Novel Localization of Sensor Nodes in Wireless Sensor Networks using Co-Ordinate Signal Strength Database

AbstractThe objective of the proposed system is to remove the dependency of radio irregularity problem in localization of static nodes in wireless sensor networks. Most of the existing range free localization algorithms are mainly suffered by the radio irregularity problem. The value of the degree of irregularity always affects the accuracy of the localization performance. The idea of this work is to calculate the location of sensor nodes using co-ordinate signal strength database. In wireless sensor networks, each flying anchor node will be equipped with a GPS receiver. The flying node calculates its position, which is transmitted as a beacon message to the sensor nodes. Upon reception of the beacon messages, each static node calculates its location using the ‘Sensor Position based on Co-ordinate Signal Strength Database’ (SP-CSSD) algorithm. The proposed idea increases the accuracy of the localization algorithm with minimal computational overhead and computational time

Intelligent UAV-Assisted Localisation to Conserve Battery Energy in Military Sensor Networks

Wireless sensor networks (WSNs) are extensively used in military applications for border area monitoring, battle-field surveillance, tracking enemy troops, where the sensor nodes run on battery power. Localisation of sensor nodes is extremely important to identify the location of event in military applications for further actions. Existing localisation algorithms consume more energy by heavy computation and communication overheads. The objective of the proposed research is to increase the lifetime of the military sensor networks by reducing the power consumption in each sensor node during localisation. For the state-of-the-art, we propose a novel intelligent unmanned aerial vehicle anchor node (IUAN) with an intelligent arc selection (IAS)-based centralised localisation algorithm, which removes computation cost and reduces communication cost at every sensor node. The IUAN collects the signal strength, distance data from sensor nodes and the central control station (CCS) computes the position of sensor nodes using IAS algorithm. Our approach significantly removes computation cost and reduces communication cost at each sensor node during localisation, thereby radically extends the lifetime and localisation coverage of the military sensor networks.Science Journal, Vol. 64, No. 6, November 2014, pp.557-563, DOI:http://dx.doi.org/10.14429/dsj.64.529

USER PROFILE BASED PROPORTIONAL SHARE SCHEDULING AND MAC PROTOCOL FOR MANETS

ABSTRACT Quality of Service(QoS

Success Prediction of Students by Integrating Communication Skills with Achievement Motivation and Personality

Abstract—A technique has been recommended for the success level prediction of student professionals. It is achieved by combining communication skills with prior, proven prediction of students ’ success with narrower domains of Big Five Personality traits [17] along with achievement motivation [16]. The goal is to assess the combined success levels of students in their life beyond academics with the novel and complete mathematical representation, architecture, development and implementation of online decision support system. Regression model has been developed for a test bed of 974 male and female students with training and testing data. Results inferred that on regressing Students-Success-NEO-Achievement Motivation-Communication (SS-N-A-C) incrementally with demographic variables, Students-Success-NEO-Achievement Motivation (SS-N-A) and Total-Communication-group (TComm-g), TComm-g has higher impact over SS-N-A in predicting collective success levels of students. The performance accuracy has been calculated using Chi-square Automatic Interactor Detector (CHAID), classification technique

Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks

Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM’s multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods