Showing papers on "Data aggregator published in 2009"

Distributed Data Aggregation Scheduling in Wireless Sensor Networks

Abstract: Data aggregation is an essential operation in wireless sensor network applications. This paper focuses on the data aggregation scheduling problem. Based on maximal independent sets, a distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks is proposed. The time latency of the aggregation schedule generated by the proposed algorithm is minimized using a greedy strategy. The latency bound of the schedule is 24D + 6 Delta + 16, where D is the network diameter and Delta is the maximum node degree. The previous data aggregation algorithm with least latency has the latency bound (Delta- Delta 1)R, where R is the network radius. Thus in our algorithm Delta contributes to an additive factor instead of a multiplicative factor, which is a significant improvement. To the best of our knowledge, the proposed algorithm is the first distributed algorithm for data aggregation scheduling. This paper also proposes an adaptive strategy for updating the schedule when nodes fail or new nodes join in a network. The analysis and simulation results show that the proposed algorithm outperforms other aggregation scheduling algorithms.

Efficient data aggregation in multi-hop wireless sensor networks under physical interference model

Abstract: Efficient aggregation of data collected by sensors is crucial for a successful application of wireless sensor networks (WSNs). Both minimizing the energy cost and reducing the time duration (or called latency) of data aggregation have been extensively studied for WSNs. Algorithms with theoretical performance guarantees are only known under the protocol interference model, or graph-based interference models generally. In this paper, we study the problem of designing time efficient aggregation algorithm under the physical interference model. To the best of our knowledge, no algorithms with theoretical performance guarantees are known for this problem in the literature. We propose an efficient algorithm that produces a data aggregation tree and a collision-free aggregation schedule. We theoretically prove that the latency of our aggregation schedule is bounded by O(R+Δ) time-slots. Here R is the network radius and Δ is the maximum node degree in the communication graph of the original network. In addition, we derive the lower-bound of latency for any aggregation scheduling algorithm under the physical interference model. We show that the latency achieved by our algorithm asymptotically matches the lower-bound for random wireless networks. Our extensive simulation results corroborate our theoretical analysis.

A fuzzy logic based approach for structure-free aggregation in vehicular ad-hoc networks

Abstract: Information aggregation is used to merge correlated data items from different nodes before redistributing them. Thus, using aggregation the number of transmissions and the communication overhead can be reduced significantly. Especially for applications which require periodic dissemination of information into a large region, aggregation is a prerequisite. Consider for example a traffic information system where each vehicle periodically disseminates information about road conditions. The aim is to provide drivers with accurate information on traffic conditions for a large road section, so drivers can be informed in time and can take alternate routes in case of traffic congestion, for example. Many aggregation approaches for VANETs use a fixed or structured segmentation of roads for these applications. Based on such segments the aggregation function is applied to fuse data. We argue that such fixed bounds contradict the real situation on roads. Thus either the segments are too large, resulting in loss of accuracy or the segments are too small, resulting in high communication load. Therefore, we propose in this work a system for completely structure-free aggregation. We employ fuzzy reasoning to allow for aggregation decisions to be based on a flexible and extensible set of criteria. These criteria can be application specific and enable a dynamic fragmentation of the road according to the applications' requirements.

A fundamental scalability criterion for data aggregation in VANETs

Abstract: The distribution of dynamic information from many sources to many destinations is a key challenge for VANET applications such as cooperative traffic information management or decentralized parking guidance systems. In order for these systems to remain scalable it has been proposed to aggregate the information within the network as it travels from the sources to the destinations. However, so far it has remained unclear by what amount the aggregation scheme needs to reduce the original data in order to be considered scalable. In this paper we prove formally that any suitable aggregation scheme must reduce the bandwidth at which information about an area at distance d is provided to the cars asymptotically faster than 1/d2. Furthermore, we constructively show that this bound is tight: for any arbitrary e>0, there exists a scalable aggregation scheme that reduces information asymptotically like 1/d(2+e).

