Data Aggregation Techniques Research Articles

An Enhanced K-Means and ANOVA-Based Clustering Approach for Similarity Aggregation in Underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) have recently been proposed as a way to observe and explore aquatic environments. Sensors in such networks are used to perform pollution monitoring, disaster prevention, or assisted navigation and to send monitored data to the sink. Compared with the traditional sensor networks, sensors in UWSNs consume more energy due to the acoustic technology used in under water communications. Node clustering is a common method to organize data traffic and reduce in-network communications while improving scalability and energy consumption. In this paper, we present a new clustering method to handle the spatial similarity between node readings. We suppose that readings are sent periodically from sensor nodes to their appropriate cluster heads (CHs). Then, a two-tier data aggregation technique is proposed. At the first level, each node periodically cleans its readings in order to eliminate redundancies before sending its data set to its CH. Once the CH receives all data sets, it applies an enhanced K-means algorithm based on a one-way ANOVA model to identify nodes generating identical data sets and to aggregate these sets before sending them to the sink. Our proposed approach is validated via experiments on real sensor data and comparison with other existing clustering and data aggregation techniques.

Addressing Data Aggregation Using Polynomial Regression in WSNs

One of the scarcest resources in wireless sensor networks (WSNs) is energy. Thus, a major issue of WSN is to prolong the network lifetime. The energy dissipation of sensor nodes should be minimized within the network for prolong network lifetime. For the sake of this, data aggregation emerged as an important field of research in modern era. Intensive research has been carried out for providing novel energy efficient and secure data aggregation techniques. Unlike conventional data aggregation techniques, polynomial regression based data aggregation technique increases the network lifetime by transmitting compressed data packets of constant size. Data compression has been carried out by performing regression function on the sensed data. These techniques provide good approximation of the sensed value and are scalable as the data compression ratio is nearly constant. In this paper, we present a detailed review on different polynomial regression based data aggregation techniques and provide a comparison among them. Keywords: Data aggregation, Data correlation, Polynomial regression, Security, Wireless sensor networks.

Efficient Load Balancing Schema using Multiple Mobile Sink

efficient and fast data aggregation technique is one of the challenging task when it is considered for large-scale wireless sensor network (WSN). The problems of presence of single sink, distance between sink and cluster, as well as type of presence of dynamic task to be performed within different cluster is extremely difficult to address for reliable data aggregation technique in WSN. Therefore, the proposed system introduces a novel cylindrical topology of WSN that ensures an efficient task allocation strategy in large-scale WSN architecture. The performance of the data aggregation process is further increased by considering presence of multiple mobile sink that adds exponential benefits to the task allocation policy proposed. The results simulated in MATLAB shows satisfactory performance by considering packet delivery ratio, delay minimization, and completion time of data aggregation process

Energy Efficient, Integrity Preserving and Secure Data Aggregation Technique for Wireless Sensor Network

Energy Efficient, Integrity Preserving and Secure Data Aggregation Technique for Wireless Sensor Network

Effective and Efficient Data Aggregation Technique for Common Web Log XML Format for Digital Forensics Investigation (DFI)

Effective and Efficient Data Aggregation Technique for Common Web Log XML Format for Digital Forensics Investigation (DFI)

Data Aggregation Techniques in Wireless Sensor Networks: Literature Survey

The wireless detector network is wide employed in totally different areas of application like military applications etc. Wireless detector network is nothing however the cluster of detector nodes those area unit human action to every alternative mistreatment radio frequencies through the bottom station. Every detector node in network has the battery that is important and crucial to outline the lifetime of detector node. Hence it’s needed to possess economical energy utilization with aim of extending the lifetime of detector networks. One approach that greatly reduces this consumption of power is reduction of within detector networks information traffic that successively reduces the information sent to the bottom station. This method of collection information and causation it to the bottom station is termed as information aggregation. There area unit has several information aggregation techniques conferred by varied researchers with aim of rising the performance of wireless detector network in terms of energy consumption, network out-turn, packet delivery quantitative relation etc. primarily the information aggregation algorithms collection and aggregating data in economical utilization of power with aim of extend the period. Throughout this review paper, we tend to area unit planning to present the various existing strategies for in-network information aggregation in WSNs.

A Survey on Energy Performance Improvement with Data Aggregation Techniques for WSN

In the recent advance in science and technology, the communication system has been marching towards quick development. Wireless sensor network is one of the important blocks for communication. The major limiting factors in wireless sensor network are power consumption requirements, life time of network, data integrity and data confidentiality. Data Aggregation is a very critical strategy in wireless sensor network. Data Aggregation is a methodology of gathering and accumulating the message information in a vital effective way, so that life time can be expanded. The paper discusses some standard peculiarity of wireless sensor network associated with the data aggregation and the different approaches of data aggregation. General Terms Performances improvement, Network protocols

Cellular phenotype database: a repository for systems microscopy data.

