The Design of Formulation of Triangulation Network Model Using Simulate Environment for Co-Operative Communication in WSN using Matlab

Abstract

Wireless Sensor Networks (WSNs) gained universal attention now a day owing to This innovative is sensing technology to incorporate an immense number sensor nodes or motes set up in an area of perceive any continuously are fluctuating physical to phenomena. These tiny sensor nodes sense and process the sensed data and transfer this information to a base station or sink via radio frequency (RF) channel. The small size of these sensors is an advantage as it can be easily embedded within any device or in any environment. This feature has attracted the use of WSNs in immense applications especially in monitoring and tracking; the most prominent being the surveillance applications. But this type of tiny sizes of sensors to nodes restricts to resourcing capabilities. Usually to WSNs are install to application area to the human intervention of quite risk and difficult. The sense information might to need to taking the critical decisions to emergency applications. So maintaining the connectivity of the network is of utmost importance. The efficient use of the available resources to the maximum extend is a necessity to prolong the network lifetime. Most of research the area of WSNs has concentrate on energy efficiency where the design of energy efficient routing protocols plays a major role. The triangulations rules were applied for 20, 50 and 100 nodes demonstration for WSNs integrated with co-operative communications.

Introduction

I. INTRODUCTION

In this project we will simulate the triangular network modelling using a wireless sensor network (WSN) for co-operative communication in Matlab. Wireless sensor network will be designed and simulate in a Matlab software in order to design a triangular network and simulate it for co-operative communication. Cooperative communication refers to selecting multiple forwarding nodes for data at the same time is  provides of additional flexibility and declare new functions for the network layers. Cooperative communication of need to considering various energy save measures. The high dynamic network save energy of maintain the reliability to nodes  an important research to directions. Through the multiple forwarding nodes is  used in existing cooperative communication protocols to improving  the reliability to the nodes. The reliability model is unpredictable. More, using to sleep  the sensing nodes to saving energy isn’t considers. Therefore it is  new cooperative communication to algorithms is needed. Multi-agent to route planning is the newly  research directions of intelligent agents to transmissions.  Multi-agent route are to overlapping between route, which results in energy waste. Furthermore, it is the necessary to studies  new multi-agent routing algorithms. The recent years, wireless sensor networks (WSNs) are the important parts of  Internet of Things. They are undergoes tremendous developments . The wireless channels of  WSN in the IoT changing dynamical and  resources of nodes is limited. It is the great challenges to provide high-performance of communication, especially to multimedia communications. The Cooperative communications is consider the solution of this challenges, this is scalable, to energy efficient to faults tolerant. The IOT have been  applied to various field and form intelligence. WSN is the network layer of the IoT. Although intelligence of mobile agent, data and deploys tasks are collected. The optimization of algorithms can cooperative communication to intelligent mobile agents is studied. To specific of contributions as following. (1) In this aspects of cooperative communications technology a new cooperative communication algorithm KCN (k-cooperative node) is to be proposed. K cooperative nodes for the transmission is using in each hop. Moreover, the reliability of transmission in dynamic network is improved. (2) To Intelligent mobile agents have a view to the whole networks. Sink node centralizes the planning of proxy routing. The proxy is routing can be control by using programming. In this fact, the idea of this software defining networks have been applied to the realization of  intelligent agents. This paper the multi-agents routing plan algorithms is study, and the directional sources of grouping based on multi-agents itinerary planning (D-MIP) are proposed. The remainder of the paper is structured as follows.

Section 2 introducing the currents research progressing and the shortcomings. In Section 3, a new cooperative communication algorithm KCN is proposed. The algorithm uses K cooperative nodes per hop for transmission, which improves the reliability of transmission in dynamic network. Section 4 studies the multi-agent routing planning algorithm and proposes the directed source packet multi-agent routing planning algorithm. Section 5 is describing the experimental processing to result analysis. In this Section 6 summarized to whole papers and the puts forwarding the next research plans.

II. OBJECTIVES

The primary objective of this research is to devise energy efficient routing protocols for WSNs. This research work aims to develop energy efficient routing protocols for WSNs with the help of co-operative communications, by intensively studying the existing energy efficient routing algorithms and highlighting both the relevance and limitations of them. A modification of the traditional LEACH algorithm, a mobile sink assisted modification of LEACH and a modification of the HEED protocol is carried out, focussing on enhancing the energy efficiency. The scope of this research is to; Study the different energy efficient routing techniques like clustering integrated with co-operative communications for modelling node communications.Study the various categories of energy efficient routing algorithms emphasising their merits and demerits for co-operative communications

III. DESIGN

A. WSN Architecture

The OSI model is most often followed in the WSNs architecture (Abed et al. 2012). Essentially the WSN architecture has five main layers namely the physical layer, data link layer, network layer, transport and application layer. Besides these layers there are three cross layer planes responsible for the coordination of the entire sensors nodes and managing the overall efficiency of the network. They are the power management plane, the mobility management plane and the task management plane. The WSN OSI architecture is given in Figure 1. 2. The power management plane coordinates the use of power. The mobility management plane is responsible for the tracking of node mobility while the task management plane schedules the sensing process.

