IRJET- Coverage Hole Avoidance using Fault Node Recovery in Mobile Sensor Network

IRJET- Coverage Hole Avoidance using Fault Node Recovery in Mobile Sensor Network

• 1.
  International Research Journalof Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 5290 COVERAGE HOLE AVOIDANCE USING FAULT NODE RECOVERY IN MOBILE SENSOR NETWORK Ms. P. Jananipriya1, Dr. T. Kowsalya2, Dr. G. Selvakumar3 1PG. Scholar Department of ECE, Muthayammal Engineering College, Tamilnadu, India. 2,3Professor, Department of ECE, Muthayammal Engineering College, Tamilnadu, India. ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract -Study the coverageandenergyutilizationcontrol in mobile heterogeneous wireless sensor networks (WSNs). By term heterogeneous, we mean that sensors in the network have various sensing radius, which is an inherent property of many functional WSNs. Two sensor deployment schemes are considered-uniform and Poisson schemes. The main focus of this work is directed towards distributed coordination algorithms for coverage in a mobile sensor network. The sensors are assumed to have non identical sensingranges, and it is desired to move them in such a way that the total sensing coverage increases as much as possible. To this end, the fieldis partitioned using the multiplicatively weighted Voronoi cells, and then different geometric methods are developed to find new locations for the sensors such that the coverage is improved. The proposed algorithms are iterative, and use the existing local information to place the sensors accurately, aiming to decrease the size of the coverageholes inthesystem. Our scheme propose defective node recovery and replacement algorithm for WSN based on the grade diffusion algorithm combined with genetic algorithm. TheFNRalgorithmrequires replacing smaller number of sensor nodes and reusesthemost routing paths, rising the WSN lifetime and reducing the replacement rate. In the model, the proposed algorithm increases the number of active nodes reduces the rate of energy utilization. Key Words:Heterogeneous Network, Voroni Cells, Fault Node Recovery Cells ,Coordination Algorithm. 1. INTRODUCTION Wireless Sensor Network (WSN) is a collection of spatially deployed wireless sensors by which to monitor various changes of environmental conditions (e.g., forest fire, air pollutant concentration, and object moving) in a collaborative manner without relying on any underlying infrastructure support. Recently, a number of research efforts have been made to develop sensor hardware and network architectures in order to effectively deploy WSNs for a variety of applications. Due to a wide diversity of WSN application requirements, however, a general-purpose WSN design cannot fulfill the needs of all applications. Many network parameters such as sensing range, transmission range, and node density have to be carefully considered at the network design stage, according to specific applications. To achieve this, it is critical to capture the impacts of network parameters on network performance with respect to application specifications. Wireless sensor network consists of a large number of distributed nodes with sensing, data processing, and communication capabilities. Those nodes are self-organized into a multi-hop wireless network and collaborate to accomplish a common task. As sensor nodes are usually battery-powered, and theyshouldbeabletooperatewithout attendance for a relatively long period of time, energy efficiency is of critical importance in design of wireless sensor network. 1.1 Related Work Faults may be due to a variety of factors, includinghardware failure, software bugs, operator (user) error, and network problems. Data delivery in sensor networks is inherently faulty and unpredictable links may fail when permanently (or) temporarily blocked by an external object (or) environmental condition. Packets may be corrupted due to the erroneous nature of communication. Fault is occurredduringtheroutingprocess in wireless sensor network because of some problem, the major problem that affect the design and performance of a WSN are as follows: Hardware and Operating System for WSN Synchronization Localization Deployment Data Dissemination
• 2.
