An Energy Balancing Strategy Based on Hilbert Curve and Genetic Algorithm for Wireless Sensor Networks

Abstract

A wireless sensor network is a sensing system composed of a few or thousands of sensor nodes. These nodes, however, are powered by internal batteries, which cannot be recharged or replaced, and have a limited lifespan. Traditional two-tier networks with one sink node are thus vulnerable to communication gaps caused by nodes dying when their battery power is depleted. In such cases, some nodes are disconnected with the sink node because intermediary nodes on the transmission path are dead. Energy load balancing is a technique for extending the lifespan of node batteries, thus preventing communication gaps and extending the network lifespan. However, while energy conservation is important, strategies that make the best use of available energy are also important. To decrease transmission energy cost and prolong network lifespan, a three-tier wireless sensor network is proposed, in which the first level is the sink node and the third-level nodes communicate with the sink node via the service sites on the second level. Moreover, this study aims to minimize the number of service sites to decrease the construction cost. Statistical evaluation criteria are used as benchmarks to compare traditional methods and the proposed method in the simulations.

Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH

“…This phenomenon means that the energy consumption of the nodes around the sink is increased, and as a result, the network lifetime can be reduced. We will study the routing considering the balance of the remaining energy and routing topology for the increase of the network lifetime [33,34,35]. In addition, Escalator provides a conflict-free schedule for convergecast traffic only.…”

Distributed Systematic Network Coding for Reliable Content Uploading in Wireless Multimedia Sensor Networks

“…To enhance the efficient uploading in WMSNs, many works have paid attention to techniques such as data aggregation [ 2 ], compressive sensing [ 3 ], information fusion [ 4 ], network lifetime [ 5 ], and energy efficiency [ 6 , 7 , 8 , 9 , 10 ]. Instead of uploading raw data, CH tries to reduce the gathered information used for uploading along with guaranteeing quality of service (QoS).…”