Review Paper on Wireless Sensor Networks WSN for Fuzzy Logy Algorithms

1. International Research Research and Development (IJTSRD) International Open Access Journal Review Paper on Wireless Sensor Networks (WSN) for Fuzzy Logy Algorithms Ashish Verma1, Mukesh Patidar2 1M. Tech (Student), 2Professor Department of Electronics and Communication Engineering, Lakshmi Narain College of Technology, Indore, Madhya Pradesh, India International Journal of Trend in Scientific Scientific (IJTSRD) International Open Access Journal ISSN No: 2456 ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume 6470 | www.ijtsrd.com | Volume - 2 | Issue – 4 Review Paper on Wir eless Sensor Networks (WSN) for Fuzzy Logy Algorithms Ashish Verma Department of Electronics and Communication Engineering i Narain College of Technology, ABSTRACT Wireless Sensor Networks is a group of spatially dispersed dedicated sensors to monitor and record an environment’s physical conditions and to organize collected data at a central location (Xiao 2004). Wireless Sensor networks (WSNs) have become one of the most interesting areas of research in the past few years. A WSN is composed of a number of wireless sensor nodes which form a sensor field and a sink. These large numbers of nodes, having the abilities to sense their surroundings, perform limited computation and communicate wirelessly form the WSNs. It is enjoy great benefits due to their very low cost, small scale, smart Applications of wireless sensor networks such as: span environmental, animal monitoring, industrial monitoring, agricultural monitoring, automation, health monitoring and many other areas. In this paper review on WSN, literature and analysis of fuzzy loge algorithm for consider different numbers of Node and cluster like 100, 200 and 300. WSN node is typically battery powered and characterized by extremely small size and low cost. characterized by extremely small size and low cost. Wireless Sensor Networks is a group of spatially dispersed dedicated sensors to monitor and record an environment’s physical conditions and to organize collected data at a central location (Xiao 2004). Wireless Sensor networks (WSNs) have become one of the most interesting areas of research in the past ew years. A WSN is composed of a number of wireless sensor nodes which form a sensor field and a sink. These large numbers of nodes, having the abilities to sense their surroundings, perform limited computation and communicate wirelessly form the is enjoy great benefits due to their very low cost, small scale, smart Applications of wireless sensor networks such as: span environmental, animal monitoring, industrial monitoring, agricultural monitoring, automation, and many other areas. In this paper review on WSN, literature and analysis of fuzzy loge algorithm for consider different numbers of Node and WSN node is typically battery powered and sensor sensor nodes nodes etc. etc. Fig. 1: Cluster based WSN Fig. 1: Cluster based WSN It is wireless network sometimes more specifically referred as Wireless Sensor and described in a Wireless sensor networks (WSNs) enable new applications and require non paradigms for protocol design due to several constraints. Wireless sensor network are using Global Positioning System (GPS) and local algorithms can be used to positioning information and obtain location. The requirement for low device complexity together with low energy consumption, a proper balance between signal/data processing capabilities must be found. signal/data processing capabilities must be found. It is wireless network sometimes more specifically referred as Wireless Sensor and Actuator Networks as described in a Wireless sensor networks (WSNs) enable new applications and require non-conventional paradigms for protocol design due to several constraints. Wireless sensor network are using Global Positioning System (GPS) and local positioning algorithms can be used to positioning information and obtain location. The requirement for low device complexity together with low energy consumption, a proper balance between Keywords: WSN, Cluster, Fuzzy logy, Sensor WSN, Cluster, Fuzzy logy, Sensor I. Wireless sensor network (WSN) is an ad technology comprising even thousands of autonomic and self-organizing nodes that combine environmental sensing, data processing, and wireless networking. The applications for sensor networks range from home and industrial environments to military uses. Unlike the traditional computer networks, a WSN is application-oriented and deployed for a specific task. WSNs are data centric, which means that messages are not send to individual nodes but to geographical locations