Motivation: The Cellular Phenotype Database (CPD) is a repository for data derived from high-throughput systems microscopy studies. The aims of this resource are: (i) to provide easy access to cellular phenotype and molecular localization data for the broader research community; (ii) to facilitate integration of independent phenotypic studies by means of data aggregation techniques, including use of an ontology and (iii) to facilitate development of analytical methods in this field.Results: In this article we present CPD, its data structure and user interface, propose a minimal set of information describing RNA interference experiments, and suggest a generic schema for management and aggregation of outputs from phenotypic or molecular localization experiments. The database has a flexible structure for management of data from heterogeneous sources of systems microscopy experimental outputs generated by a variety of protocols and technologies and can be queried by gene, reagent, gene attribute, study keywords, phenotype or ontology terms.Availability and implementation: CPD is developed as part of the Systems Microscopy Network of Excellence and is accessible at http://www.ebi.ac.uk/fg/sym.Contact: jes@ebi.ac.uk or ugis@ebi.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

GABs: A Game-Based Secure and Energy Efficient Data Aggregation for Wireless Sensor Networks

This paper endeavors to investigate the security and energy issues in wireless sensor networks from prior art such as game theory and various embodiments of methods like public-key cryptography. Due to the needs of interacting with the physical world, thanks to a plethora of innovative applications, the state of the art in wireless sensor networks (WSNs) is focusing on multiple purposes such as monitoring, tracking, and security while relying upon some assumptions about distributed data, whereas these assumptions may not hold in a real scenario. The content herein models homogeneous WSN environment of highly linked nodes, in a theoretical game with synchronized actions, where, in order to transmit their readings securely across the network in some level of hierarchy, using the technique of data aggregation, sensor nodes with communication and computational resources constraint should establish trusted and direct links between their neighbors for the privacy and integrity of their data against faulty nodes, while ensuring energy saving. The study considers the ZigBee known as IEEE 802.15.4 standard for its high trustworthiness and low power consumption in wireless sensor network and compares different topology models.

English

English

A survey on data aggregation techniques in wireless sensor networks

Wireless sensor networks WSNs are self-organised, low cost and low power utilising network, which senses, calculates and communicates the data from the source to sink. The data collection at sensor nodes consumes more energy, but the sensor nodes have only limited energy. Hence, most of the data-aggregation techniques aim to prolong the lifetime of the network by minimising power consumption and optimised data transmission. In this paper, an extensive survey of various data aggregation techniques in WSN is performed by categorising the techniques as structured, structure-free, flat and hierarchical. These techniques are analysed in terms of energy conservation, network lifetime, packet delivery ratio, latency and various other parameters. A comparison of these data aggregation techniques is also presented along with their advantages and issues.

Issues of Data Aggregation Methods in Wireless Sensor Network: A Survey

In Wireless Sensor Networks (WSN) sensor nodes are deployed in a region to sense the information. These sensor nodes sense the similar information and sends it to sink node. This thing leads to redundancy at sink node. Sink node wastes most of its energy in processing redundant packets. To save the energy of node in order to prolong the network lifetime there is need to eliminate redundancy. Data aggregation is a process in which intermediate node receives multiple input packets performs aggregation and produce single output packet in the network. This process will reduce the number of redundant packets in the network. But redundancy sustains reliability. Therefore there is need to maintain redundancy but it should be up to an adequate level. In this paper we have focused on different issues in data aggregation process such as delay, redundancy elimination, accuracy and traffic load and mentioned various methods to solve those issues and then we compared some data aggregation techniques based on strategy, delay, redundancy, average energy consumption and traffic load. Further we have proposed a model based on our study which performs data aggregation at multiple levels and not only maintains the tradeoff between energy conservation and reliability but also addresses all the issues in data aggregation technique.

A Secure Privacy-Preserving Data Aggregation Model in Wearable Wireless Sensor Networks

With the rapid development and widespread use of wearable wireless sensors, data aggregation technique becomes one of the most important research areas. However, the sensitive data collected by sensor nodes may be leaked at the intermediate aggregator nodes. So, privacy preservation is becoming an increasingly important issue in security data aggregation. In this paper, we propose a security privacy-preserving data aggregation model, which adopts a mixed data aggregation structure. Data integrity is verified both at cluster head and at base station. Some nodes adopt slicing technology to avoid the leak of data at the cluster head in inner-cluster. Furthermore, a mechanism is given to locate the compromised nodes. The analysis shows that the model is robust to many attacks and has a lower communication overhead.

An adaptive scheme for data collection and aggregation in periodic sensor networks

Low data delivery efficiency and high energy consumption are inherent problems in sensor networks. Finding accurate data is difficult and leads to expensive sensor readings. Adaptive data collection and aggregation techniques provide opportunities for reducing the energy consumption of continuous sensor data collection. In this paper, we propose an adaptive data collection and aggregation scheme for periodic sensor networks. Our approach is two-fold: firstly, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, over-sampling can be minimised and power efficiency can be improved. Furthermore, we propose a multiple levels activity model that uses behaviour functions modelled by modified Bezier curves to define application classes. Secondly, a new data aggregation method is applied on the set of data collected from all sensor nodes. We investigate the problem of finding all pairs of nodes generating similar datasets. Our proposed models are validated via simulation on real sensor data.

