Showing papers on "Data aggregator published in 2016"

INCEPTION: incentivizing privacy-preserving data aggregation for mobile crowd sensing systems

Abstract: The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other components which may affect its performance, such as data aggregation component that aggregates workers' data and data perturbation component that protects workers' privacy. Therefore, different from past literature, we capture such interactive effect, and propose INCEPTION, a novel MCS system framework that integrates an incentive, a data aggregation, and a data perturbation mechanism. Specifically, its incentive mechanism selects workers who are more likely to provide reliable data, and compensates their costs for both sensing and privacy leakage. Its data aggregation mechanism also incorporates workers' reliability to generate highly accurate aggregated results, and its data perturbation mechanism ensures satisfactory protection for workers' privacy and desirable accuracy for the final perturbed results. We validate the desirable properties of INCEPTION through theoretical analysis, as well as extensive simulations.

On the Construction of Data Aggregation Tree With Maximizing Lifetime in Large-Scale Wireless Sensor Networks

Abstract: Data aggregation protocols are generally utilized to extend the lifetime of sensor networks by reducing the communication cost. Traditionally, tree-based structured approaches that are a basic operation for the sink to periodically collect reports from all sensors were concerned about many applications. Since the data aggregation process usually proceeds for many rounds, it is important to collect these data efficiently, that is, to reduce the energy cost of data transmission. Under such applications, a tree is usually adopted as the routing structure to save the computation costs when maintaining the routing tables of sensors. In our previous work, we have demonstrated that multiple trees, as well as split trees, can provide additional lifetime extensions when certain nodes are deployed in a wireless sensor network. In this paper, we explore how the number of the family-set of trees influences the lifetime gain, and we work on the problem of constructing data aggregation trees that minimizes the total energy cost of data transmission under diverse set of scenarios and network query region. Through dividing query area, the sensory and aggregation data have been returned through a number of different forwarding trees within each sub query area, which reduces the network hot spots. To evaluate the performance of the proposed approach, we have compared and analyzed an angular division routing algorithm and query region division routing with LEACH. Theoretical and experimental results illustrate that the query region division algorithm based on angle leads to lower energy cost in comparison with the models reported in the literature.

Privacy-preserving verifiable data aggregation and analysis for cloud-assisted mobile crowdsourcing

Abstract: Crowdsourcing is a crowd-based outsourcing, where a requester (task owner) can outsource tasks to workers (public crowd). Recently, mobile crowdsourcing, which can leverage workers' data from smartphones for data aggregation and analysis, has attracted much attention. However, when the data volume is getting large, it becomes a difficult problem for a requester to aggregate and analyze the incoming data, especially when the requester is an ordinary smartphone user or a start-up company with limited storage and computation resources. Besides, workers are concerned about their identity and data privacy. To tackle these issues, we introduce a three-party architecture for mobile crowdsourcing, where the cloud is implemented between workers and requesters to ease the storage and computation burden of the resource-limited requester. Identity privacy and data privacy are also achieved. With our scheme, a requester is able to verify the correctness of computation results from the cloud. We also provide several aggregated statistics in our work, together with efficient data update methods. Extensive simulation shows both the feasibility and efficiency of our proposed solution.

On the Construction of Data Aggregation Tree with Minimum Energy Cost in Wireless Sensor Networks: NP-Completeness and Approximation Algorithms

Abstract: In many applications, it is a basic operation for the sink to periodically collect reports from all sensors. Since the data gathering process usually proceeds for many rounds, it is important to collect these data efficiently, that is, to reduce the energy cost of data transmission. Under such applications, a tree is usually adopted as the routing structure to save the computation costs for maintaining the routing tables of sensors. In this paper, we work on the problem of constructing a data aggregation tree that minimizes the total energy cost of data transmission in a wireless sensor network. In addition, we also address such a problem in the wireless sensor network where relay nodes exist and consider the cases where the link quality is not perfect. We show that these problems are NP-complete and propose $O(1)$ -approximation algorithms for each of them. Simulations show that the proposed algorithms have good performance in terms of energy cost.

