Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

Energy efficient data aggregation in sensor network using multiple sink data node

Abstract: Data collection is an important task in any network. Data collection and aggregation becomes highly challenging in a wide area network with numerous nodes like sensor nodes, data nodes, and communication nodes are present. Data aggregation from these distributed nodes is an energy consuming task as sensor nodes mainly work on consumable power. Major task of any network engineer lies in performing an energy efficient data aggregation, so that he preserves the non renewable energy supply of the sensor. In the research carried out in this paper energy efficient data aggregation approach is proposed. Two network specific algorithms are devised one can cluster the network efficiently and the other performs the aggregation from the sensor node to data node. Data node is the specialized proposed node made available in the network which can be later connected to any centralized storage like cloud.

F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

Abstract: Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other and central nodes over a wireless network and internet. Recently, due to the crucial importance of human well-being, IoT-enabled healthcare systems have gained significant attention. On the other hand, as IoT networks are large-scaled and battery-powered, developing proper energy and resource management mechanisms for them is inevitable. On account of the large amount of data generated in IoT environments, data aggregation is vital to lower energy consumption and extend network lifespan, and many researchers have endeavored to enhance its efficiency. However, there is no optimized method for the dynamic, complex, and nonlinear nature of healthcare applications. Fuzzy logic could be effective in these scenarios because it can convert qualitative data to quantitative, implement complex nonlinear functions, and present approximate solutions for cases where there is no single optimal answer, and it changes with a slight variation in conditions. This paper proposes the F-LEACH, a Fuzzy-based data aggregation scheme for IoT-enabled healthcare applications aiming to maximize the network lifetime. According to the simulation results, the proposed method outperformed similar works by 5–20%.

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory.

Abstract: Background: Biomedical research often requires large cohorts and necessitates the sharing of biomedical data with researchers around the world, which raises many privacy, ethical, and legal concerns. In the face of these concerns, privacy experts are trying to explore approaches to analyzing the distributed data while protecting its privacy. Many of these approaches are based on secure multiparty computations (SMCs). SMC is an attractive approach allowing multiple parties to collectively carry out calculations on their datasets without having to reveal their own raw data; however, it incurs heavy computation time and requires extensive communication between the involved parties. Objective: This study aimed to develop usable and efficient SMC applications that meet the needs of the potential end-users and to raise general awareness about SMC as a tool that supports data sharing. Methods: We have introduced distributed statistical computing (DSC) into the design of secure multiparty protocols, which allows us to conduct computations on each of the parties’ sites independently and then combine these computations to form 1 estimator for the collective dataset, thus limiting communication to the final step and reducing complexity. The effectiveness of our privacy-preserving model is demonstrated through a linear regression application. Results: Our secure linear regression algorithm was tested for accuracy and performance using real and synthetic datasets. The results showed no loss of accuracy (over nonsecure regression) and very good performance (20 min for 100 million records). Conclusions: We used DSC to securely calculate a linear regression model over multiple datasets. Our experiments showed very good performance (in terms of the number of records it can handle). We plan to extend our method to other estimators such as logistic regression.

PUDA – Privacy and Unforgeability for Data Aggregation

Abstract: Existing work on secure data collection and secure aggregation is mainly focused on confidentiality issues. That is, ensuring that the untrusted Aggregator learns only the aggregation result without divulging individual data inputs. In this paper however we consider a malicious Aggregator which is not only interested in compromising users’ privacy but also is interested in providing bogus aggregate values. More concretely, we extend existing security models with the requirement of aggregate unforgeability. Moreover, we instantiate an efficient protocol for private and unforgeable data aggregation that allows the Aggregator to compute the sum of users’ inputs without learning individual values and constructs a proof of correct computation that can be verified by any third party. The proposed protocol is provably secure and its communication and computation overhead is minimal.

Data Aggregation with Spatially Correlated Grouping Technique on Cluster-Based WSNs

Abstract: Wireless sensor networks have received considerable attention in recent years due to their invaluable potential applications. To achieve long-term deployment, in-network aggregation has been studied and argued as an effective data reduction technique. In this paper, an efficient algorithm to construct and maintain aggregation architecture in cluster-based sensor networks, such as LEACH, is proposed. The architecture takes the spatial and temporal correlations among nodes into account simultaneously to develop the suppression strategies. The main idea is to organize nodes inside the same cluster into highly spatial-correlated groups. One representative node of each group will be selected as base node used as a reference for compressing (using linear regression) the transmissions of the nodes inside the same group. The proposed architecture was evaluated on the real dataset, Intel Lab dataset, and the results indicate that a large amount of transmissions can be reduced without introducing large errors. In contrast to the existing aggregation architectures, such as TAG and TiNA, the results also portray that the hybrid architecture can perform better by considering both spatial and temporal correlations simultaneously.