A Data Management Perspective on Vehicular Networks

The interest of intelligent transportation systems and vehicular ad hoc networks has increased in recent years. As a fundamental building block for the development of applications for vehicular networks, new techniques are needed to handle data appropriately in the vehicles. In this paper, we present a comprehensive overview of data management for vehicular networks, where the vehicle-to-vehicle communications play a key role. We describe the technological context of vehicular networks along with the different types of data managed in that environment, and we analyze several challenges, such as the evaluation of the relevance of data regarding the occurrence of events on the roads (e.g., accidents), the design of appropriate (effective and efficient) content-based data dissemination protocols, the competition in the access to physical resources (e.g., parking spaces), the development of suitable data aggregation techniques specifically adapted to the context of vehicular networks, and query processing. This paper provides an in-depth coverage of data management for vehicular networks but keeps, at the same time, a didactic orientation. Supported by an extensive collection of relevant references, we analyze the state of the art, identify some must-read references, outline research problems, and extract conclusions and lessons learned.

Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

Due to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.

A Survey on Data Aggregation and Clustering Schemes in Underwater Sensor Networks

Energy consumption is one of the most challenging constraintsof the design and implementation of the sensor network. Underwater sensor networking is the technology that enables the applications like environment monitoring, underwater exploration, seismic monitoring and other surveillance applications. In underwater sensor network,a sensor node senses the data and transmits it to the sink. Many routing algorithms have been proposed in order to make the network phase of UWSNs more efficient.In this report, we present a review and comparison of various data collection algorithms and clustering schemes, proposed recently in order to execute the demands of the ongoing researches. The main goal of data aggregation technique is to accumulate data in an energy efficient manner for a long-term network monitoring. The main purpose of this study is to present algorithms addressing issues like deployment and localization in UWSNs under different conditions.

Real-time Event Detection on Social Data Stream

Social Network Services (SNS) are becoming more popular in our daily life, the process is boosted by various kinds of smart devices integrating utility modules such as 3G/WIFI connector, GPS tracker, Camera, Heartbeat sensor and so on. It makes the information flow (or Social Data Stream) on SNS have a real-time nature characteristic, where each SNS user is an information sensor and also a data connector for diffusing interesting news to his/her communication networks. Hiding inside the information flow are pieces of real social events. The events draw attention from users evidencing by the number of relevant announces and communication interactions toward that topic. However, traditional topic detection approaches are not designed to detect the kind of the event efficiently in real-time, particularly if the data sources are influenced by noise data and containing diverse topics. To overcome the issue, in this paper we proposed a model for extracting and tracking real social events on Social Data Stream, which can work well in real-time by using distributing computation and data aggregation technique on the discrete signals as a new representation of the original data.

Multi-Objective Meta-Heuristic Approach for Energy-Efficient Secure Data Aggregation in Wireless Sensor Networks

Energy consumption in the sensor network is primarily due to the switching states of radio transceivers and long busy states of sensor nodes in the network. Data aggregation techniques reduce the number of transmissions and improve the bandwidth utilization. Secure data aggregation and energy-efficient routing protocols establish the secure channel, and reduce the communication overhead in the network. Multi-objective optimization methods based on the weighted sum method, the utility method and meta-heuristic search methods enhance the performance of meta-heuristic algorithms. This article proposes multi-objective meta-heuristic approach for energy-efficient secure data aggregation (MH-EESDA) protocol in wireless sensor networks. The proposed protocol uses divide-and-conquer approach to form the secure clusters and perform the secure data aggregation in energy-efficient route paths of the network. The protocol functions in three phases. In the first phase, the clusters are formed, in the second phase, the secure nodes are selected and in the third phase, energy-efficient data aggregation is performed across the secure route paths of the network. The sensor node energy and data aggregation rate are evaluated for (1) minimum degree of intrusions (2) threshold-based degree of intrusions and (3) maximum degree of intrusions in the network. Simulation results illustrate significant improvements in the proposed MH-EESDA protocol.

A Middleware for the Integration of Smart Grid Elements with WSN Based Solutions

Currently, electricity distributors make use of various types of equipment divided into levels of automation. This automation enables the integration of elements such as Intelligent Electronic Devices (IEDs) to the supervision of the distribution electrical system, but there is not an appropriate environment to increase the scale of these elements. In this context, the smart grid comes with specifications that allow adding new elements to the intelligence of the power grid operation. However, the cost of communication is still an impediment to the scalability of the integration of these elements into the current structure. In this paper, we propose a middleware that optimizes the communication of this integration using wireless sensor networks (WSN). The goal is to ensure a gradual integration of new elements taking advantage of the increase in the number of sensor nodes in the network due to the scalability of the system itself. The conversion solutions have been used to allow easy communication between the WSN and the smart grid system, and we also have used data aggregation and compression techniques to increase the lifetime of the wireless sensor network.