Privacy-Preserving Data Aggregation in Mobile Phone Sensing

Abstract: Mobile phone sensing provides a promising paradigm for collecting sensing data and has been receiving increasing attention in recent years. Different from most existing works, which protect participants’ privacy by hiding the content of their data and allow the aggregator to compute some simple aggregation functions, we propose a new approach to protect participants’ privacy by delinking data from its sources. This approach allows the aggregator to get the exact distribution of the data aggregation and, therefore, enables the aggregator to efficiently compute arbitrary/complicated aggregation functions. In particular, we first present an efficient protocol that allows an untrusted data aggregator to periodically collect sensed data from a group of mobile phone users without knowing which data belong to which user. Assume there are $n$ users in the group. Our protocol achieves $n$ -source anonymity in the sense that the aggregator only learns that the source of a piece of data is one of the $n$ users. Then, we consider a practical scenario where users may have different source anonymity requirements and provide a solution based on dividing users into groups. This solution optimizes the efficiency of data aggregation and meets all users’ requirements at the same time.

System and method of preventing and remedying restricted area intrusions by unmanned aerial vehicles

Abstract: An intrusion prevention system includes an unmanned aerial vehicle (UAV), a UAV controller, and a restricted area data aggregator. The restricted data aggregator collects and stores restricted area data. The UAV controller is coupled to communicate with the UAV and the restricted area data aggregator, wherein the UAV controller receives positional data from the UAV and restricted area data from the restricted area aggregator. The UAV controller determines based on the received positional data and the received restricted area data whether the UAV is currently intruding within a restricted area or is predicted to intrude within a restricted area and wherein the UAV controller initiates actions to prevent unauthorized intrusions into restricted areas.

Big data commercialization

Abstract: We consider the emerging trade in big data through commercial transactions. Through a wide ranging systematic literature review that covers both academic and practitioner perspectives, we first demonstrate that there is increasing interest in big data commercialization, which mostly consists of exploratory theoretical development and managerial prescriptions to date. We outline the new types of businesses that seek to create and appropriate value, including the data supplier, data manager, data custodian, data aggregator, application developer and service provider. Building upon this typology, we discuss the processes of business model emergence, and the importance of data ecosystems, reviewing both ecosystem structure and dynamics. We also highlight the challenges for the trade in big data, including IP protection, regulatory complexity, pricing, the development of data agreements and privacy concerns. We conclude with an outline for future research.

NEECP: Novel energy-efficient clustering protocol for prolonging lifetime of WSNs

Abstract: In this paper, a novel energy-efficient clustering protocol (NEECP) for increasing the network lifetime in wireless sensor networks is proposed. This technique selects the cluster heads in an effective way with an adjustable sensing range and performs data aggregation using chaining approach. It also avoids transmission of redundant data by using a redundancy check function for improving the network lifetime. It is implemented by considering the data with aggregation and without aggregation. The NEECP without aggregation increases the network lifetime by 59.76 and 7.17% as compared with the hybrid energy-efficient distributed (HEED) and intra-balanced low-energy adaptive clustering hierarchy (IBLEACH), respectively. It increases the network lifetime by 122.92 and 49.53% over the HEED and IBLEACH, respectively, while considering data aggregation.

Improved throughput for Power Line Communication (PLC) for smart meters using fog computing based data aggregation approach

Abstract: The smart grid is now being deployed in many countries to provide cleaner and greener energy to the consumers. To implement this efficiently and effectively appropriate data collecting, processing and transmitting infrastructures are needed to be in place. Smart meters are used to measure the energy consumption details and patterns and transmit to meter data management system (MDMS) with the help of data aggregation units (DAU). The sheer amount of data need to be collected from the consumers are very huge and they fall under the category of big data. Currently, all the data collected from smart meters are stored in a centralized place for processing to forecast the energy demand. This approach is becoming a bottleneck for efficient data collection due to limited bandwidth capacities of Power Line Communication (PLC). In this paper, we propose a framework for distributed data aggregation approach with the help of fog computing architecture. With this approach the amount of data sent to the centralized storage space is limited and, therefore, the capacity of PLC is virtually improved without compromising the functionality.

