Improved Termite Hill Routing Protocol using ACO in WSN

2013 International Computer Science and Engineering Conference (ICSEC 2013) Improved Termite Hill Routing Protocol using ACO in WSN 1 Chandni, 2 Kanika Sharma 3 Himanshu Monga Electronics and Communication Engineering N.I.T.T.T.R Chandigarh, India. chandni.smiley08@gmail.com Electronics and Communication Engineering Baddi University Himachal Pradesh, India. Abstract -The wireless sensor networks contains of a large number of tiny sensing devices. These are capable of detecting an event, processing the information, and transmitting that processed data. In WSN network there is a sink which conveys all information to the end user. The sink is placed anywhere in the target area. The nodes which are closer to the sink easily convey their message to it but the nodes which are at a great distance from it cannot directly forward their data to the sink, they have to send their data to the node which is closer to it than its neighbor forwards its data to sink. In this way the nodes which are closer to the sink have to send the data of the distant nodes along with their own so they get depleted in terms of energy, as their energy is used in sending their data along with the data of farther nodes. This problem is called HOT SPOT problem. In the proposed work the location of the sink node is optimized by using ACO. The simulation results of our proposed algorithm on MATLAB software demonstrated that it has reduced the network s energy consumption and improved other parameters like packet delivery rate. area in order to collect the local information and make a global decision about the physical environment. These nodes are very small in size, less expensive in nature, self powered devices [1]. The sensor nodes are highly prone to failures as they possess very less power. The topology of a sensor network changes in a frequent manner. Sensor nodes are not having any global identification because of a very huge amount of overhead. Sensor networks include different types of sensors such as seismic, thermal, visual, infrared, acoustic and radar, which are able to monitor and analyze a wide variety of conditions that include the Temperature, Humidity, Vehicular movement, Lightening condition, Pressure, Soil makeup, the presence or absence of certain objects, Noise levels and mechanical stress levels on the objects attached to it, the current characteristics such as speed, direction, size of an Keywords-Mobile sink; Termite Hill routing algorithm; swarm intelligence; wireless sensor network. I. INTRODUCTION Wireless sensor network is basically a network consisting of a large number of tiny nodes which are deployed from a helicopter over a wide geographical Fig. 1 Sensor network architecture [1] 378

object. Sensor nodes can be used for sensing, event detection, location sensing, and local control of actuators. The applications of these networks can be categorized into military, health, home, environment applications, home and other commercial areas. This classification can be expanded with more categories such as space exploration, chemical processing disaster relief etc. It has a very wide range of applications nowadays [2]. A. Zigbee and 802.15.4 Overview Many WSN platforms such as MICA2, MICAz, TelosB MOTE (Xbow, 2005), and Dust Network (DustNetworks, 2005) had been developed. To avail compatibility among different systems to work together, a number of standards were needed. ZigBee/IEEE 802.15.4 standards were designed for this purpose and it came into existence in May 2003. The ZigBee Alliance [3] is basically a group of companies working together to develop a number of standards for a highly reliable, low cost, reduced power consumption in wireless networks. In Wireless sensor network based on Zigbee technology is widely used in a number of applications like military security, monitoring of environment, automation of home and so on ZigBee is constructed on the IEEE 802.15.4 standard [4] which basically defines the physical and MAC layers for reduced cost, low rate PAN (personal area networks). ZigBee defines the specifications of network layer for star, peer-topeer and tree topologies. The IEEE 802.15.4 standard [4] explains the characteristics of the physical and MAC layers for providing Low-Rate Wireless Personal Area Networks (LR-WPAN). The