One of the useful approaches to exploit redundancy in a sensor network is to keep active only a small subset of sensors that are sufficient to cover the region required to be monitored. The set of active sensors should also form a connected communication graph, so that they can autonomously respond to application queries and/or tasks. Such a set of active sensors is known as a connected sensor cover, and the problem of selecting a minimum connected sensor cover has been well studied when the transmission radius and sensing radius of each sensor is fixed. In this article, we address the problem of selecting a minimum energy-cost connected sensor cover, when each sensor node can vary its sensing and transmission radius; larger sensing or transmission radius entails higher energy cost. For the aforesaid problem, we design various centralized and distributed algorithms, and compare their performance through extensive experiments. One of the designed centralized algorithms (called CGA) is shown to perform within an O (log n ) factor of the optimal solution, where n is the size of the network. We have also designed a localized algorithm based on Voronoi diagrams which is empirically shown to perform very close to CGA and, due to its communication-efficiency, results in significantly prolonging the network lifetime. We also extend the aforementioned algorithms to incorporate fault tolerance. In particular, we show how to extend the algorithms to address the minimum energy-cost connected sensor k -cover problem, in which every point in the query region needs to be covered by at least k distinct active sensors. The CGA preserves the approximation bound in this case. We also propose a localized topology control scheme to preserve k -connectivity, and use it to extend the Voronoi-based approach to computing a minimum energy-cost k 1 -connected k 2 -cover. We study the performance of our proposed algorithms through extensive simulations.

Radio frequency identification (RFID) is a technology where a reader device can "sense" the presence of a close-by object by reading a tag device attached to the object. To improve coverage, multiple RFID readers can be deployed in the given region. In this paper, we consider the problem of slotted scheduled access of RFID tags in a multiple reader environment. In particular, we develop centralized algorithms in a slotted time model to read all the tags using near-optimal number of time slots. We consider two scenarios -one wherein the tag distribution in the physical space is unknown, and the other where tag distribution is known or can be estimated a priori. For each of these scenarios, we consider two cases depending on whether a single channel or multiple channels are available. All the above version of the problem are NP-hard. We design approximation algorithms for the single channel and heuristic algorithms for the multiple channel cases. Through extensive simulations, we show that for the single channel case, our heuristics perform close to the approximation algorithms. In general, our simulations show that our algorithms significantly outperform colorwave, an existing algorithm for similar problems.

Abstract — Sensor networks are often deployed in a redundant fashion. In order to prolong the network lifetime, it is desired to choose only a subset of sensors to keep active and put the rest to sleep. In order to provide fault tolerance, this small subset of active sensors should also provide some degree of redundancy. In this paper, we consider the problem of choosing a minimum subset of sensors such that they maintain a required degree of coverage and also form a connected network with a required degree of fault tolerance. In addition, we consider a more general, variable radii sensor model, wherein every sensor can adjust both its sensing and transmission ranges to minimize overall energy consumption in the network. We call this thevariableradiik1-Connected, k2-Cover problem. To address this problem, we propose a distributed and localized Voronoibased algorithm. The approach extends the relative neighborhood graph (RNG) structure to preserve k-connectivity in a graph, and design a distributed technique to inactivate desirable nodes while preserving k-connectivity of the remaining active nodes. We show through extensive simulations that our proposed techniques result in overall energy savings in random sensor networks over a wide range of experimental parameters. I.

P2P file sharing protocol and ad hoc wireless routing protocol share many intriguing similarities even though they are motivated on totally different basis. The goal of P2P file sharing system such as KaZaa is to locate a set of servers that contain a given file and disseminate it efficiently. The key problem of an ad hoc network routing protocol is to determine which route to take to reach a given remote host. P2P file sharing application on mobile ad hoc network (MANET) has gained more momentum as shown in the research of recent years. One natural way is to implement P2P application and ad hoc routing at different layers they belong to. In this paper, we argue that instead of stacking one on the top of the other, more work needs to be done to make both P2P file sharing protocol and MANET routing protocol interact with each other. We extract the commonalities of these two and design a common query/response framework on which ad hoc network routing and P2P file sharing are integrated seamlessly. The extensive experiments show that our strategy performs better than the layered approach in terms of traffic, average delay and packet delivery ratio.

In overdeployed sensor networks, one approach to conserve energy is to keep only a small subset of sensors active at any instant. We consider the problem of selecting a minimum size connected K-cover, which is defined as a set of sensors M such that each point in the sensor network is "covered" by at least K different sensors in M, and the communication graph induced by M is connected. For the above optimization problem, we design a centralized approximation algorithm that delivers a near-optimal (within a factor of O(lg n)) solution, and present a distributed version of the algorithm. We also present a communication-efficient localized distributed algorithm which is empirically shown to perform well.

One of the useful approaches to exploit redundancy in a sensor network is to actively keep only a small subset of sensors that are sufficient to cover the region required to be monitored. The set of active sensors should also form a connected communication graph, so that they can autonomously respond to application queries and/or tasks. Such a set of active sensors is known as a connected sensor cover, and the problem of selecting a minimum connected sensor cover has been well studied when the transmission radius and sensing radius of each sensor is fixed. In this article, we address the problem of selecting a minimum energy-cost connected sensor cover, when each sensor node can vary its sensing and transmission radius; larger sensing or transmission radius entails higher energy cost. For the above problem, we design various centralized and distributed algorithms, and compare their performance through extensive experiments. One of the designed centralized algorithms (called CGA) is shown to perform within an O(log n) factor of the optimal solution, where n is the size of the network. We have also designed a localized algorithm based on Voronoi diagrams which is empirically shown to perform very close to CGA, and due to its communication-efficiency, results in significantly prolonging the network lifetime.