Data aggregator

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

Multi-Objective Meta-Heuristic Approach for Energy-Efficient Secure Data Aggregation in Wireless Sensor Networks

Abstract: Energy consumption in the sensor network is primarily due to the switching states of radio transceivers and long busy states of sensor nodes in the network. Data aggregation techniques reduce the number of transmissions and improve the bandwidth utilization. Secure data aggregation and energy-efficient routing protocols establish the secure channel, and reduce the communication overhead in the network. Multi-objective optimization methods based on the weighted sum method, the utility method and meta-heuristic search methods enhance the performance of meta-heuristic algorithms. This article proposes multi-objective meta-heuristic approach for energy-efficient secure data aggregation (MH-EESDA) protocol in wireless sensor networks. The proposed protocol uses divide-and-conquer approach to form the secure clusters and perform the secure data aggregation in energy-efficient route paths of the network. The protocol functions in three phases. In the first phase, the clusters are formed, in the second phase, the secure nodes are selected and in the third phase, energy-efficient data aggregation is performed across the secure route paths of the network. The sensor node energy and data aggregation rate are evaluated for (1) minimum degree of intrusions (2) threshold-based degree of intrusions and (3) maximum degree of intrusions in the network. Simulation results illustrate significant improvements in the proposed MH-EESDA protocol.

Aggregation on the fly: reducing traffic for big data in the cloud

Abstract: As a leading framework for processing and analyzing big data, MapReduce is leveraged by many enterprises to parallelize their data processing on distributed computing systems. Unfortunately, the all-to-all data forwarding from map tasks to reduce tasks in the traditional MapReduce framework would generate a large amount of network traffic. The fact that the intermediate data generated by map tasks can be combined with significant traffic reduction in many applications motivates us to propose a data aggregation scheme for MapReduce jobs in cloud. Specifically, we design an aggregation architecture under the existing MapReduce framework with the objective of minimizing the data traffic during the shuffle phase, in which aggregators can reside anywhere in the cloud. Some experimental results also show that our proposal outperforms existing work by reducing the network traffic significantly.

Data aggregation and data fusion techniques in WSN/SANET topologies - a critical discussion

Abstract: WSN and SANET topologies generate huge amount of heterogeneous data, which has to be transmitted in a dynamically changing network infrastructure. Especially in the domain of wireless low-power applications, the energy-efficiency and the prioritisation of communication tasks is critical. Several research areas deal with this issue. They optimising the respective hardware components as well as the protocols within the PHY, MAC or network layer. But for an optimised media transport in the topology also the data management and the task scheduling on the application layer is essential. Here, the key challenge is to minimise the data amount without decreasing the information quality. Related research work in the field of data aggregation and data fusion offers interesting techniques for an efficient data handling. In this paper, we discuss usual ways for data aggregation, including the adapted communication process. We critically analyse the benefits in theory and compare these conceptual advantages with measured real-world results. The evaluation was done in two steps. The first one is based on simulation scenarios of typical WSN/SANET applications. In a second step, we implement a demonstrator platform for a respective real-world environment. The test bed configuration is similar to the simulation scenario and provides comparable data. Based on the results and the respective analysis, we propose feasible methods for optimising data aggregation techniques. We clarify, that these improvements are essential for an efficient usage in resource-limited, embedded sensor network environments.

Distributed and Efficient Minimum-Latency Data Aggregation Scheduling for Multichannel Wireless Sensor Networks

Abstract: Data aggregation is a critical operation in wireless sensor networks (WSNs). Many applications have strict requirements for the latency of data aggregation. This paper focuses on the latency problem of data aggregation. Two factors determine the latency of data aggregation. First, because of the existence of interference, efficient collision-free scheduling is crucial for reducing data aggregation latency. Second, the tree structure has an important impact on data aggregation latency. In this paper, we propose a novel approach called distributed and efficient data aggregation scheduling over multichannel links (DEDAS-MC). DEDAS-MC minimizes the latency in routing the aggregated data to the sink over multichannel links. In DEDAS-MC, we first present a scheduling algorithm to schedule sensors to avoid interference and minimize the latency of data aggregation on a given tree. Then, a distributed algorithm for constructing minimum-latency data aggregation trees is proposed by employing the Markov approximation method. In DEDAS-MC, the value of $\beta $ is adaptive. The Markov approximation method-based adaptive- $\beta $ is more flexible and efficient than the single $\beta $ approximation. The experiments show that DEDAS-MC outperforms the existing competing schemes.

An Energy-Efficient Reliable Trust-Based Data Aggregation Protocol for Wireless Sensor Networks

Abstract: Security data aggregation plays an important role in reducing the amount of data transmission and prolonging the life of wireless sensor networks (WSN). When the security of the aggregation nodes is threatened, the networks can generate many aggregated data errors, leading to trouble in a security measure. In this paper, we propose an energy-efficient reliable trust-based data aggregation protocol for WSN called the ERTDA protocol. Based on the observations of the nodal behavior, the ERTDA protocol calculates, monitors and evaluates the trust values of the nodes; it also detects and excludes the compromised nodes in a timely manner. The simulation results illustrate that the ERTDA protocol can effectively improve the accuracy of the aggregation, reduce the nodal mortality rate, reduce the nodal energy consumption, improve the reliability of the data transmission and extend the life of the networks.