A Novel Data Aggregation Scheme for Energy Efficient Wireless Sensor Networks

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1 1 A Novel Data Aggregation Scheme for Energy Efficient Wireless Sensor Networks 1 J.Martin Sahayaraj, 2 Dr.N.Dhasarathan 1 Assistant Professor, Department of ECE, Kurinji College of Engineering and Technology, Tamilnadu, India, 2 Associate Professor,Department of ECE, Ponnaiya Ramajayam Engineering college, Tamilnadu, India, ABSTRACT The main objective of routing in wireless sensor networks (WSN) is to minimize the energy as well as energy while sending the information to sink. Since, the nodes in the WSNs are resource constrained nature; hence the reduced utilization of energy with minimum delay is an important factor. Some of the application in WSN needs continuous monitoring (i.e. the sensors from the Region of Interest (RI) periodically monitor the field and send the sensed data to the sink) which significantly increases the data transmission cost in terms of energy, since the nodes in the WSN are resource constrained in nature. In order to reduce the energy utilization of the nodes in these types of applications, the aggregation mechanism plays vital role for providing better trade-off between energy and delay. In this proposed approach a novel Data Aggregation Based Routing Scheme (DABRE) is proposed for time constrained applications of WSNs. In this DABRE an aggregator node is selected from the RI and duly transmits the aggregated information to sink in order to avoid the unnecessary data transmissions as well as unwanted energy utilization. Key Words: WSN, Routing, Data Aggregation, Energy, Delay. 1. INTRODUCTION WSN consists of a more number of wireless nodes named sensor nodes and sink as one or more base stations. The amount of nodes are deployed in the sense collaborate to form an ad hoc network capable of reporting to data collection sink (base station). The Figure.1 A sensor nodes are generates data and its sensing technique to sensed and transmit the data to the BS (sink). The BS is located very long distance from sensor nodes since this is direct transmission methods [3]

2 2 WSN have various applications like habitat monitoring, building monitoring, home automation, industrial sensing, intelligent transportation systems, health monitoring, and target tracking to gather physical measures from the monitored environment[3][4]. The new deployment applications based on Wireless Sensor Networks (WSNs) are chip miniaturization and energy consumption. A WSN is an ad-hoc network composed of tiny devices with limited energy and computational resources, and it equipped with sensors in order. Since these WSNs, sensors collect the required information, mostly, according to a fixed temporal schedule, and send it to the sink, which interfaces with a server or a computer. At this point data from sensors can be processed, before being stored. Figure1.Architecture of the wireless sensor network A WSN is produced by large number of nodes, that channel in wireless to communicate have each other. Different type of nodes is reducing the power equipments for being small memories, battery power and equipped with low-performance processors. The main five WSN components are memories (DRAM, SRAM, Flash...), sensors, processing unit (microcontroller, processor, FPGA), actuators, multiple communication, layers (physical radio, Medium Access Control, Routing,..) and a power supply (external power supply, batteries, solar cells,...).the design objectives with suitable for these type of components. The power consumption is the main problem in WSNs, due to the sensor is mostly in battery power. For example, a battery-operated sensor device that wakes up once every few minutes to check an environmental parameter needs to consume as little power as possible in order to minimize the battery replacement. The nodes are affected in harsh environments, such as underground or underwater, where replacing battery could be an unfeasible operation, network life time and energy efficiency [9]. The great deal efficiency encompasses network node deployment, routing mechanisms and data aggregation. Network is deployed routing mechanism lifetime is increases. Sensor value is determined based on the device datasheet. Applications of WSN have several restrictions limited energy supply and limited bandwidth of the wireless links. Main design Goals of WSNs is to carry out data communication of energy management techniques. [3] [9] Node deployment of sensor nodes are scattered randomly creating an infrastructure in ad

