Coding, Scheduling, and Cooperation in Wireless Sensor Networks

TL;DR

This paper explores optimizing the lifetime of wireless sensor networks by jointly considering source coding, channel coding, and scheduling, highlighting the complexity and potential energy savings of cooperative sensor data transmission.

Contribution

It introduces a max-min optimization framework for sensor network lifetime that incorporates source-channel coding and scheduling, demonstrating the problem's NP-hardness and proposing algorithms and heuristics.

Findings

Joint source-channel coding and scheduling significantly extend network lifetime.

The optimization problem remains NP-hard despite simplifications.

Proposed heuristics offer practical solutions for complex scenarios.

Abstract

We consider a single-hop data gathering sensor cluster consisting of a set of sensors that need to transmit data periodically to a base-station. We are interested in maximizing the lifetime of this network. Even though the setting of our problem is very simple, it turns out that the solution is far from easy. The complexity arises from several competing system-level opportunities available to reduce the energy consumed in radio transmission. First, sensor data is spatially and temporally correlated. Recent advances in distributed source-coding allow us to take advantage of these correlations to reduce the number of transmitted bits, with concomitant savings in energy. Second, it is also well-known that channel-coding can be used to reduce transmission energy by increasing transmission time. Finally, sensor nodes are cooperative, unlike nodes in an ad hoc network that are often modeled as competitive, allowing us to take full advantage of the first two opportunities for the purpose of maximizing cluster lifetime. In this paper, we pose the problem of maximizing lifetime as a max-min optimization problem subject to the constraint of successful data collection and limited energy supply at each node. By introducing the notion of instantaneous decoding, we are able to simplify this optimization problem into a joint scheduling and time allocation problem. We show that even with our ample simplification, the problem remains NP-hard. We provide some algorithms, heuristics and insight for various scenarios. Our chief contribution is to illustrate both the challenges and gains provided by joint source-channel coding and scheduling.