Data Aggregation Techniques Research Articles

HYBRID APPROACH OF DATA AGGREGATION (HADA) BASED ON iLEACH IN WSNs

Data aggregation techniques lessen the communique cost of WSNs as it reduces the unneeded data by aggregation. In this, a homogeneous protocol has been proposed. On the other hand, definite advance has been proposed for the cluster head choice optimization as well to improve the network lifetime further. Also, an additive and divisible function has been used to aggregate data to overcome the problem of flooding. The overall goal of this paper is to propose a novel protocol i.e. PROPOSED_DA which will use hybrid approach of data aggregation (HADA) based iLEACH to develop the outcome further. The design and execution has been ended in MATLAB. The proportional study has also been done to prove the effectiveness of the proposed technique over the available technique. Keywords—Wireless sensor networks, hybrid approach, data aggregation, Network lifetime

Data Aggregation in Wireless Sensor Networks Based on Environmental Similarity: A Learning Automata Approach

Sensor networks are established of many inexpensive sensors with limited energy and computational resources and memory. Each node can sense special information, such as the temperature, humidity, pressure and so on and then send them to the central station. One of the major challenges in these networks, is limit energy consumption and one of the ways for reducing energy consumption in wireless sensor networks, is reducing the number of packets that are transmitted in the network. Data Aggregation technique that combines related data together and prevents sending additional packets on the network can be effective in reducing the number of packets sent over the network. In this paper a Data Aggregation method based on learning automata is presented and with identifying sensors that are in the similar area, and produce the same data and enable the sensor nodes periodically avoid sending additional packets on the network, and significantly saves energy and increases the lifetime of the network. Simulation results show the optimal performance of the proposed method.

Asset Commonality and Systemic Risk Among Large Banks in the United States

In this paper, we present a compellingly simple yet innovative approach to capturing the buildup of systemic risk associated with commonalities in banks’ asset holdings. We draw on a growing strand of theoretical literature that studies the systemic externalities of banks’ balance sheet asset side allocations. By applying data aggregation and clustering techniques to publicly available balance sheet data, we uncover interesting patterns in the asset holdings of the major bank holding companies in the United States during the years 2001-2013. We augment our findings with theoretical analysis and insight. Based on our analysis, we construct a novel measure of systemic risk, ACRISK, where AC stands for asset commonality. This measure captures well the buildup of systemic risk that culminated in the global credit crisis, and provides empirical support to the asset commonality theoretical notions.

An Investigational Study of Energy Conservation Techniques in Hierarchical Routing Protocols in Wireless Sensor Network

In the advent of wireless networking, wireless sensor network (WSN) has been a constant target of research due to its potential data aggregation techniques in hostile environment. Even after crossing more than a decade, wireless sensor network is still more under research and development and less on commercial deployment when it comes to large scale wireless environment. Although, there are various issues exists in WSN that ranges from quality of service to security policies, it was frequently seen that root cause of majority of the issues originates from the energy that backs up the sensor motes to transmit the data to base station. The past research work has witness massive volumes of algorithms using various sophisticated technologies in order to mitigate the issues of energy problems in sensor motes, however, till date none of the prior studies has yet been standardized and hence the issues of unwanted power depletion still persist because of numerous unsolved factors. This paper is an attempt to study only the prominent techniques that has been introduced in the past for energy efficiency exclusively for hierarchical routing protocols. A brief review of some prior Swarm Intelligence (SI) techniques is also given a special focus for the similar purpose in this paper.

A new data aggregation technique to improve landscape metric downscaling

Scale is a fundamental concept in landscape ecology and considerable attention has been given to the scale-dependent relationships of landscape metrics. Many metrics have been found to exhibit very consistent scaling relationships as map resolution (i.e., pixel or grain size) is increased. However, these scaling relationships tend to break down when attempting to ‘downscale’ them, and the scaling function is often unable to accurately predict metric values for finer resolutions than the original data. The reasons for this breakdown are not well understood. This research examines the downscaling behavior of metrics using various data aggregation techniques in an attempt to better understand the characteristics of metric scaling behavior. First, downscaling performance is examined using the traditional method of aggregation known as ‘majority rules’. Second, a new data aggregation technique is introduced that utilizes fractional land cover abundances obtained from sub-pixel remote sensing classifications in order to capture a greater amount of the spatial heterogeneity present in the landscape. The goal of this new aggregation technique is to produce a more accurate scaling relationship that can be downscaled to predict metric values at fine resolutions. Results indicate that sub-pixel classifications have the potential to transform data aggregation to allow more accurate downscaling for certain landscapes, but accuracy is linked to the spatial heterogeneity of the landscape.