3 3 hoc manner. Multihop WSN of each node plays a dual role as data sender and data router. Data reporting have been divided as time-driven, event-driven, query driven and hybrid. Normal sensor chosen from the deployed sensors or can be more powerful transmission to the BS is handled by the set of cluster-heads. Lack of power, physical damage or environmental interference may fail of sensor nodes and it is not affect the overall task of sensor network. Multiple levels redundancy needs in a fault tolerance of sensor network. Sensor protocols should be scalable enough to respond to events in the environment. Traditional problems associated with a wireless channel having kb/s. Network topology is being variable and the network size is shrinking, it s due to the sensor node failures. Each sensor node is limited for both range and accuracy. A data aggregation is being the duplicate suppression, minima, maxima and average values. The beam form of to combine the incoming signals and also reducing the noise. Energy gets depleted; the network may be required to reduce the quality of the results in order to reduce the energy dissipation. 1.1 DATA AGGREGATION: Data aggregation is a process of aggregating the sensor data. The general data aggregation algorithm works as shown in the below figure.2 A Sensor node aggregates an algorithm such as LEACH (low energy adaptive clustering hierarchy), TAG (Tiny Aggregation) etc. This both transfer to the sink node by selecting the efficient path. Aggregation having the different types of approach likes.., Centralized Approach, In- Network Aggregation, Tree-Based Approach, and Cluster-Based Approach [5]. Figure 2: Architecture of the Data Aggregation Algorithm A WSN Data transmission between sensor nodes, aggregators and the receiver consumes lot of energy. In Figure 3 consists data aggregation model and Non Data aggregation model in which sensor nodes 1,2,3,4,5,6

4 4 are regular nodes that gathering the data packet and reporting to the upper sensor nodes where 7,8 are perform sensing aggregators. An aggregation model 4 data packet is travelled with in the network and only one data packet is travelled to the BS. The help of Data aggregation process to decrease the number of data burden before they are transmitted to the BS or Sink [1]. Figure 3: Data Aggregation and Non Data Aggregation Model 2. RELATED WORKS Kiran Maraiya, Kamal Kant, Nitin Gupta [1] this paper proposed an approach which addresses the Data aggregation issues of wireless sensor network. The data aggregation process reduces the energy consumption by eliminating data redundancy, when wireless sensor network deployed in remote areas or hostile environment. This paper discusses the data aggregation approaches based on the routing protocols WSNs. Finally, they discussed the advantages and disadvantages or various performance measures of the data aggregation in the network. Prakashgoud Patil,Umakant Kulkarni [5] proposed In Data aggregation WSNs is major role of at the same time the aggregation must be energy efficient and a lesser amount of delay. This paper proposed to solve the problem in Delay Efficient Distributed Data Aggregation (DEDA) Scheduling Algorithm for Wireless Sensor Network (WSN) to handle the delay and energy tradeoff in the process of aggregation using the timeout concept. In this paper first to building the aggregated tree, then DEDA scheduling algorithm to solved

