ISBN-10:
0471684724
ISBN-13:
9780471684725
Pub. Date:
09/02/2005
Publisher:
Wiley
Handbook of Sensor Networks: Algorithms and Architectures / Edition 1

Handbook of Sensor Networks: Algorithms and Architectures / Edition 1

by Ivan Stojmenovic

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Product Details

ISBN-13: 9780471684725
Publisher: Wiley
Publication date: 09/02/2005
Series: Wiley Series on Parallel and Distributed Computing Series , #49
Pages: 552
Product dimensions: 6.30(w) x 9.60(h) x 1.30(d)

About the Author

IVAN STOJMENOVIC, PHD, is Professor of Computer Science, School of Information Technology and Engineering, University of Ottawa. Dr. Stojmenovic has published more than 200 scholarly papers in peer-reviewed journals and conferences. He is also the editor of Handbook of Wireless Networks and Mobile Computing and coeditor of Mobile Ad Hoc Networking.

Read an Excerpt

Handbook of Sensor Networks


By Ivan Stojmenovic

John Wiley & Sons

ISBN: 0-471-68472-4


Chapter One

Introduction to Wireless Sensor Networking

FERNANDO MARTINCIC and LOREN SCHWIEBERT Wayne State University, Detroit, Michigan

This chapter introduces the topic of wireless sensor networks from the applications perspective. A wireless sensor network consists of a possibly large number of wireless devices able to take environmental measurements such as temperature, light, sound, and humidity. These sensor readings are transmitted over a wireless channel to a running application that makes decisions based on these sensor readings. Authors describe some examples of proposed wireless sensor applications, and consider the following two questions to motivate an application-based viewpoint. What aspects of wireless sensors make the implementation of applications more challenging, or at least different? One widely recognized issue is the limited power available to each wireless sensor node, but there are other challenges such as limited storage or processing. What services are required for a wireless sensor network application to achieve its intended purpose? A number of widely applicable services, such as time synchronization and location determination are briefly discussed in this chapter. Other services are needed to support database requirements, such as message routing, topology management, and data aggregation and storage. As most of these topics are covered in separate chapters, this chapter serves to provide a broad framework to enable the reader to see how these different topics tie together into a cohesive set of capabilities for building wireless sensor network applications.

1.1 INTRODUCTION

A wireless sensor network consists of a possibly large number of wireless devices able to take environmental measurements. Typical examples include temperature, light, sound, and humidity. These sensor readings are transmitted over a wireless channel to a running application that makes decisions based on these sensor readings. Many applications have been proposed for wireless sensor networks, and many of these applications have specific quality of service (QoS) requirements that offer additional challenges to the application designer. In this chapter, we introduce the topic of wireless sensor networks from the perspective of the application.

Along with some examples of proposed wireless sensor applications, we consider two questions to motivate an application-based viewpoint:

1. What aspects of wireless sensors make the implementation of applications more challenging, or at least different? One widely recognized issue is the limited power available to each wireless sensor node, but other challenges such as limited storage or processing capabilities play a significant role in constraining the application development. 2. What services are required for a wireless sensor network application to achieve its intended purpose? A number of widely applicable services, such as time synchronization and location determination are briefly discussed. Other services are needed to support database requirements, such as message routing, topology management, and data aggregation and storage.

Because some of these topics are covered in separate chapters, this discussion serves to provide a broad framework to enable the reader to see how these different topics tie together into a cohesive set of capabilities for building wireless sensor network applications.

1.2 DESIGN CHALLENGES

Several design challenges present themselves to designers of wireless sensor network applications. The limited resources available to individual sensor nodes implies designers must develop highly distributed, fault-tolerant, and energy-efficient applications in a small memory-footprint. Consider the latest-generation MICAz [1,2] sensor node shown in Figure 1.1.

