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Heterogeneous wireless sensor network (HWSN) fulfills the requirements of researchers in the design of real life application to resolve the issues of unattended problem. But, the main constraint faced by researchers is the energy source available with sensor nodes. To prolong the life of sensor nodes and thus HWSN, it is necessary to design energy efficient operational schemes. One of the most suitable approaches to enhance energy efficiency is the clustering scheme, which enhances the performance parameters of WSN. A novel solution proposed in this article is to design an energy efficient clustering protocol for HWSN, to enhance performance parameters by EECPEP-HWSN. The proposed protocol is designed with three level nodes namely normal, advanced, and super, respectively. In the clustering process, for selection of cluster head we consider different parameters available with sensor nodes at run time that is, initial energy, hop count, and residual energy. This protocol enhances the energy efficiency of HWSN and hence improves energy remaining in the network, stability, lifetime, and hence throughput. It has been found that the proposed protocol outperforms than existing well-known LEACH, DEEC, and SEP with about 188, 150, and 141 percent respectively.

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Background & Objective: Mobile target tracking based on data from wireless sensor networks (WSN) is a hot topic that has been investigated both from a theoretical and practical point of view in the literature. Tracking the position and velocity of a target moving in WSN (especially in the context of uncertain noisy measurement channel) is a very challenging task. To deal with the uncertainty in system dynamics as well as uncertainty in target states, an Observer Based Self Recurrent Neural Network (OBSRNN) is proposed in this paper. Methods: The proposed algorithm employs a state observer based tracking control strategy and thereby allows for accurate estimation of mobile target moving along a predefined route in WSN. The Self Recurrent Neural Network (SRNN) framework is used to approximate the uncertainty in the system dynamics, while a full-order state observer is used to estimate the unknown state vector. Conclusion: The simulation analysis is performed to evaluate the efficacy of the proposed work.