A sensor network in the field is usually required to support multiple sensing tasks or missions to be accomplished simultaneously. Since missions might compete for the exclusive usage of the same sensing resource we need to assign individual sensors to missions. Missions are usually characterized by an uncertain demand for sensing resource capabilities. In this paper we model this assignment problem by introducing the Sensor Utility Maximization (SUM) model, where each sensor-mission pair is associated with a utility offer. Moreover each mission is associated with a priority and with an uncertain utility demand. We also define the benefit or profit that a sensor can bring to a mission as the fraction of mission's demand that the sensor is able to satisfy, scaled by the priority of the mission. The goal is to find a sensor assignment that maximizes the total profit, while ensuring that the total utility cumulated by each mission does not exceed its uncertain demand. SUM is NP-Complete and is a special case of the well known Generalized Assignment Problem (GAP), which groups many knapsack-style problems. We compare four algorithms: two previous algorithms for problems related to SUM, an improved implementation of a state-of-the-art pre-existing approximation algorithm for GAP, and a new greedy algorithm. Simulation results show that our greedy algorithm appears to offer the best trade-off between quality of solution and computation cost.
One of the main goals of sensor networks is to provide accurate information about a sensing field for an extended
period of time. This requires collecting measurements from as many sensors as possible to have a better view
of the sensor surroundings. However, due to energy limitations and to prolong the network lifetime, the number
of active sensors should be kept to a minimum. To resolve this conflict of interest, sensor selection schemes
are used. In this paper, we survey different schemes that are used to select sensors. Based on the purpose of
selection, we classify the schemes into (1) coverage schemes, (2) target tracking and localization schemes, (3)
single mission assignment schemes and (4) multiple missions assignment schemes. We also look at solutions to
relevant problems from other areas and consider their applicability to sensor networks. Finally, we take a look
at the open research problems in this field.
Ad-hoc sensor networks need to create their own network after deployment. Various schemes have been suggested
for sensors to create a better coverage pattern than if they are randomly deployed. A better coverage pattern
translates into a geometry of having disks cover an area completely and even redundantly. In this paper, we
present two coverage arrangements which turn out to be equivalent to grid lattice arrangements and analyze
their efficacy.
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