An Enhanced Data Mining Technique for Energy Efficiency in WSN

Transcription

1 An Enhanced Data Mining Technique for Energy Efficiency in WSN Research paper Anisha kamboj #1, TanuSharma *2 1 M.Tech.,CSEDepartment, JMIT, Radaur, Kurukshetra University, India 2 Assistant Professor,CSE Department, JMIT, Radaur, Kurukshetra University, India Abstract In this paper we have define how to improve the energy efficiency of the network by using data mining technique by discovery, usage, and understanding of patterns and knowledge, mining moving object data, mining text, Web, and other unstructured data. There has recently been a considerable amount of research work done on using data compression techniques to minimize the volume of the transmitted traffic, and the consequently assist in reducing power consumption levels in Wireless Sensor Networks. A Wireless Sensor Network is the network of small sensor nodes which are energy constraint devices and have limited data computational and transmission power. The nodes are arranged in the form of cluster around a Base Station (Sink). NS2 consist two key languages: Object-oriented Tool Command Language and C++. in the large numbers, they provide with a very real picture of the field being sensed. A sensor network is composed of a large amount of small sensor nodes, which are organized closely either inside network or very close to it. The sensor nodes, in wireless sensor network monitor physical situations such as temperature, sound, pressure, etc. and to pass data through the network to main position. Keywords wireless sensor network, data mining, clustering, energy efficiency. I. INTRODUCTION Wireless Sensor Network includes small sensor nodes; these sensor nodes are the highly energy constraint devices, have low computational power and limited data transmission. These sensor nodes takes information from the environment, processes the data and then transmits the data via radio signals. The advantages of wireless sensor network are improved coverage, energy efficiency, enhanced target tracking and superior channel capacity. Generally, wireless sensor network have no fixed structure, and sometimes there is no monitoring station of these sensor nodes during lifetime of the network. Therefore, it is necessary that a wireless sensor network must have mechanisms for adaptation and self-configuration in case of failure, deletion or insertion of a sensor node. Sensor nodes are the electronic devices and the main components of sensor nodes are units of storage, sensing, processing, and transmission. This sensor node captures the information from the environment, processes the data and transmits the data through radio signal. Usually, these nodes have limited battery life due to some constraints such as limited battery life (due to cost, size etc.), very low data rates, low memory, low bandwidth processing, variable link quality and little computing capability. Despite these constraints, the sensors were deployed Fig: 1 Data communication in clustered network WSN is a collection of small, light-weight sensor nodes deployed in large numbers to control the ambient conditions. WSN have many advantages, Hence energy consumption is a major criterion. Collection of sensor nodes is called cluster. Each cluster consists of one or more cluster heads. From their cluster members the cluster head gathers all the data. The collected information is routed to the Base Station (BS). The Base Station is a fixed node, which is capable to receive and transmit the data within the entire network. The number of cluster head selection depends on the number of sensor nodes. Energy consumption is dynamically controlled by selecting more than one cluster head. Analysis of energy consumption depends on the number of cluster heads needed, when the nodes are increased. The main reason of energy spending in WSN relate with communicating the sensor readings from the sensor nodes to remote sinks. These readings are typically relayed using an ad hoc multi hop routes in the WSN. If the energy spent in ISSN: Page 137

2 relaying data can be saved then network lifetime can be extended. Recent research work has proved the applicability of the mobile elements (mobile robots, cars etc.) for the retrieval of sensory data from smart dust motes in comparison with the multi hop transfers to a centralized element. Mobile sink awakening through the network deployment region can collect the data from static sensor nodes over a single hop radio link when accessing within radio range of the sensor nodes or with limited hop transfers if sensor nodes are located further. This reduces energy consumption at SNs near the base station, avoids long-hop relaying and prolonging the network lifetime Clustering in WSN The clustering technique in wireless sensor network deals with sensor constraints. The hierarchical network structure authorize grouping of sensor nodes into cluster and to assign a specific task to the sensor in the cluster, before moving information to the higher level. Wireless sensor network clustering technique help to achieve high energy efficiency and assure about the long network lifetime. In the hierarchical manner each cluster has a cluster leader, which supervised all the cluster node known as cluster head and several common sensor nodes are the member of cluster. The cluster formulation process eventually leads to the two levels hierarchy where the cluster head nodes form higher level and the cluster- member nodes form lower level. The sensor nodes transmit the data to the corresponding cluster head