Energy Efficient Data Aggregation Technique Using Load Shifting Policy for Wireless Sensor Network
01 Jan 2016-pp 299-310
TL;DR: Cl clustering-based load shifting policy (LSP) is used to eliminate redundancy up to an adequate level to achieve optimization point of redundancy in WSN and Simulation result shows that LSPDA has lesser average energy consumption and longer lifetime than traditional cluster-based data aggregation method.
Abstract: Data redundancy is quite common in wireless sensor networks (WSNs) where nodes are deployed densely. The reason behind such deployment is to achieve reliability from communication failure. Communication failure happens when particular node transmitting data fails. In WSN there is no other way than keeping redundant nodes to solve communication failure problem. If redundant nodes are available then at the time of node failure the data of failed node can be recovered from its redundant nodes. Though we can achieve reliability through redundant nodes presence of redundant nodes will generate more number of redundant packets which will consume more energy of network. Because nodes which are densely deployed will sense the same information and send it to sink node and sink will waste its energy in processing redundant data and also redundancy will generate heavy traffic in network. Hence, there is a need to trade-off between energy conservation and reliability. To do this trade-off we need to find optimization point of redundancy in WSN. So that reliability and energy conservation both will be maintained. In this paper we have used clustering-based load shifting policy (LSP) to eliminate redundancy up to an adequate level to achieve optimization point of redundancy. Data aggregation eliminates redundancy from WSN. We are performing data aggregation at two levels and at the same time we are keeping redundant nodes up to 50 % to achieve reliability. In this paper, we have done comparison of traditional cluster-based data aggregation with our LSP-based data aggregation. Simulation result shows that LSPDA has lesser average energy consumption and longer lifetime than traditional cluster-based data aggregation method.
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TL;DR: The main goal of data aggregation is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced and to provide energy efficiency the authors have designed energy efficient data aggregation method named E-BIN.
Abstract: The fast advancement of hardware technology has enabled the development of tiny and powerful sensor nodes, which are capable of sensing, computation and wireless communication. This revolutionizes the deployment of wireless sensor network for monitoring some area and collecting regarding information. However, limited energy constraint presents a major challenge such vision to become reality. Data communication between nodes consumes a large portion of the total energy consumption of the WSNs. Consequently, Data Aggregation techniques can greatly help to reduce the energy consumption by eliminating redundant data traveling back to the base station (sink).This paper signifies the various data aggregation techniques in wireless sensor network and implementation of a data aggregation technique in wireless sensor networks. The main goal of data aggregation is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. To provide energy efficiency we have designed energy efficient data aggregation method named E-BIN. We have considered a cluster-based wireless sensor network. Our method executes on each cluster independently and provides an energy efficient data aggregation in a cluster and hence maximize network lifetime for whole network.
134 citations
14 Sep 2012
TL;DR: A new energy efficient routing protocol called An Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks (WSN) using data aggregation technique, data aggregation is essentially used to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced.
Abstract: In this paper we have developed a new energy efficient routing protocol called An Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks (WSN) using data aggregation technique, data aggregation is essentially used to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. Data aggregation protocols aims at eliminating redundant data transmission. Power consumption is an important aspect to be considered in the data aggregation which is a scarce resource and they are irreplaceable. In addition to power consumption, reliability is also of major concern in data aggregation. In this paper, we propose to design an energy efficient reliable data aggregation technique for wireless sensor networks (EERDAT). Initially we form clusters and a coordinator node (CN) is selected near the cluster in order to monitor the nodes in the cluster. The CN selects a cluster head (CH) in each cluster based upon the energy level and the distance to the CN. The packets sent by the sensor nodes are aggregated at the CH and transmitted to the CN. The CN measures the loss ratio and compares it with a threshold value of loss ratio. Depending upon this value, the forward node count is incremented or decremented and the cluster size is adaptively changed, ensuring reliability and balanced energy consumption. We have compared the new protocol with Efficient Reliable Data collection (eRDC) using NS-2, evaluated the protocol by varying numbers of nodes. The result shows that EERDAT outperforms the eRDC.
