Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks

Maximize Lifetime of Wireless Rechargeable Sensor Networks with Mobile Energy-Limited Charging Device

Mobile charging devices (MCDs) have been regarded as a promising way to solve the energy shortage of wireless sensor networks. Due to ignoring some important factors, such as redundant sensor nodes, there is still room to improve network lifetimes. We propose a charging strategy for wireless sensor networks with one energy-limited MCD. To give the best support for sensor nodes which need charging the most, an algorithm is proposed to find the minimum sensor nodes which keep the coverage and connectivity of the network and have the least energy requirements. Then, the goal of maximizing network lifetime is changed into how to utilize the limited energy of the MCD to guarantee the minimum sensor nodes work as long as possible. If the MCD has enough energy for all sensor nodes, the charging algorithm is designed to minimize the outage time of the network and maximize charging efficiency. Otherwise, if the energy capacity is larger than the least energy requirement, the charging target minimizes the outage time of the minimum sensor node; otherwise the charging problem becomes maximizing the lifetime of minimum sensor nodes, which has lower complexity. The results of simulation experiments confirm that our scheme prolongs network lifetime and improves charging efficiency.

Research on energy saving algorithm of field animal monitoring based on cluster sensor network1

Monitoring the diversity of wild animals is a core part of the research and protection of wild animals. Due to the harsh outdoor environment, researchers cannot squat in the deep forest for a long time. Therefore, designing a sensor network system for wildlife monitoring is of great value to wildlife research, protection, and management. When deploying a wildlife monitoring network in the wild environment, it is necessary to solve the problem of the effective use of energy. To this end, this paper proposes an energy-saving optimization method for node scheduling and a wake-up scheme based on a cultural genetic algorithm. This method achieves the purpose of energy saving by making redundant nodes fall asleep and waking up sleep nodes to repair the coverage blind area caused by dead nodes. Simulation results show that this method can activate fewer sensor nodes to monitor the required sensing area, and its performance is better than other known solutions.

A Byzantine Sensing Network Based on Majority-Consensus Data Aggregation Mechanism

In the current Internet of Things era, digital devices form complex interconnections. The statuses of objects of interest are monitored using sensors, and distributed wireless sensor networks are formed from numerous sensor nodes. Many Byzantine fault tolerance mechanisms in wireless sensor networks (WSNs) were proposed from Byzantine agreement which even with a few faulty nodes in a sensor network, most healthy nodes can reach a consensus, perform data transmission tasks, and maintain network operation. In this study, this mechanism was utilized together with the majority function technique; in particular, the proposed method uses original sensor signals to define a threshold to assert a binary value of one or zero, thereby performing data judgment and aggregation. This approach reduces node energy consumption and enables the nodes to quickly reach a consensus. Moreover, the operating performance of the network can be maintained even when problems such as node failure and faults occur within the fault tolerance range. Compared with existing algorithms, the proposed data aggregation mechanism exhibits a better network life cycle and can effectively extend the flexibility of network operations.