MICAz motes are equipped with an Atmel128L [4] processor capable of a maximum throughput of 8 millions of instructions per second (MIPS) when operating at 8 MHz. It also features an IEEE 802.15.4/Zigbee compliant RF transceiver, operating in the 2.4-2.4835-GHz globally compatible industrial scientific medical (ISM) band, a direct spread-spectrum radio resistant to RF interference, and a 250-kbps data transfer rate. The MICAz runs on TinyOS [5] (v1.1.7 or later) and is compatible with existing sensor boards that are easily mounted onto the mote. A partial list of specifications given by the manufacturers of the MICAz mote is presented in Figure 1.2.

For wireless sensor network applications to have reasonable longevity, an aggressive energy-management policy is mandatory. This is currently the greatest design challenge in any wireless sensor network application. Considering that in the MICAz mote the energy cost associated with transmitting a byte over the transceiver is substantially greater than performing local computation, developers must leverage local processing capabilities to minimize battery-draining radio communication. Several key differences between more traditional ad hoc networks and wireless sensor networks exist [6]:

Individual nodes in a wireless sensor network have limited computational power and storage capacity. They operate on nonrenewable power sources and employ a short-range transceiver to send and receive messages. The number of nodes in a wireless sensor network can be several orders of magnitude higher than in an ad hoc network. Thus, algorithm scalability is an important design criterion for sensor network applications. Sensor nodes are generally densely deployed in the area of interest. This dense deployment can be leveraged by the application, since nodes in close proximity can collaborate locally prior to relaying information back to the base station. Sensor networks are prone to frequent topology changes. This is due to several reasons, such as hardware failure, depleted batteries, intermittent radio interference, environmental factors, or the addition of sensor nodes. As a result, applications require a degree of inherent fault tolerance and the ability to reconfigure themselves as the network topology evolves over time.

Wireless sensor networks do not employ a point-to-point communication paradigm because they are usually not aware of the entire size of the network and nodes are not uniquely identifiable. Consequently, it is not possible to individually address a specific node. Paradigms, such as directed diffusion [7,8], employ a data-centric view of generated sensor data. They identify information produced by the sensor network as kattribute, valuel pairs. Nodes request data by disseminating interests for this named data throughout the network. Data that matches the criterion are relayed back toward the querying node.

Even with the limitations individual sensor nodes possess and the design challenges application developers face, several advantages exist for instrumenting an area with a wireless sensor network [9]:

Due to the dense deployment of a greater number of nodes, a higher level of fault tolerance is achievable in wireless sensor networks. Coverage of a large area is possible through the union of coverage of several small sensors. Coverage of a particular area and terrain can be shaped as needed to overcome any potential barriers or holes in the area under observation. It is possible to incrementally extend coverage of the observed area and density by deploying additional sensor nodes within the region of interest. An improvement in sensing quality is achieved by combining multiple, independent sensor readings. Local collaboration between nearby sensor nodes achieves a higher level of confidence in observed phenomena. Since nodes are deployed in close proximity to the sensed event, this overcomes any ambient environmental factors that might otherwise interfere with observation of the desired phenomenon.

1.3 WIRELESS SENSOR NETWORK APPLICATIONS

Several applications have been envisioned for wireless sensor networks [6]. These range in scope from military applications to environment monitoring to biomedical applications. This section discusses proposed and actual applications that have been implemented by various research groups.

1.3.1 Military Applications

Wireless sensor networks can form a critical part of military command, control, communications, computing, intelligence, surveillance, reconnaissance, and targeting (C4ISRT) systems. Examples of military applications include monitoring of friendly and enemy forces; equipment and ammunition monitoring; targeting; and nuclear, biological, and chemical attack detection.

By equipping or embedding equipment and personnel with sensors, their condition can be monitored more closely. Vehicle-, weapon-, and troop-status information can be gathered and relayed back to a command center to determine the best course of action. Information from military units in separate regions can also be aggregated to give a global snapshot of all military assets.

