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In recent days, IoT has been widely accepted and WSN (Wireless Sensor network) is being used for variety of the applications such as transportation, medical, environmental, military, it moreover the main aim to deploy the WSN is to collect the data about the given set of phenomena. The common task of WSN is to sense the data and send over the network. Moreover, due to the various purpose such as statistical analysis, the data aggregation is required. However, the when the dynamic network topology is considered, it is considered to be the very difficult task to provide the secure and efficient data aggregation. The main issue here is to ensure the security and accuracy of the data aggregation. Hence, in this research we have proposed an algorithm named as E-SDA (Efficient Secure Data Aggregation) in order to provide the secure data. In this, the algorithm provides the flexibility to detect the dishonest honest through neighbor monitoring. Later, extensive simulation has been done in order to prove the convergence of our algorithm.

Wireless sensor network can be applied to both abominable and military environments. A primary goal in the design of wireless sensor networks is lifetime maximization, constrained by the energy capacity of batteries. One well-known method to reduce energy consumption in such networks is data aggregation. Providing efcient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present privacy-preserving data aggregation scheme for additive aggregation functions. The Cluster-based Private Data Aggregation (CPDA)leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

As the rate of annual data generation grows exponentially, there is a demand to aggregate and summarise vast amounts of information quickly. In the past, frequency scaling was relied upon to push application throughput. Today, Dennard scaling has ceased and further performance must come from exploiting parallelism. Single instruction-multiple data (SIMD) instruction sets offer a highly efficient and scalable way of exploiting data-level parallelism (DLP). While microprocessors originally offered very simple SIMD support targeted at multimedia applications, these extensions have been growing both in width and functionality. Observing this trend, we use a simulation framework to model future SIMD support and then propose and evaluate five different ways of vectorising data aggregation. We find that although data aggregation is abundant in DLP, it is often too irregular to be expressed efficiently using typical SIMD instructions. Based on this observation, we propose a set of novel algorithms and SIMD instructions to better capture this irregular DLP. Furthermore, we discover that the best algorithm is highly dependent on the characteristics of the input. Our proposed solution can dynamically choose the optimal algorithm in the majority of cases and achieves speedups between 2.7x and 7.6x over a scalar baseline. ; The research leading to these results has received funding from the RoMoL ERC Advanced Grant GA no 321253 and is supported in part by the European Union (FEDER funds) under contract TTIN2015-65316-P. Timothy Hayes is supported by a FPU research grant from the Spanish MECD. ; Peer Reviewed ; Postprint (published version)

As the rate of annual data generation grows exponentially, there is a demand to aggregate and summarise vast amounts of information quickly. In the past, frequency scaling was relied upon to push application throughput. Today, Dennard scaling has ceased and further performance must come from exploiting parallelism. Single instruction-multiple data (SIMD) instruction sets offer a highly efficient and scalable way of exploiting data-level parallelism (DLP). While microprocessors originally offered very simple SIMD support targeted at multimedia applications, these extensions have been growing both in width and functionality. Observing this trend, we use a simulation framework to model future SIMD support and then propose and evaluate five different ways of vectorising data aggregation. We find that although data aggregation is abundant in DLP, it is often too irregular to be expressed efficiently using typical SIMD instructions. Based on this observation, we propose a set of novel algorithms and SIMD instructions to better capture this irregular DLP. Furthermore, we discover that the best algorithm is highly dependent on the characteristics of the input. Our proposed solution can dynamically choose the optimal algorithm in the majority of cases and achieves speedups between 2.7x and 7.6x over a scalar baseline. ; The research leading to these results has received funding from the RoMoL ERC Advanced Grant GA no 321253 and is supported in part by the European Union (FEDER funds) under contract TTIN2015-65316-P. Timothy Hayes is supported by a FPU research grant from the Spanish MECD. ; Peer Reviewed ; Postprint (published version)

This article focusses on an important but often overlooked dimension of aggregation in organizational research, the temporal dimension. As more researchers begin to appreciate the dynamic quality of their subject matter, longitudinal studies will become more common. Lacking fully developed research paradigms, however, such studies are likely to encounter a number of problems. In this paper, three particular problems, the interval problem, the differential period problem, and the morphogenetic problem, are discussed in the context of two concrete studies. The kinds of issues each problem poses are identified and possible solutions are proposed.