Future generation information technology : first International Conference, FGIT 2009, Jeju Island, Korea, December 10-12, 2009 : proceedings

Abstract: Keynotes.- Computer Science: Where Is the Next Frontier?.- Video Forgery.- Data Analysis, Data Processing, Advanced Computation Models.- Intelligent Data Granulation on Load: Improving Infobright's Knowledge Grid.- Data Analysis Methods for Library Marketing.- HMM Approach for Classifying Protein Structures.- Investigation of Average Mutual Information for Species Separation Using GSOM.- Speech Recognition System and Formant Based Analysis of Spoken Arabic Vowels.- A Study on Mental Tasks Discriminative Power.- A Straight Line-Based Distance Measure to Compute Photographic Compositional Dissimilarity.- Data Gathering for Gesture Recognition Systems Based on Mono Color-, Stereo Color- and Thermal Cameras.- Object Surface Reconstruction from One Camera System.- The Study of Development Strategy for Bank Distribution Network through the Analysis of Inter-regional Financial Transaction Network.- Global Synchronization Properties for Different Classes of Underlying Interconnection Graphs for Kuramoto Coupled Oscillators.- Predicting the Performance of a GRID Environment: An Initial Effort to Increase Scheduling Efficiency.- Towards an Integrated Vision across Inter-cooperative Grid Virtual Organizations.- Effective GIS Mobile Query System.- Modeling and Simulation of Tandem Tollbooth Operations with Max-Algebra Approach.- Security, Software Engineering, Communication and Networking.- Intrusion Detection Based on Back-Propagation Neural Network and Feature Selection Mechanism.- Automatic Detection for JavaScript Obfuscation Attacks in Web Pages through String Pattern Analysis.- Fragmentation Point Detection of JPEG Images at DHT Using Validator.- Secure and Energy Efficient Key Management Scheme Using Authentication in Cluster Based Routing Protocol.- Automatic Detection of Infinite Recursion in AspectJ Programs.- A Hierarchical Test Model and Automated Test Framework for RTC.- A Bi-objective Model Inspired Greedy Algorithm for Test Suite Minimization.- Analysing Object Type Hierarchies to Identify Crosscutting Concerns.- A Bayesian Inference Tool for NHPP-Based Software Reliability Assessment.- AGILE Rate Control for IEEE 802.11 Networks.- Low Density Parity Check Code for the Single Carrier Frequency Division Multiple Access.- Dual Optimization of Dynamic Sensor Function Allocation and Effective Sensed Data Aggregation in Wireless Sensor Networks.- Depth-Spatio-Temporal Joint Region-of-Interest Extraction and Tracking for 3D Video.- Dynamic Routing Algorithm for Reliability and Energy Efficiency in Wireless Sensor Networks.- QoS Multicast Routing Algorithms Based on Tabu Search with Hybrid Candidate List.- A Lifetime Enhancing Node Deployment Strategy in WSN.- Data Analysis, Data Processing, Advanced Computation Models.- Experimental Investigation of Three Machine Learning Algorithms for ITS Dataset.

Privacy-preserving Robust Data Aggregation in Wireless Sensor Networks

Abstract: In-network data aggregation in wireless sensor networks (WSNs) is a technique aimed at reducing the communication overhead—sensed data are combined into partial results at intermediate nodes during message routing. However, in the above technique, some sensor nodes need to send their individual sensed values to an aggregator node, empowered with the capability to decrypt the received data to perform a partial aggregation. This scenario raises privacy concerns in applications like personal health care and the military surveillance. A few other solutions exist where the data are not disclosed to the aggregator (e.g., using privacy homomorphism (PH)), but these solutions are not robust to node or communication failure. The contributions of this paper are two-fold: first, we design a private data aggregation protocol that does not leak individual sensed values during the data aggregation process. In particular, neither the base station (BS) nor the other nodes are able to compromise the privacy of an individual node's sensed value. Second, the proposed protocol is robust to data-loss; if there is a node-failure or communication failure, the protocol is still able to compute the aggregate and to report to the base station the number of nodes that participated in the aggregation. To the best of our knowledge, our scheme is the first one that efficiently addresses the above issues all at once. Copyright © 2009 John Wiley & Sons, Ltd.

