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Data collection is one of the most important operations in wireless sensor networks. Currently, many researches focus on using a connected dominating set to construct a virtual backbone for data collection in WSNs. Most researchers concentrate on how to construct a minimum connected dominating set because a small virtual backbone incurs less maintenance. Unfortunately, computing a minimum size CDS is NP-hard, and the minimum connected dominating sets may result in unbalanced energy consumption among nodes. In this paper, we investigate the problem of constructing an energy-balanced CDS to effectively preserve the energy of nodes in order to extend the network lifetime in data collection. An energy-balanced connected dominating set scheme named DGA-EBCDS is proposed, and each node in the network can effectively transmit its data to the sink through the virtual backbone. When constructing the virtual backbone in DGA-EBCDS, we prioritize selecting those nodes with higher energy and larger degree. This method makes the energy consumption among nodes more balanced. Furthermore, the routing decision in DGA-EBCDS considers both the path length and the remaining energy of nodes in the path; it further prolongs the lifetime of nodes in the backbone. Our conclusions are verified by extensive simulation results.

With the emergence of edge computing, a large number of devices such as sensor nodes have been deployed in the edge network to sense and process data. However, how to provide real-time on-demand energy for these edge devices is a new challenge issue of edge networks. In real-world applications of edge computing, sensor nodes usually have different task burdens due to the environmental impact, which results in a dynamic change of the energy consumption rate at different nodes. Therefore, the traditional periodical charging mode cannot meet the nodes charging demand that have dynamic energy consumption. In this paper, we propose a real-time on-demand charging scheduling scheme (RCSS) under the condition of limited mobile charger capacity. In the process of building the charging path, RCSS adequately considers the dynamic energy consumption of different node, and puts forward the next node selection algorithm. At the same time, a method to determine the feasibility of charging circuit is also proposed to ensure the charging efficiency. During the charging process, RCSS is based on adaptive charging threshold to reduce node mortality. Compared with existing approaches, the proposed RCSS achieves better performance in the number of survival nodes, the average service time and charging efficiency.

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Recent studies reveal that great benefit can be achieved by employing mobile collectors to gather data in wireless sensor networks. Since the mobile collector can traverse the transmission range of each sensor, the energy of nodes may be saved near maximally. However, for directly receiving data packet from every node, the length of mobile collector route should be very long. Hence it may significantly increase the data gathering latency. To solve this problem, several algorithms have been proposed. One of them called BRH-MDG found that data gathering latency can be effectively shortened by performing proper local aggregation via multihop transmissions and then uploading the aggregated data to the mobile collector. But, the BRH-MDG algorithm did not carefully analyze and optimize the energy consumption of the entire network. In this paper, we propose a mathematical model for the energy consumption of the LNs and present a new algorithm called EEBRHM. The simulation results show that under the premise of bounded relay hop, compared with BRH-MDG, EEBRHM can prolong the networks lifetime by 730%.