The study of coverage problem in uncertain WSN environment requires the consideration of this uncertainty by taking the best possible decisions, since it is impossible to explicitly represent all the combinatorics to produce a conditional active/passive state nodes' planning in the area of interest, and allow reasoning on various environmental states of the partially known physical world. This paper addresses the problem of area coverage based on the Dempster-Shafer theory. The authors aim to ensure the full area coverage while using a subset of connected nodes, with minimal costs using a minimal number of dominant nodes regardless of the type of used deployment (random or deterministic). This is ensured by activating a single node in each subset of each geographic sub-area, thus extending the lifetime of the wireless sensor network to its maximum. The comparison of the proposed model denoted evidential approach for area coverage (EAAC) with two well-known protocols and with a recent one showed a better performance and a slight improvement in the covered area.

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Deterministic methods used to address the coverage problem in an uncertain deployment environment have not proven to be very successful. For this purpose, the original idea of this paper is to deal with the coverage problem in an uncertain environment with uncertain theories. We consider the imperfection in the deployment environment and in the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in WSN is uncertain. As a consequence, this paper proposes a hybrid Fuzzy-Possibilistic model to schedule the Active/ Passive state of sensor nodes. This model helps to plan the scheduling of node states (Active / Passive) based on the possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated the proposed model with (a) a running example, (b) a statistical evaluation (calculation of the confidence interface), and (c) a comparison with maximum sensing coverage region problem (MSCR), Coverage Maximization with Sleep Scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP) and PEAS with location information protocols. The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem.

Deterministic methods used to address the coverage problem in an uncertain deployment environment have not proven to be very successful. For this purpose, the original idea of this paper is to deal with the coverage problem in an uncertain environment with uncertain theories. We consider the imperfection in the deployment environment and in the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in WSN is uncertain. As a consequence, this paper proposes a hybrid FuzzyPossibilistic model to schedule the Active/ Passive state of sensor nodes. This model helps to plan the scheduling of node states (Active / Passive) based on the possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated the proposed model with (a) a running example, (b) a statistical evaluation (calculation of the confidence interface), and (c) a comparison with maximum sensing coverage region problem (MSCR), Coverage Maximization with Sleep Scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP) and PEAS with location information protocols. The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem.