We focus on data gathering problems in energy-constrained networked sensor systems. We study store-and-gather problems where data are locally stored at the sensors before the data gathering starts, and continuous sensing and gathering problems that model time critical applications. We show that these problems reduce to maximization of network flow under vertex capacity constraint. This flow problem in turn reduces to a standard network flow problem. We develop a distributed and adaptive algorithm to optimize data gathering. This algorithm leads to a simple protocol that coordinates the sensor nodes in the system. Our approach provides a unified framework to study a variety of data gathering problems in networked sensor systems. The performance of the proposed method is illustrated through simulations. State-of-the-art sensors (e.g., Smart Dust [1]) are powered by batteries. Replenishing energy by replacing the batteries is infeasible because the sensors are typically deployed in harsh terrains. Also, the cost of replacing batteries can be prohibitively high. These sensors, which are usually unattended, need to operate over a long period of time after deployment. Energy efficiency is thus critical. Techniques ranging from low power hardware design [2, 3] and energy-aware routing [4, 5] to application level optimizations [6, 7] have been proposed to improve energy efficiency of networked sensor systems. An important application of networked sensor systems is to monitor the environment. Examples of such applications include vehicle tracking and classification in the battlefield, patient health monitoring, pollution detection, and so on. In these applications, a fundamental operation is to sense the environment and transmit the sensed data to the base station for further processing. In this article, we study energy-efficient data gathering in networked sensor systems from an algorithmic perspective. Compared with sensing and computation, communication is the most expensive operation (in terms of energy consumption) in the context of data gathering [8] . Generally, data transfers are performed via multihop communications where each hop is a shortrange communication. This is due to the well-known fact that long-distance wireless communication is expensive in terms of both implementation complexity and energy dissipation, especially when using the low-lying antennae and near-ground channels typically found in networked sensor systems. Short-range communication also enables efficient
In REF , authors aim at maximizing the throughput of data received by the base station.
Maximum Data Gathering in Networked Sensor Systems
International Journal of Distributed Sensor Networks
Computer Science