This paper presents IRIS, an integrated interest dissemination and convergecasting solution for wireless sensor networks (WSNs). The interest dissemination protocol is used to build and maintain the network topology and for task/instruction assignment, while convergecasting implements data gathering at the network sink. Convergecasting heavily exploits cross-layering in that MAC and routing operation are performed jointly and relay selection is based on flexible cost functions that take into account information from different layers. The definition of the IRIS cost function enables tradeoff between key end-to-end performance metrics. In addition, it provides mechanisms for supporting efficient network behavior such as in-network data aggregation or processing. Energy usage is minimized by exploiting density estimation, sleeping modes and duty cycle control in a distributed and autonomous manner and as a function of the traffic intensity. Finally, IRIS is self adaptive, highly localized and imposes limited control overhead. IRIS performance is evaluated through ns2 simulations as well as through experiments on a WSN testbed. Comparative performance results show that IRIS outperforms previous cross-layer solutions. The flexibility introduced by the IRIS cross-layer approach results in higher robustness than that of well-known approaches such as BoX-MAC and CTP. (C) 2011 Elsevier B.V. All rights reserved.

IRIS: Integrated data gathering and interest dissemination system for wireless sensor networks

ROSSI, MICHELE;ZORZI, MICHELE
2013

Abstract

This paper presents IRIS, an integrated interest dissemination and convergecasting solution for wireless sensor networks (WSNs). The interest dissemination protocol is used to build and maintain the network topology and for task/instruction assignment, while convergecasting implements data gathering at the network sink. Convergecasting heavily exploits cross-layering in that MAC and routing operation are performed jointly and relay selection is based on flexible cost functions that take into account information from different layers. The definition of the IRIS cost function enables tradeoff between key end-to-end performance metrics. In addition, it provides mechanisms for supporting efficient network behavior such as in-network data aggregation or processing. Energy usage is minimized by exploiting density estimation, sleeping modes and duty cycle control in a distributed and autonomous manner and as a function of the traffic intensity. Finally, IRIS is self adaptive, highly localized and imposes limited control overhead. IRIS performance is evaluated through ns2 simulations as well as through experiments on a WSN testbed. Comparative performance results show that IRIS outperforms previous cross-layer solutions. The flexibility introduced by the IRIS cross-layer approach results in higher robustness than that of well-known approaches such as BoX-MAC and CTP. (C) 2011 Elsevier B.V. All rights reserved.
2013
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2574679
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