Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Heliomote: enabling long-lived sensor networks through solar energy harvesting
Proceedings of the 3rd international conference on Embedded networked sensor systems
Network coding: an instant primer
ACM SIGCOMM Computer Communication Review
Harvesting aware power management for sensor networks
Proceedings of the 43rd annual Design Automation Conference
Telos: enabling ultra-low power wireless research
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
Integrating concurrency control and energy management in device drivers
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
XORs in the air: practical wireless network coding
IEEE/ACM Transactions on Networking (TON)
Network Coding: An Introduction
Network Coding: An Introduction
IEEE Transactions on Information Theory
Scalable network coding for wireless sensor network energy conservation
International Journal of Autonomous and Adaptive Communications Systems
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Wireless sensor network (WSN) motes of small form factor are operated in resource-constrained settings. This demands for low-power designs and energy-aware operation, but especially for optimization of costly wireless transmissions. A promising approach is to find network structures where network coding can be applied to optimize energy efficiency of information flows. Schemes exist for global optimization of the load on WSN's resources. Nevertheless, applying them in practical settings is often not feasible due to complex computations and the need for centralized knowledge. The gain from optimization methods fades away for scaled and autonomous operation of WSNs due to control overhead. This paper presents LINDONCS: Localized In-Network Detection Of Network Coding Structures. Communication patterns are adapted autonomously without paying off impact on network scalability. We show how to detect structures and apply network coding to improve network lifetime over state-of-the-art solutions.