characteristics advantages of an LR-WPAN are ease of installation, high reliability in data transfer, short-range operation, extremely reduced cost, and a reasonable amount of battery life, Fig. 2 ZigBee functional layer architecture [17] besides this it maintains a simple and flexible protocol stack. ZigBee [3] basically standardizes the higher layers of the protocol stack. The network layer (NWK) performs the function of organizing and providing routing path over a multihop network, while the Application Layer (APL) performs the function for application development and data communication. APO software is used to control the hardware unit like transducer which is embedded on the device. Each APO is locally allotted a unique endpoint value. This APO can use this number to interact with other APOs. The ZigBee Device Object (ZDO) provides a number of services to the APOs like it makes APOs enable to discover the devices in the network. Its main function is to provide data communication and security management. The Application Sublayer (APS) enables data transfer operations for the APOs and the ZDO. The basic framework of zigbee 802.15.4 allows a 10-meter range of data communications providing a transfer rate of 250 kbit/s. Tradeoffs among the data rates and power consumption will be there in some cases. If lower power consumption is desired the then one has to compromise with the data rates. II. RELATED WORK Several numbers of researches are made in the field of routing in WSN towards improving the network lifetime, focusing on static applications. Recently, the sink was considered by [5] to be mobile to 379

improve the lifetime of the network. Other protocols which supports mobile sink are Improved Energy Efficient Ant Based Routing algorithm (IEEABR) [6], Flooded Forward ant routing FF [7], Ad-hoc On-demand Distance Vector (AODV) [8], and Sensor-driven and Cost-aware ant routing (SC) among others. Several researches based on swarm intelligence are adopted in [6], and honey bees [9] to improve the lifetime of network. The study of termite behavior has obtained remarkable achievements in the communication capabilities as compared to ants and honey bees. In [10], termite agents [11] were modeled for mobile wireless ad-hoc networking (MANET). In [13] the sink is considered to be mobile using termites but speed of sink is random. There are a number of routing protocols which supports the sink mobility. Thanigaivelu et al. [1] investigated the effect of sink mobility in two scenarios in WSNs by using random way point mobility and random walk mobility. Marta et al. [12], propose a solution to decide sinks movements when the paths are not predetermined in WSNs supporting multihop communication. Luo et al. [13], proposed a routing protocol for WSNs which can predict a path using a mobile sink to prolong the network lifetime. The work defined in [14] is extended to use multiple mobile sinks to solve the problem of network growth. The drawback of all the surveyed work is that, they tend to sacrifice network average packet delivery rate in order to balance the network lifetime. In our proposed algorithm, we have considered that the sink to be in mobile scenario. We have used ACO to optimize the position of the sink. This approach has improved the average packet delivery rate, efficiency and energy consumption of the sensor network to a significant level. The simulation is made in MATLAB simulator. Fig. 3 Termite s nest [15] III. PROPOSED ALGORITHM The proposed algorithm is simulated by MATLAB software. The energy consumption model is implemented to compute energy consumed by transmitting and receiving data packets. a) Assumptions b) Initialization of parameters. c) Deployment of sensor nodes. d) Deployment of sink. e) Selection of a sensor node to communicate with the sink. f) Sink position optimization using ACO. g) Sink movement with speed. h) Data Transmission. A) Assumptions Assume that the maximum range of sink is 60m i.e. sink can make communication only with the nodes that are lying in this particular range. The minimum energy of each sensor node is assumed to be 5 joules. B) Initialization of Parameters The simulation is carried out using MATLAB software version R2011b. The simulation parameters are shown in table. Sensor nodes are randomly deployed in the network area. 380

