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International Journal of Hybrid Intelligence
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International Journal of Hybrid Intelligence (2 papers in press)
Collaboration adaptive filtering model for data reduction in Wireless Sensor Networks by Walaa M. Elsayed, Hazem M. El-Bakry, Salah M. El-Sayed Abstract: Wireless sensor networks (WSNs) are collecting data periodically by randomly dispersed sensors (motes) that typically exploit high energy in monitoring a specified application. Furthermore, dissemination mode in WSN is resulting noisy or missing information that affects the behaviour of WSN. So, data prediction-based filtering is an important approach to reduce redundant data transmissions, conserve node energy and overcome the defects resulted from data dissemination. Therefore, in this article, a Distributed Data Reduction Model (DDRM) is proposed to prolong the network lifetime by decreasing the energy consumption of sensor nodes. It is built upon a distributive clustering model for predicting diffusion-faults in WSN. The proposed model is developed using the RLS adaptive filter integrated with a FIR filter for minimizing the amount of transmitted data and provide high convergence of the signals. A dataset of atmospheric changes was handled. The results clarify that DDRM reduced the rate of data transmission to ~ 20 %. Also, it depressed the energy consumption to ~ 95 % throughout the dataset sample. DDRM effectively upgraded the performance of the sensory network by about 19.5 %, and hence extend its lifetime. Keywords: WSN; Cluster head; Data dissemination; Adaptive RLS filter; Data prediction; Value failure.
Multi Area Power Dispatch Strategy Considering Economic and Environmental Aspects Using NDSGA II by Abhik Hazra, Saborni Das, Ashish Laddha, Mousumi Basu Abstract: Multi area economic environmental dispatch strategies (MAEEDS), corresponding to centralized fossil fuel fired power plants, remain significant for allocating power among committed units of various regions in an optimalized manner. The allocation must ensure simultaneous reduction in total fuel cost and emission level in the best possible ways. At the same time, equality alongside inequality constraints likes production-demand balance, power production capacity, and tie line capacity have to be taken into consideration. The aforementioned optimalization task involves multiple objective optimization with contradictory behaving goals. The presented article suggests nondominated sorting genetic algorithm II (NDSGA II) to achieve solutions for the MAEEDS task. Solutions obtained through the NDSGA II for a four area test system prove the superiority of the suggested technique. A comparison has also been made among the solutions attained through the suggested technique, strength pareto evolutionary algorithm II (STPEA II), and other well-known optimization techniques available in the literature. The comparative analysis shows superiority of the suggested NDSGA II for the considered four area MAEEDS task. Keywords: Multi area economic environmental dispatch strategies; multiple objective optimization; nondominated sorting genetic algorithm II; strength pareto evolutionary algorithm II.