5 5 the delay aware data aggregation and optimized energy efficiency. Decision Making Unit (DMU) has been used and to control the handle energy and delay tradeoff by generating the timeout concept. Nandini. S. Patil, Prof. P. R. Patil [2] are developed in WSNs offer an increasingly sensor nodes, takes minimum power for transmitting time. The multiple sending sensed data to BS in more number of sensor nodes. Sensor nodes are battery driven, an efficient utilization of power, network life time, tradeoff data and packet size is low. The most preferable data aggregation approach is collecting the data in distributed system architectures and dynamic access via wireless connectivity. This proposed method, a data aggregation framework wireless sensor networks is presented. The data aggregation framework is performed as, to measure middleware data by a number of nodes within a network. In this paper, WSNs have limited computational power, low memory and battery power. The often results in closely network protocols are leads to increased complexity for design applications. The performance of this proposed method, to compare the data for using with data aggregation and without data aggregation of TAG. Mukesh Kumar,Kamlesh Dutta[7], dealt with large number of resource constrained sensor nodes in wireless sensor networks. It is communicate over wireless medium to perform a functionally collection of messages. In this paper the wireless medium, data security and data sensing overlapping for output in sensor nodes. The LDAT (Linguistic Fuzzy Trust based Data Aggregation and Transmission) is proposed in this paper that evaluates the trustworthiness of sensor nodes during Data Aggregation to improve the reliability and accuracy of aggregated data. Thus, security and reliability of the transmitted data get reduced. Compromised nodes can inject false data, drop all the data, selectively forward data to an attacker, copy legal nodes to join routing paths, and disrupt data transmission during the data aggregation operation. In protocol LDAT, security of the Data Aggregation process is ensured by selecting the trusted data aggregator using Linguistic Fuzzy Trust mechanism. XiaoHua Xu, ShiGuang Wang, XuFei Mao, ShaoJie Tang, and XiangYang Li [6],In this paper the problem of distributed aggregation scheduling in sensor networks and propose a distributed scheduling algorithm with latency bound 16R + Δ 14.here R is network radius and Δ is the maximum node degree in the communication graph of WSNs. This is a nearly constant approximate algorithm which significantly reduces the aggregation latency. In this paper compare to the previous data aggregation algorithm that has a latency bound of 24D+6Δ+16 time-slots, where D is the network diameter (Note that D can be as large as 2R). A Data aggregation is an efficient primitive in wireless sensor network (WSN) applications. This paper focuses on data aggregation scheduling problem to minimize the latency method of collision-free schedule for data aggregation in WSNs. Finally prove that the lower-bound of latency of data aggregation under any interference model is max {log n, R} where n is the network size. 3. PROPOSED WORK In this proposed work, the issues like energy, delay and throughput of the time sensitive application of WSNs are addressed. The aggregator node is also being a node in the group; purposely it will select for aggregating the information from its neighbors in order to enhance the performance of the network.

6 6 3.1 Data Aggregation Based Routing (DABRE) In general the node in the WSNs are highly constrained and limited in physical size i.e. limited power, bandwidth, memory, transceiver equipments and processing elements. Hence the implementation of weighted algorithms causes the hardware more complex and the light weight algorithm with a little bit modifications from the existing infrastructure leads overall performance of the network effectively. In this approach the following assumptions has to be taken into account. This proposed technique closely suitable for Wireless Sensor and Actors Network (WSAN). Since the time bounded applications of the WSNs need very precise information s for triggering the actors network. 1. The aggregator node is one of the normal nodes. 2. The aggregator is a dynamic one i.e. the node will change in every round. Figure4: Proposed DABRE architecture The WSAN, the number of normal nodes are covered with aggregator events. That event is sensed by the number of nodes nearby to the event occurred in periodically. The node will change every zone, i.e. dynamic aggregator one. The value sensed by the node may not be equal to and so on. The nodes which are very closer to the event are detected highly accurate value compared to the nodes far from the nodes in RI. The sensed data is process of aggregated algorithms. The Information is transmitted to the Aggregator node. An aggregator node buffer is doing this operation (Ex: Min, max, sum, avg).the aggregation function performed in the aggregator is X and the maximum accurate value of the aggregator is found by Y. Then the

7 7 error value of the lossy aggregation function can be calculated by event nodes and average events. Then optimized error value is calculated by..which is difference to the maximum DABRE plays vital role in the phase of lossy aggregation and provides most fitted value from the region of interest. An Aggregation methodology is comparing the entire result (i.e. aggregated value) with max event detected value sent to the aggregator. The energy required to the transmitting and receiving nodes. Let the no. of nodes in the region of interest is.where,. no. of nodes detected the event (i.e.) around the event is The value sensed by the node may not be equal to and so on. The nodes which are very closer to the event are detected highly accurate value compared to the nodes far from the nodes in RI. The aggregation function performed in the aggregator is, (1) The maximum accurate value of the aggregator is found by, (2) Then the error value of the lossy aggregation function can be calculated by, (3) Then the optimized error value is calculated by, (4) The initial energy of the node is, the energy utilized by the transmitting and receiving data are and respectively. The residual energy of the respective node is and it can be calculated from the equation (5). (5)

