Elsevier

Computer Networks

Volume 51, Issue 16, 14 November 2007, Pages 4599-4616
Computer Networks

BiSNET: A biologically-inspired middleware architecture for self-managing wireless sensor networks

https://doi.org/10.1016/j.comnet.2007.06.006Get rights and content

Abstract

This paper describes BiSNET (Biologically-inspired architecture for Sensor NETworks), a middleware architecture that addresses several key issues in multi-modal wireless sensor networks (MWSNs) such as autonomy, scalability, adaptability, self-healing and simplicity. Based on the observation that various biological systems have developed mechanisms to overcome these issues, BiSNET follows certain biological principles such as decentralization, food gathering/storage and natural selection to design MWSN applications. In BiSNET, each application consists of multiple software agents, which operate on the BiSNET middleware platform in individual sensor nodes, and each agent exploits certain biologically-inspired mechanisms such as energy exchange, pheromone emission, replication, migration and death. This is analogous to a bee colony (application) consisting of multiple bees (agents). This paper describes the biologically-inspired mechanisms in BiSNET, and evaluates their impacts on the autonomy, scalability, adaptability, self-healing and simplicity of MWSNs. Simulation results show that BiSNET allows sensor nodes (agents and platforms) to be scalable with respect to network size, autonomously adapt their sleep periods for power efficiency and responsiveness of data collection, adaptively aggregate data from different types of sensor nodes, and collectively self-heal (i.e., detect and eliminate) false positive sensor data. The BiSNET platform is implemented simple in its design and lightweight in its memory footprint.

Introduction

This paper describes a middleware architecture for multi-modal wireless sensor networks (MWSNs),1 called BiSNET (Biologically-inspired architecture for Sensor NETworks), which inherently addresses five challenges in MWSNs. The first challenge is autonomy. Since sensor nodes can be deployed in an unattended area (e.g., forest and ocean) or physically unreachable area (e.g., inside a building wall), they are required to operate with the minimum aid from base stations or human administrators [1], [2].

The second challenge is scalability. In order to cover large spatial extents or monitor the extents at a high-resolution, sensor networks are required to scale to a large number of sensor nodes2 and a large amount of data generated by sensor nodes [1], [3].

The third challenge is adaptability. Sensor nodes are required to adapt their operations to the environmental conditions that they monitor (e.g., temperature and carbon monoxide (CO)) [3], [4], [5], [6]. For example, sensor nodes may increase their duty cycle intervals (sleep periods) when there is no significant change in their sensor readings. This results in less power consumption in the nodes. Also, when neighboring nodes report environmental changes (e.g., changes in temperature or CO level), a sensor node may draw inference from the reports and decrease its sleep period to be more watchful for a potential local environmental change in the future. This can increase responsiveness of the node to transmit its sensor data to a base station. In addition, a sensor node may aggregate data from different types of sensor nodes (e.g., temperature and CO data) and transmit the aggregated data to a base station. This can reduce power consumption in the nodes on a path toward the base station.

The fourth challenge is self-healing. Sensor reading usually contains some noise; it may be a false positive due to, for example, malfunction of sensors. Sensor nodes are required to self-heal (i.e., detect and eliminate) false positives in their sensor readings instead of transmitting them to base stations [5], [7]. This can reduce power consumption of sensor nodes because in-node data processing consumes much less power than data transmission does [8].

The fifth challenge is simplicity. Sensor control software (e.g., applications and middleware) needs to be simple in its design and small in its footprint because of limited availability of CPU power, memory and battery.

In order to address the above five issues, BiSNET provides a middleware platform, called the BiSNET platform. The BiSNET platform hides low-level operating and networking details (e.g., network I/O and state control of sensor nodes) from applications, and implements a series of mechanisms to support autonomous, scalable, adaptive and self-healing applications. BiSNET also provides a high-level programming abstraction to aid the simple and rapid development of applications. The design of BiSNET is motivated by the observation that various biological systems have already developed mechanisms necessary to overcome those challenges [9], [10]. For example, bees act autonomously, influenced by local conditions and local interactions with other bees. A bee colony can scale to a massive number of bees because all activities of the colony are carried out without centralized control. A bee colony adapts to dynamic environmental conditions. When the amount of honey in a hive is low, many bees leave the hive to gather nectar from flowers. When the hive is full of honey, bees expand the hive. Also, bees recover (or self-heal) their pheromone traces to flowers when a part of them is lost. The structure and behavior of each bee is very simple; however, a group of bees autonomously exhibits desirable system characteristics such as adaptability and self-healing through collective behaviors and interactions among bees. Based on this observation, the authors of the paper believe that, if MWSN applications are designed after certain biological principles and mechanisms, they may be able to meet the requirements in MWSNs (i.e., autonomy, scalability, adaptability, self-healing and simplicity).

