An environmental energy harvesting framework for sensor networks
Proceedings of the 2003 international symposium on Low power electronics and design
Power utility maximization for multiple-supply systems by a load-matching switch
Proceedings of the 2004 international symposium on Low power electronics and design
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
Tiny Dragon - An Encryption Algorithm for Wireless Sensor Networks
HPCC '08 Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications
Fine-grained energy profiling for power-aware application design
ACM SIGMETRICS Performance Evaluation Review
Energy conservation in wireless sensor networks: A survey
Ad Hoc Networks
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A Wireless Sensor Network (WSN) powered using harvested energies is limited in its operation by instantaneous power. Since energy availability can be different across nodes in the network, network setup and collaboration is a non trivial task. At the same time, in the event of excess energy, exciting node collaboration possibilities exist; often not feasible with battery driven sensor networks. Operations such as sensing, computation, storage and communication are required to achieve the common goal for any sensor network. In this paper, we design and implement a smart application that uses a Decision Engine, and morphs itself into an energy matched application. The results are based on measurements using IRIS motes running on solar energy. We have done away with batteries; instead used low leakage super capacitors to store harvested energy. The Decision Engine utilizes two pieces of data to provide its recommendations. Firstly, a history based energy prediction model assists the engine with information about in-coming energy. The second input is the energy cost database for operations. The energy driven Decision Engine calculates the energy budgets and recommends the best possible set of operations. Under excess energy condition, the Decision Engine, promiscuously sniffs the neighborhood looking for all possible data from neighbors. This data includes neighbor's energy level and sensor data. Equipped with this data, nodes establish detailed data correlation and thus enhance collaboration such as filling up data gaps on behalf of nodes hibernating under low energy conditions. The results are encouraging. Node and network life time of the sensor nodes running the smart application is found to be significantly higher compared to the base application.