27 citations
21 Oct 2013
TL;DR: Support Vector Machine (SVM) based Data Redundancy Elimination for Data Aggregation in WSN (SDRE) has been proposed in this work and it minimizes the redundancy and to eliminate the false data to improve performance of WSN.
Abstract: The data aggregation is most important in Wireless Sensor Networks (WSN) due to constraint of resources. There is lot of data redundancy in WSN due to dense deployment. So, it is necessary to minimize the data redundancy by adopting suitable aggregation techniques. To resolve this problem, Support Vector Machine (SVM) based Data Redundancy Elimination for Data Aggregation in WSN (SDRE) has been proposed in this work. First, we build aggregation tree for the given size of the sensor network. Then, SVM method was applied on tree to eliminate the redundant data. The Locality Sensitive Hashing (LSH) is used minimize the data redundancy and to eliminate the false data based on the similarity. The LSH codes are sent to the aggregation supervisor node. The aggregation supervisor finds sensor nodes that have the same data and selects only one sensor node among them to send actual data. The benefit of this approach is it minimizes the redundancy and to eliminate the false data to improve performance of WSN. The performance of proposed approach is measured using the network parameters such as Delay, Energy, Packet drops and Overheads. The SDRE perform better in all the scenarios for different size network and varying data rate.
26 citations
21 Oct 2013
TL;DR: Bandwidth Efficient Heterogeneity aware Cluster based Data Aggregation (BHCDA) algorithm presents the solution for the effective data gathering with in-network aggregation by using the correlation of data within the packet for applying the aggregation function on data generated by nodes.
Abstract: The fundamental challenge in design of Wireless Sensor Network (WSNs) is proper utilization of resources which are scare. One of the critical challenges is to maximize the bandwidth utilization in data gathering from sensor nodes and forward to sink. The main design objective of this paper is to utilize the available bandwidth efficiently in terms of reduced packet delivery ratio and throughput. Bandwidth Efficient Heterogeneity aware Cluster based Data Aggregation (BHCDA) algorithm presents the solution for the effective data gathering with in-network aggregation. It considers the network with heterogeneous nodes in terms of energy and mobile sink to aggregate the data packets. It embodies the optimal approach by Intra and inter-cluster aggregation on the randomly distributed nodes with variable data generation rate while routing data to sink. It uses the correlation of data within the packet for applying the aggregation function on data generated by nodes. BHCDA shows significant improvement in packet delivery ratio (67.66 % & 19.62 %) and throughput (37.01 % & 17.16 %) as compared with the state-of-the-art solutions, Two Tier Cluster based Data Aggregation (TTCDA) and Energy Efficient Cluster based Data Aggregation (EECDA).
24 citations
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TL;DR: A Dynamic Balanced Spanning Tree (DBST) for data aggregation in WSNs which considers several criteria to balance the energy consumption between sensor nodes, and uses a discrete first order radio model which is adaptable to the limitation of Sensor hardware platforms.
Abstract: Data gathering is one of the most popular energy-efficient routing protocols in Wireless Sensor Networks (WSNs). In this research, we introduce a Dynamic Balanced Spanning Tree (DBST) for data aggregation in WSNs which considers several criteria to balance the energy consumption between sensor nodes. A cost function is presented which can follow the conditions of network. It considers various factors such as distance, residual energy, and node weight and also controls the trade-off between these factors. DBST not only minimizes the maximum energy consumption among the sensor nodes, but also can balance the traffic load in the network by dynamic design of the routing tree. DBST is equipped with a root selection mechanism which only needs localized information and considers both distance and residual energy criteria. In this way, the role of the root is distributed among the nods fairly. Furthermore, it guarantees that critical nodes are not selected as root node. DBST also uses a discrete first order radio model which is adaptable to the limitation of Sensor hardware platforms. Our extensive experiments demonstrate efficiency of the proposed DBST in balancing the traffic loads in the network.
18 citations