Data aggregator

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

Evolutionary game-based data aggregation model for wireless sensor networks

Abstract: Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes. Since multi-source data obtained from different nodes represent redundancy or complement property, as an effective tool to deal with the conflicts, the use of game theory for WSNs is provided. The authors propose a common aggregation model, which is independent of the specific application environments, based on the evolutionary game theory called evolutionary game-based data aggregation model (EGDAM) in WSNs. EGDAM made up of formal definition, functional model and general process is defined to map the competition and cooperation in aggregation procedure into games, and well-avoid perfect rationality. The authors then put the theoretic model into application. Guided by our model, an evolutionary game-based adaptive weighting algorithm named EGWDA is provided for the pixel-level data aggregation with homogeneous sensors. Reasonable weights distribution of sensors can be achieved during the aggregation in WSNs. The experiments on both the self-constructed data and the one from reference made satisfied performances.

A study on data aggregation techniques in wireless sensor network in static and dynamic scenarios

Abstract: Small-size sensor nodes are used as the basic component for collecting and sending the data or information in the ad hoc mode in wireless sensor network (WSN). This network is generally used to collect and process data from different regions where the movement of human is very rare. The sensor nodes are deployed in such a region for collecting data using ad hoc network where, at any time, the unusual situation may happen or there is no fixed network that can work positively and provide any transmission procedure. The location may be very remote or some disaster-prone area. In disaster-prone zone, after disaster, most often no fixed network remains alive. In that scenario, the ad hoc sensor network is one of the reliable sources for collecting and transmitting the data from that region. In this type of situation, sensor network can also be helpful for geo-informatic system. WSN can be used to handle the disaster management manually as well as through an automated system. The main problem for any activity using sensor node is that the nodes are very much battery hunger. An efficient power utilization is required for enhancing the network lifetime by reducing data traffic in the WSN. For this reason, some efficient intelligent software and hardware techniques are required to make the most efficient use of limited resources in terms of energy, computation and storage. One of the most suitable approaches is data aggregation protocol which can reduce the communication cost by extending the lifetime of sensor networks. The techniques can be implemented in different efficient manners, but all are not useful in same application scenarios. More specifically, data can be collected by dynamic approach using rendezvous point (RP), and for that purpose, intelligent neural network-based cluster formation techniques can be used and for fixing the targeted base station, the ant colony optimization algorithm can be used. In this work, we have made a comprehensive study of such energy efficient integrated sensor-based system in order to achieve energy efficiency and to prolong network lifetime.

Data aggregation in UAV-aided random access for the Internet of Vehicles

Abstract: Recently, the Internet of Vehicles (IoV) has been employed as an enabling technology for smart transportation, which can be further enhanced by integrating space-air-ground integrated networks (SAGIN). Since the data packets of vehicular user equipments (VUEs) are generally short, random access is usually considered for VUEs to connect to the network. However, collisions caused by the multiple VUEs initiating random access simultaneously are inevitable. To relieve the performance degradation by collisions, data aggregation can be carried out in the IoV, where aggregated packets can be relayed to a base station (BS). In this paper, we first propose an aggregators-aided random access (ARA) scheme for the IoV, where unmanned aerial vehicles (UAVs), as one of the key components in SAGIN, are deployed as data aggregators to help transmissions of VUEs. Then, a Semi-Markov chain is used to analyze the average number of aggregated packets and the metric of average data to overhead ratio (ADOR) is presented to evaluate the efficiency of aggregation. Finally, the altitude of UAVs and the duration of data aggregation are optimized to maximize the ADOR. By numerical simulations, the accuracy of the analysis as well as the effectiveness of the proposed scheme are validated.

Query Planning for Continuous Aggregation Queries over a Network of Data Aggregators

Abstract: Continuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a user desires to obtain the value of some aggregation function over distributed data items, for example, to know value of portfolio for a client; or the AVG of temperatures sensed by a set of sensors. In these queries a client specifies a coherency requirement as part of the query. We present a low-cost, scalable technique to answer continuous aggregation queries using a network of aggregators of dynamic data items. In such a network of data aggregators, each data aggregator serves a set of data items at specific coherencies. Just as various fragments of a dynamic webpage are served by one or more nodes of a content distribution network, our technique involves decomposing a client query into subqueries and executing subqueries on judiciously chosen data aggregators with their individual subquery incoherency bounds. We provide a technique for getting the optimal set of subqueries with their incoherency bounds which satisfies client query's coherency requirement with least number of refresh messages sent from aggregators to the client. For estimating the number of refresh messages, we build a query cost model which can be used to estimate the number of messages required to satisfy the client specified incoherency bound. Performance results using real-world traces show that our cost-based query planning leads to queries being executed using less than one third the number of messages required by existing schemes.

An Energy-efficient UAV-based Data Aggregation Protocol in Wireless Sensor Networks

Abstract: Energy efficiency is an important issue in Wireless Sensor Networks (WSNs). The energy is mainly consumed by data sensing, data transmission and movement of sensors. The energy consumed by data transmission is much larger than data sensing. A potential solution for energy saving is applying the external devices to collect data to prolong the network lifetime. In this paper, we adopt an unmanned aerial vehicle (UAV) serving as the data mule and propose a novel energy-efficient UAV-based data aggregation protocol in WSNs to reduce the energy consumption of sensors. By considering a clustered WSN, our approach computes an optimal path for data mule through all cluster heads (CHs) while achieving a relatively high system-wide energy efficiency. Moreover, we introduce a genetic algorithm to derive a near-optimal solution for a large-scale WSN while reducing the computing time. We compare our protocol with state-of-the-art approaches, and the simulation results demonstrate that the proposed algorithm improves the network performance on energy efficiency.