Latency of wireless sensor networks with uncoordinated power saving mechanisms

Abstract

We consider a wireless sensor network, where nodes switch between an active (on) and a sleeping (off) mode, to save energy. The basic assumptions are that the on/off schedules are completely uncoordinated and that the sensors are distributed according to a Poisson process and their connectivity ranges are larger or equal to their sensing ranges. Moreover, the durations of active and sleeping periods are such that the number of active nodes at any particular time is so low that the network is always disconnected.Is it possible to use such a network for time-critical monitoring of an area? Such a scenario requires indeed to have bounds on the latency, which is the delay elapsed between the time at which an incoming event is sensed by some node of the network and the time at which this information is retrieved by the data collecting sink. A positive answer is provided to this question under some simplifying assumptions discussed in the paper. More precisely, we prove that the messages sent by a sensing node reach the sink with a fixed asymptotic speed, which does not depend on the random location of the nodes, but only on the network parameters (node density, connectivity range, duration of active and sleeping periods). The results are obtained rigorously by using an extension of first passage percolation theory.

Bounds on the information propagation delay in interference-limited ALOHA networks

“…We also observe that the lower bound on the time constant increases with p. From the upper and lower bounds we observe that µ scales like β √ λ. Since we do not have T (o, x + y) ≤ T (o, x) + T (o, y), Kingman's subadditive ergodic theorem [9] cannot be directly applied to (8). But since T T (o,x) (x, y) d = T (x, y), there is hope that such a result holds.…”

“…The main difference between an epidemic process and the process we consider is that the spreading (of packets) depends on a subset of the population (due to interference) and is not independent from node to node. In [8], the latency for a message to propagate in a sensor network is analyzed using similar tools. They consider a Boolean connectivity model with randomly weighted edges and derive the properties of first-passage paths on the weighted graph.…”

Energy Management for Wireless Sensor Network Nodes

“…In research [4] the nodes switched between active and sleeping mode independently of each other. The sensors are distributed based on a Poisson process.…”

Large Deviations for the Graph Distance in Supercritical Continuum Percolation

“…The large deviations of the graph distance and the shape theorem are important properties of the infinite component and can be applied to communication networks, particularly to the large-scale randomly distributed wireless sensor networks (WSNs), which can be modeled using the infinite component of continuum percolation. For instance, Dousse [3] constructed a two-dimensional continuum percolation to study a sort of WSN and obtained some results about the delay of the networks. In our model, we think of the Poisson points as the locations Large deviations for the graph distance 155 of the sensor nodes in the WSNs: every node has the same transmitting radius and can transmit data to the nodes in their transmitting range directly.…”