STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Energy efficient Modulation and MAC for Asymmetric RF Microsensor Systems
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Minimizing Service and Operation Costs of Periodic Scheduling
Mathematics of Operations Research
Ultra Low-Energy Transceivers for Wireless Sensor Networks
Proceedings of the 15th symposium on Integrated circuits and systems design
A study of energy consumption and reliability in a multi-hop sensor network
ACM SIGMOBILE Mobile Computing and Communications Review - Special issue on wireless pan & sensor networks
Triage: balancing energy and quality of service in a microserver
Proceedings of the 5th international conference on Mobile systems, applications and services
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In sensor networks applied to monitoring applications, individual sensors may perform preassigned or on-demand tasks, or missions. Data updates (info-pages) may be sent to sensors from a command center, via a time-division broadcast channel. Sensors are normally put in sleep mode when not actively listening, in order to conserve energy in their batteries. Hence, a schedule is required that specifies when sensors should listen for updates and when they should sleep. The performance of such a schedule is evaluated based on data-related costs and sensor-related costs. Data-related costs reflect the obsoleteness of current sensor data, or the delay while sensors wait for updated instructions. Sensor-related costs reflect the energy that sensors consume while accessing the broadcast channel and while switching between the active and sleeping modes (rebooting). Our goal is a schedule with the minimum total cost. Previous related work has explored data-related costs, but listening cost has been addressed only under the assumption that the rebooting operation is free. This paper formulates a new cost model, which recognizes the cost of sensor rebooting. We derive an optimal schedule for the single-sensor setting. We proceed to consider schedules of multiple sensors; we formulate a mathematical program to find an optimal fractional schedule for this setting and provide a solution to the lower bound. Several heuristics for scheduling multiple sensors are introduced and analyzed, and various tradeoffs among the cost factors are demonstrated.