Enable Efficient Data Aggregation for Region-Based Top-K Queries in Wireless Sensor Networks

We study data aggregation for region-based top-k queries in wireless sensor networks, which is one kind of internet of things. Because the energy of sensor nodes is limited and a sensor node will die if it has no energy left, one of the important targets for all protocols in wireless sensor networks is to decrease the energy consumption of the sensor nodes. For a sensor node, communication cost is much more than other kinds of energy cost such as energy cost on computation and data storage. Thus, a very efficient way to decrease the energy cost of the sensor nodes is to decrease the quality of the sensing data that will be transmitted to the base station. In this paper, we use the technique of data aggregation to achieve this goal, and propose an algorithm to construct a novel Data Aggregation Tree (DAT) in the query region. To check the efficiency of DAT, we have made a simulation on OMNET, and the results show that DAT can shrink large quality of data when they are transmitted to the base station, and the life time of the sensor networks can thus be prolonged..

An IEEE 802.11 energy efficient mechanism for continuous media applications

The widespread deployment of wireless mobile communications enables an almost permanent usage of portable devices, which imposes high demands on the battery of these devices. Indeed, battery lifetime is becoming one the most critical factors on the end-users satisfaction when using wireless communications. In this work, the optimized power save algorithm for continuous media applications (OPAMA) is proposed, aiming at enhancing the energy efficiency on end-users devices. By combining the application specific requirements with data aggregation techniques, OPAMA improves the standard IEEE 802.11 legacy Power Save Mode (PSM) performance. The algorithm uses the feedback on the end-user expected quality to establish a proper tradeoff between energy consumption and application performance. OPAMA was assessed in the OMNeT++ simulator, using real traces of variable bitrate video streaming applications, and in a real testbed employing a novel methodology intended to perform an accurate evaluation concerning video Quality of Experience (QoE) perceived by the end-users. The results revealed the OPAMA capability to enhance energy efficiency without degrading the end-user observed QoE, achieving savings up to 44% when compared with the IEEE 802.11 legacy PSM.

Asymmetric Concealed Data Aggregation Techniques in Wireless Sensor Networks: A Survey

The wireless communication nature of remotely deployed sensor nodes make the attacks more easily to be happened in wireless sensor networks (WSNs). But traditional security algorithms are infeasible in WSNs due to the limited computing, communication power, storage, band width and energy of sensor nodes. So energy efficient secure data aggregation schemes are necessary in resource constrained WSNs. Concealed Data Aggregation (CDA) based on privacy homomorphism (PH) gives a critical solution for energy efficient secure data aggregation in WSNs. PH based algorithms allow aggregation to be happened on cipher texts. Thus, it eliminates the power consuming decryption operations at the aggregator node for the data aggregation and further encryption for the secure transmission of aggregated data. It also avoids the aggregator node from the burden of keeping the secret key information and thereby it achieves energy efficiency and reduces the frequency of node compromise attacks in aggregator nodes. Among the CDA techniques, asymmetric PH based CDA techniques are exploring due to their combination with elliptic curve cryptography having reduced key size. We present an overview of asymmetric concealed data aggregation techniques that achieve both end to end data confidentiality and non delayed data aggregation.

Maximum Likelihood Item Easiness Models for Test Theory Without an Answer Key.

Cultural consensus theory (CCT) is a data aggregation technique with many applications in the social and behavioral sciences. We describe the intuition and theory behind a set of CCT models for continuous type data using maximum likelihood inference methodology. We describe how bias parameters can be incorporated into these models. We introduce two extensions to the basic model in order to account for item rating easiness/difficulty. The first extension is a multiplicative model and the second is an additive model. We show how the multiplicative model is related to the Rasch model. We describe several maximum-likelihood estimation procedures for the models and discuss issues of model fit and identifiability. We describe how the CCT models could be used to give alternative consensus-based measures of reliability. We demonstrate the utility of both the basic and extended models on a set of essay rating data and give ideas for future research.

Data Aggregation based Cooperative MIMO System for Wireless Sensor Networks: Performance Analysis

increase the life span of wireless sensor network, energy consumption must be minimized while satisfying given throughput and delay requirement. In this paper, we described an energy model based on cooperative MIMO scheme considering of both transmission and data aggregation energy is proposed for wireless sensor network. Based on this consequence, jointly considering both cooperative MIMO and data aggregation techniques which shows performance enhancement over conventional SISO system for same signal- to-noise ratio. Moreover, at critical distance value MIMO system outperforms SISO system is compared. The results show that MQAM is the best modulation technique is proved by simulations and suitable diversity technique for energy miniaturization to send a given number of bits are analyzed. Keywordsdiversity schemes, Data aggregation, Energy Efficiency, Wireless sensor network.