Latency-constrained aggregation in sensor networks

Abstract: A sensor network consists of sensing devices which may exchange data through wireless communication; sensor networks are highly energy constrained since they are usually battery operated. Data aggregation is a possible way to save energy consumption: nodes may delay data in order to aggregate them into a single packet before forwarding them towards some central node (sink). However, many applications impose constraints on the maximum delay of data; this translates into latency constraints for data arriving at the sink.We study the problem of data aggregation to minimize maximum energy consumption under latency constraints on sensed data delivery, and we assume unique communication paths that form an intree rooted at the sink. We prove that the offline problem is strongly NP-hard and we design a 2-approximation algorithm. The latter uses a novel rounding technique.Almost all real-life sensor networks are managed online by simple distributed algorithms in the nodes. In this context we consider both the case in which sensor nodes are synchronized or not. We assess the performance of the algorithm by competitive analysis. We also provide lower bounds for the models we consider, in some cases showing optimality of the algorithms we propose. Most of our results also hold when minimizing the total energy consumption of all nodes.

Design of Structure-Free and Energy-Balanced Data Aggregation in Wireless Sensor Networks

Abstract: Since sensor nodes are energy-constrained, energy saving is a critical issue in wireless sensor networks. By reducing the number of transmissions, data aggregation is an effective approach to save energy. In the literature, most of data aggregation protocols rely on a structured architecture to accomplish the data gathering task. Such structure-based methods suffer from high maintenance overhead in a dynamic environment where sensor nodes may move or fail unexpectedly. In this paper, we propose a structure-free and energy-balanced data aggregation protocol(SFEB). The two-phase aggregation and dynamic aggregator selection of SFEB enable both efficient data gathering and balanced energy consumption. Extensive simulations verify the superiority of our SFEB.

Optimal stochastic policies for distributed data aggregation in wireless sensor networks

Abstract: The scenario of distributed data aggregation in wireless sensor networks is considered, where sensors can obtain and estimate the information of the whole sensing field through local data exchange and aggregation. An intrinsic tradeoff between energy and aggregation delay is identified, where nodes must decide optimal instants for forwarding samples. The samples could be from a node's own sensor readings or an aggregation with samples forwarded from neighboring nodes. By considering the randomness of the sample arrival instants and the uncertainty of the availability of the multiaccess communication channel, a sequential decision process model is proposed to analyze this problem and determine optimal decision policies with local information. It is shown that, once the statistics of the sample arrival and the availability of the channel satisfy certain conditions, there exist optimal control-limit-type policies that are easy to implement in practice. In the case that the required conditions are not satisfied, the performance loss of using the proposed control-limit-type policies is characterized. In general cases, a finite-state approximation is proposed and two on-line algorithms are provided to solve it. Practical distributed data aggregation simulations demonstrate the effectiveness of the developed policies, which also achieve a desired energy-delay tradeoff.

A Cluster-Based Protocol to Enforce Integrity and Preserve Privacy in Data Aggregation

Abstract: Data fusion or information collection is one of the fundamental functions in the future cyber-physical systems. But, privacy concerns must be addressed and security must be assured in such systems. It is very challenging to achieve the synergy of privacy and integrity, because privacy preserving schemes try to hide or interfere with data, while integrity protection usually needs to enable peer monitoring or public access of the data. Therefore, privacy and integrity can be the conflicting requirements, one may barricade the implementation of the other.In this paper, we address both privacy of individual sensory data and integrity of aggregation result simultaneously by proposing a protocol called iCPDA, which piggybacks on a cluster-based privacy-preserving data aggregation protocol(CPDA). We implement the add-on feature to protect integrity of aggregation result. To show the efficacy and efficiency of the proposed scheme, we present simulation results. To the best of our knowledge, this paper is among the first protocols to preserve privacy and integrity in data aggregation.

