Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism works as a vital role in the cloud computing. Thus my protocol is designed to minimize the switching time, improve the resource utilization and also improve the server performance and throughput. This method or protocol is based on scheduling the jobs in the cloud and to solve the drawbacks in the existing protocols.Here we assign the priority to the job which gives better performance to the computer and try my best to minimize the waiting time and switching time. Best effort has been made to manage the scheduling of jobs for solving drawbacks of existing protocols and also improvise the efficiency and throughput of the server.

Abstract-Recently, a concept of wireless sensor networks has attracted much attention due to its wide-range of potential applications. Wireless sensor networks also pose a number of challenging optimization problems. One of the fundamental problems in sensor networks is the coverage problem, which reflects the quality of service that can be provided by a particular sensor network. The coverage concept is depending from several points of view due to a variety of sensors and a wide-range of their applications. One fundamental issue in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal is to determine the degree of coverage of a sensor network, which is covered by at least k sensors, where k is a predefined value. The sensing ranges of sensors can be same or different. Performance evaluation of our protocol indicates that degree of coverage of wireless sensor networks can be determined within small period of time. Therefore energy consumption of the sensor networks can be minimized.

IoT devices enhance efficiency, accuracy and economic advantages along with less involvement of human resources, thus our different daily applications have become more flexible and convenient. But, in IoT we have many security and privacy challenges emerging on regular basis. Earlier this issue has been addressed by introduction of many approaches for achieving privacy-preserving in data aggregation process. In this aspect this paper presents an outline of IoT-oriented approach for achieving privacy preservation together with minimizing communication overhead. This paper reviews the latest Privacy Preserving Data Aggregation (PPDA) techniques along with their comparative analysis. Latest techniques are investigated here to give a detail analysis of the each and every step of these techniques. In addition, every mathematical operation used in several PPDA schemes is analyzed here. Also current study will be advantageous to researchers in designing solutions in terms of energy efficiency and computational feasibility for ensuring user privacy in different IoT application.

Carpooling hicle on the roa ment this system nger for improvi to find the opt ng approach.

In this paper we study the increase of coverage and connectivity in a sensor network with a view to improving coverage, while preserving the network’s coverage. We also examine the impact of on the related problem of coverage-boundary detection. We reduce both problems to the computation of Voronoi diagrams and intersectional point method prove and achieve lower bounds on the solution of these problems and present efficient distributed algorithms for computing and maintaining solutions in cases of sensor failures or insertion of new sensors. We prove the correctness and termination properties of our distributed algorithms, and analytically characterize the time complexity and the traffic generated by our algorithms. Our algorithms show that the increase coverage & Connectivity in wireless sensor density.