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This paper proposes a multi-objective queuing charging strategy for electric vehicles (EVs) based on metrics of public interest. It combines common charging modes, such as random charging mode, tariff-guided mode and stop-and-charge mode. It introduces the problem of queuing charging for EVs by considering the realistic imbalances of vehicle-pile ratios in these common modes. A travel model and a charging model were developed in this study. Experiments prove that the proposed strategy has the highest comprehensive evaluation index, achieves the aim of low charging cost and high travel rate and considers the queuing problem, which is unavoidable in reality. It improves the convenience of life and reduces the charging cost. The proposed strategy smoothens the EV charging load curve, largely reducing the burden of charging load fluctuations on the grid and achieving a win-win situation for both supply and demand.

In recent years, evolutionary algorithms based on swarm intelligence have drawn much attention from researchers. This kind of artificial intelligent algorithms can be utilized for various applications, including the ones of big data information processing in nowadays modern world with heterogeneous sensor and IoT systems. Differential evolution (DE) algorithm is one of the important algorithms in the field of optimization because of its powerful and simple characteristics. The DE has excellent development performance and can approach global optimal solution quickly. At the same time, it is also easy to get into local optimal, so it could converge prematurely. In the view of these shortcomings, this article focuses on the improvement of the algorithm of DE and proposes an adaptive dimension differential evolution (ADDE) algorithm that can adapt to dimension updating properly and balance the search and the development better. In addition, this article uses the elitism to improve the location update strategy to improve the efficiency and accuracy of the search. In order to verify the performance of the new ADDE, this study carried out experiments with other famous algorithms on the CEC2014 test suite. The comparison results show that the ADDE is more competitive.

Target tracking is one of the important applications of wireless sensor networks. For a visual sensor network, the visual sensors have particular characteristics such as directional sensing, limited angle of view, and line-of-sight view. Target tracking with visual sensors is different from that with scalar sensors. Moreover, the quality of sensed visual data can be much important in many applications. In this paper, the concept of Voronoi diagram is utilized for target tracking in visual sensor networks. The structure of Voronoi cells is suitable for the design of a distributed/localized algorithm and it exists a property of bisector for dividing a distance line or region. This paper proposed a Voronoi-based distributed sensor handover protocol for visual sensor networks. The simulation result shows the benefits of our proposed approach in terms of target-detected latency, target-tracked ratio, and average target distance, which indicates that the quality of target tracking service can be improved with the proposed approach for visual sensor networks.

An efficient wireless sensor network (WSN) should maintain full sensing coverage and topology connectivity within its sensing field. Once holes occur due to failed sensors, the functionality and performance of the WSN will be affected. In this work, the proposed hole healing strategy aims to shorten the delivery hop count by a dynamic hole healing process in order to improve the data delivery time while maintaining the coverage ratio and topology connectivity. The criteria used to determine which hole should have the highest priority for healing in the next round include the weighted distance, angle magnitude, and depth of the hole. This study proposes a mobile robot, operating within the WSN, which carries redundant sensors to patch the holes by an optimum healing path. This path is determined based on the proposed EDPS (equally divided path selection) algorithm. Simulation results show the superiority of the proposed hole healing scheme over other general methods.

A directional sensor network is different from conventional wireless sensor networks. It uses directional sensors instead of omnidirectional ones in the network for different applications, and the effective sensing range is characterized by directionality and size-specific sensing angle. Therefore, conditions of directional sensor networks are dissimilar to those of generic wireless sensor networks for researches, especially on the sensing coverage. This study proposed a distributed approach to enhance the overall field coverage by utilizing mobile and direction-rotatable sensors in a directional sensor network. The algorithm makes sensors self-redeploy to the new location and new direction without global information by utilizing the features of geometrical Voronoi cells. Simulations were used to evaluate and prove the effectiveness of the proposed algorithm. The results show that the approach contributes to significant field coverage improvement in directional sensor networks.