  International Research Journalof Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 5291 Data Aggregation Fig 1: Fault Occurred in WSN Network In a wireless sensor network (WSN), sensor nodes are provided with batteries that can operate for only a short period of time, which results in short network lifespan. The short lifespan disables the application of WSNsforlongterm tasks such as road condition maintenance for bridges and tunnels, border surveillance, and so on. 2. EXISTING SYSTEM The MW-Voronoi diagram is used in this work to develop sensor deployment algorithms. Every sensor has a circular sensing area whose size is not the same for all sensors. Consider each sensor as a weighted node whose weight is equal to its sensing radius, and draw the MW-Voronoi diagram. It is a straightforward results that if a sensor cannot detect a phenomenon in its region, there is no other sensor that can detect it either. This implies that tofindthecoverageholes in the sensing field, it would sufficient to compare the MW- Voronoi region of every node with its local coverage area. Disadvantages: • To enhance the life time of a wireless sensor network when some of the sensor nodes stop their working. • It consumes more energy and decreasethe network life time. • the coverage hole problem may occur 3. PROPOSED SYSTEM Farthest Point Boundary Strategy (FPB) is proposed. In this algorithm, each sensor moves towardthefarthestpointin its MW-Voronoi region such that any existing coverage hole in its region can be covered. This point is denoted by X i,far for the i -th region. In fact, once a sensor detectsa coveragehole, it calculates the farthest point and moves toward it continuously until X is covered The following definition is used to calculate the farthest point in each MW-Voronoi region. Due to Fault node also, the coverage hole problem may occur. To avoid this fault node recovery algorithm is established. It uses genetic algorithm to find out fault nodes. Table is maintained for every node, having its energy level, location and payload. If energy level dropstothreshold,then corresponding region is covered neighbouring nodes using voronoi cell algorithm. If it does not node will be replaced. Advantages:  It tends tomove the sensors in proper directions such that the network configuration becomescloser to an ideal configuration.  Reducing the rate of data loss and rate of energy consumption  Increase the network life time. MODULES 1. Sensor Deployment 2. Hole Identification 3. Hole Healing 4. Healing a Hole with Obstacle 3.1 SENSOR DEPLOYMENT Constructing Project design in NS2 should takes place. Wireless Sensor Nodes are deployed over a region where some phenomenon is to be monitored. Each node in the Wireless Sensor Network maintains the detailsofneighbour node.
• 3.
  International Research Journalof Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 5292 3.2 HOLE IDENTIFICATION In this module we assess the existence of a hole, which is done by identifying stuck nodes which are border of hole. Stuck nodes & Border Nodes are similar to this Algorithm. That is why we need to carry out the network boundary- node identification to avoid that the hole discovery process be launched by those nodes. 3.3 HOLE HEALING After determining the Stuck nodes & Boundary nodes, the nodes in the local area of the holes involved in the healing process. Nodes that receiveforces fromtheholecenter,move towards it. 3.4 HEALING A HOLE WITH OBSTACLE In this module, we add the Distributed Virtual Force Algorithm to deploy mobile wireless sensors in an area containing obstacles in order to ensure coverage and connectivity. And also the DVFA algorithm should cover the boundary of the network. Here we have the figure 1 to explain the system which we are going to propose. In this figure we have the number of nodes from that we need to send the message from one node to another one, in between that some obstacles are present so that it have to penetrate through the obstacle and then move to the sink node. 4. FAULT NODE RECOVERY ALGORITHM Fault node recovery algorithm is combination of grade diffusion algorithm with genetic algorithm fig shows the flow chart of FNR . In wireless network ,the sensor nodes have less battery supplies and less energy, It also result in replacement of sensor node and replacement cost and using same routing path when few nodes are not working. Fault node recoveryalgorithmforwirelesssensornetwork is combination of genetic and grade diffusion algorithm. This algorithm replace non working node and reuse the routing path reduce the replacement cost and increase the life time. Proposes an algorithm to search for and replace fewer sensor nodes and to reuse the most routing paths.. One scheme, the genetic algorithm (GA). The fault node recovery (FNR) algorithm based on the Grade Diffusion (GD) algorithm combined with the GA. 5. THE BASIC WIRELESS MODEL IN NS The wireless model essentially consists of the Mobile Node at the core, with additional supporting features that allows simulations of multi-hop ad-hoc networks, wireless LANs etc. The Mobile Node object is a split object. The C++ class Mobile Node is derived from parent class Node. A Mobile Node thus is the basic Node object withaddedfunctionalities of a wireless and mobile node like ability to move within a given topology, ability to receive and transmit signals to and from a wireless channel etc. A major difference between them, though, is that a MobileNode is not connected by means of Links to other nodes or mobile nodes. In this section we shall describe the internals of Mobile Node, its routing mechanisms, the routing protocols dsdv, aodv, tora and dsr, creation of network stack allowingchannel accessin Mobile Node, brief description of each stack component, trace support and movement/traffic scenariogenerationfor wireless simulations. 6. IMPLEMENTATION TOOLS 6.1 THE NETWORK SIMULATOR 2.33 (NS2) Network Simulator (NS2) is a discrete event driven simulator developed at UC Berkeley. It is part of the VINT project. The goal of NS2 is to support networking research and education. It is suitable for designing new protocols, comparing different protocols and traffic evaluations.NS2 is developed as a collaborative environment. It is distributed freely and open source. A large amount of institutes and people in development and research use, maintain and develop NS2. This increases the confidence in it. 6.2 MOBILE NETWORKINGG IN NS2.33 This section describes the wirelessmodel thatwasoriginally ported as CMU’s Monarch group’s mobilityextensionto NS2. The first section covers the original mobility model ported from CMU/Monarch group. In this section, we cover the internals of a mobile node,routingmechanismsandnetwork components that are used to construct the network stack for a mobile node. The components that are covered briefly are Channel, Network interface, Radio propagation model, MAC protocols, Interface Queue, Link layer and Address resolution protocol model (ARP). CMU trace support and Generation of node movement and traffic scenario files are also covered in this section. The original CMU model allows simulation of pure wireless LANs or multihop ad-hoc networks. Further extensions were made to this model to
• 4.
