The complete area coverage problem in Wireless Sensor Networks (WSNs) has been extensively studied in the literature. However, many applications do not require complete coverage all the time. For such applications, one effective method to save energy and prolong network lifetime is to partially cover the area. This method for prolonging network lifetime recently attracts much attention. However, due to the hardness of verifying the coverage ratio, all the existing centralized or distributed but nonparallel algorithms for partial coverage have very high time complexities. In this work, we propose a framework which can transform almost any existing complete coverage algorithm to a partial coverage one with any coverage ratio by running a complete coverage algorithm to find full coverage sets with virtual radii and converting the coverage sets to partial coverage sets via adjusting sensing radii. Our framework can preserve the characteristics of the original algorithms and the conversion process has low time complexity. The framework also guarantees some degree of uniform partial coverage of the monitored area.

Wireless sensor networks can be used for event warning applications. Till date, in most of the proposed schemes, the raw or aggregated sensed data are periodically sent to a data consuming center. However, with those schemes, the occurrence of an emergency event such as a fire is hardly reported timely, which is a strict requirement for event warning applications. In wireless sensor networks, it is also highly desired to conserve energy so that network lifetime can be maximized. Furthermore, to ensure the quality of surveillance, some applications require that if an event occurs, it needs to be detected by at least k sensors, where k is a user-defined parameter. In this work, we examine the Timely Energy-efficient k-Watching Event Monitoring (TEKWEM) problem and propose a scheme, which involves an event detection model and a warning delivery model, for monitoring composite events and delivering warnings to users. Theoretical analysis and simulation results are shown to validate the proposed scheme.

The complete area coverage problem in wireless sensor networks (WSNs) where every point inside an area is covered by an active sensor has been extensively studied in the literature. However, there are many applications that do not always require complete coverage. For such applications, an effective method to save energy and prolong network lifetime is to partially cover the area. However, due to the hardness to verify the ratio of the covered area over the entire monitored area (coverage ratio), all the existing algorithms for partial coverage have very high time complexities (either centralized algorithms or distributed but non-parallel algorithms). Besides, all the existing algorithms are intentionally designed for partial coverage, thus they do not utilize the various exiting methods for the complete coverage problem. In this work, we propose a framework that can convert almost any existing algorithm for complete coverage to a one for partial coverage with any coverage ratio. Our framework can preserve the characteristics of the original algorithms and the conversion process has low time complexity. The framework also guarantees some degree of uniform partial coverage of the monitored area.

Data aggregation is a fundamental yet time-consuming task in wireless sensor networks. We focus on the latency part of data aggregation. Previously, the data aggregation algorithm of least latency [1] has a latency bound of (Delta - 1)R, where Delta is the maximum degree and R is the network radius. Since both Delta andRcould be of the same order of the network size, this algorithm can still have a rather high latency. In this paper, we designed an algorithm based on maximal independent sets which has an latency bound of 23R+ Delta - 18. Here Delta contributes to an additive factor instead of a multiplicative one; thus our algorithm is nearly constant approximation and it has a significantly less latency bound than earlier algorithms especially when Delta is large.

Sensor networks can be used for event alarming applications. To date, in most of the proposed schemes, the raw or aggregated sensed data is periodically sent to a data consuming center. However, with this scheme, the occurrence of an emergency event such as a fire is hardly reported in a timely manner which is a strict requirement for event alarming applications. In sensor networks, it is also highly desired to conserve energy so that the network lifetime can be maximized. Furthermore, to ensure the quality of surveillance, some applications require that if an event occurs, it needs to be detected by at least k sensors where k is a user-defined parameter. In this work, we examine the timely energy-efficient k-watching event detection problem (TEKWEO). A topology-and-routing-supported algorithm is proposed which constructs a set of detection sets that satisfy the short notification time, energy conservation, and tunable quality of surveillance requirements for event alarming applications. Simulation results are shown to validate the proposed algorithm.

In this paper, we consider the problem of maximizing the lifetime of a target-covering sensor network in which each sensor can adjust its sensing range. The network model consists of a large number of sensors with adjustable sensing ranges being deployed to monitor a set of targets. Since more than one sensor can cover a target, in order to be energy efficient, one can activate successive subsets of sensors that cover all targets. This paper addresses the problem of maximizing the total lifetime of such an activation schedule. In contrast to the approach taken by Cardei et al. (2005), our formulation directly maximizes the network lifetime rather than maximizing the number of sensor covers. We give a mathematical model of this problem using a linear program with exponential number of variables and solve this linear program using the approximation algorithm of Garg-Konemann (1998). Our experimental results on simulated data show a 4times increase in lifetime when compared with the previous approach taken by Cardei et al. (2005)