Tee: Traffic-based Energy Estimators for duty-cycled Wireless Sensor Networks

Energy is classically considered as a critical resource in Wireless Sensor Networks (WSNs). These networks are composed of tiny devices that auto-organize around one or few gateways, which may have various roles from simple reference or traffic sinks to full network orchestrator. Such a gateway could influence the network behavior, for instance by decreasing activity when energy becomes scarce. It however needs to be able to estimate the nodes remaining energy. Indeed, this gateway is on the path of all traffic going in or out the WSN. This traffic sample could be used to acquire a coarse estimate of individual nodes energy consumption. The accuracy of this estimation can then be improved by explicit signaling if needed. This paper presents Tee, a set of such Traffic-based energy estimators that operates at the WSN gateway. We evaluate, by simulation, the accuracy of two such estimators in IEEE 802.15.4 networks running RPL and ContikiMAC, a duty cycled MAC layer. Results show that such silent estimators benefit from information already available at the gateway, such as the routing topology. However, they still underestimate the consumption due to the routing control messages, to the packets strobing, or to contention and collisions and can easily be complemented by lightweight explicit calibrations.