By deploying wireless sensor networks in critical areas, enemy troop and vehicle movements can be tracked in detail. Sensor nodes can be programmed to send notifications whenever movement through a particular region is detected. Unlike other surveillance techniques, wireless sensor networks can be programmed to be completely passive until a particular phenomenon is detected. Detailed and timely intelligence about enemy movements can then be relayed, in a proactive manner, to a remote base station.

In fact, some routing protocols have been specifically designed with military applications in mind [10]. Consider the case where a troop of soldiers needs to move through a battlefield. If the area is populated by a wireless sensor network, the soldiers can request the location of enemy tanks, vehicles, and personnel detected by the sensor network (Fig. 1.3). The sensor nodes that detect the presence of a tank can collaborate to determine its position and direction, and disseminate this information throughout the network. The soldiers can use this information to strategically position themselves to minimize any possible casualties.

In chemical and biological warfare, close proximity to ground zero is needed for timely and accurate detection of the agents involved. Sensor networks deployed in friendly regions can be used as early-warning systems to raise an alert whenever the presence of toxic substances is detected. Deployment in an area attacked by chemical or biological weapons can provide detailed analysis, such as concentration levels of the agents involved, without the risk of human exposure.

1.3.2 Environmental Applications

By embedding a wireless sensor network within a natural environment, collection of long-term data on a previously unattainable scale and resolution becomes possible. Applications are able to obtain localized, detailed measurements that are otherwise more difficult to collect. As a result, several environmental applications have been proposed for wireless sensor networks [6,9]. Some of these include habitat monitoring, animal tracking, forest-fire detection, precision farming, and disaster relief applications.

Habitat monitoring permits researchers to obtain detailed measurements of a particular environment in an unobtrusive manner. For example, applications such as the wireless sensor network deployed on Great Duck Island [11] allow researchers to monitor the nesting burrows of Leach's Storm Petrels without disturbing these seabirds during the breeding season. Deployment of the sensor network occurs prior to the arrival of these offshore birds. Monitoring of the birds can then proceed without direct human contact. Similarly, the PODS project [12,13] at the University of Hawaii uses wireless sensor networks to observe the growth of endangered species of plants. Data collected by the sensor network is used to determine the environmental factors that support the growth of these endangered plants. These two applications are discussed in detail in Sections 1.3.4 and 1.3.5.

Consider a scenario where a fire starts in a forest. A wireless sensor network deployed in the forest could immediately notify authorities before it begins to spread uncontrollably (see Fig. 1.4). Accurate location information [14] about the fire can be quickly deduced. Consequently, this timely detection gives firefighters an unprecedented advantage, since they can arrive at the scene before the fire spreads uncontrollably.

Precision farming [15] is another application area that can benefit from wireless sensor network technology. Precision farming requires analysis of spatial data to determine crop response to varying properties such as soil type [16]. The ability to embed sensor nodes in a field at strategic locations could give farmers detailed soil analysis to help maximize crop yield or possibly alert them when soil and crop conditions attain a predefined threshold. Since wireless sensor networks are designed to run unattended, active physical monitoring is not required.

Disaster relief efforts such as the ALERT flood-detection system [17] make use of remote field sensors to relay information to a central computer system in real time. Typically, an ALERT installation comprises several types of sensors, such as rainfall sensors, water-level sensors, and other weather sensors. Data from each set of sensors are gathered and relayed to a central base station.

1.3.3 Health Applications

Potential health applications abound for wireless sensor networks. Conceivably, hospital patients could be equipped with wireless sensor nodes that monitor the patients' vital signs and track their location. Patients could move about more freely while still being under constant supervision. In case of an accident-say, the patient trips and falls-the sensor could alert hospital workers as to the patient's location and condition. A doctor in close proximity, also equipped with a wireless sensor, could be automatically dispatched to respond to the emergency.

