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By Mustapha Reda Senouci, Abdelhamid Mellouk

Wireless Sensor Networks: concept and perform for Deployment addresses WSNs deployment, a compulsory and demanding step within the strategy of constructing WSNs recommendations for real-life purposes. The authors deal with basic techniques to set up static WSNs, then exploring more sophisticated techniques to install cellular WSNs. that includes unique investigations of deployment-related matters reminiscent of deployment rate, insurance, connectivity, sensors reliability, and cruel deployment environments, this booklet will equip you with the fundamentals and a sophisticated view of either the theoretical and sensible facets, in addition to wisdom of the guidelines for WSNs deployment.

  • Provides either the theoretical foundation and useful applications
  • Features an in-depth dialogue of deployment-related issues
  • Covers simple techniques and terminologies in addition to highlighting open difficulties within the examine parts that can assist you clear up your deployment-related matters

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Devised a greedy heuristic called Max-Cov-Tp. The main advantage of the Max-Cov-Tp algorithm consists of avoiding the maximization of the detection probability of target points that have been already covered which reduces the deployment cost. The computational complexity of Max-Cov-Tp is O(n6 ). The same authors proposed in [SEN 14d] a polynomial-time deployment algorithm called simulated annealing-based sensor deployment algorithm (SADA). The obtained results show that SADA algorithm provides better results than other heuristics such as MIN_MISS and BDA.

3. Power-law This distribution is characterized by the following two features [ISH 04b]: first, the density of sensors is higher near the sink, and second, the degree of the sensors follows a power law. 5] 0 ≤ r ≤ R, 0 ≤ θ ≤ 2π, −1 ≤ α ≤ 1 The characteristics of the Power-law placement are similar to those of the R-random placement. 4. Exponential In this model, the distribution follows an exponential law [ZHA 04a]. 8. Here, the number of sensors is 436, the RoI is 300 m × 300 m, and λ = 100. 8.

The number of sensors Min. the number of sensors Min. the number of sensors Min. the number of sensors Min. the number of sensors Min. the number of sensors Min. the number of sensors Min. the number of sensors Min. the number of sensors Min. the number of sensors Min. the number of sensors Min. 1. 3. m) where n × m is the size of the RoI. The convergence and the effectiveness of EBDA are reported in [SEN 11, SEN 15c]. In [SEN 12b], Senouci et al. considered two additional constraints, namely preferential coverage and network connectivity.

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