  International Research Journalof Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 5293 allow combined simulation of wired and wireless networks. MobileIP was also extended to the wireless model. 7. CONCLUSIONS Efficient sensor coordination algorithms are developed in this work to increase sensing coverage in a network of mobile sensors with non-identical sensing ranges. The sensing field is first partitioned using the multiplicatively weighted Voronoi (MW-Voronoi) diagram, and three distributed deployment algorithms are subsequently developed. Under the proposed algorithms, the sensors move iteratively in such a way that coverage holes are reduced in size. The algorithms tend to move the sensors in proper directions such that the network configuration (in terms of the distance of sensors from the vertices of the MW-Voronoi regions) becomes closer to an ideal configuration. To avoid complex non-convex optimization problems, novel geometric methods are used to findthenewsensorlocations in the MW-Voronoi regions. The proposed algorithms are compared with other techniques with different number of sensors. Our system proposes a faulty node recovery and replacement algorithm for WSN basedonthegradediffusion algorithm combined with genetic algorithm. The FNR algorithm requires replacing fewer sensor nodesandreuses the most routing paths, increasing the WSN lifetime and reducing the replacement cost. In the simulation, the proposed algorithm increases the number of active nodes reduces the rate of energy consumption. REFERENCES [1] Z. Zou, Y. Bao, H. Li, B. F. Spencer, and J.Ou, “Embedding compressive sensing-based data lossrecoveryalgorithminto wireless smart sensors for structural health monitoring,” IEEE Sensors Journal, vol. 15, no. 2, pp. 797–808, 2015. [2] X. Wang, S. Han, Y. Wu, and X. Wang, “Coverage and energy consump- tion control in mobile heterogeneous wireless sensor networks,” IEEE Transactions on Automatic Control, vol. 58, no. 4, pp. 975–988, 2013. [3] S. Zhu and Z. Ding, “Distributed cooperative localization of wireless sensor networks with convex hull constraint,” IEEE Transactions on Wireless Communications, vol. 10, no. 7, pp. 2150–2161, 2011. [4] M. Pourali and A. Mosleh, “Afunctional sensorplacement optimization method for power systems healthmonitoring,” IEEE Transactions on Industry Applications, vol. 49, no. 4, pp. 1711–1719, 2013. [5] T. Shu, M. Krunz, and S. Liu, “Secure data collection in wireless sensor networks using randomized dispersive routes,” IEEE Trans. Mobile Comput., vol. 9, no. 7, pp. 941- 954, Jul. 2010. [6] M. Kurt and S. Peter, Algorithms andData Structures:The Basic Toolbox. Springer, 2008. pp. 198-199 [6] G. Zhan, W. Shi, and J. Deng, “Design and implementation of TARF: A trust-aware routing framework for WSNs,” IEEE Trans. Dependable Secure Comput.,vol.9,no.2, pp.184-197, Mar./Apr. 2012. [7] A. Ahmed, K. A. Bakar, M. I. Channa, K. Haseeb, and A. W. Khan, “TERP: A Trust andEnergyAwareRoutingProtocol for Wireless Sensor Network,” IEEE Sensors J. vol. 15,no.12, pp. 6962-6972, Dec. 2015. [8] Y. Liu, M. Dong, K. Ota, and A. Liu, “ActiveTrust: Secure and Trustable Routing in Wireless Sensor Networks,” IEEE Trans. Inf. Forensics Security., vol. 11, no. 9, pp. 2013-2027, Sep. 2016. [9] X. Chen, J.-H Cho, and S. Zhu, “GlobalTrust: An Attack- Resilient Reputation System for Tactical Networks,” in Proc. IEEE SECON 2014. Singapore, 2014, pp. 275-283. [10] T. Shu, M. Krunz, and S. Liu, “Secure data collection in wireless sensor networks using randomized dispersive routes,” IEEE Trans. Mobile Comput., vol. 9, no. 7, pp. 941- 954, Jul. 2010. [11] S. Li, L. D. Xu, and S. Zhao, “The Internet of Things: A survey,” Inf. Syst. Frontiers, vol. 17, no. 2, pp. 243-259, Apr. 2015. [12] J.-H. Cho, A. Swami, and I.-R Chen, “A Survey on Trust Management for Mobile Ad Hoc Networks,” IEEE Commun. Surv