Balanced Cluster Lifetime Prolonging Protocol for Wireless Sensor Network

sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in diverse conditions. One of the crucial matter in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a balanced cluster routing protocol called balance cluster lifetime prolonging protocol (BCLPP). In BCLPP, we divide the network into four static quadrants which have almost equal number of nodes, which reduce the overhead of dynamic clustering thus increase the lifetime of our network. Simulation-based evaluations are performed to compare the performance of BCLPP against Energy-Efficient Protocol with Static Clustering for Wireless Sensor Networks (EEPSC) Our experiment results show that BCLPP outperforms EEPSC in terms of network lifetime and power consumption minimization. Keywordstechnique, Energy efficiency, network lifetime and wireless sensor network.

Comparative Analysis of FBMC and OFDM Multicarrier Techniques for Wireless Communication Networks

MC) techniques are highly attractive for the development of modern wireless communication systems such as 4 th Generation (4G) and Long Term Evolution (LTE) systems. So far, Orthogonal Frequency Division Multiplexing (OFDM) is the most popular and highly recommended modulation technique for Dynamic spectrum access (DSA) and Cognitive Radio (CR) applications. Despite their numerous advantages, OFDM system suffers from few drawbacks. Filter bank Multicarrier (FBMC), another modulation technique evolved from OFDM which overcomes these drawbacks is examined. FBMC applies filter banks and omits the severe out of band leakage of OFDM. This paper presents the comparative performance analysis of conventional OFDM system and FBMC system in terms of Signal to noise ratio (SNR), Bit error rate (BER), Packet delivery ratio(PDR) and number of packets dropped with the help of computer simulations performed using NETWORK SIMULATOR NS-2.34. KeywordsFBMC, DSA, OFDM-OQAM, NS2, MAC

Decomposability of Projective Curvature Tensor in Recurrent Finsler Space (WR-F_n)

The decomposition of curvature tensor field was studied by K. Takano[1]. The decomposability of curvature tensor in Finsler manifold was studied by Pandey[2]. The purpose of the present chapter is to decompose the Projective curvature tensor in recurrent Finsler space and study the properties of conformal decomposition tensor.

Approximate Solution of Volterra-Fredholm Integral Equation with Hilbert Kernel

Blood Flow through a Stenosed Artery with the Effect of Transverse Magnetic Field using a Non-Newtonian Model

dimensional, steady, Herschel-Bulkley fluid flow of blood through a stenosed artery under the effect of external magnetic field is studied. The blood is assumed as incompressible. The governing equations are solved analytically. This model has been used to study the influence of yield stress on blood flow through the stenosed artery. The effects of magnetic field on axial velocity, flow rate and wall shear rate has been shown graphically. The effects of all the parameters are quite significant on axial velocity, flow rate and wall shear rate as evidence from the results.

Cooperative Communication Simulation using TCP

A Parametric Estimation Approach for Effective Cluster Selection in WPAN

WPAN is the most advanced communication network that combines the capabilities of Mobile network and sensor network in a complex global area scenario. This network defines various integrated processes under communication scenario specification. Cluster Selection process is the most common requirement of any WPAN scenario because of the mobility feature for vehicle nodes. In this work, an effective soft-Cluster Selection mechanism is defined under positional and communication parameter analysis. The work is defined for global highway scenario. The obtained results from system shows the effective comm-unication rate and lesser communication loss.

Adapting k-means for Clustering in Big Data

data if used properly can bring huge benefits to the business, science and humanity. The various properties of big data like volume, velocity, variety, variation and veracity render the existing techniques of data analysis ineffective. Big data analysis needs fusion of techniques for data mining with those of machine learning. The k-means algorithm is one such algorithm which has presence in both the fields. This paper describes an approximate algorithm based on k-means. It is a novel method for big data analysis which is very fast, scalable and has high accuracy. It overcomes the drawback of k-means of uncertain number of iterations by fixing the number of iterations, without losing the precision.

A Survey on Biometric Templates Binarization

Survey on Energy Efficient Routing Protocols in WSNs