Many sensor network applications require consistent coverage of the region in which they are deployed over the course of the network lifetime. However, because sensor networks may be deployed randomly, node distribution and data redundancy in some regions of the network may be lower than in others. The sensors in the sparsest regions should be considered more critical to the sensor network application since their removal would likely result in unmonitored regions in the environment. For this reason, sensors in the more densely deployed regions should be considered more favorable as candidates to route the traffic of other nodes in the network. In this work, we propose several coverage-aware routing costs that allow traffic to be routed around the sparsely deployed regions so that the coverage of the environment can remain high for a long lifetime. We also propose an integrated route discovery and sensor selection protocol called DAPR that further lengthens network lifetime by jointly selecting routers and active sensors, again with the goal of minimizing the use of sensors in sparsely covered areas. Simulation results show the effectiveness of our approach in extending network lifetime nearly to the extent that can be reached using a centralized approach based on global network knowledge.

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.

In multi-hop wireless sensor networks that are characterized by many-to-one (converge-east) 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.

In this work, we present a method for the selection of a subset of nodes in a wireless sensor network whose application is to reconstruct the image of a (spatially) bandlimited physical value (e.g., temperature). The selection method creates a sampling pattern based on blue noise masking and guarantees a near minimal number of activated sensors for a given signal-to-noise ratio. The selection method is further enhanced to guarantee that the sensor nodes with the least residual energy are the primary candidates for deselection, while enabling a tradeoff between sensor selection optimality and balanced load distribution. Simulation results show the effectiveness of these selection methods in improving signal-to-noise ratio and reducing the necessary number of active sensors compared with simpler selection approaches.

As wireless sensor networks continue to attract more attention, new ideas for applications are continually being developed, many of which involve consistent coverage of a given surveillance area. Recently, several protocols and architectures have been proposed to maintain network connectivity and adequate coverage quality while minimizing the drain on the scarce energy resources of the sensor nodes. In this paper, we propose DAPR, an integrated protocol for routing and coverage preservation that is distinctly different from the previously proposed solutions. Guided by the intuition that certain sensors are more important to the sensing application than others because of limited neighborhood redundancy, we introduce a new routing metric-an "application cost"-that aims to avoid the use of sensors in areas of critically sparse sensor deployment as routers. We have implemented DAPR in the ns-2 simulator and present simulation results showing the effectiveness of the protocol in extending network lifetime.

In this work, we address the problem of maximizing the lifetime of an application that requires a minimum level of quality of service from a network of energy-constrained wireless sensors. The problem is formulated as a generalized maximum flow graph problem with additional constraints and an optimal solution is found through linear programming. The result of the optimization is a schedule that determines the mode that all sensors should operate in and allows redundant sensors to turn off and conserve energy whenever possible. We show through simulations of typical sensing applications that by intelligently managing sensors, application lifetime can be extended by up to a factor of 2 in some cases.