Confidentiality and integrity for data aggregation in WSN using peer monitoring

Abstract: Summary Hop-by-hop data aggregation is a very important technique used to reduce the communication overhead and energy expenditure of sensor nodes during the process of data collection in a wireless sensor network (WSN). However, the unattended nature of WSNs calls for data aggregation techniques to be secure. Indeed, sensor nodes can be compromised to mislead the base station (BS) by injecting bogus data into the network during both forwarding and aggregation of data. Moreover, data aggregation might increase the risk of confidentiality violations: If sensors close to the BS are corrupted, an adversary could easily access to the results of the ‘in network’ computation performed by the WSN. Further, nodes can also fail due to random and non-malicious causes (e.g., battery exhaustion), hence availability should be considered as well. In this paper we tackle the above issues that affect data aggregation techniques by proposing a mechanism that: (i) provides both confidentiality and integrity of the aggregated data so that for any compromised sensor in the WSN the information acquired could only reveal the readings performed by a small, constant number of neighboring sensors of the compromised one; (ii) detects bogus data injection attempts; (iii) provides high resilience to sensor failures. Our protocol is based on the concept of delayed aggregation and peer monitoring and requires local interactions only. Hence, it is highly scalable and introduces small overhead; detailed analysis supports our findings. Copyright © 2009 John Wiley & Sons, Ltd.

Decentralized Strategies for Monitoring Structures using Wireless Smart Sensor Networks

Abstract: Driven by the needs to address problems with our rapidly aging civil infrastructure, structural health monitoring (SHM) has arisen as an important tool to improve maintenance and operation. Introduced as a promising alternative to the traditional wired sensors, wireless smart sensors offer unique features (low cost, wireless communication, onboard computation, and small size) that enable deployment of dense array of sensors essential for assessing structural damage. The centralized data collection approach, which the wired sensor system commonly employs, is not suitable to wireless smart sensor networks (WSSNs) due to limitations in the wireless communication; decentralized data aggregation and processing is required in the WSSNs. Rather than collecting uncondensed raw sensor data at a centralized location, in-network data processing, made possible by the onboard computational capability of smart sensors, is utilized to condense the raw data and extract meaningful information. By transferring only the condensed data to the centralized location, data communication over the wireless links can be greatly reduced. Decentralized data aggregation approaches can be placed in two broad categories: (a) independent processing (each node processes sensor data independently), and (b) coordinated processing (sensor nodes collaborate to process sensor data by sharing information). This report outlines the implementation of both decentralized data aggregation approaches for the WSSNs employing MEMSIC’s Imote2 smart sensor platform (http://www.memsic.com). Design considerations for developing WSSN applications are described herein, including network-wide flow and timing, fault-tolerant feature, and network topology to account for the decentralized data aggregation. WSSN applications introduced in this report can be downloaded at the Illinois SHM Project website (http://shm.cs.uiuc.edu).

A Sensor Network Data Aggregation Technique

Abstract: —Existing sensor network data aggregation techniques assume that the nodes are preprogrammed and send data to a central sink for offline querying and analysis. This approach faces two major drawbacks. First, the system behavior is preprogrammed and cannot be modified on the fly. Second, the increased energy wastage due to the communication overhead will result in decreasing the overall system lifetime. Thus, energy conservation is of prime consideration in sensor network protocols in order to maximize the network's operational lifetime. In this paper, we give an energy efficient approach to query processing by implementing new optimization techniques applied to in-network aggregation. We first discuss earlier approaches in sensors data management and highlight their disadvantages. We then present our approach and evaluate it through several simulations to prove its efficiency, competence and effectiveness.