Review on: Data Aggregation and Dynamic Routing in Wireless Sensor Network

A wireless sensor network (WSN) has important applications such as monitoring temperature, humidity, level, speed, remote environmental monitoring and target tracking. Main critical issue in sensor network is maximization of network lifetime. All sensor nodes are required large amount of energy to send and receive data; also it depends upon distance between two nodes. Therefore with the help of Data aggregation technique we reduce the size of data. In wireless sensor network’s (WSN’s) nodes need less power for processing as compared to send data. Second technique is to reduce amount of energy to send data by using shortest path that can be achieve with the help of dynamic routing. Our proposed technique is attribute aware data aggregation. Attributes means the identifier of sampled data by different sensors node such as humidity sensors, temperature sensors etc., should be gathered together. Attribute-aware Data Aggregation mechanism which can collect packets with same attribute as much as possible and hence improve the efficiency of data aggregation. This aim can’t be achieved by static routing technique of data aggregation mechanisms since they construct routes before transmitting the sampled data and thus cannot dynamically forward packets in response to the variation of packets at intermediate. Therefor we use potential-based dynamic routing scheme to transfer data into network.

A Reliable Routing Approach for In-Network Aggregation in Wireless Sensor Networks

Wireless Sensor Networks are deployed densely in close proximity to the phenomenon to be monitored accurately in different applications. As these networks are highly populated, the data generated will be enormous. Since communication is the most expensive activity in terms of energy, Data aggregation technique should be made use of while transmitting data to the sink to save energy. With data fusion the size and number of packets transmitted can be reduced by aggregating the messages from sources in the intermediate nodes, thus decreasing communication cost, data traffic and energy consumption. For the network using data aggregation, the routing of data to the sink is an important and critical part in the processing, as the loss of aggregated data packets will have immense impact for data inference at the sink. Moreover, the routing technique used should be capable of reliable data transmission in all situations. In this paper, a novel data routing approach for in-network aggregation called DRINA (Data Routing for In-Network Aggregation) is proposed, that has the features such as reduced number of messages for setting up the routing tree, maximized number of overlapping routes, high aggregation rate and reliable data transmission even in the nodes failure situation. DRINA is compared with two other known methods: Information Fusion- based Role Assignment (InFRA) and Shortest Path Tree (SPT) algorithms using Network Simulator and is proved to be outperforming in all the evaluated scenarios. The future work will incorporate method to reduce the network overhead and to improve the aggregation rate.

Effective Approach for Data Aggregation in Wireless Sensor Network: A Structure Free Approach

AbstractWireless Sensor Network consist of thousands sensor node which have limited Power, Computation, Sensing and Communication capabilities. Among all operation of Sensor Node, Wireless Communication consumes most of the energy. So it is necessary to decrease the number of packets transmitted through the network. Many Sensor Node could detect similar event, which increases the overall bandwidth utilization to transmit redundant data. Here Nodes computation is cheaper than communication in terms of energy. So the technique of Data Aggregation is applied to summarize data which decreases the amount of data transmitted in the network, which in turn increases the lifetime of the network. Many Data aggregation protocols are based on a structured approach which is suitable for data collection application. But maintenance of the structure is an extra overhead and this approach is not suitable for dynamic scenario. So we propose an ad-hoc data aggregation protocol for dynamic scenario mainly event based application.

Energy Balanced Routing Method for In-Network Data Aggregation in Wireless Sensor Networks

A wireless sensor network (WSN) consists of a large number of wireless sensor nodes. Since wireless sensor nodes are battery powered devices, they have limited processing and transmission power. In order to transmit sensing data to receiver effectively, it is important to design a routing protocol for WSNs. Since energy conservation is a key issue in WSNs, data aggregation should be exploited in order to save energy. In this case, redundant data can be aggregated at intermediate nodes reducing the size and number of exchanged messages and, thus, decreasing communication costs and energy consumption. This paper propose an Enhanced Forward Aware Factor-Energy Balanced Routing Method (EFAF-EBRM) based on Data aggregation technique that has some key aspects such as a reduced number of messages for setting up a routing tree, maximized number of overlapping routes, high aggregation rate, and reliable data aggregation and transmission.. In the experiments, proposed method is compared with FAF-EBRM and LEACH. Experimental results show that proposed method outperforms FAF-EBRM and LEACH, which balances the energy consumption, prolongs the network function lifetime and provides the best aggregation quality .

