Wireless sensor networks (WSN) are emerging in various fields. A large number of sensors in these applications are unattended and work autonomously. Lifetime is an important parameter which is critical for different algorithms for data transfer. Moreover it is responsible for the throughput and the failure of the network. Heterogeneous wireless sensor network, on top of clustering technique, has evolved as the major parameter to increase the lifetime of the Sensor network, data transfer, energy consumption and the scalability of the sensor network. This paper surveys the different clustering algorithm and dependencies for heterogeneous wireless sensor network. This paper is for scholars to gain sufficient knowledge of wireless sensor network (WSN), its important characteristics, and performance metrics with factors responsible for a WSN system. It can help a scholar to start a quick research by understanding all the respective parameters and energy oriented strategies in WSN.

Any electromechanical device can be termed as Robot, which imitates human actions and in some of the situation can be used as a replacement for human. These days Robots are the integral part of our life and can be applied in several applications and tasks by giving respective commands. The research in robotics domain is to make it as autonomous and as much independent as it can be. The problem that arises is of controlling a mobile robot with the energy constraint. A lot of energy is wasted, if it takes wrong trajectory motion, this motion depends upon the robot knowledge which indeed in not constant. The variation in the environment results in making difficult for the robot to take precise and accurate measurements to reach the destination without much of the energy loss. An autonomous robot is expected to take decision according to the situation. For this precise decisions of robot path planning there are algorithms like A*, Dijkstra, D* etc. In this paper we have done analysis on partially known environment situation. Optimal path is planned by new heuristic approach over the A star algorithm, robot moving at an appropriate angle cuts down the unnecessary cost of path planning. Experimental results show that the proposed algorithm is much effective for more than 8% than the conventional A* algorithm in the same map environment.