Data aggregation for drilling operations

Abstract: A method for aggregating data for a drilling operation. The method includes acquiring the data from a number of data sources associated with the drilling operation, synchronizing a timing of the data for aggregating the data to generate synchronized aggregated data, determining a drilling context based on the synchronized aggregated data, and assigning the determined drilling context to the synchronized aggregated data. The method further includes analyzing the synchronized aggregated data in the drilling context to generate an analysis and presenting the analysis to at least one user.

A Hybrid Approach for Clustering-Based Data Aggregation in Wireless Sensor Networks

Abstract: In a wireless sensor network application for tracking multiple mobile targets, large amounts of sensing data can be generated by a number of sensors. These data must be controlled with efficient data aggregation techniques to reduce data transmission to the sink node. Several clustering methods were used previously to aggregate the large amounts of data produced from sensors in target tracking applications. However, such clustering based data aggregation algorithms show effectiveness only in restricted type of sensing scenarios, while posing great problems when trying to adapt to various environment changes. To alleviate the problems of existing clustering algorithms, we propose a hybrid clustering based data aggregation scheme. The proposed scheme can adaptively choose a suitable clustering technique depending on the status of the network, increasing the data aggregation efficiency as well as energy consumption and successful data transmission ratio. Performance evaluation via simulation has been made to show the effectiveness of the proposed scheme.

Integrated channel assignment and data aggregation routing problem in wireless sensor networks

Abstract: In wireless sensor networks, data aggregation routing could reduce the number of data transmission so as to achieve efficient total energy consumption. However, this kind of data aggregation introduces data retransmission that is caused by co-channel interference from neighbouring sensor nodes. Hence, more data aggregation leads to more extra energy consumption and significant retransmission delay from retransmission. This could jeopardise the benefits of data aggregation. One possible solution to circumvent retransmission caused by co-channel interference is to assign different channel to every sensor node that is within each other's interference range on the data aggregation tree. As the number of non-overlapping channels is limited in wireless networks, it is unlikely that we could assign a different channel to every sensor node on the data aggregation tree. Then, an interesting problem is to perform data aggregation routing in conjunction with channel assignment to minimise total transmission power under limited number of non-overlapping channels. This problem is an NP-complete problem. We devise heuristic algorithm, Iterative Channel Adjustment Data Aggregation Routing algorithm (ICADAR), and other three heuristics, to tackle this problem. From the simulation results, the ICADAR algorithm outperforms the other three algorithms under all experimental cases.

Privacy-Preserving Integrity-Assured Data Aggregation in Sensor Networks

Abstract: Data aggregation in sensor network can improve both efficiency and privacy of network traffic.Recent work in integrity-assured data aggregation has considered aggregation as only an efficiency primitive. In this work, we address for the first time the problem of integrity-assured data aggregation with efficiency and privacy as a joint objective. Our solutions show the inherent tension between privacy-preservation and integrity-assurance of data aggregation.

Fault-Tolerant Aggregation by Flow Updating

Abstract: Data aggregation plays an important role in the design of scalable systems, allowing the determination of meaningful system-wide properties to direct the execution of distributed applications. In the particular case of wireless sensor networks, data collection is often only practicable if aggregation is performed. Several aggregation algorithms have been proposed in the last few years, exhibiting different properties in terms of accuracy, speed and communication tradeoffs. Nonetheless, existing approaches are found lacking in terms of fault tolerance. In this paper, we introduce a novel fault-tolerant averaging based data aggregation algorithm. It tolerates substantial message loss (link failures), while competing algorithms in the same class can be affected by a single lost message. The algorithm is based on manipulating flows (in the graph theoretical sense), that are updated using idempotent messages, providing it with unique robustness capabilities. Furthermore, evaluation results obtained by comparing it with other averaging approaches have revealed that it outperforms them in terms of time and message complexity.

