Utility-based bandwidth adaptation in mission-oriented wireless sensor networks

Author:

Eswaran Sharanya1,Misra Archan2,Bergamaschi Flavio3,Porta Thomas La4

Affiliation:

1. Telcordia Technologies, University Park, PA

2. Singapore Management University

3. IBM, U.K.

4. Pennsylvania State University

Abstract

This article develops a utility-based optimization framework for resource sharing by multiple competing missions in a mission-oriented wireless sensor network (WSN) environment. Prior work on network utility maximization (NUM) based optimization has focused on unicast flows with sender-based utilities in either wireline or wireless networks. In this work, we develop a generalized NUM model to consider three key new features observed in mission-centric WSN environments: i) the definition of the utility of an individual mission (receiver) as a joint function of data from multiple sensor sources; ii) the consumption of each sender's (sensor) data by multiple missions; and iii) the multicast-tree-based dissemination of each sensor's data flow, using link-layer broadcasts to exploit the “wireless broadcast advantage” in data forwarding. We show how a price-based, distributed protocol (WSN-NUM) can ensure optimal and proportionally fair rate allocation across multiple missions, without requiring any coordination among missions or sensors. We also discuss techniques to improve the speed of convergence of the protocol, which is essential in an environment as dynamic as the WSN. Further, we analyze the impact of various network and protocol parameters on the bandwidth utilization of the network, using a discrete-event simulation of a stationary wireless network. Finally, we corroborate our simulation-based performance results of the WSN-NUM protocol with an implementation of an 802.11b network.

Funder

U.K. Ministry of Defence

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

Reference34 articles.

1. Bertsekas D. 1999. Non-Linear Programming. Athena Scientific. Bertsekas D. 1999. Non-Linear Programming. Athena Scientific.

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