In multi-hop wireless sensor networks that are characterized by many-to-one (converge-cast) traffic patterns, problems related to energy imbalance among sensors often appear. When the transmission range is fixed for nodes throughout the network, the amount of traffic that sensors are required to forward increases dramatically as the distance to the data sink becomes smaller. Thus, sensors closest to the data sink tend to die early. Network lifetime can be improved to a limited extent by the use of a more intelligent transmission power control policy that balances the energy used in each node by requiring nodes further from the data sink to transmit over longer distances (although not directly to the data sink). Alternatively, policies such as data aggregation allow the network to operate in a more energy efficient manner. Since the deployment of an aggregator node may be significantly more expensive than the deployment of an ordinary microsensor node, there is a cost tradeoff involved in this approach. This paper provides an analysis of these policies for mitigating the sensor network hot spot problem, considering energy efficiency as well as cost efficiency. 1

Abstract Cross-layer designs have received much attention recently. While not as general as layered architectures, they prove to be more tunable and energy-efficient in many scenarios. This flexibility can be exploited by a middleware whose principal task is to adapt quality of service provided by the network to the application’s needs using the pre-defined parameters of the cross-layer protocol. In this paper, we study the ways in which a middleware (Milan) can control a cross-layer protocol for wireless sensor networks (DAPR), thereby ensuring that the network provides the application’s required quality of service while removing this burden from the application designer. Keywords: Cross-layer protocols, middleware, wireless sensor networks 1.

Abstract—In multihop wireless sensor networks that are often characterized by many-to-one (convergecast) traffic patterns, problems related to energy imbalance among sensors often appear. Sensors closer to a data sink are usually required to forward a large amount of traffic for sensors farther from the data sink. Therefore, these sensors tend to die early, leaving areas of the network completely unmonitored and reducing the functional network lifetime. In our study, we explore possible sensor network deployment strategies that maximize sensor network lifetime by mitigating the problem of the hot spot around the data sink. Strategies such as variable-range transmission power control with optimal traffic distribution, mobile-data-sink deployment, multiple-data-sink deployment, nonuniform initial energy assignment, and intelligent sensor/relay deployment are investigated. We suggest a general model to analyze and evaluate these strategies. In this model, we not only discover how to maximize the network lifetime given certain network constraints but also consider the factor of extra costs involved in more complex deployment strategies. This paper presents a comprehensive analysis on the maximum achievable sensor network lifetime for different deployment strategies, and it also provides practical cost-efficient sensor network deployment guidelines. Index Terms—Wireless sensor networks, data dissemination, linear programming, deployment strategies. Ç 1

Abstract. Sensor sleeping is a widely-used and cost-effective technique to save energy in wireless sensor networks. Protocols at different stack levels can, either individually or simultaneously, make the sensor sleep so as to extend the application lifetime. To determine the best choice for sensor sleeping under different network conditions and application requirements, we investigate single layer and multi-layer sleeping schemes at the routing and MAC layers. Our results show that routing layer sleeping performs better when there is high network redundancy or high contention, while MAC layer sleeping performs better when there is low contention or in small networks. Moreover, multi-layer sleeping requires cross-layer coordination to outperform single layer sleeping under low contention. Therefore, our conclusions can not only guide the implementation of practical sensor networks, but they also provide hints to the design of cross-layer power management to dynamically choose the best sleeping scheme under different network and application scenarios.

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The lifetime of wireless sensor networks can be increased by minimizing the number of active nodes that provide complete coverage, while switching off the rest. In this paper, we propose a distributed and scalable node-scheduling algorithm that conserves overall system energy by minimizing the number of active nodes, localizing the execution to the dying sensor(s), and minimizing the frequency of execution by reacting only to the occurrence of a sensing hole. This effects an increased system lifetime while maintaining coverage over an application-defined threshold value. We compare our algorithm to a network with a centralized nodescheduling algorithm. Our results show equivalent coverage degree over a wide range of sensor networks.

Single-hop centralized wireless sensor networks are widely used for applications ranging from security and surveillance to medical monitoring. Often the goal of these networks is to provide satisfactory quality of service (QoS) to the application under different system states, but it is difficult to determine how the appropriate sensor sets should be selected over time, given the knowledge of all possible sensor sets to support the application QoS requirements. To address this problem, in this paper we propose a novel cost function called SUI (Sensor Usage Index) that is based on a sensor’s relative ideal lifetime (SRIL) and can be used to select sensor sets so as to meet application QoS requirements for extended periods of time. Simulation results show that utilizing the proposed cost function for sensor set selection enables the network to meet application QoS requirements for longer than using other standard sensor selection schemes. In fact, our scheme approaches the optimal network lifetime, which can be found using global knowledge of the sensors and the system dynamics.

Abstract – Video-based wireless sensor networks continue to gain increasing interest due to their ability to collect visual information for a wide range of applications. However, knowledge about these types of networks is mostly related to visual algorithms, leaving the networking perspective aside. In this work, we analyze how an algorithm designed for traditional wireless sensor networks, which integrates the coverage and routing problem, behaves in video-based networks. Our results show that because of the unique way that cameras capture data, the sensor network algorithm does not give the expected results in terms of coverage preservation of monitored areas. We discuss the main differences between traditional wireless sensor networks and video-based networks that lead to such a result, and we provide ideas for how protocols should be designed for the unique features of video-based networks. I.

Abstract Given n sensors and m targets, a monitoring schedule is a partition of the sensor set such that each part of the partition can monitor all targets. Monitoring schedules are used to maximize the time all targets are monitored when there is no possibility of replacing the batteries of the sensors. Each part of the partition is used for one unit of time, and thus the goal is to maximize the number of parts in the partition.

during the winter of 2006. Her primary research interests lie in the area of wireless communication and networking, wireless sensor networks, and visual (camera-based) networks. ii Acknowledgements During the last five years I had the pleasure to work in the Wireless Lab. Among all the wonderful people I met during this period, I would first like to thank to my advisor, Professor Wendi Heinzelman for the guidance and encouragement she provided. I learned many lessons from her, but the most important were to be enthusiastic about my work and life, and to be persistent. Her intelligence, great life attitude and passion for research truly inspired me. I would also like to thank to Professor Mark Bocko for his generous support at the times I needed help most. My cordial thanks extend to Professor Gaurav Sharma on his collaboration and to Professor Kai Shen and Professor Lane Hemaspaandra for being on my dissertation defense committee. I would like to thank to all members of the Sensors group at the University of Rochester for their