nodes. The criteria for selection of the cluster head are based on the minimum average distance from base station and residual energy of the nodes within the clusters. The CH nodes transmit the data to the sink either directly or via intermediate communication with other cluster head node. In order to balance energy consumption among all network nodes, there is rotation of cluster head in WSN [10]. Cluster Support Sensor Networks The energy limitations in the sensor network makes hard to managing the sensor energy and safeguarding of the power is an important factor for attaining prolonged network lifetime. Clustering sensors into the group is more adaptive and efficient approach is adopted for routing and for node communication by many studies on sensors energy consideration. Cluster-based mechanism is employed for routing and for node communication. In the clustered wireless sensor network the sensors communicate data only to cluster-head (CH) then the cluster heads transfers the aggregated data to the base station or to the processing canter which is called as sink shown in Fig. 2 The base station (BS) is a specialized device or one of the sensors. Bandyopadhyay and Coyle selecting a set of cluster head by all of the nodes in the network is an essential operation. With the help of these heads it also clusters the other nodes. The cluster heads are selected according to some of the negotiated rules. Normally the node which is more powerful in the topology play the role of cluster and the other nodes will forward the sensing data to the cluster nodes. Fig 2: Cluster -based Sensor network In the energy consumption sensors, communication is the main factor to be considered. The amounts of energy used are totally dependent on the distance of the data that has to travel between sender and the receiver. Since the sensors only communicate to the cluster heads over smaller distances in clustered sensor network, total number of sensors in the network is much smaller than the situation in which each and every sensor communicates directly to the sink [6]. Data Mining With significant increase in the volume of data stored on the need for the better methods, cheaper and faster to analyse them was felt. And if this purpose can t be effective and efficient mechanism to extract the knowledge from huge volumes of data design, then all data available in the world will be meaningless. A number of scientists discovered the necessity of such need and thus was born the science of data mining. Wireless Sensor Networks with large number of sensor nodes are used to monitor the sensing field. Sensor network data-transfer reliability and Sensor node energy efficiency are the primary design parameters. Some of the applications may require heterogeneous sensor nodes with different sensing phenomena or the different hardware characteristics. The heterogeneity imposes an added constraint to mining of useful information from network. At the same time, a stream of the data that is frequently reported from each node to the base station may be needed. ISSN: Page 138

3 Use of Data Mining for Energy Efficiency A Wireless Sensor Network consists of a large number of the sensor nodes that co-operatively monitors specific region of interest. Typically, a sensor node is the small hardware device consisting of a sensing unit, a power unit, processing unit, and communication unit that are used for sensing, communication purposes and data processing. These nodes collectively gather sensed information and forward it to the special node called sink or base station. This acts as interface between the sensor nodes and users. The uniqueness of the sensor node lies in its small size and the light weight. However, there are lots of constraints such as limits on resources in terms of energy, bandwidth computational speed, memory, and so on. Because of these constraints the interactions between the sensors are limited to the short distances and low data rates. These sensors are used in wide range of applications and real time applications such as security purpose, military applications, habitat monitoring, nuclear power plants, etc. Sensors gather useful information in a timely manner and send to a centralized node named base station or sink. Sink node which is also known as base station and is responsible for the further processing such as node query. Due to large number of the sensor nodes and the voluminous data that should be reported, the data communication should be done in the energy efficient manner. Centralized solutions for the data collection are not recommended. Obvious the drawbacks of this type of solutions include the hindering the network since the sink node becomes bottleneck, the bandwidth allocated is not efficiently used and all the sensor nodes consume a lot of valuable power and scarce to communicate with the sink node. Hence, the optimum solution becomes a distributed data collection algorithm, where the data mining techniques such as clustering are applied to sensor nodes. When there are large number of the sensors in the sensing field, sensors will be clustered to reduce data redundancy. The cluster head CH will take care of this work. Clustering of the sensor nodes is to be considered as one of the very successful techniques to mining useful information from the distributed environment. It is particularly useful technique especially for the applications that require scalability to hundreds and the thousands of nodes. Clustering also supports the aggregation of data in order to summarize overall transmitted data. However, the