The simulation takes place for 100 seconds. The batteries of nodes are initialized with 30 joules. The participating nodes are stationary in nature. The sink is working in mobile scenario. The data transmission is carried out by using multihop routing. C) Deployment of sensor nodes The sensor nodes are randomly deployed in the network area and have uniform density and random location throughout the network. TABLE I. SIMULATION PARAMETERS Parameters Values Routing Protocols Advance Termite Hill Routing Algorithm, Termite-hill Routing Algorithm Number of Nodes 9 nodes Simulation Time 100 Seconds Nodes Energy 30Joules Simulation area 200m x 200m Packet size 64 bytes Simulator MATLAB R2011b software Protocol 802.15.4 There are 9 sensor nodes and they are randomly deployed in an area of 200m * 200m. D) Deployment of sink The sink is deployed after the deployment of sensor nodes. The transmission range of sink is assumed to be 60m. The sink is considered to be in mobile scenario. Initially it is static. When it finds a suitable position then it moves towards that location. E) Selection of a sensor node to communicate with the sink The range of the sink is assumed to be 60m. Firstly it checks the nodes that whether they are in its range or not. If no neighboring node is found then pit flag is set to 1. If some neighbors are found then the sink checks their battery values that whether their battery values are above the threshold value or not. If they are below the threshold value then pit flag is set to1 otherwise the node with shortest distance from the sink gets selected to communicate with the sink. F) Sink position optimization using ACO The sink is lying anywhere in the target field initially. The ants are also lying at that position. The range of ants is assumed to be 100m. After a short time period the ants starts searching the area. They intimate the sink about the new location. If they find a position with a high energy value than the current position of sink only then the sink moves towards that location. If the current position is best suited than new location then it will not move. d = [{node(x) sink(x)} 2 +{node(y) sink(y)} 2 ]½ G) Sink movement with speed The sink will move only if it finds a new location with higher energy than the current position. If the distance between the new location and current location is large then the sink will move at a higher pace by increasing the step size. If the distance between the two positions is not large enough then the sink will move at a slower pace by reducing the step size. Fig. 4 Data transmission through diagonal routing 381

H) Data Transmission Data Transmission takes place through diagonal routing i.e. if the node is lying in the range of sink then it will directly sends its data to sink. But if it is not lying in the range of the sink then it will check for its nearest neighbor in terms of distance from the sink. In this way the data is routed towards the sink. I) Energy Consumption Model We have considered the signal to be a DPSK modulated and Rayleigh fading channel is assumed. For such modulation schemes, the Eb/N0 required for a given BER performance in a Rayleigh fading channel can be found out through simulation studies [16]. For the system under consideration, the following equations apply: Pt = [pr x (4) 2 d n x M L x N F ]/(G t Gr λ 2 ) (1) IV. EXPERIMENTAL RESULTS AND DISCUSSIONS Fig. 5.Comparison graph of Throughput (Mobile Scenario) Pr= E/NoNoRb (2) Fig. 6 Comparison graph of Consumption (Mobile Scenario) E t (d)= p t /R b (3) E rece (k)= E elect x k (4) E trans (k,d)= E elect x k + E t (d) x k (5) P t - transmitted power, P r - received power, M L - link margin compensating the hardware, N F - receiver noise figure, N 0 - single sided thermal noise power spectral density. G t - gain of the transmitter, G r - gain of the receiver k -length of the message (bits) to be transmitted, λ - wavelength of the signal E trans is the transmitting cost, E rece is the receiving cost. n is path-loss exponent typically ranges from 2 5. For our simulation purpose we take n as 2 and G t and G r as unity. E elec is the energy dissipation to run the transmitter or receiver circuitry.e t (d) is the energy dissipation for the transmission amplifier for single bit. Fig. 7 Comparison graph of Efficiency (Mobile Scenario) TABLE II. IMPACT OF SINK MOBILITY ON NETWORK LIFETIME Parameters Termite Hill Algorithm Proposed Algorithm Throughput 4kbps 29.11kbps Energy 4kb/joule 38kb/joule Efficiency Energy Consumption 30joules 8.8joules 382