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

Abstract: With the growing popularity of information and communications technologies and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the advanced message queuing protocol. We provide a comprehensive analysis on the effect of adaptive and nonadaptive window size in segmentation of time series using SensorSAX and symbolic aggregate approximation (SAX) approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with three parameters, namely window size parameter of the SAX algorithm, sensitivity level, and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilized segmentation approach, due to the fact that each geographically different sensory environment has got a different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.

VDDA: automatic visualization-driven data aggregation in relational databases

Abstract: Contemporary RDBMS-based systems for visualization of high-volume numerical data have difficulty to cope with the hard latency requirements and high ingestion rates of interactive visualizations. Existing solutions for lowering the volume of large data sets disregard the spatial properties of visualizations, resulting in visualization errors. In this work, we introduce VDDA, a visualization-driven data aggregation that models visual aggregation at the pixel level as data aggregation at the query level. Based on the M4 aggregation for producing pixel-perfect line charts from highly reduced data subsets, we define a complete set of data reduction operators that simulate the overplotting behavior of the most frequently used chart types. Relying only on the relational algebra and the common data aggregation functions, our approach is generic and applicable to any visualization system that consumes data stored in relational databases. We demonstrate our visualization-driven data aggregation using real-world data sets from high-tech manufacturing, stock markets, and sports analytics, reducing data volumes by up to two orders of magnitude, while preserving pixel-perfect visualizations, as producible from the raw data.

Comment on “Privacy-Enhanced Data Aggregation Scheme Against Internal Attackers in Smart Grid”

Abstract: Quite recently, Fan et al. ( IEEE Trans. Ind. Informat. , vol. 10, no. 1, pp. 666–675, 2014) proposed a new data aggregation scheme for smart grid communications, and claimed that it can achieve not only user’s privacy-preservation, but also data integrity requirement. However, in this paper, we show that Fan et al. ’s scheme has a serious security flaw and cannot meet data integrity requirement at all. Specifically, by observing the user registration procedure in Fan et al. ’s scheme, we find that each user’s private key can be easily derived from the information published by the aggregator. Then, with the derived private key, an attacker can inject polluted data to user’s real data without being detected. As a result, data integrity will be completely violated. We hope that with our comment, similar mistakes can be avoided in future design of privacy-preserving data aggregation with data integrity protection.

Comparison of data aggregation techniques in Internet of Things (IoT)

Abstract: Internet of Things is a new paradigm which is tremendously gaining ground in the scenario of modern age. Wireless sensor network is a major constituent of it. Data aggregation schemes play a vital role in enhancing overall efficiency of such networks. The main goal of data aggregation scheme is to collect and aggregate data packets in an efficient manner in order to reduce power consumption, traffic congestion, and to increase network lifetime and data accuracy. In this paper, we present a comparative analysis of a few data aggregation techniques in Internet of Things with reference to the parameters like energy dissipation, network lifetime, throughput, latency and number of nodes alive.

WDARS: A Weighted Data Aggregation Routing Strategy with Minimum Link Cost in Event-Driven WSNs

Abstract: Realizing the full potential of wireless sensor networks (WSNs) highlights many design issues, particularly the trade-offs concerning multiple conflicting improvements such as maximizing the route overlapping for efficient data aggregation and minimizing the total link cost. While the issues of data aggregation routing protocols and link cost function in a WSNs have been comprehensively considered in the literature, a trade-off improvement between these two has not yet been addressed. In this paper, a comprehensive weight for trade-off between different objectives has been employed, the so-called weighted data aggregation routing strategy (WDARS) which aims to maximize the overlap routes for efficient data aggregation and link cost issues in cluster-based WSNs simultaneously. The proposed methodology is evaluated for energy consumption, network lifetime, throughput, and packet delivery ratio and compared with the InFRA and DRINA. These protocols are cluster-based routing protocols which only aim to maximize the overlap routes for efficient data aggregation. Analysis and simulation results revealed that the WDARS delivered a longer network lifetime with more proficient and reliable performance over other methods.

