In wireless sensor networks (WSNs) nodes often operate unattended in a collaborative manner to perform some tasks. In many applications, the network is deployed in harsh environments such as battlefield where the nodes are susceptible to damage. In addition, nodes may fail due to energy depletion and breakdown in the onboard electronics. The generalization comprises a wide range of objective functions including total power consumption of the network, maximum power consumption among the nodes in the network and log-sum of the power consumptions of the nodes in the network, any modulation scheme and any scheduling algorithm. In this article we presents the various scheme related to energy management in wireless networked control systems, here we defined the comparative study for the energy management in wireless sensor networks.

A Wireless sensor network (WSN) consists of wireless sensor nodes or motes, which are devices equipped with a processor, a radio interface, an analog-to- digital converter, sensors, memory and a power supply. Cognitive radio technology can significantly improve spectrum utilization efficiency via enabling secondary users to access licensed spectrum dynamically without harmful interference to primary users.

In the past years due to the progress of digital technology in producing digital cameras and availability of high-speed Internet connections, the amount of digital images has been grown rapidly. Those factors have provided a fast and simple way to generate and propagate visual content worldwide. Fully automatic extraction of visual as well as textual contents of an image for retrieving the similar images is really a challenging task for a machine. Such task is complex yet efficient for several applications, for instance, to help the visually impaired person to understand the image contents (i.e. visual or textual) and to help a tourist to read the contents given in another language. In this paper we presents the comparative study for the content based retrieval and classification for various number of application using the machine learning, data mining and other techniques.