Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes

Abstract

In order to maximize network lifetime and balance energy consumption when sink nodes can move, maximizing lifetime of wireless sensor networks with mobile sink nodes (MLMS) is researched. The movement path selection method of sink nodes is proposed. Modified subtractive clustering method, k-means method, and nearest neighbor interpolation method are used to obtain the movement paths. The lifetime optimization model is established under flow constraint, energy consumption constraint, link transmission constraint, and other constraints. The model is solved from the perspective of static and mobile data gathering of sink nodes. Subgradient method is used to solve the lifetime optimization model when one sink node stays at one anchor location. Geometric method is used to evaluate the amount of gathering data when sink nodes are moving. Finally, all sensor nodes transmit data according to the optimal data transmission scheme. Sink nodes gather the data along the shortest movement paths. Simulation results show that MLMS can prolong network lifetime, balance node energy consumption, and reduce data gathering latency under appropriate parameters. Under certain conditions, it outperforms Ratio_w, TPGF, RCC, and GRND.

Lifetime Optimization Algorithm with Mobile Sink Nodes for Wireless Sensor Networks Based on Location Information

“…It can be solved by genetic method, graph theory, and other methods. Because the number of anchors is not large, nearest neighbor interpolation algorithm is used to find the approximate solution of shortest path [17]. The implementation steps of m th sink node are as follows.…”

Contiki-AMAC — The enhanced adaptive radio duty cycling protocol: Proposal and analysis

“…Figure 6 stresses the trade-off between network delivery ratio and lifetime. Besides, proper selection of network sink node locations [15] should reduce large differences in nodes battery consumptions at end of networklifetime; and the optimal selection of such location would theoretically reduce nodes battery capacities coefficient of variation to zero. As compared to reference scenario, Figure 9 shows great correlation between battery capacities coefficient of variation for network nodes with network lifetimes.…”

Energy Efficient Routing Protocol for Maximum Lifetime in Wireless Sensor Networks

“…However, continuous data transmission through the shortest path can lead to an energy whole problem due to constant energy dissipation of sensor nodes transmitting to neighbouring nodes along the same path. In recent years, researchers have taken the advantage of mobility of the base station for the purpose of gathering data from the sensor nodes in a more reliable and efficient manner [7]. However, using a mobile base station for data collection has two major problems.…”