An efficient privacy-preserving aggregation and billing protocol for smart grid

Abstract: Smart grid is an electrical grid that uses digital information and communication technology to gather information. Like other digital systems, security and privacy are crucial for smart grid. However, security and privacy protection will inevitably introduce computational complexity and overhead. As smart grid systems are usually operated in a large scale, computational efficiency is a challenging issue. In this paper, we propose an efficient and secure billing system for smart grid, featuring privacy-preserving and data aggregation. We show that our system offers better privacy protection and computational efficiency, in comparison with an existing protocol. Our security analysis indicates that our protocol achieves privacy-preserving on electricity reading aggregation and billing, perfect forward secrecy of system session keys, identity authentication, data integrity, and confidentiality. It also shows that even if we allow the collusion of server and gateways, user privacy can still be achieved. Copyright © 2016 John Wiley & Sons, Ltd.

Mobility and Heterogeneity Aware Cluster-Based Data Aggregation for Wireless Sensor Network

Abstract: Internet of things is the modern era, which offers a variety of novel applications for mobile targets and opens the new domains for the distributed data aggregations using wireless sensor networks. However, low cost tiny sensors used for network formation generate the large amount of redundant sensing data and hence, results in energy and bandwidth constraints. In this context, the paper proposes the sink mobility and nodes heterogeneity aware cluster-based data aggregation algorithm (MHCDA) for efficient bandwidth utilization and an increase in network lifetime. The proposed algorithm uses a predefined region for the aggregation of packets at the cluster head for minimizing computation and communication cost. MHCDA exploits correlation of data packets generated by nodes with a variable packet generation rate to reduce energy consumption by 8.66 %. Also, it prolongs the network life by 23.53 % as compared to with and without mobility of the sink and state of the-art solutions.

An efficient privacy preserving data aggregation approach for mobile sensing

Abstract: The advances in sensing capabilities of smartphones give rise to a variety of mobile participatory sensing applications that collect users' personal data. Because of the existence of both sensitive, private personal data, and untrusted aggregator, serious privacy concerns on users arise. Currently, existing privacy preserving data collection methods either require bidirectional communications between an untrusted aggregator and mobile users in every aggregation period, or have high computation or communication overhead. To address these problems, we propose an efficient data aggregation approach by which an untrusted aggregator in mobile sensing can collect the statistics over the data contributed by multiple mobile users, while supporting privacy preservation of each user and data integrity verification. In this approach, information hiding and homomorphic encryption are applied to guarantee the data privacy of mobile users. In detail, a breadth-first search tree is first constructed at the initial phase among the mobile users, and then the original datum of each user is perturbed among its neighbors in ciphertext space by using information hiding and homomorphic encryption. The evaluations of our approach show that our protocol requires lower communication and computation overhead and thus more feasible for the computation constrained mobile devices. Copyright © 2016 John Wiley & Sons, Ltd.

Self-regulatory information sharing in participatory social sensing

Abstract: Participation in social sensing applications is challenged by privacy threats. Large-scale access to citizens’ data allow surveillance and discriminatory actions that may result in segregation phenomena in society. On the contrary are the benefits of accurate computing analytics required for more informed decision-making, more effective policies and regulation of techno-socio-economic systems supported by ‘Internet-of Things’ technologies. In contrast to earlier work that either focuses on privacy protection or Big Data analytics, this paper proposes a self-regulatory information sharing system that bridges this gap. This is achieved by modeling information sharing as a supply-demand system run by computational markets. On the supply side lie the citizens that make incentivized but self-determined decisions about the level of information they share. On the demand side stand data aggregators that provide rewards to citizens to receive the required data for accurate analytics. The system is empirically evaluated with two real-world datasets from two application domains: (i) Smart Grids and (ii) mobile phone sensing. Experimental results quantify trade-offs between privacy-preservation, accuracy of analytics and costs from the provided rewards under different experimental settings. Findings show a higher privacy-preservation that depends on the number of participating citizens and the type of data summarized. Moreover, analytics with summarization data tolerate high local errors without a significant influence on the global accuracy. In other words, local errors cancel out. Rewards can be optimized to be fair so that citizens with more significant sharing of information receive higher rewards. All these findings motivate a new paradigm of truly decentralized and ethical data analytics.