Voronoi Fuzzy Clustering Approach for Data Processing in WSN

AbstractClustering for data aggregation is essential nowadays for increasing the wireless sensor network (WSN) lifetime, by collecting the monitored information within a cluster at a cluster head. The clustering algorithm reduces overall transmission of data from each sensor to the sink node thus energy spent by individual sensor node is minimized. The cluster heads collect all sensed information from their respective cluster members and performs data aggregation to transmit the data to the sink node. In this paper novel Voronoi Fuzzy multi hop clustering (V-FCM) algorithm is proposed for grouping the sensor node. This algorithm is a mixture of Voronoi diagram and modified Fuzzy C- Means clustering algorithm. In addition to clustering, data aggregation technique such as MAX, MIN and AVG is computed in each cluster head for further reduction of the number of data transmissions. Finally, the simulations are performed and the results are analyzed within the simulation set up to determine the performance of t...

Energy Consumption Analysis of Secure and Clustered Wireless Sensor Network

Due to limited energy resources, different design strategies have been proposed in order to achieve better energy efficiency in wireless sensor networks, and organizing sensor nodes into clusters and data aggregation are among such solutions. In this work, secure communication protocol is added to clustered wireless sensor network. Security is a very important requirement that keeps the overall system usable and reliable by protecting the information in the network from attackers. The proposed and implemented AES block cipher provides confidentiality to the communication between nodes and base station. The energy efficiency of LEACH clustered network and with added security is analyzed in detail. In LEACH clustering along with the implemented data aggregation technique 48% energy has been saved compared to not clustered and no aggregation network. The energy consumption overhead of the AES-based security is 9.14%. The implementation is done in Contiki and the simulation is carried out in Cooja emulator using sky motes.

Construction of Data Aggregation Tree for Multi-objectives in Wireless Sensor Networks through Jump Particle Swarm Optimization

As a typical data aggregation technique in wireless sensor networks, the spanning tree has the ability of reducing the data redundancy and therefore decreasing the energy consumption. However, the tree construction normally ignores some other practical application requirements, such as network lifetime, convergence time and communication interference. In this case, the way how to design a tree structure subjected to multi-objectives becomes a crucial task, which is called as multi-objective steiner tree problem (MOSTP). In view of this kind of situation, a multi-objective optimization framework is proposed, and a heuristic algorithm based on jump particle swarm optimization (JPSO) with a specific double layer encoding scheme is introduced to discover Pareto optimal solution. Furthermore, the simulation results validate the feasibility and high efficiency of the novel approach by comparison with other approaches.

Genetically derived secure cluster‐based data aggregation in wireless sensor networks

In wireless sensor networks (WSNs), the current cluster-based data aggregation technique consumes more energy. Also the secured data transmission are vital for enhancing the data authentication and confidentiality. In order to overcome these issues, in this study, the authors propose a genetically derived secure cluster-based data aggregation in WSN. Initially the cluster heads are selected based on the node connectivity, which acts as a data aggregator. Then, the clustering process is executed using the genetic algorithm. When a cluster member wants to transmit the data to aggregator, a data encryption technique are utilised that offers authenticity, confidentiality and integrity. By simulation results, the authors show that the proposed technique minimises the energy consumption, ensures data security and reduces the transmission overhead.

Deploying a LEACH Data Aggregation Technique for Air Quality Monitoring in Wireless Sensor Network

The aim of this paper is to develop an effective aggregation technique for wireless sensor networks that is mainly designed to suit air quality monitoring application. The solution is based on LEACH by Heinzelman et al.; a well known data aggregation technique for wireless sensor networks. Experimental results show the effectiveness of our solution in extending the network lifetime, while efficiently monitoring air quality.

A Survey on Privacy Preserving Data Aggregation Protocols for Wireless Sensor Networks

The data aggregation is a widely used mechanism in Wireless Sensor Networks (WSNs) to increase lifetime of a sensor node, send robust information by avoiding redundant data transmission to the base station. The privacy preserving data aggregation is a challenge in wireless communication medium as it could be eavesdropped; however it enhances the security without compromising energy efficiency. Thus the privacy protecting data aggregation protocols aims to prevent the disclosure of individual data though an adversary intercept a link or compromise a node’s data. We present a study of different privacy preserving data aggregation techniques used in WSNs to enhance energy and security based on the types of nodes in the network, topology and encryptions used for data aggregation.