A utility-based sensing and communication model for a glacial sensor network

Abstract

This paper reports on the development of a utility-based mechanism for managing sensing and communication in cooperative multi-sensor networks. The specific application considered is that of GLACSWEB, a deployed system that uses battery-powered sensors to collect environmental data related to glaciers which it transmits back to a base station so that it can be made available world-wide to researchers. In this context, we first develop a sensing protocol in which each sensor locally adjusts its sensing rate based on the value of the data it believes it will observe. Then, we detail a communication protocol that finds optimal routes for relaying this data back to the base station based on the cost of communicating it (derived from the opportunity cost of using the battery power for relaying data). Finally, we empirically evaluate our protocol by examining the impact on efficiency of the network topology, the size of the network, and the degree of dynamism of the environment. In so doing, we demonstrate that the efficiency gains of our new protocol, over the currently implemented method over a 6 month period, are 470%, 250% and 300% respectively. Categories and Subject Descriptors General TermsAlgorithms, Design, Experimentation KeywordsAgents and ambient intelligence, agents and novel computing paradigms, Agent-based sensor networks.

A Survey on Sensor Networks from a Multiagent Perspective

“…However, a comparison of the gain in terms of performance of these mechanisms in front of other well-known energy-efficient routing protocols is not provided. In [21] Padhy et al propose a new utility-based energy-aware self-organizing routing protocol combined with adaptive sampling (namely Utility-based Sensing and Communication protocol, USAC) that finds the cheapest cost route from an agent to the sink. The idea here is that it might be preferable for a sensor to transmit its data via a more energy-consuming route if the least energy-consuming route contains a sensor in a highly dynamic environment.…”

“…In [21], Padhy et al develop, as part of their USAC protocol, a novel mechanism for adaptive sampling that allows each sensor to adjust its sampling rate depending on the environment dynamics. Each sensor uses a regression model to forecast the future data as a function of the last measurements and the optimal sampling rate is the one than keeps the confidence interval within a fixed limit.…”

A survey of agent technologies for wireless sensor networks

“…Backcasting [33] Adaptive data sampling by communicating with the base station Energy-efficient data sampling but centralized approach controlled by the base station SORA [34] Adaptive data sampling by Wellman's market-oriented programming Energy-efficient data sampling but centralized approach where the price of each action is set by the central coordinator USAC [35] Adaptive data sampling and routing Energy-efficient data sampling but confidence interval value remains static throughout the systems' operation Kho [32] Decentralized adaptive sampling Energy-efficient data sampling but transmitting rate and schedule are fixed Kho's routing [36] Fixed and flexible routing Flexible routing delivers more packets but does not scale well Stranders et al [37] Coordination approach for mobile sensors to collect data Mobile sensor nodes quickly find the events but when the number of nodes increase, movement paths to necessary locations decrease Le et al [38] Task assignment Solution to task assignment problem by using agents but sensors should be static and missions are independent capabilities in order to improve their energy management. A node may schedule its sampling interval based on its past set of observed data and the set of data that it believes it will observe, so as to achieve the network's goals [32].…”

“…Padhy et al [35] defined a utility-based sensing and communication model (USAC) that included sensing and communication protocols by the decentralized control of the system. USAC has two main features: a mechanism for adaptive sampling, and a routing protocol.…”

Computational Service Economies: Design and Applications

“…In the former case, we have made advances in the areas of auctions (Dash et al, 2007Rogers et al, 2007a;Vetsikas et al, 2007;Gerding et al, 2008), coalition formation (Dang and Jennings, 2006;Fatima et al, 2009;Rahwan and Jennings, 2007;Chalkiadakis et al, 2008;, automated negotiation (Fatima et al, 2006;Karunatillake et al, 2009;Ramchurn et al, 2007;Fatima et al, 2004), trust and reputation , flexible reasoning strategies for workflows (Stein et al, 2009a) and decentralized coordination (Rogers et al, 2007b, Farinelli et al, 2008. In the latter case, we have built applications using these techniques in areas such as: virtual organizations (Norman et al, 2004), emergency response (Chapman et al, 2009), sensor networks (Padhy et al, 2006;Kho et al, 2009;, mobile sensors (Stranders et al, 2009), computational grids (Stein et al, 2009b) and personalized recommendations (Wei et al, 2005;Payne et al, 2006).…”