Cooperative Processing Model for Wireless Sensor Networks

Abstract

Wireless sensor networks offer a distributed processing environment. Many sensor nodes are deployed in fields that have limited resources such as computing power, network bandwidth, and electric power. The sensor nodes construct their own networks automatically, and the collected data are sent to the sink node. In these traditional wireless sensor networks, network congestion due to packet flooding through the networks shortens the network life time. Clustering or in-network technologies help reduce packet flooding in the networks. Many studies have focused on saving energy in the sensor nodes because the limited available power leads to an important problem of extending the operation of sensor networks as long as possible. However, we focus on the execution time because clustering and local distributed processing already contribute to saving energy by local decision making. In this paper, we present a cooperative processing model based on the processing timeline. Our processing model includes validation of the processing, prediction of the total execution time, and determination of the optimal number of processing nodes for distributed processing in wireless sensor networks. The experiments demonstrate the accuracy of the proposed model, and a case study shows that our model can be used for the distributed application.

CONASENSE:Vision, Motivation and Scope

“…Sensing is not done anymore by few complex sensor devices, wired connected to some control interface, but by a huge number of low-cost, tiny, untethered, batterypowered low-cost MEMS (micro-electro-mechanical systems) devices with limited on-board processing capabilities, storage and short-range wireless communication links based on radio technology, as well as sensing capabilities. Therefore, both data collection and processing are no longer centralized but distributed [10] and the best compromise between local processing and estimation accuracy must be found taking into account the other important metrics such as power consumption and reduction of data transmission. The concept of distributing the "sensing" to different "smaller" sensors, wirelessly connected in an ad hoc manner, is interesting also for space applications.…”