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This article focuses on the problem of scheduling the optimal paths of multiple mobile elements (e.g. robots, vehicles, etc.) to minimize the travel distance and balance the energy consumption and the data gathering latency in wireless sensor networks for smart cities. To partition the network for the multiple mobile elements and compute the trajectories of the multiple mobile elements, we utilize the sensor's communication range and construct a multiple mobile elements scheduling problem. A heuristic mobile data gathering approach is proposed to solve this problem, which includes the following three steps. The sensor nodes are preliminarily partitioned into four levels, and then the clusterheads are further partitioned, and the traveling tour is scheduled for each cluster. After the first two steps, all the sensor nodes are partitioned reasonably for the multiple mobile elements. In the last step, the traveling tour is scheduled for each cluster, and the meeting point of each clusterhead is determined. We compare the proposed heuristic mobile data gathering with the existing approaches. The results indicate that the travel distance and the data gathering latency are reduced significantly, which further validates that the communication range is beneficial to minimize the travel distance. [ABSTRACT FROM AUTHOR]

In the body sensor networks (BSN), sensor nodes transmit data to remote receiving terminal via sink node. During the process of data aggregation, interference and self-similar character of wireless business have significant influence on traffic queue performance of sink node. To balance traffic quality of service (QoS) requirement and system present available resource and decide optimum resource distribution project. Hence, a complex function of sink node cache length, distributed channel rate and packet loss probability, and queue delay is built in this paper. Based on the function, by combining multiple target optimizing method and considering joint constraint of queue delay and packet loss probability, we proposed an admission control method based on traffic aggregation QoS constraint. In a BSN consisting of a large amount of sensor nodes, by admission control of sink node, reasonable acceptance of new nodes, and distribution sink node resource, the method we proposed can meet needs of important parameters in WSN design. [ABSTRACT FROM AUTHOR]

As a promising technology in signal detection, the chaotic detection system can significantly improve the accuracy of weak signal detection in strong background noise. It benefits from its characteristics of the sensitivity to the initial condition and the immunity to the Additive White Gaussian Noise. However, the fundamental challenges of the existing chaotic detection system are the sensitivity to narrow-band noise and the influences of multi-target detection with adjacent frequency, which bring great difficulties in the real application. To address these problems, in this article, we focus on the weak multi-target detection with adjacent frequency under the narrow-band noise, and a novel chaotic detection system that integrates the detection algorithm based on period-chaos duration ratio is proposed. In order to enhance the robustness to narrow-band noise, the Melnikov method is used to analyze the Duffing difference system. To realize the detection of weak multi-target with adjacent frequency, we proposed the detection system using the rule named general critical state. Furthermore, simulation results corroborate that the proposed system based on period-chaos duration ratio can achieve satisfactory performance in terms of the weak multi-target detection under narrow-band noise, and it is well investigated by extensive simulation for testing its effectiveness. [ABSTRACT FROM AUTHOR]