Hybrid clustering based data aggregation method for multi-target tracking in wireless sensor network

Abstract: Provided are a sensor network structure, a data aggregation method, and a clustering method for efficient multi-target tracking. The multi-target tracking may be efficiently performed in a heterogeneous sensor network by combining clustering methods and adaptively varying the clustering methods. As such, an energy consumption problem in a sensor network may be reduced, and a data transmission delay problem or a data traffic problem may be solved by reducing the amount of data to be transmitted.

An Efficient Data Aggregation Algorithm for Cluster-based Sensor Network

Abstract: Data aggregation in wireless sensor networks eliminates redundancy to improve bandwidth utilization and energyefficiency of sensor nodes. One node, called the cluster leader, collects data from surrounding nodes and then sends the summarized information to upstream nodes. In this paper, we propose an algorithm to select a cluster leader that will perform data aggregation in a partially connected sensor network. The algorithm reduces the traffic flow inside the network by adaptively selecting the shortest route for packet routing to the cluster leader. We also describe a simulation framework for functional analysis of WSN applications taking our proposed algorithm as an example.

Efficient Data Aggregation in Multi-Hop WSNs

Abstract: Data aggregation is a primitive communication task in wireless sensor networks (WSNs). In this paper, we study designing data aggregation schedules under the Protocol Interference Model for answering queries. Given a network consisting of a set of nodes V distributed in a two-dimensional plane, we address different kinds of queries in this paper. First and foremost, we consider a single one-off query which requires a subset of source nodes V′ ⊆ V to send data to a distinguished sink node, we propose a delay-efficient algorithm that produces a collision-free schedule and theoretically prove that the delay achieved by our algorithm is nearly a small constant factor of the optimum. We further extend our discussion to the multiple one-off queries case and periodic query case and propose our data aggregation scheduling algorithms respectively with theoretical performance analysis.

A New Approach for Energy-Balanced Data Aggregation in Wireless Sensor Networks

Abstract: A wireless sensor network (WSN) is composed of a large number of sensor nodes which are resource constraints, e.g., limited power. This drives research on how to design routing protocols to gather data efficiently so that the life of the network can be prolonged. A usual concept to collect data by a sink node is to transfer data from sensor nodes to the sink node by multi-hop. However, it gives rise to two problems. The first one is the hotspot problem, in which the sensor nodes closer to the sink run out of energy sooner than other nodes. As a result, the network loses its service ability, regardless of a large amount of residual energy of the other nodes. The second one is that the network generates unnecessary traffic during data transmission for choosing a proper data sending path. To resolves the problems, we, in this paper, propose a new energy balanced and efficient data aggregation scheme for WSNs, called designated path (DP) scheme. In DP scheme, a set of paths is pre-determined and run them in round-robin fashion so that all the nodes can participate in the workload of gathering data and transferring the data to the sink. Through analytical performance evaluations and simulation results, we show that our DP scheme is more energy efficient and it can prolong the life time of the network than the existing schemes directed diffusion (DD) and hierarchical data aggregation (HDA).

A New Approach to Secure Aggregation of Private Data in Wireless Sensor Networks

Abstract: Many applications require the privacy of the sampled data while they travel from the source sensor nodes to data collecting device, say data sink. Providing an efficient data aggregation scheme with preserving data privacy is a challenging problem in the research of wireless sensor networks (WSNs). Although the secure data aggregation in WSNs has been well studied in the recent years, there exists a little work, for instance PDA (Privacy-preserving Data Aggregation), which focuses on protecting sensor data not only from adversaries but also from the participating trusted sensor nodes. However, PDA suffers from one main problem which is the high communication cost due to unnecessary traffics in the network during data transmissions. To resolve the problem, we, in this paper, propose a secure aggregation scheme of private data for WSNs. The proposed scheme applies the additive property of complex number to unite sensor data and for their privacy during their transmissions to a sink node. With our analytical performance evaluations, we show that our scheme is more efficient than the PDA scheme in terms of communicational cost.

