Sensor nodes are prone to failure due to energy depletion and some other reasons in Distributed Sensor Networks (DSNs). In this regard fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation DSNs. This paper proposes a node fault detection and recovery by using Genetic Algorithm (GA), when some of the sensor nodes faulty in DSN. The main objective of this work is to provide fault tolerance mechanism, which is energy efficient and responsive to network by using GA which is used to detect the faulty of nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). We have evaluated the performance parameters for the proposed scheme.

Distributed Sensor Network consists set of distributed nodes having the capability of sensing, computation and wireless communications. Power management, various routing and data dissemination protocols have been specifically designed for DSN, where energy consumption is an essential design issues for routing. Optimization of routing method is an essential for routing of DSN because of long communication distances between distributed sensor nodes and sink node in a network can greatly drain the energy of sensors and decrease the lifetime of the network.
In this paper, simulation is carried out for optimization of routing in DSNs using MATLAB software. The objective is to maximize the network life time and improve the energy efficiency using heuristic technique. A proposed Genetic Algorithm based routing protocol is used for solving an optimization through the evolution of genes parameters, which are coded by strings of characters or numbers and genetic operations (selection, crossover and mutation) are iterated. Finally, the performance parameters for the proposed scheme are evaluated and are shown in terms of energy and routing efficiency, time computation and network lifetime.