The BiSNET platform operates atop TinyOS in each sensor node to host applications (Fig. 1). In BiSNET, each application consists of multiple agents,3 which follow several biological principles such as decentralization, autonomy, food gathering/storage and natural selection. This is analogous to a bee colony (application) consisting of multiple bees (agents) running on multiple platforms (hives). Each agent contains a set of data and program code, which is interpreted by the BiSNET platform at runtime. Each agent reads sensor data with the underlying sensor device, and discards or reports it to a base station using biological behaviors such as pheromone emission, replication and migration. The BiSNET platform consists of a container and platform services (Fig. 1). A container provides an execution environment for agents, and controls the state of the local sensor node (e.g., sleep, listen and broadcast). Platform services are used by agents to read sensor data and perform their behaviors.

This paper describes the biologically-inspired mechanisms in BiSNET and evaluates their impacts on the autonomy, scalability, adaptability, self-healing and simplicity of MWSN applications. Simulation results show that BiSNET allows sensor nodes (agents and platforms) to autonomously adapt their sleep periods for power efficiency, draw inference on potential environmental changes from sensing activities of neighboring nodes, adaptively aggregate data from different types of nodes, and collectively self-heal (i.e., detect and eliminate) false positive sensor data. The BiSNET platform is lightweight thanks to a set of simple biologically-inspired mechanisms.

Section snippets

Contributions

This section summarizes the contributions of this work.

  • Adaptive and decentralized duty cycle management: BiSNET is the first attempt to investigate dynamic duty cycle management that adaptively balances the tradeoff between power efficiency and sensing responsiveness for environmental changes (i.e., the risk to miss significant environmental changes during sleep period). The BiSNET platform allows each sensor node to autonomously adjusts its sleep period in a decentralized manner.

  • A simple and

Design principles for BiSNET agents

In BiSNET, agents are designed after the following biological principles.

  • 1.

    Decentralization: Similar to biological systems (e.g., bee colonies), there are no centralized entities in BiSNET to control and coordinate agents. Decentralization allows agents to be scalable and simple by avoiding a single point of performance bottlenecks and failures [11], [12] and by avoiding any central coordination in deploying agents [13].

  • 2.

    Autonomy: Similar to biological entities (e.g., bees), agents sense their

BiSNET

This section describes a programming abstraction for agents, the default agent the BiSNET platform provides, and the functions of the BiSNET platform.

Simulation results

This section shows a series of simulation results to evaluate BiSNET in terms of adaptability, scalability, self-healing, inference, power efficiency9 and simplicity. BiSNET is implemented on TinyOS and evaluated in the PowerTOSSIM simulator [14].

Related work

This paper describes the research findings extending several previous works [17], [18]. In [17], BiSNET did not support MWSNs; it supported only one type of sensor nodes throughout a network. In this paper, BiSNET is extended to support MWSNs by introducing the concept of pheromones. Pheromones are used, in the agent replication behavior, for each agent to aggregate different types of sensor data and self-heal false positive data. Pheromones are also used for each platform to perform adaptive

Concluding remarks

This paper describes a biologically-inspired sensor networking architecture, called BiSNET, which addresses several key issues in MWSNs such as autonomy, scalability, adaptability, self-healing and simplicity. Inspired from biological systems, BiSNET provides a set of simple yet generic solutions that address those issues simultaneously rather than focusing on them one by one or in an ad-hoc manner. This paper describes the biologically-inspired mechanisms in BiSNET and evaluates their impacts

Pruet Boonma is a second year Ph.D. student at the University of Massachusetts, Boston. His research interests include adaptive distributed systems and biologically-inspired wireless sensor networks. He received his B.E. in Computer Engineering from Chiangmai University, Thailand, and M.IT. in Computer Science from Monash University, Australia. He worked for the Department of Computer Engineering, Chaingmai University, as a faculty member since 1997. In 2001, he received Australian Government

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    Pruet Boonma is a second year Ph.D. student at the University of Massachusetts, Boston. His research interests include adaptive distributed systems and biologically-inspired wireless sensor networks. He received his B.E. in Computer Engineering from Chiangmai University, Thailand, and M.IT. in Computer Science from Monash University, Australia. He worked for the Department of Computer Engineering, Chaingmai University, as a faculty member since 1997. In 2001, he received Australian Government Scholarship (AusAID) for his master program in Australia. In 2005, he received Thai Government Scholarship for his PhD. work in the United States.

    Junichi Suzuki received the Ph.D. in computer science from Keio University, Japan, in 2001. He joined the Department of Computer Science, University of Massachusetts, Boston in September 2004, where he is currently an assistant professor. From 2001 to 2004, he was with the School of Information and Computer Science, University of California, Irvine (UCI), as a postdoctoral research fellow. Before joining UCI, he was with Object Management Group Japan, Inc., as the Technical Director. His research interests include autonomous adaptive distributed systems, biologically-inspired (e.g., ecological, genetic, immunological and developmental) software designs, and model-driven software engineering. In these areas, he has authored two books, published over 70 refereed papers including five award papers, gave over 100 invited talks, and served on technical program committees of over 25 conferences including IEEE AINA 2008 and IEEE/ACM/Create-Net/ICST BIONETICS 2007.

    He is an active participant and contributor of the International Standard Organization SC7/WG19 and the Object Management Group, Super Distributed Objects SIG. He is a member of IEEE and ACM.

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