HUMS: An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks

Abstract

Sink mobility has attracted much research interest in recent years because it can improve network performance such as energy efficiency and throughput. An energy-unconscious moving strategy is potentially harmful to the balance of the energy consumption among sensor nodes so as to aggravate the hotspot problem of sensor networks. In this paper, we propose an autonomous moving strategy for the mobile sinks in data-gathering applications. In our solution, a mobile sink approaches the nodes with high residual energy to force them to forward data for other nodes and tries to avoid passing by the nodes with low energy. We performed simulation experiments to compare our solution with other three data-gathering schemes. The simulation results show that our strategy cannot only extend network lifetime notably but also provides scalability and topology adaptability.

MA-LEACH: Energy Efficient Routing Protocol for WSNs using Particle Swarm Optimization and Mobile Aggregator

“…Though, the resulting path needs to meet our model's constraints. In our model, the contributions are two folds: Firstly, we adopt a hierarchical protocol, LEACH in contrast to the work of Gu et al [29] and we do not mobilize the sink as in [30]. Secondly, we limit the number of nodes a mobile aggregator needs to visit to a smaller set of CHs.…”

Virtually Moving Base Stations for Energy Efficiency in Wireless Sensor Networks

“…To completely solve the energy-hole problem, researchers propose to physically move BSs [13,4,15,9,16,2,14]. These schemes have high energy-efficiency because they increase the degree of freedom for evenly distributing the communication load.…”

“…3 Calculate the shortest path Luv from each sensor node v ∈ C to the remote server S via each BS u ∈ F . 4 Assign the service costs guv (u ∈ F, v ∈ C) and the facility costs hu (u ∈ F) as shown in (23) and (24), respectively. 5 Set Fo ←− ∅ and set Cu ←− C. 6 while Cu = ∅ do denotes the set containing all connected customers j ∈ C\Cu whose qij < 0 and containing k unconnected customers j ∈ Cu with the smallest positive qij.…”

A Global Best Path Meteorological Data Gathering Algorithm for Wireless Sensor Networks

“…Many studies focus on movement patterns of mobile sink and their main mentality is determining the best position for mobile sink previously based on the assumption that the mobile sink can arrive everywhere in sensor fields [8]. Actually, it is not practical in most real applications.…”