Node Positioning for Increased Dependability of Wireless Sensor Networks in .NET

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Node Positioning for Increased Dependability of Wireless Sensor Networks
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Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250
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Department of Computer Science, Southern Illinois University, Carbondale, IL 62901
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9.1 INTRODUCTION Advances in microelectronics have enabled the development of very tiny sensor nodes that have the ability of measuring ambient conditions such as temperature, pressure, humidity, light intensity, vibration, and so on. The sensed data can then be transmitted through an onboard radio transmitter to a single or multiple base stations where it can be further processed. The cost and size advantage of such emerging sensor nodes has encouraged practitioners to explore using them collaboratively in a network formed in ad hoc manner. Such networked sensor systems are not only cost effective but can also provide fast and accurate information gathering in remote and risky areas. Figure 9.1 depicts a typical sensor network architecture. The base station acts as a gateway for linking the sensors to multiple command nodes. The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in applications such as disaster management, combat eld reconnaissance, border protection, and security surveillance [1, 2]. Sensors in these applications are expected to be remotely deployed and to operate autonomously in unattended environments. While the initial view of the community was that WSNs will play a complementary role that enhances the quality of these applications, recent research results have encouraged practitioners to envision an increased reliance
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Algorithms and Protocols for Wireless Sensor Networks, Edited by Azzedine Boukerche Copyright 2009 by John Wiley & Sons Inc.
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Figure 9.1. A sensor network for a disaster management application.
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on WSNs. In order to best realize their potential, dependable design and operation of WSNs have to be ensured. Dependability is a property that indicates the ability of a system to deliver services to the user subject to a required level of quality. Dependability can be speci ed in terms of attributes, such as responsiveness, availability, safety, security, and so on. Dependability in WSNs is complicated by many factors including the following: (1) Sensors are signi cantly constrained in the amount of available resources such as energy, storage, and computation; (2) sensors are expected to be deployed in very large numbers in normal as well as forbidding environments; and (3) WSNs suffer from structural weakness and limited physical protection. Moreover, dependability requirements may vary according to a network s mission, such as eld of deployment (e.g., hostile versus friendly), type of application (e.g., monitoring, tracking, data collection), mode of operation (e.g., normal, exception, post-event recovery), and time. The bulk of the research on WSNs has focused on the effective support of the functional (e.g., data latency) and the nonfunctional (e.g., data integrity) requirements while coping with the resource constraints and on the conservation of available energy in order to prolong the life of the network. Contemporary design schemes for WSNs pursue optimization at the various layers of the communication protocol stack. Popular optimization techniques at the network layer include multihop route setup, in-network data aggregation, and hierarchical network topology [3]. For medium access control, collision avoidance, minimizing idle listening of radio receivers, and output power control are a sample of proposed schemes [1, 4]. At the application layer, examples include adaptive activation of nodes, lightweight data authentication and encryption, load balancing, and query optimization [5, 6].
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One of the design optimization strategies is to deterministically place the sensor nodes in order to meet the desired performance goals. In such cases, the coverage can be ensured through careful planning of node densities and eld of view, and thus the network topology can be established at setup time. However, in most WSN applications, sensors deployment is random and little control can be exerted in order to ensure coverage and yield uniform node density while achieving strongly connected network topology. Also, the location of the base station can have great in uence on the network performance. For example, routing data to a base station that is distant from the source sensor usually involves numerous relaying nodes and thus increases the aggregate delay and energy consumption and risks a packet loss due to link errors. Therefore careful selection of the base-station location may affect various performance metrics such as energy consumption, delay, and throughput. Unlike sensors deployment, positioning of the base stations can be somewhat controlled and is feasible in many application setups. Optimal node placement is a very challenging problem that has been proven to be NP-complete for base stations [7] and for most of the formulations of sensors deployment [8 10]. To tackle such complexity, several heuristics have been proposed to nd suboptimal solutions [7, 11 14]. However, the context of these optimization strategies is mainly static in the sense that assessing the quality of candidate positions is based on structural quality metric such as distance, network connectivity, and/or basing the analysis on a xed topology. Therefore, we classify them as static approaches. We note, however, that dynamically adjusting nodes location can further increase the dependability of WSNs since the optimality of the initial positions may become void during the operation of the network depending on the network state and various external factors. For example, traf c patterns can change based on the monitored events, load may not be balanced among the nodes causing bottlenecks, application-level interest can vary over time, and the available network resources may change due to the depletion of energy of some nodes and/or the addition of more nodes. In this chapter we opt to categorize the various strategies for statically positioning base stations and sensor nodes in WSNs. We contrast a number of published approaches highlighting their strengths and limitations. Our aim is to help applications designers identify alternative solutions and select appropriate strategies. In addition to surveying static positioning approaches, we show that dynamically repositioning nodes while the network is operational can be a very effective means for boosting the network s dependability attributes. We describe scenarios for which node relocation can be pursued to counter holes in coverage, achieve/maintain strong network connectivity, preserve sensor s energy, increase data timeliness, and boost the node s physical security. We highlight the issues, report on the state of the art, and outline open esearch problem. The chapter is organized as follows. The next section is dedicated to static strategies for node positioning. We separately cover approaches for sensor placement as well as single and multiple base stations positioning. In Section 9.3 we turn our attention to dynamic positioning schemes. We highlight the technical issues and describe sample techniques that exploit sensors and base-station repositioning to enhance the network dependability. The bulk of the discussion applies to a single base-station setup or to
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