current literatures either focus on the node or data clustering alone. Clustering of the sensor nodes deals with two main operations: 1) assigning nodes to respective cluster heads, and 2) identifying cluster heads. These two operations should be done at very energy-efficient level. On the other hand, data clustering deals with the collecting the similar data for aggregation purposes. Mining Data from Sensor Networks With the popularity of the sensor networks, cellular phones, other mobile devices, GPS, tremendous amount of moving data object has been collected, calling for the effective analysis. This is especially true in much scientific, business, engineering applications. Sensor networks are finding number of applications in many domains, including smart buildings, battle fields, and the human body. Moreover, sensors must process a continuous stream of data. Data mining in wireless sensor networks is a challenging area, as the algorithms need to work in constrained and extremely demanding environment of sensor networks (like limited energy, storage, bandwidth, computational power). Wireless sensor network also require highly decentralized algorithms. In the designing algorithms for sensor networks, it is important to keep in mind that the power consumption has to be minimized. Gathering the distributed sensor data in single site could be expensive in terms of battery power consumed, some attempts have been made towards making data collection task energy efficient and balance the energy-quality trade-offs. Clustering the nodes of sensor networks is an essential optimization problem. Nodes that are clustered together can be easily communicate with each other, which can be applied to developing optimal algorithms and energy optimization for clustering sensor nodes. Energy Saving Models for WSN The SeReNe framework is stands for Selecting Representatives in a sensor Network (SeReNe) are an environment for the identification of energysaving models to efficiently query sensor networks. Figure shows the SeReNe framework integrated into sensor network architecture. From the sink, Sensor nodes frequently receive queries (i.e., base station). The query is implementing by each node over its sensor data and the result sends to the base station by means of a multi-hop data collection protocol. Before transmitting the query results, the sensor data can be sometimes compressed by compression /reduction techniques. These in-network processing techniques are complementary approach that address the sensor network efficiency at a different level and that can be successfully applied besides SeReNe. SeReNe generates sensor network models by means of two steps: (1) Selection of sensor representatives and (2) Correlation analysis. ISSN: Page 139

4 towards the target using the shortest path. Moreover, localization can be accomplished without any assistance from the stationary sensor networks. Simulation results authenticate nearly a 40% reduction in target accretion time compared to a random walk model. Fig 3: Architecture of the SeReNe framework Temporal correlations among sensor data streams are catches by the correlation analysis block, in terms of correlation time, correlated sensors and strength. Furthermore, it discovers spatial correlations among faraway sensors and neighbouring sensors. From the set of correlated sensors, the second phase allows to out a subset of sensors from the network, called Representative- Sensors which also known as R- sensors, which best represent all network nodes. Several criteria may be considered (e.g. transmission cost among sensors and distance) during the selection of R-Sensors. The time window in which the sensor network model is effectively acting the network state is computed by guaranteeing a user-provided the error bound τ. Furthermore, the network model can be reliably altered to network changes by continuously analysing the data, collected through the network itself, thus allowing the apprehension of deviations from the modelled behaviour Sensor network models generated by SeReNe are exploited to efficiently query the network. Smart sensors are characterized by sensing capabilities computational, and communication. The execution plan and the query are broadcast to (interested) sensors to collect sensor data. Hence, a transmission schedule must be generated. Transmission schedule generation block of the SeReNe Framework goal is to identifying an energy-saving model, which minimizes communication costs. II. LITERATURE REVIEW Cesare Alippi, Giuseppe Anastasi (2009) uses effective energy management strategies should be include policies for an economic use of energyhungry sensors, which become one of the components affecting the network lifetime. In this paper, the author surveys the main approaches for efficient energy management in the sensor networks with energy-hungry sensors. QingquanZhanga,GeraldE.Sobelmana(2009) propose a novel gradient-based method which uses the statistical techniques to evaluate the position of a stationary target. Mobile nodes can be directed Xueli Shen and Huan Zhang (2011) present the radio propagation path loss and the previous weighted centroid location algorithm is enhanced. The weighted factor in the algorithm is accessed, which makes positioning accuracy and positioning error of the unknown nodes better. Firstly, the paper uses three-point location to figure location of unknown nodes approximately and secondly, the paper uses a modified weighted centroid location algorithm to figure the unknown nodes precisely. Finally, the algorithm is