or regions based on the data content. A locations or regions based on the data content. A INTRODUCTION Wireless sensor network (WSN) is an ad-hoc network technology comprising even thousands of autonomic organizing nodes that combine environmental sensing, data processing, and wireless networking. The applications for sensor networks range from e and industrial environments to military uses. Unlike the traditional computer networks, a WSN is oriented and deployed for a specific task. WSNs are data centric, which means that messages are not send to individual nodes but to geographical communication communication and and @ IJTSRD | Available Online @ www.ijtsrd.com @ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 4 | May-Jun Jun 2018 Page: 1971

2. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 several sensor network applications such as inter cluster communication, node localization and so on. Clustering algorithms have extensive applications in the precedent years and algorithms have been consumption in recent years in all of these algorithms, and nodes are structured as clusters, superior energy nodes are called as Cluster Head (CH) and other nodes are called as normal sensor nodes. III. LITERATURE REVIEW 1.D. Naga Ravikiran and C. G. Dethe (2018) “Improvements in Routing Algorithms to Enhance Lifetime of Wireless International Journal of Computer Networks & Communications (IJCNC) Vol.10 (2) 2018. In this paper improvements in various parameters are compared for three different routing algorithms. First, it is started with Low Energy Adaptive Cluster Hierarchy (LEACH)which is a famed clustering mechanism that elects a CH based on the probability model. Then, work describes a Fuzzy logic system initiated CH selection algorithm for LEACH. 2.Vineet Mishra et al. (2015). “Performance Analysis Of Cluster Formation In Wireless Sensor Networks”, Global Engineering Technologies and Sciences, 2(11). The energy consumption is the principal concern in WSN. It system is built on an IEEE 802.15.4 wireless mesh network. Networking is a network of wireless sensor nodes deployed in an area. The wireless sensor network consists of the sensor nodes. The idea of development of wireless sensor networks was initially motivated by military applications. A WSN provides a reliable, low maintenance, low power method for making measurements in applications where cabled sensors are impractical or otherwise undesirable. A wireless sensor network (WSN) is a wireless network using sensors to cooperatively monitor physical or environmental conditions such as humidity, pressure, temperature, sound, vibration. 3.T. Arivanantham et al. (2014). “Clustering Techniques to Analyze Communication Overhead in Wireless Sensor Network”, International Journal of Computational Engineering Research, 4(5). The major problem with wireless sensor network is their limited source of energy, the courage constraint and high traffic load. In this paper we introduce various clustering techniques common clustering proposed for energy Sensor Networks” Fig. 2: WSN Technology In recent years an efficient design of a Wireless Sensor Network has become a leading area of research. A Sensor is a device that responds and detects some type of input from both the physical or environmental conditions, such as pressure, heat, light, etc. The output of the sensor is generally an electrical signal that is transmitted to a controller for further processing. II. Types of WSNs (Wireless Sensor Networks) Depending on the environment, the types of networks are decided so that those can be deployed underwater, underground, on land, and so on. Different types of WSNs include: 1.Terrestrial WSNs 2.Underground WSNs 3.Underwater WSNs 4.Multimedia WSNs 5.Mobile WSNs Journal of Advance Wireless Sensor II. ENERGY CONSUMPTION Wireless Sensor Network (WSN) plays an extremely significant role in usual lives. Wireless Networks in provisions of constraints of their resources. The energy consumption is the principal concern in Wireless Sensor Network (WSN). Therefore, a numerous researchers focused on energy efficient algorithms in WSNs for extending the life time of sensors. These differ depending on the deployment of node, the network design, the characteristics of the cluster head nodes and the network operation. Energy is proficient of save by grouping nodes as clusters. A. Cluster Head Clustering is used in order to advance the scalability of network performance. Clustering is useful in @ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 4 | May-Jun 2018 Page: 1972

3. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 which are to be used to reduce communication overhead and increase network’s lifetime. In the present work, the comparative evaluation of communication overhead for the wireless sensor network based on clustering technique is carried out. As a result of these experiments, we evaluated the communication overhead in WSN using K- means clustering algorithm and Fuzzy clustering algorithm. 4.Labisha R.V et al. (2014). “Energy Efficient Clustering Algorithms in Networks-An Analytical View” International journal 9(3). Wireless sensor network refers to a group of spatially distributed and dedicated sensors for monitoring and recording the physical conditions of environment like temperature, humidity, sound, pollution levels, and wind speed with direction and pressure. Sensors are self powered nodes which also possess limited processing capabilities communicate wirelessly through a gateway. The capability of sensing, communication found in sensor networks lead to a vast number of applications of wireless sensor networks in areas such as environmental monitoring, warfare, education, agriculture to name a few. In the present work, the comparative evaluation of communication overhead for the wireless sensor network based off on clustering technique is carried out. 5.Amrinder Kaur et al. (2013). “Simulation of Low Energy Adaptive Clustering Hierarchy Protocol for Wireless Sensor Network” International Journal of Scientific Engineering, 3(7). Recent advances in wireless sensor networks have settled into an important part of one’s everyday life and gained attention from community and actual users. In wireless sensor networks energy alertness is an essential consideration & it explored to many new protocols specifically networks. This paper presents the overview of wireless sensor networks and their routing challenges and design issues that is faced in designing a protocol for sensor networks. 6.Anshul Shrotriya et al. (2013). “Energy Efficient Modeling of Wireless Sensor Networks Based on Different Modulation Schemes Using QualNet” International Journal of Scientific Engineering and Technology, 1(3). I have study in the paper that it account for the analysis of energy consumption in existing energy models of Wireless Sensor Network (WSN) based upon different modulation. There are two main energy models preferred in Wireless Sensor Network, MOTES and MICAZ. So here, we have investigated the more energy efficient energy model under different modulation schemes. So I can simply find the better energy model as well as the better modulation scheme to efficiently utilize the energy in WSN. It has been found that, based upon the modulation schemes; mica z performs better than mica mote energy model. 7.Shiv Prasad Kori et al. (2013). “Performance Comparison in Terms Overhead for Wireless Sensor Network Based on Clustering Technique” International Journal of Electronics Communication Engineering, 4(3). The present work, the comparative evaluation overhead for the wireless sensor network based of on clustering technique is carried out. It has been be observed that overhead in cluster based protocol is not much dependent upon update time. Simulation a result indicates that cluster based protocol has low communication overheads compared with the BBM based protocol when sink mobility is high. In these applications a large number of sensors are expected, requiring careful architecture and management of the network. Grouping nodes into clusters has been the most popular approach for support scalability in WSNs. Significant attention has been paid to clustering strategies and algorithms yielding a large number of publications. IV. METHODOLOGY The System has been implemented in the MATLAB. The wireless sensor network is design with following specification in table 1. The method of design simulation has been given below: Table 1: Specification Fuzzy Algorithms S. No. Specification 1 NO. of Node and cluster 2 Length of network area 3 Maximum range Wireless Sensor of Communication and Computer of communication and the nodes processing and both the research designed for sensor Value 200, 300 and 400 1×1m 500 m 4 5 6 7 Noise power in dBm 50 dbm Transmitted power Operating frequency Technology 1 Mw 2.4 GHZ Fuzzy @ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 4 | May-Jun 2018 Page: 1973

4. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 A. Fuzzy C-Means Clustering This algorithm works by assigning membership to each data point corresponding to each cluster center on the basis of distance between the cluster center and the data point. For more the data is near to the cluster center more is its membership towards the particular cluster center. In clearly, summation of membership of each data point should be equal to one. VI. CONCLUSION Wireless network is considered as most common service used in industrial and commercial application due to its technological enhancement in the process, interaction and utilisation of low power embedded computational devices. Wireless sensor networks (WSNs) utilize fast, cheap, and effective applications to imitate the human intelligence capability of sensing on a wider distributed scale The energy consumption reduction has been taken as a objective in this project. The clustering based network architecture has been proposed in this project to reduce the communication overhead which in turn reduces the energy consumption. The various clustering techniques are available for locating the access point for WSN. Following path is generated for consider number of nodes 100, 200 and 300 respectively using Matlab toll R2013a. Path= 6, 57, 18, 8 (Fig. 3) Path= 6, 18, 27, 8 (Fig. 4) Path= 6, 295, 175, 8 (Fig. 5) 1000 88 60 81 32 91 67 80 25 64 6 44 73 78 900 49 11 93 43 31 70 76 35 90 4 38 800 47 50 87 77 36 58 16 37 7 61 700 99 9 45 39 2 89 94 30 22 59 42 72 600 40 68 3 57 66 48 86 18 23 500 28 65 74 75 92 79 97 71 41 17 82 13 26 400 95 1 98 12 8 54 29 55 300 24 15 63 5 69 83 85 200 34 100 84 10 51 33 52 19 100 62 56 21 27 46 14 20 53 96 0 0 100 200 300 400 500 600 700 800 900 1000 Fig: 3: Performance of Fuzzy clustering for 100 Nodes REFERENCES 1.D. “Improvements in Routing Algorithms to Enhance Lifetime of Wireless International Journal of Computer Networks & Communications (IJCNC) Vol.10 (2) 2018. 1000 130 169 129 143 195 96 93 163 197 75 77 37 100 51 38 12 Naga Ravikiran and C.G. Dethe, 147 187 110 900 192 132 43 186 74 200 157 85 173 112 182 114 126 800 73 125 80 97 165166 76 159 156 14 155 177 111 115 138 150 42 181 Sensor Networks” 170 24 36 17 41 104 136 47 174 89 700 64 91 55 22 145 31 8 44 92 52 40 193 600 58 107 106 144 33 46 168 196 83 108 81 71 60 146 34 191 7 148 178 53 188 134 4 105 183 500 48 167 180 162 82 171 18 6 13 23 95 87 194 109 11 20 78 29 2.Amrinder Kaur, “Simulation of Low Energy Adaptive Clustering Hierarchy Protocol for Wireless Sensor Network”, Volume 3, Issue 7, July 2013. 198 120 153 400 137 160 50 94 161 39 79 68 27 69 172 189 45 3 185 300 59 19 184 116 152 15 54 88 158 151 131 61 118 123 135 133 56 66 10 200 142 98 199 21 32 72 49 30 128 5 139 117 2 90 103 140 102 122 84 6263 164 57 127 179 86 175 16 100 99 35 154 1 141 101 176 26 121 70 25 3.Amitabh Basu , Jie Gao, Joseph S. B. Mitchell, Girishkumar Sabhnani , “Distributed localization using noisy distance and angle information” 7th AC international symposium on Mobile ad hoc networking and computing, 2006,pp. 262 – 273. 28 65 124 149 190 9 67 119 113 0 0 100 200 300 400 500 600 700 800 900 1000 Fig: 4: Performance of Fuzzy clustering for 200 Nodes 1000 30 128 165 206207 195 69 263 29 43 95 223 261 237 235 299 97 242 145 255 120 219 254 180 266 119 110 182 116 103 233 283 63 262 47 169 10 101 122 130 159 194 192 197 900 92 244 161 32 250 86 4.Rui Zhang,Myung J.Lee, Seong-Soon Joo, “Mobile sink update local information through these Aps”, 2008. 87 102 231 178 176 111 173 100 104 136 57 12 249 280 14 26 77 258 260 73 800 82 65 56 66 162 216 227 239 55 164 121 270 205 221 11 36 8 15 247 38 59 259 131 25 218 50 171 81 70 4 251 300 74 700 134 45 177 291 228 297 285 225 124 264 213 93 163 222 5.Shiv Prasad Kori, “Performance Comparison in Terms of Communication Overhead for Wireless Sensor Network Based on Clustering Technique”, Volume 4, Issue 3, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209. 208 236 181 275 600 6 44 68 267 7 253 96 132 109 139 243 46 78 168 232 91 295 48 108 202 230 294 198 276 88 241 34 147 277 500 196 67 62 252 190 105 185 200 83 298 5 71 144 106 238 9 94 80 107 226 215 279 129 203 245 98 53 175 214 400 286 37 20 172 292 149 183 210 273 246 113 117 229 60 265 186 158 187 61 118 248 140 170 151 272 89 85 300 127 142 240 16 256 72 112 155 268 52 220 84 58 135 193 51 123 31 209 166 217 35 33 293 200 42 148 167 18 23 64 174 204 278 28 284 99 157 271 281 39 189 17 115 269 274 6.Labisha R.V, 2Baburaj E, “Energy Efficient Clustering Algorithms Networks-An Analytical Number3, May 2014. 133 152 3 2 199 288 282 137 138 22 40 79 114 143 184 290 27 76 153 75 100 141 211 287 154 54 296 in Wireless Sensor View”, Volume9, 41 1 125 191 212 21 257 234 224 289 49 90 201 150 156 24 160 188 126 146 19 13 179 0 0 100 200 300 400 500 600 700 800 900 1000 Fig: 5: Performance of Fuzzy clustering for 300 Nodes @ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 4 | May-Jun 2018 Page: 1974

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