These cross layer optimizations augment in conserving the energy and other resources.

The sensor nodes vary in size and functionality and are designed specifically on the application prerequisite as they are designated for specific application scenarios. The general architecture of a wireless sensor node is given in Figure 1.3. A sensor node entails a microcontroller, a radio transceiver, a power source and other wireless communicating devices. The entire network functions in chorus, by using sensor nodes of different dimensions and appropriate routing algorithm for data transmission.

The energy consumption of the wireless sensor node is dependent on the amount of energy consumed by each component for its various operations. The sensor module consumes power for sensing (periodic, sleep/wake), signal sampling, modulation, and for the analogue to digital conversion. The processing module spends energy when it is in idle, run or sleep states. The main operations of this module are controlling the sensing process, communication and data processing. The power consumption of wireless communication module is dependent on many factors like the hardware, operating frequency etc. The capacity of the power supply module is dependent on the manufacturer as well as the model of the node.

The unique characteristics and behaviour of WSNs demands a diverse method of traffic flow compared to the traditional networks. WSNs adopt two main traffic patterns; the single hop and multi hop methods for satiating its application requisite. This differentiation is on the basis of the number of nodes involved in the data transfer to destination. It is always preferred to have single hop communication as it more power efficient (Pantazis et al. 2013). But the range limits of the sensor nodes demand multihop communication.

The different multi hop communication patterns are local communication, point to point routing, convergence, aggregation and divergence. It is through the local communication that the status of the node is broadcasted to its neighbours. Figure 1.4(a) represents the local communication. This method is also used if data has to be transmitted between two immediate nodes. For transmitting data in wireless LAN environments point to point routing is used as mentioned in Figure 1.4 (b). The pattern of data movement from multiple nodes to a single BS is convergence while the reverse process is termed divergence. The convergence pattern is represented in Figure 1.4(c) and the divergence in Figure 1.4(e). When data processing happens at the nodes and the results are routed to the BS, it is called as aggregation. The diagrammatic representation of data aggregation is given in Figure 1.4 (d)

IV. RESULTS AND DISCUSSION

Conclusion

WSNs are used for various applications in our daily life. Since numerous sensors are usually deployed on remote and inaccessible places, specifically for monitoring and surveillance applications, the deployment and maintenance should be easy and scalables. As the sensor nodes are resource constrained, the efficient use of these resources especially the energy resource is very much decisive for retaining the life time of the WSNs. As energy is utilised more for communication purpose, efficient routing protocol design is a need for WSNs for prolonging the network lifetime. The triangulations rules were applied for 20, 50 and 100 nodes demonstration for WSNs The protocols developed during this research, attains the basic objective of the research work, energy efficient routing in WSNs. The outcome shows an increase in life time, average energy of nodes and packet delivery ratio and a decrease in the average packet delay which proves the energy efficiency of the modified protocols. These protocol was developed to mainly keeping in the mind in the agriculture sector. They can be customised according to the requirements in the practical scenario.

References

[1] www.mdpi.com/journal/Sensors 2019, 19, 4690; doi:1 0.3390/s19214690, A Survey of Collaborative UAV–WSN Systems for Ecient Monitoring Dan Popescu, Florin Stoican, Grigore Stamatescu, Oana Chenaru and Loretta Ichim [2] HAL Id: hal-01376584, https://hal.archives-ouvertes.fr/hal-01376584, 5 Oct 2016, Cooperative communication for wireless sensors Network : a Mac protocol solution, Bastien Mainaud, Vincent Gauthier, Hossam Afifi [3] Alhasanat A. I., Matrouk K. D., Alasha H. A., and Al-qadi Z. A., “Connectivity-Based Data Gathering with Path-Constrained Mobile Sink in Wireless Sensor Networks,” Wireless Sensor Network, vol. 6, no. 1, pp. 118-128, 2014. [4] Abed A., Alkhatib A., and Baicher G. S., “Wireless Sensor Network Architecture,” International Journal on Computer Networks Communication Systems, vol. 35, pp. 11-15, 2012. [5] Journal of Emerging Trends in Computing and Information Sciences, VOL. 3, NO. 7 July, 2012, Performance Analysis of Cooperative Communication Protocols, Rahat Ali Khan, Muhammad Abdul Aleem, Asad Ali Shaikh. [6] Ali, Q. I. “Simulation Framework of Wireless Sensor Network ( WSN ) Using MATLAB / SIMULINK Software,” MATLAB - A Fundamental Tool for Scientific Computing and Engineering Applications,vol. 2, no. 1,pp. 263-284, 2012. [7] IETE TECHNICAL REVIEW | VOL 28 | ISSUE 5 | SEP-OCT 2011, Cooperative-Communication: A Review, Himanshu Katiyar, Ashutosh Rastogi and Rupali Agarwal. [8] Almi\'ani K.,Viglas A., Libman L. Mobile Element Path Planning for Time-Constrained Data Gathering in Wireless Sensor Networks in Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Application, pp. 843–850, 2010.

Copyright

Copyright © 2022 Ganesh Dighorikar, Alankar Hedaoo, Kavi Katekhaye, Khushali Zode, Prof. Vivek. N. Mahawadiwar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.