Data collection using miniature aerial vehicles in wireless sensor networks

Abstract: Energy constraints of sensor nodes in wireless sensor networks (WSNs) is a major challenge and minimising the overall data transmitted across a network using data aggregation, distributed source coding, and compressive sensing have been proposed as mechanisms for energy saving. Similarly, use of mobile nodes capable of relocating within the network has been widely explored for energy saving. In this study, the authors propose a novel method for using miniature aerial vehicles for data collection instead of actively sensing from a deployed network. The proposed mechanism is referred as data collection fly (DCFly). It is suitable for data collection from WSNs deployed in harsh- undulating terrain with the base station located far from the sensing region. The DCFly is compared with data collection based on multi-hop data aggregation and data collection with mobile sinks. The numerical results justify that the proposed data collection mechanism is effective and efficient for use in WSNs. 1 Introduction Wireless sensor networks (WSNs) are deployed in an area to be monitored, referred to in this paper as the sensing region, for diverse applications such as habitat and environmental monitoring, battlefield surveillance, and industrial monitoring. Using active node mobility in the network, overall communication in the network can be reduced and specifically nodes close to the base station (BS) could be prevented from draining out. Various utilities that are possible using active node mobility in the network are: mobile relay, mobile data mule, and mobile BS. Alternatively, in-network data processing mechanisms such as data aggregation, network coding, and compressive sensing could be utilised for minimising the overall communication of the network. These two approaches, that is, node mobility, and in-network processing have been extensively explored individually and independently. This paper presents a mechanism for data collection using a data collection fly (DCFly). The DCFly would aerially collect data from the network. This is the first work that explores the potential of using miniature aerial vehicles (MAVs) for collecting data, collectively involving node mobility and in-network data processing. The underlying framework adopted in this paper has been presented in our preliminary work (1). The remaining paper has been structured as follows: in Section 2, previous work relating to node mobility and in-networking data processing has been presented. Section 3 provides an insight into factors governing optimal number of cluster heads (CHs) and inter-relation with position of BS, data collection operation by the DCFly; these aspects influence the network architecture. The factors governing the DCFly's possible use in the sensing region and the network operational conditions considered for evaluation are presented in Section 4. The DCFly's governing factors formulated as an optimisation problem to determine the feasible operational utility are presented in Section 5. The optimum number of clusters that can be covered by a single DCFly are presented in Section 6, along with the comparative evaluation of the proposed DCFly with a multi-hop data aggregation mechanism (referred as DA mechanism). The DCFly-based data collection is also compared with data collection from the network relying on mobile elements (MEs) in Sections 6.1 and 6.2. Finally, this paper is concluded in Section 7. 2 Related work As stated earlier, the methods for minimising the energy consumption in the network can be broadly categorised into node mobility and in-network data processing-based mechanisms.

A comparison study on node clustering techniques used in target tracking WSNs for efficient data aggregation

Abstract: Wireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregation process. In this paper, we have highlighted the gains of the existing schemes for node clustering-based data aggregation along with a detailed discussion on their advantages and issues that may degrade the performance. Also, the boundary issues in each type of clustering technique have been analyzed. Simulation results reveal that the efficacy and validity of these clustering-based data aggregation algorithms are limited to specific sensing situations only, while failing to exhibit adaptive behavior in various other environmental conditions. Copyright © 2016 John Wiley & Sons, Ltd.

