An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

Abstract

Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

Energy harvesting techniques for routing issues in wireless sensor networks

“…Energy harvested from different sources and power greedy sensors are used to gather data from the deployed area. The lifespan of the network is much improved due to the harvested energy adapt the sampling rate in correlation with the classical ASA (Srbinovski et al, 2016).…”

“…At end the BS receives a message from the node (t TX and P TX ) consisting of the collected assessment. So the active period can be demonstrated by using equations (2) and (3) (Srbinovski et al, 2016).…”

“…Longer acquisition times are required in some applications as compared to the transmission for data. The energy used for data transmission is lower than the energy used by sensors themselves, so the techniques used by the MAC protocol are the efficient energy management mechanisms for the sensors (Srbinovski et al, 2016).…”

Energy harvesting techniques for routing issues in wireless sensor networks

“…In this way, it is expressed as inactive link quality estimation in light of the fact that a perception time is required. Srbinovski et al (2016) presented a new technique which is the combination of energy management technique with Adaptive Sampling Algorithm (ASA) i.e., an optimised version of energy harvesting for WSNs. The approach is named Energy Aware Adaptive Sampling Algorithm (EASA).…”

Less is more

“…A further study [146] introduces an energy-aware adaptive sampling approach. This method is a combination of an adaptive sampling algorithm and a power-management technique tailored to energy-harvesting sensor networks.…”