Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Low-Power Wireless Sensor Networks
VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
Utilizing Solar Power in Wireless Sensor Networks
LCN '03 Proceedings of the 28th Annual IEEE International Conference on Local Computer Networks
Performance aware tasking for environmentally powered sensor networks
Proceedings of the joint international conference on Measurement and modeling of computer systems
Solar-aware clustering in wireless sensor networks
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
Design considerations for solar energy harvesting wireless embedded systems
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Perpetual environmentally powered sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
IEEE Communications Magazine
Opportunistic routing in wireless sensor networks powered by ambient energy harvesting
Computer Networks: The International Journal of Computer and Telecommunications Networking
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This paper presents low-latency routing strategies that take account of energy-harvesting sensor nodes in wireless sensor networks. Currently, in the case of tree-based network connectivity, 'lazy' nodes with a long interval between computational activities may slow down communication if deployed along a routing path. To cope with this problem, we suggest a random graph in which a node may have multiple parent nodes, thus allowing a choice of path to the sink node. We also propose three path selection schemes for the network topology on the basis of (a) the cost of a single hop from the current node, (b) the cost of two hops, and (c) the cost of a single hop plus a cost estimate for the remainder of the path to the sink, respectively. The proposed strategies are shown to outperform the existing routing algorithm for a tree-structured energy-harvesting sensor network.