Energy Efficient Data Aggregation in Mobile Agent Based Wireless Sensor Network

Abstract: Energy efficiency have always been a priority while designing wireless sensor networks. Introduction of mobile agent technology in wireless sensor networks for collaborative signal and information processing has provided the new scope for efficient processing and aggregation of data. Mobile agent based distributed computing paradigm offers numerous benefits over the existing and commonly used client/server computing paradigm in wireless sensor networks. Mobile agent performs the task of data processing and data aggregation at the node level rather than at the sink, thus, eliminating the redundant network overhead. One of the most important issues in mobile agent based paradigm is planning of an itinerary for agent traversal. In this paper, we have proposed a dynamic mobile agent based data aggregation approach that takes into consideration energy efficiency, network lifetime, end to end delay and aggregation ration while making the decision for migration of agent in multihop sensor network. As our approach focuses on finding the most informative route by traversing comparatively less number of nodes consequently mobile agent takes less time to return to processing element, thus, exhibiting lower delay.

Safe Integration of SIEM Systems with Internet of Things: Data Aggregation, Integrity Control, and Bioinspired Safe Routing

Abstract: Internet of Things (IoT) is one of the most rapidly developing information technology concept in the world. Ensuring the safety of IoT is a complex task which has not been completely solved so far. A prospective approach for security providing in IoT is development of a security information and event management (SIEM) system for IoT. For realizing this approach, it is necessary to solve tasks of data volume reduction, data integrity, and guaranteed data transportation to SIEM system. This paper proposes a solution for all of these tasks using data aggregation, data digital signing, and swarm routing algorithm.

Lightweight and confidential data aggregation in healthcare wireless sensor networks

Abstract: Medical wireless sensor networks MWSNs provide efficient solutions to the ubiquitous healthcare systems. Deployment of MWSNs for healthcare monitoring minimizes the need for healthcare professionals and helps the patients and elderly people to safely maintain an independent life. In hospitals, medical data sensors on patients produce an enormous volume of increasingly diverse real-time data. However, it is still critical to efficiently aggregate the different types of MWSNs data to the central servers. The security of collected and transmitted data from medical sensors is critical, whether inside the network or when stored at central servers. Efficient and secure aggregation of data is thus very essential to ensure integrity of data delivery, as well as the privacy of these data. In this research, we propose a priority-based compressed data aggregation scheme with integrity preservation to improve the aggregation efficiency of different types of health data. We use compressed sensing as a sampling procedure to reduce the communication overhead and minimize power consumption. Then, the compressed data are encrypted, and integrity is protected by a cryptographic hash algorithm to preserve data integrity. Finally, according to different data priorities, we apply an aggregation function and then send the data for diagnosis. The security analysis focuses on security properties assured by our scheme. Then, we will present experimental results for the evaluation of the proposed system on e-health sensor platform. Copyright © 2015 John Wiley & Sons, Ltd.

SDAW: secure data aggregation watermarking-based scheme in homogeneous WSNs

Abstract: Redundant data retransmission problem in wireless sensor networks (WSNs) can be eliminated using the data aggregation process which combines similar data to reduce the resource-consumption and consequently, saves energy during data transmission. In the recent days, many researchers have focused on securing this paradigm despite the constraints it imposes such as the limited resources. Most of the solutions proposed to secure the data aggregation process in WSNs are essentially based on the use of encryption keys to protect data during their transmission in the network. Indeed, the key generation and distribution mechanisms involve additional computation costs and consume more of energy. Considering this, in this paper, we propose a new security mechanism to secure data aggregation in WSNs called SDAW (secure data aggregation watermarking-based scheme in homogeneous WSNs). Our mechanism aims to secure the data aggregation process while saving energy. For this, the mechanism uses a lightweight fragile watermarking technique without encryption to insure the authentication and the integrity of the sensed data while saving the energy. The links between the sensor nodes and the aggregation nodes, and also the links between the aggregation nodes and the base station are secured by using the watermarking mechanism.

Data clustering in wireless sensor network implemented on self organization feature map (SOFM) neural network

Abstract: Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.