Highly-resilient, energy-efficient multipath routing in wireless sensor networks
ACM SIGMOBILE Mobile Computing and Communications Review
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
ESRT: event-to-sink reliable transport in wireless sensor networks
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
FLSS: a fault-tolerant topology control algorithm for wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Deploying sensor networks with guaranteed capacity and fault tolerance
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Network coding-based protection of many-to-one wireless flows
IEEE Journal on Selected Areas in Communications - Special issue on network coding for wireless communication networks
IEEE Communications Letters
Reliable wireless broadcasting with near-zero feedback
INFOCOM'10 Proceedings of the 29th conference on Information communications
Survivability strategies in multihop wireless networks
IEEE Wireless Communications
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
In this paper, we present a new technique that uses deterministic binary network coding in a distributed manner to enhance the resiliency of sensor-to-base information flow against packet loss. First, we show how to use network coding to tolerate a single packet loss by combining the data units from sensor nodes to produce k+1 combinations such that any k of them are solvable. After that, we extend the solution to tolerate multiple losses. Moreover, we study the coding efficiency issue and introduce the idea of relative indexing to reduce the coding coefficients overhead. To tolerate node or link failures, we introduce a simple routing protocol that can find maximally disjoint paths from the k sensor nodes to the base station (BS). We study the relationship between the probability of successful recovery of all data units at the BS, and the number of sources protected together taking into consideration their hop distance from the BS. From this study, we can decide on the appropriate number of sources to be protected together, so that the probability of successful recovery is higher than a certain threshold. Finally, we show through a simulation study that our approach is highly scalable and performs better as the network size increases.