Vehicular Ad-Hoc Networks (VANETs) are a fast growing technology that many governments and automobile manufacturers are investing in to provide not only safer and more secure roads, but also informational and entertainment-based applications for drivers. The applications developed for VANETs can be classified into multiple categories (safety, informational, entertainment). Most VANET applications, regardless of their category, depend on having certain vehicular data(vehicular speed, X position and Y position) available. Although these applications appear to use the same vehicular data, the characteristics of this data (i.e., amount, accuracy, and update rate) will vary based on the application category. For example, safety applications need an accurate version of the vehicular datawith high frequency, but over short distances. Informational applications relax the data frequency constraint as they need the vehicular data to be reasonably accurate with less frequency, but over longer distances. If each of these applications shares the vehicular data with only its peers using its own mechanism, this behavior will not only introduce redundant functionalities (sending, receiving, processing, etc.) for handling the same data, but also wastefully consume the bandwidth by broadcasting the same data multiple times. Despite the differences in the data characteristics needed by each application, this data can be still shared. Vehicular networks introduce the potential for many co-existing applications. If we do not address the problem of data redundancy early, it may hinder the deployment and usefulness of many of these applications. Therefore, we developed a framework, cluster-based accurate syntactic compression of aggregated data in VANETs (CASCADE), for efficiently aggregating and disseminating commonly-used vehicular data. CASCADE is architccted as a layer that provides applications with a customized version of the vehicular data, based on parameters that each application registers with CASCADE. Additionally, the framework performs the common data handling functionalities (sending, receiving, aggregating, etc.) needed by the applications. This dissertation makes the following contributions: (1) a lossless data compression technique based on differential coding that is tailored for the characteristics of vehicular data; (2) a syntactic data aggregation mechanism that can represent the vehicular data in a 1.5 km area in one IEEE 802.11 frame; (3) a light-weight position verification technique that quickly detects false data with very low false positives; (4) a probabilistic data dissemination technique that alleviates the spatial broadcast storm problem and effectively uses the bandwidth to disseminate data to distant areas in a short amount of time inaddition to having less redundancy and more coverage than other techniques. (5) a mechanism for recovering from the communication discontinuity problem inshort time based on the traffic density in the opposite direction; (6) an investigation of the possible data structures for representing the vehicular data in a searchable format; (7) a parametric mechanism for matching the vehicular data and providing a customized version of the data that satisfies certain characteristics based on the parameter value. CASCADE through its four major components, local view, extended view, data security and data dissemination, provides an efficient solution for the problem of scalability for VANET applications.

Wireless sensor networks (WSN) consists of many sensor nodes that are placed on unattended environments such as military sites in order to collect important information. Implementing a secure protocol that can prevent forwarding forged data and modifying content of aggregated data and has low delay and overhead of communication, computing and storage is very important. This paper presents a new protocol for concealed data aggregation (CDA). In this protocol, the network is divided to virtual cells, nodes within each cell produce a shared key to send and receive of concealed data with each other. Considering to data aggregation in each cell is locally and implementing a secure authentication mechanism, data aggregation delay is very low and producing false data in the network by malicious nodes is not possible. To evaluate the performance of our proposed protocol, we have presented computational models that show the performance and low overhead in our protocol.

PDF of a powerpoint presentation from the Military Communications Conference (MILCOM), Baltimore, Maryland, October 7, 2014. Also available on Slideshare. ; https://digitalcommons.odu.edu/computerscience_presentations/1033/thumbnail.jpg

International audience ; Many resource-constrained applications make use of data aggregation in order to prolong the network lifetime. However, the resource-constrained devices are usually deployed in unattended environments in which providing security is of paramount importance. This leads to various attacks that can occur during data aggregation process. Internal attacks such as selective forwarding represent the most dangerous ones since they cannot be detected by existing cryptography- based protocols proposed to secure data aggregation. In this work, we propose a two-levels verification, in which data is verified using cryptography and intrusion detection techniques. Indeed, a lightweight homomorphic encryption is combined with a game-theory based technique to efficiently secure data aggregation. Our analysis and results show the applicability of the system for aggregation-based resource-constrained applications, especially those considering sensitive information (e.g. health monitoring, military) where the time-efficient detection is crucial.