Towards energy-efficient rf-enabled sensor networks in internet of things

Abstract

. A crucial conduct norm for a sensor network is to avoid network failures and packet drop. One of the other essential requirements is to effectively manage the energy levels of the nodes according to the states of the operation required for an application. This paper proposes an energy management model with the aim of allowing energy optimization of Radio Frequency (RF) enabled Sensor Networks (RSN) through Energy Harvesting (EH) and Energy Transfer (ET) techniques. The harvested and transferred energy is used by the RSN tags to maintain the energy levels of the network and prevent dead state of a node. It is observed that these Energy Management (EM) techniques are different from the existing solutions in the literature, where managing the energy consumption by the transceivers and the radio is tedious in integrated systems of sensors and tags. Stochastic Backscattering Algorithm (SBA) based on the trade-off between EH rate and transmission range is also proposed. Performance analysis of the proposed EM model is carried out by characterizing the RSN nodes using Semi Markov Decision Process (SMDP). Numerical results show that through RF-based EH and SBA based ET, the lifetime of the network can be improved by around 25%, comparatively to conventional sensor networks. The results are quantified empirically for Internet of Things (IoT) contexts in terms of energy costs, lifetime of sensors and successful packet transmission.