Statistical evaluation of energy harvesting system models in wireless networks: An empirical perspective
JAYA DIPTI LAL, Dr Dolly Thankachan
Energy, harvesting, sensor, renewable, lifetime, computational complexity
Energy harvesting is a traditional concept, which is used by a wide variety of physical systems for converting naturally available energy into electric power. Sources of power include wind, solar, mechanical vibrations, magnetic fields, temperature variations, application specific power transforming sources, etc. Wireless sensor networks (WSNs) consist of a wide variety of limited power nodes, each of which continuously require energy for operation. These nodes are placed at remote locations, due to which continuous physical monitoring is not possible. This limits the capability to replace power sources (batteries) for these nodes, thus affecting their normal functioning. As an alternate to power source replacement, wireless network researchers have proposed use of energy harvesting in wireless sensor nodes. In order to perform this task, a wide variety of energy harvesting models are proposed by researchers, each of which vary in terms of computational complexity, harvesting efficiency, energy efficiency, and size of harvesting models. Due to which it becomes difficult for network designers to select the best possible energy harvesting model for their deployments. To reduce this difficulty of model selection, this text reviews a wide variety of network models directed at energy harvesting. These models are compared in terms of deployment application type, energy efficiency, computational complexity, etc. Upon referring this comparison, researchers and network designers can select the best suited model for their deployment, which will assist in improving network lifetime and harvesting performance. Moreover, this text also proposes various model level enhancements which will assist in improving performance of already defined energy harvesting techniques. This text also performs application wise statistical comparison of reviewed models, which further assists in selecting deployment specific models for highly effective network design.
Article Details
Unique Paper ID: 152789

Publication Volume & Issue: Volume 8, Issue 4

Page(s): 449 - 461
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