Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2011, Article ID 672313, 19 pages doi:10.1155/2011/672313 Research Article An Energy-Aware Middleware for Integrating Wireless Sensor Networks and the Internet Jeisa Domingues, Antonio Damaso, Rilter Nascimento, and Nelson Rosa Informatics Center, Federal University of Pernambuco, P.O. Box 7851, 50740-540 Recife, PE, Brazil Correspondence should be addressed to Jeisa Domingues, jpo@cin.ufpe.br Received 1 September 2010; Revised 29 December 2010; Accepted 7 February 2011 Copyright © 2011 Jeisa Domingues et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Wireless sensor networks (WSNs) have great potential for many distributed applications in different scenarios. As they grow in popularity and importance, it becomes increasingly desirable and necessary to share their data over the Internet. This paper presents an energy-aware middleware that focuses on integrating the Internet and WSNs at service level by providing transparency of access, location, and technology. In particular, it describes and evaluates the implemented strategies to reduce the energy consumption, which are essential for prolonging the WSN lifetime. 1. Introduction Wireless sensor networks (WSNs) have great potential for many applications in scenarios such as military target tracking and surveillance, natural disaster relief, biomedical health monitoring, hazardous environment exploration, and seismic sensing. For that reason, they have received increas- ing attention over the past few years. As WSNs become more numerous and their data more valuable, it becomes increasingly important to have common means to share data over the Internet [1]. Since WSNs can be easily deployed in various envi- ronments to monitor target objects and various conditions and to collect information, they are considered one essen- tial infrastructure for pervasive computing systems. Sensor nodes can be embedded in walls, chairs, watches or stick on the cups, bags, and so on, to gather users’ information. These data can be crucial to pervasive computing applications, as they provide the fundamental information of users’ context and the underlying execution environment [2]. Also, the WSN is one of the many networks that will compose the Ambient Networks [3]. For all those reasons, integrating WSN with the Internet has become increasingly desirable and necessary. A number of solutions have been proposed in recent years to allow the WSN and Internet integration [4, 5, 16– 25]. Most of them aim at integrating those networks through mapping protocol stacks and logical address formats used in both networks. Those solutions focus on accessing the network nodes through their logical addresses, which raises several problems. In this context, this paper extends a solution that aims at integrating applications instead of networks (i.e., protocols stack and/or logical address formats mapping) by incorpo- rating strategies to reduce the WSN power consumption. The proposed solution is an energy-aware middleware, namely, WISeMid, that allows integrating applications, which are considered services, in a transparent way. The unique contributions of this paper are the proposed power-saving mechanisms: Aggregation service, which aggregates the last n data sensed by a node; Reply Storage Timeout, which avoids sending equivalent messages to the sensor nodes while the last sensed data is considered up-to-date; Automatic Type Conversion, which removes unnecessary bytes from the messages; the implementation of invocation asynchrony patterns, which prevents the sensor application from wasting power for being blocked during a service requesting. In order to analyze the impact of the proposed strategies, we also present a power consumption evaluation in an actual application. The remainder of this paper is organized as follows. Section 2 outlines details of WISeMid’s main characteristics, architecture, and implementation. Next, Section 3 describes the power-saving strategies implemented in WISeMid.