continuous values in continuous domain directly. Meanwhile, a Latin Hypercube based Resampling Particle Swarm Optimization (LH-RPSO) algorithm is proposed to effectively solve the problem. To validate the proposed algorithm, we compared it with standard Particle Swarm Optimization (PSO) and Resampling Particle Swarm Optimization (RPSO). Simulation results for an outdoor planar regions illustrated the efficiency of the proposed algorithm.

In the field of mobile computing, after the placement of sensor nodes, energy efficiency of data transmission in the sensor network is becoming a critical issue as the sensor nodes are battery-operated devices. Most of the proposed protocols implement clustering based methods in order to balance the energy consumption among the sensor nodes through data aggregation. In this paper, a new method named a two-stage RPSO-ACS based protocol is proposed to improve the energy efficiency and network operation quality in the data transmission process, prolonging the lifespan of the network. Firstly, in the process of cluster head (CH) selection, the resampling particle swarm optimization (RPSO) is introduced, which shows better precision and efficiency than particle swarm optimization (PSO) and genetic algorithm (GA). Secondly, after the selection of cluster head, instead of the single-hop transmission from the cluster heads to base station (BS), which is used in many papers, we adopt multi-hop transmission to save energy. The ant colony optimization (ACO) is used to select the relay nodes between the cluster heads and the base station for the purpose of minimizing the energy consumption in the transmission process. In addition, we determine the reasonable threshold based on the energy consumption curve of the transmission. In this way, the nodes within a radius of the threshold from BS communicate with BS directly to save energy. Experimental results show that the proposed methods achieve the goal of improving the energy efficiency and operation quality of the sensor network in comparison to traditional methods.

Sensor placement and charging in proximity service(ProSe) are becoming critical issues. In this paper, novel methods are proposed to address the coverage optimization problem and charging problem of camera networks with mobile nodes. Because the sensing angle of camera is limited, the placement of camera network is more complicated compared to omnidirectional sensor network. Aiming at finding the best positions and working angles of all camera nodes in the coverage optimization problem, we propose a novel RPSO based process. Firstly, the resampling particle swarm optimization (RPSO) is introduced, which has better precision and efficiency than particle swarm optimization (PSO) and genetic algorithm (GA). Secondly, because of the huge number of variables in the problem, we propose a hierarchical strategy, which divides the whole optimization process into several sub-processes in consideration with overlay redundancy and coverage area of all camera nodes, reducing the variables at each step. In this way, the precision of optimization results could be improved significantly. After deploying the camera network, we consider charging it to extend its lifetime. Fuzzy C-means (FCM), an fuzzy clustering algorithm, is used to classify the sensor nodes according to their positions. Then, a charging station is set in each cluster, the position of which is determined based on the (RPSO) to minimize the distance from it to all of the sensor nodes in the cluster. So, the charging efficiency is improved than before. Experimental results show that the proposed methods achieve the goal of maximizing the coverage rate and improving the charging efficiency of camera network in comparison to traditional methods.