Senceive: A Middleware for a Wireless Sensor Network

Abstract

A significant amount of research effort is being carried out by the research community to increase the scope and usefulness of wireless sensor networks; to optimise life time by developing energy efficient power management, self-organising, medium access and routing protocols; and to reduce the cost of sensing nodes so that dense and robust deployment is possible. Though much has already been achieved, currently the cost of commercially available wireless sensor nodes is considerable and the wide applicability of proposed or existing protocols is still under investigation. One essential problem associated with cost or wide applicability of protocols is that sensor networks are application-specific. Protocols and in-network algorithms are optimised for particular sensing tasks. On the other hand, in research environments researchers would like to experiment not with a single application but with many applications. Considering the not-so-cheap sensing nodes available on the market and the management overhead of deploying wireless sensor networks, it is not economical or efficient to dedicate wireless sensor networks just to a single application, not at present at any rate. We therefore propose a middleware that enables researchers to experiment with multiple applications while providing them with essential in-network functionalities to satisfy individual application's requirements. The middleware cleanly separates sensing from network management so that application developers can obtain data from the wireless sensor networks without having to deal with management concerns.

Programming a Sensor Network in a layered middleware architecture

“…However, SINA does not provide schemes to hide the faulty nature of both sensor operations and wireless communication, leaving to the application layer the responsibility to provide robustness and reliability for data services. Senceive (Hermann & Dargie, 2008) is similar to the previous approaches, but based on a graphical interface to define the operations to be performed for data gathering, in terms of SQL-like queries. The WSN is accessed from a special server, that is also a sink to the WSN.…”

“…Query optimization evaluates the various alternatives of task allocation over the sensors, to choose the one that minimizes energy consumption. Examples of the Database approach are TinyDB (Madden et Al., 2003), Cougar (Yao & Gehrke, 2002), MaDWiSe (Amato et Al., 2010), SINA (Shen et Al., 2001), and Senceive (Hermann & Dargie, 2008). TinyDB (Madden et Al., 2003), Cougar (Yao & Gehrke, 2002) and MaD-WiSe (Amato et Al., 2010) are all based on a pure database paradigm.…”

Building an intelligent sensing system: a case study

“…A comprehensive treatment of the technical aspect of the Senceive system is given in Hermann and Dargie (2008). 2 Note the discrepancy between the schedule specified in Table 1 and the way things progressed in reality.…”

Building the Senceive System

“…Moreover, wireless 1 The acronym stands for Sensing and Perceiving. A comprehensive treatment of the technical aspect of the Senceive system is given in [7]. sensor networks were one of the main research focus of the Chair, and the expected results could be exploited by some of the actively running research projects.…”