Aiming at high network energy consumption and data delay induced by mobile sink in wireless sensor networks (WSNs), this paper proposes a cluster-based energy optimization algorithm called Cluster-Based Energy Optimization with Mobile Sink (CEOMS). CEOMS algorithm constructs the energy density function of network nodes firstly and then assigns sensor nodes with higher remaining energy as cluster heads according to energy density function. Meanwhile, the directivity motion performance function of mobile sink is constructed to enhance the probability of remote sensor nodes being assigned as cluster heads. Secondly, based on Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) architecture, the energy density function and the motion performance function are introduced into the cluster head selection process to avoid random assignment of cluster head. Finally, an adaptive adjustment function is designed to improve the adaptability of cluster head selection by percentage of network nodes death and the density of all surviving nodes of the entire network. The simulation results show that the proposed CEOMS algorithm improves the cluster head selection self-adaptability, extends the network life, reduces the data delay, and balances the network load.

In wireless sensor networks (WSNs), finding the best transmission route from each node to the mobile sink is not a simple task because of the dynamic characteristics of sink node and cluster transmission. In order to balance energy consumption and reduce collisions in wireless transmissions, this paper proposes a Dynamic Spanning Tree with Mobile Sink (DSTMS) routing algorithm. Firstly, based on the Low Energy Adaptive Clustering Hierarchy Protocol (LEACH), a multi-layer transmission framework with dynamic minimum spanning tree (MST) is constructed to optimize data transmission route. In this framework, the selected rendezvous points, according to the motion parameters of mobile sink, build the dynamic rendezvous layer to constraint the hierarchical transmission of DSTMS. In addition, considering some factors of transmission energy consumption and residual energy distribution,the weight factor based on energy efficient function is introduced to evaluate the cost of transmission path within the MST. Moreover, the proposed algorithm not only reduces network energy consumption caused by frequent location updates of mobile sink, but also reduces data transmission congestion by constructing dynamic hierarchical transmission strategy. The simulation results show that the DSTMS algorithm can effectively prolong the network life, balance the network load and reduce collisions in wireless transmissions.

When some nodes cooperatively track moving targets in a wireless sensor network, some things including network working node selection and network energy consumption are influenced. Thus, this paper proposes an improved algorithm LEACH-MTC (LEACH with Moving Target Constraint) based on low energy adaptive clustering hierarchy protocol (LEACH). First, based on the two-step linearization of the nonlinear dynamic model, the state of the nonlinear moving target is predicted by the extended Kalman filter (EKF). Second, combining the state prediction of the moving target and the performance of collaborative monitoring, this paper constructs an ellipse monitoring area of some working nodes to consist with the direction of the target movement. Subsequently, the node sleep strategy corresponding to the state prediction of moving target is designed. Finally, the cluster head selection strategy is proposed based on energy balance utilizing the state prediction of the moving target. Simulation results show that the proposed LEACH-MTC algorithm can not only ensure the real-time consistency between the changing direction of area and the direction of target movement, but also increase the number of working nodes’ survival and reduce the network energy consumption.

In multisensor cooperative detection network, some random disturbances, energy carried by sensor, distance between target and sensor node, and so on all affect the sensor selection scheme. To effectively select some sensors for detecting the target, a novel sensor selection method considering uncertainty disturbance is proposed under constraints of estimation accuracy and energy consumption. Firstly, the sensor selection problem is modeled as a binary form optimization problem with a penalty term to minimize the number of sensors. Secondly, some factors (precision, energy, and distance, etc.) affecting the sensor selection scheme are analyzed and quantified, and energy consumption matrix and estimation precision threshold are given by matrix transformation. Finally, the problem of minimizing sensor number after relaxation is solved by convex optimization method, obtaining sensor selection scheme by discretization and legitimization of the suboptimal solution after convex relaxation. Simulation results show that the proposed algorithm can ensure the minimum number of sensors, improving accuracy of state estimation and saving network energy.