The improvement in the throughput of network for proposed algorithm results from the implementation of ACO to find the best suited location for the sink. The throughput is improved to a significant level as shown in the comparison graphs. The maximum value of throughput of proposed algorithm is 26kbps when the speed of sink is 40m/s while the value is 4kbps at a speed of 20m/s in case of termite Hill routing algorithm. The termite hill algorithm is having maximum consumption of 30joules when sink is moving at a speed of 100 m/s joules and in proposed algorithm it is 4 joules when the speed of sink is 40m/s. V. CONCLUSION We simulated our routing algorithm in MATLAB considering the sink to be mobile. In this paper, we have investigated the impact of sink mobility on different parameters of the network. Nodes near the sink get depleted faster in terms of their energy which might creates holes in the network thus resulting in network isolation. With the mobility of sink, the nodes around the sink always changes, thus reducing the energy consumption in the network. Our proposed algorithm has mitigated the problem of network isolation to a significant extent by reducing the energy consumption of the network. Besides this it has also improved the throughput, efficiency of the network as seen in the graph. REFERENCES [1] V.Chandrasekaran, Dr.A.Shanmugam, A Review on Hierarchical Cluster Based Routing In Wireless Sensor Networks, Journal of Global Research in Computer Science, vol. 3 no. 2 pp. 12-16, 2012. [2] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Computer Networks of Elsevier, vol. 38 pp.393 422, 2002. [3] ZigBee Alliance, ZigBee Specifications, version 1.0, April 2005. [4] Institute of Electrical and Electronics Engineers, Inc., IEEE Std. 802.15.4-2003 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low Rate Wireless Personal Area Networks (LR-WPANs), IEEE Press 2003. [5] K. Thanigaivelu, and K. Murugan, Impact of Sink Mobility on Network Performance in Wireless Sensor Networks, First IEEE International Conference on Networks & Communications, pp. 7-11, 2009. [6] A. M. Zungeru, L. -M. Ang, S. R. S. Prabaharan, and K. P. Seng, Ant Based Routing Protocol for Visual Sensors, ICIEIS, part II, CCIS252, pp. 250-264, 2011. [7] Y. Zhang, L.D. Kuhn, and M.P.J. Fromherz, Improvements on Ant Routing for Sensor Networks:Ant Colony Optimization and Swarm Intelligence, LNCS, 3172, pp. 289-313, 2004. [8] C. Perkins, and E. Royer, Ad-hoc on-demand distance vector routing, Proceedings of Second IEEE Workshop on Mobile Computing Systems and Applications, 1999. [9] M. Saleem, and M. Farooq, Beesensor: A bee-inspired power aware routing algorithms, Proceedings Evocomnet, LNCS vol. 349, pp. 136-146, 2005. [10] M. Roth, and S. Wicker, Termite: ad-hoc networking with stigmergy, IEEE Global Telecommunications Conference, vol. 5 pp. 2937-2941, 2003. [11] Termites 2011; [Accessed 18 October 2011], [online] Available at: < http://en.wikipedia.org/wiki/termite> [12] M. Marta, and M. Cardei, Using Sink Mobility to Increase Wireless Sensor Networks Lifetime, Proceedings of the Ninth IEEE International Symposium of World of Wireless, Mobile and Multimedia Networks, pp. 1-10, 2008. [13] A.M. Zungeru, Li-Minn Ang, Kah Phooi Seng, Termitehill: Routing Towards a Mobile Sink for Improving Network Lifetime in Wireless Sensor Networks,Third IEEE International Conference on Intelligent Systems Modelling and Simulation, pp. 622-627, 2012. [14] J. Luo, J. Panchard, M. Piorkowski, M. Grossglauser, and J. Hubaux, MobiRoute: Routing towards a Mobile Sink for Improving Lifetime in Sensor Networks, In: Proceedings Second IEEE/ACM International Conferene of Distributed Computing in Sensor Systems, pp. 480-497, 2006. [15] M. Dorigo, Thomas Sttuzle, Ant Colony Optimization, MIT press, pp. 356-360, 2004. [16] Srikanth. B, Harish. M, Bhattacharjee. R, An Energy Efficient Hybrid MAC Protocol for WSN containing Mobile Nodes, International conference of IEEE (ICICS), pp. 6-11, 2011. [17] Paolo Baronti b,c, Prashant Pillai, Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards, Computer Communications of Elsevier, vol. 30, pp. 1655-1695, 2007. 383