Data aggregation for machine-to-machine communication with energy harvesting

Abstract

Machine-to-Machine (M2M) communications have emerged as a new concept for the next generation of sensing and actuating systems. With the recent emergence of energy harvesting technologies, the current communication solutions have addressed the problem of controlling the data communication to regulate efficiently the energy consumption, aiming to avoid under and overuse of energy. However, these solutions do not consider data aggregation as means of controlling the network traffic. To fill this gap, this paper proposes the Data Aggregation for energy harVesting NETworks (DAV-NET), which regulates the energy consumption in accordance with the residual energy stored in the batteries, exploiting the data aggregation capabilities to control the network traffic. The performed simulations show that the proposed solution is able to regulate the energy consumption in case of abundant or scarce energy, controlling the aggregation level in a distributed fashion.978-1-4799-1860-7/15/$31.00 ©2015 IEEE This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.

M2M Communication Assessment in Energy-Harvesting and Wake-Up Radio Assisted Scenarios Using Practical Components

“…The technological challenges that are under intensive research include low power consumption of the devices [ 6 ], and the methods for obtaining or harvesting energy efficiently from different sources, as well as storing the harvested energy for later use [ 7 , 8 , 9 , 10 , 11 ]. It is worth noting, that the energy efficiency may be improved by applying energy optimization methods based on, for example, coalition formation with QoS knowledge [ 12 , 13 ] or data-aggregation [ 14 ]. Thorough study on optimization of energy efficient resource allocation in M2M communications with energy harvesting [ 15 ] attempts to minimize the total energy consumption of the network via jointly controlling power and time allocation while taking into account circuit power consumption as well as potential QoS and latency constraints.…”