8 8 The threshold value of the nodes is calculated from equation (6) The aggregator node is finalized using energy metrics, when satisfying the condition. (6) The total number of nodes in the group is, where the energy utilized for individual member node in the group is calculated in equation (7), (7) The be the energy utilization of individual node, be the energy utilization for transmission and reception of packets. The total energy utilized for all the nodes in the cluster is in equation (8), (8) 4. PERFORMANCE EVALUATION The performance evaluation of the proposed algorithm DABRE is compared to the Non data aggregated data. It is observed from Figure 5 that energy consumption of proposed DABRE is less than the without data aggregation approach, when the number of nodes in the network ranges from 20 to 80. The proposed algorithm gives better performance in terms of energy consumption. From Figure 6, it is noticed that, the delay of our proposed DABRE is less as compare to the delay in Non Data aggregation model.

9 9 Figure 5: No. of nodes Vs Energy Figure 6: No. of nodes Vs Delay The Figure 7,shows that the number of messages transmitted in propsed DABRE is higher than the double of without data aggregation model.however the number of nodes is increased, that time existing method delay also increased.but the propsed DABRE scheme to reduced the dalay.

10 10 Figure 7: No. of Messages Transmitted Figure 8: Reduction of error value The Figure 8, calculated for optimized error value (Equation 4) in DABRE results scheme, when the number of node ranges from 20 to 80. The optimized error value is less than compared to the general errors. The show, that above figure is better performance of in terms of error value. Overall, the proposed algorithm performs better with respect to energy, delay, number of messages transmitted and error values.

11 11 5. CONCLUSION This paper addresses the problem of unnecessary energy utilization in wireless sensor networks by implementing a novel data aggregation scheme in this paper. In this proposed DABRE initially the aggregator node is selected based on its residual energy. After the selection the aggregator node data aggregation process has been performed, in this scheme the lossy data aggregation process is assumed. In order to retrieve the accurate information from the RI, The error value and optimized error values are calculated, which gives most accurate value regarding the environment. Due to this aggregation process the no. of data transmission has minimized and duly the energy utilization has minimized. It leads better energy delay trade off which is one of the very big deal in WSNs. This proposed technique closely suitable for Wireless Sensor and Actors Network (WSAN) and the applications like continuous monitoring (i.e. temperature, gas, chemical measurements form critical areas). The simulated outcomes clearly states that the proposed DABRE performs well compared to the normal mode of operation. REFERENCES 1. Kiran Maraiya, Kamal Kant, Nitin Gupta, Wireless Sensor Network: A Review on Data Aggregation International Journal of Scientific & Engineering Research Volume 2, Issue 4, April ISSN Nandini. S. Patil, Prof. P. R. Patil, Data Aggregation in Wireless Sensor Network 2010 IEEE International Conference on Computational Intelligence and Computing Research 3. Luo, J, Hu, J, Wu, D, and Li, R, Opportunistic Routing Algorithm for Relay Node Selection in Wireless Sensor Networks, IEEE Transactions on Industrial Informatics, 2015, Vol. 11, Issue 1, pp Srijeevitha, S, Alwarsamy, R. An Efficient Data Transmission using Relay Node Based Opportunistic Routing, International Journal of Innovative Research in Advanced Engineering, Vol.3, Issue. 4, April. 2016, pp Prakashgoud Patil,,Umakant Kulkarni, Delay Efficient Distributed Data Aggregation Algorithm in Wireless Sensor Networks, International Journal of Computer Applications ( ) Volume 69 No.1, May XiaoHua Xu, ShiGuang Wang, XuFei Mao, ShaoJie Tang, and XiangYang Li, An Improved Approximation Algorithm for Data Aggregation in Multi-hop Wireless Sensor Networks 7. Mukesh Kumar* and Kamlesh Dutta, LDAT: LFTM based data aggregation and transmission protocol for wireless sensor networks, (Springer)Journal of Trust 0023-y Management (2016) 3:2 DOI /s

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