A structure-free aggregation framework for vehicular ad hoc networks

Abstract: One of the major difficulties for cooperative, decentralized information dissemination in vehicular networks is the heavily varying node density, which can lead to capacity issues of the wireless channel when many vehicles are driving or standing closely together. At the same time, a number of applications do not require exact information from all participating nodes, but higher-level aggregated information. For example, reports on road conditions or on flow of traffic can be aggregated before further dissemination, since remote drivers just need to know a coarse-grained picture of the situation. In this paper, we propose an information aggregation framework using the example of cooperative traffic congestion detection. The difference of our aggregation framework compared to other approaches is that it completely abandons any predefined structures such as grids and any group establishment. First evaluation results show that our approach works well for average speed dissemination on a highway.

An Energy-Efficient Data Gathering Protocol for Wireless Sensor Networks

Abstract: An energy-efficient data gathering protocol called E2DGP that takes advantage of spatial and temporal correlation of sampling data for WSNs is proposed in this paper. E2DGP includes a clustering method of balancing energy consumption, a data prediction transmission strategy and an energy-aware multihop routing algorithm. In clustering process phase, the initial probability of node for cluster head election is derived from mathematical relation between application’s seamless coverage fraction and numbers of required cluster heads. In data aggregation phase, the spatial correlation of data within a cluster is utilized by cluster head to aggregate sampling data. According to temporal correlation of sampling data, cluster heads send data to sink node using prediction transmission strategy while satisfying the transmission precision in the data transmission phase, and the lifetime of network is greatly prolonged by this strategy. In order to mitigate the hot spot problem among cluster heads, a greedy geographic and energy-aware multihop routing algorithm is presented for inter-cluster communication. Simulation results show that E2DGP outperforms EECS and LEACH in terms of network lifetime by balancing energy consumption and decrease of transmission while meeting desired application-specific requirements.

Capability-Aware Information Aggregation in Peer-to-Peer Grids

Abstract: Information aggregation is the process of summarizing information across the nodes of a distributed system We present a hierarchical information aggregation system tailored for Peer-to-Peer Grids which typically exhibit a high degree of volatility and heterogeneity of resources Aggregation is performed in a scalable yet efficient way by merging data along the edges of a logical self-healing tree with each inner node providing a summary view of the information delivered by the nodes of the corresponding subtree We describe different tree management methods suitable for high-efficiency and high-scalability scenarios that take host capability and stability diversity into account to attenuate the impact of slow and/or unstable hosts We propose an architecture covering all three phases of the aggregation process: Data gathering through a highly extensible sensing framework, data aggregation using reusable, fully isolated reduction networks, and application-sensitive data delivery using a broad range of propagation strategies Our solution combines the advantages of approaches based on Distributed Hash Tables (DHTs) (ie, load balancing and self-maintenance) and hierarchical approaches (ie, respecting administrative boundaries and resource limitations) Our approach is integrated into our Peer-to-Peer Grid platform Cohesion We substantiate its effectiveness through performance measurements and demonstrate its applicability through a graphical monitoring solution leveraging our aggregation system

Data Aggregation Security Challenges in Wireless Sensor Networks

Abstract: Data aggregation in wireless sensor networks (WSN) is a rapidly emerging research area. It can greatly help conserve the scarce energy resources by eliminating redundant data thus achieving a longer network lifetime. However, securing data aggregation in WSN is made even more challenging, by the fact that the sensor nodes and aggregators deployed in hostile environments are exposed to various security threats. In this paper, we survey the current research related to security in data aggregation in wireless sensor networks. We have classified the security schemes studied in two main categories: cryptographic based scheme and trust based scheme. We provide an overview and a comparative study of these schemes and highlight the future research directions to address the flaws in existing schemes.