affected. Balasubramaniam Natarajan, Ahmad Ababnahet et.al (2011) develops a novel optimal control theory based formulation of this sensor deployment problem. Exploiting affinity between the problem at the hand and linear quadratic regulator, an analytical solution is tested and derived. Unlike prior efforts that await purely on heuristics, the proposed optimal control framework gives a theoretical basis for the resulting solution. Hongbo Jiang, Chonggang Wang et.al (2011) develops an energy-efficient framework for the clustering-based data collection in WSNs by integrating adaptively enabling & disabling prediction scheme. The framework is clustering based. All sensor nodes are representing by the cluster head in the cluster and collects data values from them. To the realize prediction techniques efficiently in WSNs, The author present adaptive scheme to control prediction used in our framework, analyse the performance trade-off between limiting prediction cost and reducing communication cost, and design algorithms to exploit the profit of adaptive scheme to enable & disable prediction operations. Nithyakalyani and S. Suresh Kumar (2012)presents K-Means Data Relay (KMDR) clustering algorithm for grouping the sensor nodes there by reducing number of nodes transmitting data to sink node(base station), it increases the network performance by reducing the communication overhead. Furthermore Observe and Conserve Modes algorithm reduces the numbers of the nodes within cluster there by without compromising the analysis face major challenges such as constraints in power supply and storage resources region of it and limited communication bandwidth. The contribution of K-MDR is to reduce power consumption; the simulation experimental results show that the time efficiency of the algorithm is achieved. ISSN: Page 140

5 LuigiCoppolino,SalvatoreD Antonio(2013) propose a lightweight, hybrid, distributed Intrusion Detection System for WSNs. This Intrusion Detection System uses both anomaly-based and misuse-based detection techniques. It alleviate Central Agent, which performs highly accurate intrusion detection by using the data mining techniques, and a number of the Local Agents running lighter anomaly-based detection techniques on motes. The accuracy of the proposed IDS has been validated and measured through an extensive experimental campaign. V. Karthik (2013) proposed the data collection method involves deployment of the multiple mobile robots whose goal is to gather data from the nodes whose energy level is below the threshold value. Navigation of mobile robots is to collect the data from partitioned nodes usually achieved by the time and the location based strategies. In proposed hybrid scheduling, navigation of mobile robots justified by both the combination of location and time based approaches with multiple region scheduling. M. Vijayalakshmi, V. Vanitha (2013) proposed Prediction and Clustering techniques, which use the temporal correlation among sensor data, provide a chance for reducing energy consumption of the continuous sensor data collection. Thus it can achieve prolongs and stability network lifetime. An adaptive scheme is presented which is used to control the prediction and analyse the performance trade-offs between reducing prediction cost and communication cost, and design the algorithms to take the benefit of adaptive scheme to enable or disable prediction operations. Localized the prediction scheme is performed, which takes the advantages over the previous dual-prediction scheme to minimize computation and communication cost thereby reducing the energy consumption. Shruti Kukreja, Prof Poonam Dabas (2015) propose a refine clustering method for improving the network lifetime. Unlike LEACH where the clustering process is based on the random selection of cluster heads, the author use k-nearest neighbour (k-nn) to create cluster in the network then the author use local heuristic search technique- search to find CHs. So that minimum energy is consumed in any transmission. The authors reviewed different techniques for clustering and optimization and finally propose the hybrid method of these two techniques. III. PROPOSED SYSTEM To make the lifetime of the entire network to be increased without comprising coverage area within its cluster by minimizing the number of nodes contributing to sense and forward data Algorithm for sensor activation selects subset of nodes into the monitoring state while remaining sensor nodes go to conserve mode. N nodes are uniformly and randomly dispersed in a square field of size M*M. All the nodes and the base station are stationary. All nodes can use power control for different distances from the transmitter to receiver. All the nodes are the location unaware (i.e. they are not equipped with GPS-devices).All the nodes are homogeneous (all capacities).for long range transmissions the CH are dominant for performing computations to the base station. Each and every sensor node transfers data directly only with the other nodes in its cluster. A new approach named proposed AODV (pattern variation discovery) is used for solve this problem. This approach works in following four steps: A. Selection of a reference frame. This frame consists of directions along which we want to look for irregularities among the multiple sensory attributes. An analyst can be explicitly specifying the reference frame. It s also possible to discover reference frame that results in a lot of the irregularities. B. Definition of normal patterns. This definition can be models of the multiple sensory