On-line load balancing with applications to machine scheduling and virtual circuit routing
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
The coverage problem in a wireless sensor network
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Integrated coverage and connectivity configuration in wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
Coverage and hole-detection in sensor networks via homology
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
A survey of energy-efficient scheduling mechanisms in sensor networks
Mobile Networks and Applications
Boundary recognition in sensor networks by topological methods
Proceedings of the 12th annual international conference on Mobile computing and networking
Throughput-competitive on-line routing
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
Genetic coverage verification without location information using dimension reduction
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
On solving coverage problems in a wireless sensor network using voronoi diagrams
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Distance Estimation Using Bidirectional Communications Without Synchronous Clocking
IEEE Transactions on Signal Processing
Performance analysis of relative location estimation for multihop wireless sensor networks
IEEE Journal on Selected Areas in Communications
Positioning in ad hoc sensor networks
IEEE Network: The Magazine of Global Internetworking
SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
INOC'11 Proceedings of the 5th international conference on Network optimization
Sensor activation and radius adaptation (SARA) in heterogeneous sensor networks
ACM Transactions on Sensor Networks (TOSN)
Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing
Energy-efficient markov chain-based duty cycling schemes for greener wireless sensor networks
ACM Journal on Emerging Technologies in Computing Systems (JETC)
On energy-efficient trap coverage in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Achieving full-view coverage in camera sensor networks
ACM Transactions on Sensor Networks (TOSN)
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Wireless Sensor Networks are emerging as a key sensing technology, with diverse military and civilian applications. In these networks, a large number of sensors perform distributed sensing of a target field. Each sensor is a small battery-operated device that can sense events of interest in its sensing range and can communicate with neighboring sensors. A sensor cover is a subset of the set of all sensors such that every point in the target field is in the interior of the sensing ranges of at least $k$ different sensors in the subset, where k is a given positive integer. The lifetime of the network is the time from the point the network starts operation until the set of all sensors with non-zero remaining energy does not constitute a sensor cover. An important goal in sensor networks is to design a schedule, that is, a sequence of sensor covers to activate in every time slot, so as to maximize the lifetime of the network. In this paper, we design a polynomial-time, distributed algorithm for maximizing the lifetime of the network and prove that its lifetime is at most a factor O(log n * log nB) lower than the maximum possible lifetime, where n is the number of sensors and B is an upper bound on the initial energy of each sensor. Our algorithm does not require knowledge of the locations of nodes or directional information, which is difficult to obtain in sensor networks. Each sensor only needs to know the distances between adjacent nodes in its transmission range and their sensing radii. In every slot, the algorithm first assigns a weight to each node that is exponential in the fraction of its initial energy that has been used up so far. Then, in a distributed manner, it finds a O(log n) approximate minimum weight sensor cover which it activates in the slot. Our simulations reveal that our algorithm substantially outperforms several existing lifetime maximization algorithms.