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As wireless sensor platforms become less expensive and more powerful, the promise of their wide-spread use for everything from health monitoring to military sensing continues to increase. Like other networks, sensor networks are vulnerable to malicious attack; however, the hardware simplicity of these devices makes defense mechanisms designed for traditional networks infeasible. This work explores the denial-of-sleep attack, in which a sensor node's power supply is targeted. Attacks of this type can reduce sensor lifetime from years to days and can have a devastating impact on a sensor network. This work identifies vulnerabilities in state-of-the-art sensor network medium access control (MAC) protocols that leave them susceptible to denial-of-sleep attack. It then classifies these attacks in terms of an attacker's knowledge of the MAC layer protocol and ability to bypass authentication and encryption protocols. Attacks from each category in the classification are modeled to show the impacts on four current sensor network MAC protocols: S-MAC, T-MAC, B-MAC and G-MAC. To validate the effectiveness and analyze the efficiency of the attacks, implementations of selected attacks on S-MAC and T-MAC are described and analyzed in detail. This research goes on to introduce a suite of mechanisms designed to detect and mitigate the effects of denial-of-sleep attacks on sensor networks. The Clustered Anti Sleep-Deprivation for Sensor Networks, or Caisson, suite includes a lightweight, platform-independent anti-replay mechanism, an adaptive rate-limiter and a jamming detection and mitigation mechanism. These tools are designed to be applied selectively or in concert to defend against denial-of-sleep attacks depending on the specific vulnerabilities in the MAC protocol used in a particular sensor network deployment. This work makes two major contributions to state-of-the-art wireless sensor network research. First, it fully explores the denial-of-sleep attack, to include the implementation of a subset of these attacks on actual sensor devices and an analysis of the efficiency of these attacks. Second, it provides a set of tools by which these attacks are detected and defeated in a lightweight, platform-independent, and protocol-independent way. If sensor networks are to live up to current expectations, they must be robust in the face of newly emerging network attacks, to include denial-of-sleep. / Ph. D.

Approved for public release; distribution in unlimited. / a beam towards the UAV. A simulation model was developed and implemented in MATLAB programming language to study the effectiveness of beamforming using sensor clusters for establishing a communication link to the UAV. Results showed that the antenna main lobe remained relatively unchanged in the presence of position errors and sensor node failures or when the density of the sensor nodes changed. Additionally, the maximum average power gain of the main lobe can be increased by increasing the density of the sensor cluster, thereby increasing the transmission range between the sensor clusters and the UAV. / Civilian, Singapore Ministry of Defense