attributes or constraints among the multiple attributes. C. Discovery of irregularity. Whenever normal pattern is broken at the some point along the reference frame, irregularity appears. That is, the pattern variation happens IV. EXPERIMENTAL RESULT AND ANALYSIS Implementation of the whole system carried out using network simulator NS2 as version 2.34.tar.gz. NS-2 stands for the Network Simulator version 2. NS-2 is a discrete event simulator for the networking research. This simulator works at the packet level. NS2 is simply an event driven simulation tool that has proved useful in the studying dynamic nature of the communication networks. NS-2 uses a TCL as scripting language. Simulation of wired as well as wireless network protocols and functions (e.g., TCP, UDP, routing algorithms) can be done using NS2. In general, NS2 accommodate users with a way of specifying such network protocols and simulating their corresponding behaviours. NS2 consist two key languages: Object-oriented Tool Command Language and C++. Firstly, the proposed data mining technique is implemented in the TCL and the results are simulated under the environment of the Network Silulator-2 (NS-2). Firstly, analysis has been made to check the number of packets with anomalies populate in wireless sensor networks (WSNs) as we keep on increasing simulation time. We have total 1000 packets. The following table and line graph predict the analysis that has been made: ISSN: Page 141

6 V. TABLE I SHOWING VALUES OF PACKETS WITH ANOMALIES FOR SIMULATION TIME=15 SEC Simulation Time Packet with Anomalies Fig.4 In the fig below, a line graph between the Number of Packets (abnormal) and the simulation time. After the analysis, we applied our data mining technique to obtain results that shows that, this technique is quite helpful to save the of wireless sensor networks. There are total 30 nodes in the network and out of which only 7 are using the energy of complete WSN. Below are the screenshots of the results that are obtained by using ns-2 simulator. Fig.5 We have taken total of thirty nodes in our simulation evaluation process as shown in the figure above. In above figures, it is being observed that in the starting of the simulation process we have network with thirty nodes and the packets are transmitted from one node to other node, as soon the packets with the anomalies or irregularities are detected. As the Anomalies are detected, Energy consumption by network is less which increases the efficiency of the Wireless Sensor Networks. V. CONCLUSION Sensor nodes are capable of transmitting and sensing. They collect large amount of data in a highly decentralized manner. The collected data contain all the information about the region. But sometimes users are need only the specific information and rest of information is treated as irrelevant. So, here we filter out that irrelevant data or irregularities for the benefit of the users. In future, same can be used to extract the desired information from the set of the large information. AODV is being compared with the proposed AODV. Proposed AODV has better performance as efficiency of the system is being increased by filtering out the unnecessary data. The proposed AODV has been compared with existing AODV protocol at different simulation time. After that the result shows that the packets with anomalies are being filtered out using the data mining concept and the proposed AODV is more energy efficient than the existing AODV. For getting the best results filtering concept is added. VI. FUTURE SCOPE Future aspects for the proposed system are bright. We can use the concept of the clustering for filtering out data with anomalies. Other mining concepts are also available. ISSN: Page 142

7 References [1] Cesare Alippi, Giuseppe Anastasi, Energy Management in Wireless Sensor Networks with Energy-hungry Sensors, IEEE Instrumentation and Measurement Magazine,April [2] QingquanZhanga, Gerald E. Sobelmana, Gradient-based target localization in robotic sensor networks, Pervasive and Mobile Computing, February [3] Xueli Shen and Huan Zhang*, Improvement of Centroid Location Algorithm for Wireless Sensor Networks,International Conference on Computer Science and Information Technology,2011. [4] Balasubramaniam Natarajan, Ahmad Ababnah, Optimal Control-Based Strategy for Sensor Deployment,IEEE transactions on systems, man, and cybernetics,january2011. [5] Hongbo Jiang, Chonggang Wang, prediction or not? an energy-efficient framework for clustering-based data collection in wireless sensor networks,ieee transactions on parallel and distributed systems,june2011. [6] Nithyakalyani and 2S. Suresh Kumar, Data Relay Clustering Algorithm for Wireless Sensor Networks: A Data Mining Approach,Journal of Computer Science 8 (8), [7] Luigi Coppolino, Salvatore D Antonio, Applying Data Mining Techniques to Intrusion Detection in Wireless Sensor Networks,Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, [8] V. Karthik, Region Based Scheduling With Multiple Mobile Robots for Data Collection Strategies in Wireless Sensor Networks, International Journal of Innovative Research in Science, Engineering and Technology,July [9] M. Vijayalakshmi, V. Vanitha, Cluster based adaptive prediction scheme for energy efficiency in wireless sensor networks, International Research Journal of Mobile and Wireless Communications, June2013. [10] Shruti Kukreja, Prof Poonam Dabas, Clustering in WSN Using Data Mining and Classification Technique,International Journal of Advanced Research in Computer Science and Software Engineering,February ISSN: Page 143