Glucose-level monitoring is a potential application suitable for wireless sensor networks [18]. Individuals with diabetes require constant monitoring of blood sugar levels to lead healthy, productive lives. Embedding a glucose meter within a patient with diabetes could allow the patient to monitor trends in blood-sugar levels and also alert the patient whenever a sharp change in blood-sugar levels is detected. Information could be relayed wirelessly from the monitor to a wristwatch display. It would then be possible to take corrective measures to normalize bloodsugar levels in a timely manner before they get to critical levels. This is of particular importance when the individual is asleep and may not be aware that their bloodsugar levels are abnormal.

The Smart Sensors and Integrated Microsystems (SSIM) project at Wayne State University and the Kresge Eye Institute are working on developing an artificial retina [18]. One of the project goals is to build a chronically implanted artificial retina that allows a visually impaired individual to "see" at an acceptable level. Currently, smart sensor chips equipped with 100 microsensors exist that are used in ex vivo retina testing. The smart sensor comprises an integrated circuit (with transmit and receive capabilities) and an array of sensors. Challenges in this application include establishing a communication link between the retinal implant and an external computer to determine if the image is correctly seen. Regulating the amount of power used by the system to avoid damage to the retina and surrounding tissue is also a primary concern.

1.3.4 Habitat Monitoring on Great Duck Island

Leach's Storm Petrel (Fig. 1.5) is a common elusive seabird in the western North Atlantic. Most of their lives are spent off-shore, only to return to land during the breeding season. During this time, they nest in burrows located in soft, peaty soil, and are active predominantly at night. It is believed Great Duck Island, located 15 km off the coast of Maine, has one of the largest petrel breeding colonies in the eastern United States.

Petrel activity monitoring is a delicate problem, since disturbance or interference on the part of humans can lead to nest abandonment or increased predation on chicks or eggs.

(Continues...)



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Table of Contents

Preface.

Contributors.

1. Introduction to Wireless Sensor Networking (Fernando Martincic and Loren Schwiebert).

2. Distributed Signal Processing Algorithms for the Physical Layer of Large-Scale Sensor Networks (An-swol Hu and Sergio D. Servetto).

3. Energy Scavenging and Non-traditional Power Sources for Wireless Sensor Networks (Shad Roundy and Luc Frechette).

4. A virtual infrastructure for wireless sensor networks (Stephan Olariu, Ashraf Wadaa, Qingwen Xu, and Ivan Stojmenovic).

5. Broadcast authentication and key management for secure sensor networks (Peng Ning and Donggang Liu).

6. Embedded operating systems for wireless micro sensor nodes (Brian Shucker, Jeff Rose, Anmol Sheth, James Carlson, Shah Bhatti, Hui Dai, Jing Deng and Richard Han).

7. Time Synchronization and Calibration in Wireless Sensor Networks (Kay Roemer, Philipp Blum and Lennart Meier).

8. The Wireless Sensor Network MAC (Edgar H. Callaway).

9. Localization in sensor networks (Jonathan Bachrach, and Christopher Taylor).

10. Topology construction and maintenance in wireless sensor networks (Jennifer Hou, Ning Li, and Ivan Stojmenovic).

11. Energy efficient broadcasting, activity scheduling and area coverage in sensor networks (David Simplot-Ryl, Ivan Stojmenovic, and Jie Wu).

12. Geographic and energy aware routing in sensor networks (Hannes Frey and Ivan Stojmenovic).

13. Data-centric protocols for wireless sensor networks (Ivan Stojmenovic and Stephan Olariu).

14. Path exposure, target location, classification and tracking in sensor networks (Kousha Moaveni-Nejad, and XiangYang Li).

15. Data gathering and fusion in sensor networks (Wei-Peng Chen and Jennifer Hou).

Index.

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"...Stojmenovic has succeeded in compiling a diverse and very useful tool for the professional and student alike." (CHOICE, June 2006)

"…the book contains excellent contributions to the research literature…I undoubtedly recommend it." (Computing Reviews.com, December 13, 2005)

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