Zhou Yangfan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 102-112). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction and Background Study --- p.1 / Chapter 1.1 --- Wireless Sensor Networks --- p.1 / Chapter 1.1.1 --- Wireless Integrated Network Sensors --- p.1 / Chapter 1.1.2 --- Main Challenge of In-situ Sensing with Sensor Nodes: Limited Energy Resource --- p.3 / Chapter 1.1.3 --- Networking the Sensor Nodes --- p.4 / Chapter 1.2 --- Applications of Wireless Sensor Networks --- p.4 / Chapter 1.3 --- Characteristics of Wireless Sensor Networks: A Summary --- p.6 / Chapter 1.4 --- Energy-Efficient and Reliable Wireless Sensor Networks --- p.9 / Chapter 2 --- PORT: A Price-Oriented Reliable Transport Protocol --- p.12 / Chapter 2.1 --- Reliable Sensor-to-Sink Data Communications in Wireless Sensor Networks --- p.14 / Chapter 2.2 --- Related Work --- p.17 / Chapter 2.3 --- Protocol Requirements --- p.20 / Chapter 2.4 --- Design Considerations --- p.25 / Chapter 2.4.1 --- The concept of node price --- p.25 / Chapter 2.4.2 --- Link-loss rate estimation --- p.28 / Chapter 2.4.3 --- Routing scheme --- p.29 / Chapter 2.5 --- Protocol Description --- p.31 / Chapter 2.5.1 --- Task initialization --- p.31 / Chapter 2.5.2 --- Feedback of newly desired source reporting rates --- p.32 / Chapter 2.5.3 --- Feedback of wireless communication condition --- p.32 / Chapter 2.5.4 --- Fault tolerance and scalability considerations --- p.33 / Chapter 2.6 --- Protocol Evaluation: A Case Study --- p.34 / Chapter 2.6.1 --- Simulation model --- p.34 / Chapter 2.6.2 --- Energy consumption comparison --- p.36 / Chapter 2.6.3 --- The impact of reporting sensors' uncertainty distribution --- p.39 / Chapter 2.7 --- Conclusion --- p.40 / Chapter 3 --- Setting Up Energy-Efficient Paths --- p.41 / Chapter 3.1 --- Transmitter Power Setting for Energy-Efficient Sensor-to-Sink Data Communications --- p.46 / Chapter 3.1.1 --- "Network, communication, and energy consumption models" --- p.46 / Chapter 3.1.2 --- Transmitter power setting problem for energy-efficient sensor-to-sink data communications --- p.49 / Chapter 3.2 --- Setting Up the Transmitter Power Levels for Sensor-to-Sink Traffic --- p.51 / Chapter 3.2.1 --- BOU: the basic algorithm --- p.52 / Chapter 3.2.2 --- Packet implosion of BOU: the challenge --- p.53 / Chapter 3.2.3 --- Determining the waiting time before broadcasting --- p.56 / Chapter 3.2.4 --- BOU-WA: an approximation approach --- p.60 / Chapter 3.3 --- Simulation Results --- p.62 / Chapter 3.3.1 --- The comparisons of BOU and BOU-WA --- p.63 / Chapter 3.3.2 --- The approximation of BOU-WA --- p.65 / Chapter 3.4 --- Related Work --- p.67 / Chapter 3.5 --- Conclusion Remarks and Future Work --- p.69 / Chapter 4 --- Solving the Sensor-Grouping Problem --- p.71 / Chapter 4.1 --- Introduction --- p.73 / Chapter 4.2 --- The Normalized Minimum Distance i:A Point-Distribution Index --- p.74 / Chapter 4.3 --- The Sensor-Grouping Problem --- p.77 / Chapter 4.3.1 --- Problem Formulation --- p.80 / Chapter 4.3.2 --- A General Sensing Model --- p.81 / Chapter 4.4 --- Maximizing-i Node-Deduction Algorithm for Sensor-Grouping Problem --- p.84 / Chapter 4.4.1 --- Maximizing-i Node-Deduction Algorithm --- p.84 / Chapter 4.4.2 --- Incremental Coverage Quality Algorithm: A Benchmark for MIND --- p.86 / Chapter 4.5 --- Simulation Results --- p.87 / Chapter 4.5.1 --- Number of Groups Formed by MIND and ICQA --- p.88 / Chapter 4.5.2 --- The Performance of the Resulting Groups --- p.89 / Chapter 4.6 --- Conclusion --- p.90 / Chapter 5 --- Conclusion --- p.92 / Chapter A --- List of Research Conducted --- p.96 / Chapter B --- Algorithms in Chapter 3 and Chapter 4 --- p.98 / Bibliography --- p.102

Wireless sensor networks offer the advantages of low cost, flexible measurement of phenomenon in a wide variety of applications, and easy deployment. Since sensor nodes are typically battery powered, energy efficiency is an important objective in designing sensor network algorithms. These algorithms are often application-specific, owing to the need to carefully optimize energy usage, and since deployments usually support a single or very few applications.<p> This thesis concerns applications in which the sensors monitor a continuous scalar field, such as temperature, and addresses the problem of determining the location of a contour line in this scalar field, in response to a query, and communicating this information to a designated sink node. An energy-efficient solution to this problem is proposed and evaluated. This solution includes new contour detection and query propagation algorithms, in-network-processing algorithms, and routing algorithms. Only a small fraction of network nodes may be adjacent to the desired contour line, and the contour detection and query propagation algorithms attempt to minimize processing and communication by the other network nodes. The in-network processing algorithms reduce communication volume through suppression, compression and aggregation techniques. Finally, the routing algorithms attempt to route the contour information to the sink as efficiently as possible, while meshing with the other algorithms. Simulation results show that the proposed algorithms yield significant improvements in data and message volumes compared to baseline models, while maintaining the integrity of the contour representation.

University of Macau / Faculty of Science and Technology / Department of Electrical and Electronics Engineering

Wireless sensor networks offer the advantages of low cost, flexible measurement of phenomenon in a wide variety of applications, and easy deployment. Since sensor nodes are typically battery powered, energy efficiency is an important objective in designing sensor network algorithms. These algorithms are often application-specific, owing to the need to carefully optimize energy usage, and since deployments usually support a single or very few applications.<p> This thesis concerns applications in which the sensors monitor a continuous scalar field, such as temperature, and addresses the problem of determining the location of a contour line in this scalar field, in response to a query, and communicating this information to a designated sink node. An energy-efficient solution to this problem is proposed and evaluated. This solution includes new contour detection and query propagation algorithms, in-network-processing algorithms, and routing algorithms. Only a small fraction of network nodes may be adjacent to the desired contour line, and the contour detection and query propagation algorithms attempt to minimize processing and communication by the other network nodes. The in-network processing algorithms reduce communication volume through suppression, compression and aggregation techniques. Finally, the routing algorithms attempt to route the contour information to the sink as efficiently as possible, while meshing with the other algorithms. Simulation results show that the proposed algorithms yield significant improvements in data and message volumes compared to baseline models, while maintaining the integrity of the contour representation.