Data aggregator

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

New data aggregation approach for time-constrained wireless sensor networks

Abstract: A fundamental challenge in the design of wireless sensor networks (WSNs) is to maximize their lifetime especially because they have a limited and non-exchangeable energy supply. One of the most important methods to prolong the lifetime of a WSN is data aggregation. Indeed, data aggregation reduces the number of broadcasts, hence collisions and energy consumption. Many methods has been proposed in the literatures to organize the network for data aggregation like clustering which has been proved to provide the best performance in term of energy saving, aggregation gain and packet delivery accuracy, or structure free which has been proved to perform better than a structure-based data gathering. To benefit all the advantages of these two approaches, we propose a hybrid method that combines multi-level structure-based approaches for time-constrained wireless sensor networks. Simulation results show that our proposed method can improve aggregation gain and energy consumption.

EDA: Event-oriented data aggregation in sensor networks

Abstract: Data aggregation is a crucial technique for energy constrained sensor networks. Previous researches on data aggregation are featured as query-oriented, which only provide partial information of the event happened in the deployment area at the base station. In this paper, we propose an event-oriented data aggregation approach, called EDA. EDA presents the distributed algorithm by exploiting Cloud Membership model of fuzzy logic to aggregate the information of events in the sensor networks. It also presents a distributed algorithm to collect and aggregate the event information. The base station will restore the whole event information when receiving the aggregated packets of event features. EDA can balance the tradeoff between delay, traffic savings, and precision of the restored events. The performance has been evaluated through both theoretical analysis and simulations. We also confirm the performance with the traces of our offshore sensor network testbed (OceanSense).

Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks

Abstract: With the exponential growth of the data generated by Internet of Things (IoT) devices, computation offloading becomes a promising method to alleviate the computation burden of local IoT device and improve processing latency. In order to address the bottleneck problem of limited resources in IoT device more efficiently and provide security guarantee in data processing and forwarding process, in this paper, we propose a privacy and energy co-aware data aggregation computation offloading for fog-assisted IoT networks. Specifically, a fog-assisted three-layer security computing architecture is developed to counteract security threats and enable the aggregation operation can be performed in ciphertext. Meanwhile, a momentum gradient descent based energy-efficient offloading decision algorithm is developed to minimize the total energy consumption of computation tasks, which can achieve the optimal value with fast convergence rate. Finally, the security and performance evaluations reveal that the developed data aggregation offloading scheme is a secure data processing scheme and achieves significant performance advantage in energy consumption. For example, the total energy consumption can be reduced by an average of 23.1% compared with benchmark PGCO solution.

Improved trust management based on the strength of ties for secure data aggregation in wireless sensor networks

Abstract: Secure data aggregation exerts more and more important effects on the research of wireless sensor network. There occurs many groping research subject, via managing nodes' trust and reputation to keep data aggregation secure. The nodes in wireless sensor networks are often compromised and those compromised nodes will do a lot of harmful behaviors to decrease the security of wireless sensor networks. These behaviors include impersonate legal nodes to join routing paths, selectively forward data to an adversary, inject erroneous data, and disrupt data transmission. Thus, the conception of nodes' trust is introduced into data aggregation to judge and evaluate whether the nodes are trustable or not. In this paper, we propose an improved trust management method for data aggregation based on the relationship between nodes that is called the strength of the ties between the nodes. The improved method is developed from the trust model in the iRTEDA protocol and increasing the utilization efficiency of second-hand information coming from neighboring nodes. The aim of the proposed trust model is to obtain a higher level of security for data aggregation. The simulation results show us that data aggregation, based on proposed trust management method, can get higher accuracy of evaluating nodes' trust and reputation and achieve higher data accuracy of aggregating.

A novel data aggregation using multi objective based male lion optimization algorithm (DA-MOMLOA) in wireless sensor network

Abstract: Wireless sensor network efficiently aggregates and transmits data in an internet of things (IoT) environment. Machine Learning algorithms can minimize data transmission rates by utilizing the distributive features of the network. This study proposes a novel cluster-based data aggregation method using multi-objective based male lion optimization algorithm (DA-MOMLOA) for evaluating the network based on energy, delay, density and distance. The data aggregation method is employed with the help of cluster head wherein data aggregated from similar clusters are forwarded to the sink node following by application of machine learning algorithms. Hence, the proposed method shows promising results as it significantly increases the network efficiency and reduces the packet drop owing to a smaller number of aggregation processes.