For the CASA approach, a neural network model is built up to app

For the CASA approach, a neural network model is built up to approximate the desire performance. For the DASA approach, a probabilistic model using the concept of geometry is applied to abstract the properties of the algorithm. Moreover, based on the analysis, the sensor lifetime and cluster lifetime is further explored to show how the operations of the proposed schemes may prolong the network lifetime.The organization of this paper is as follows: Section 2 reviews the current literature on the sensor scheduling management. Section 3 describes the system model and algorithm for sensor scheduling in a cluster-based network topology. In Section 4, a neural network model and a probabilistic model are built up to approximate the desire performance and estimate the sensing rounds of the proposed schemes.

Section 5 summarizes the performance of the proposed scheduling methodology. Finally, Section 6 draws conclusions and shows future research directions.2.?Literature ReviewA large number of sensor scheduling and coverage maintenance protocols have been proposed [8-35]. However, due to the sensing objectives, these management protocols can be different. Yan et al. [1] presented an energy-efficient sensing protocol to achieve the desired sensing coverage. Nodes decide their active periods by exchanging reference points among neighbors. In [2], the authors investigated coverage intensity of the proposed sleep scheduling protocols. Ren et al. [3] provided a generic analytical framework that can be widely used for sensing scheduling protocol design with detection quality requirements.

Turau et al. [4] tried to route packet with the minimum time and energy and aimed to distribute the transmission time slots dynamically among sensor nodes such that the network congestion can be avoided.Hohlt et al. [5] proposed a scheduling scheme for considering energy savings in a data collection process. Schrage et al. [6] applied an ant colony optimization method for scheduling the visiting order of targeted areas in the sensing field such that their energy consumptions are minimized. Decker et al. [7] developed a scheduler to manage Brefeldin_A the competition for resources among different sensing tasks at a single sensor node. Chamberland et al. [8] investigated the relationship between sleeping duration, detection delay and energy consumption in a stationary sensing field.

References [9, 10, 11] are clustering-based protocols that attempt to minimize the energy dissipation in sensor networks.Cheng et al. [12] proposed a bio-inspired scheduling scheme which is a kind of adaptive scheduling scheme which uses only local information for making scheduling decisions. Premkumar et al. [13] considered the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active.

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