Data aggregator

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

Multi-dimensional sensor data aggregator for adaptive network management in M2M communications

Abstract: This paper proposes a method of aggregating tempo-spatial data generated by sensors deployed in buildings or houses. The size of each sensor data such as temperature is usually small, but it often involves many additional data to represent its attribute values like time, location, data type and data precision. This would often increase the traffic volume between sensor gateway at building/home side and service providers at server side. In our method, such sensor data are packed into multidimensional matrices indexed by those attribute values for more compact representation, and the compressed sensing technique is adaptively applied to further reduce the data size. The method was applied to a field trial with KDDI corporation to collect data from 29 community facilities, and the traffic volume was reduced to 50% with reasonable precision of data restoration.

Wireless sensor network data reliable aggregation method based on continuous-collecting single-transmitting model

Abstract: The invention discloses a wireless sensor network data reliable aggregation method based on a continuous-collecting single-transmitting model. The method comprises data transmission mechanism connection establishing, data transmission congestion control and data transmission selective retransmission. When establishment of the data transmission mechanism connection is started, sensor nodes put forward data transmission requirements to data aggregation nodes, data transmission control information is transmitted incidentally in a link establishment process, and data source node control information cost is reduced. After connection establishment is completed, the method turns into a data transmission phase. The sensor nodes and the data aggregation nodes respectively carry out network congestion control through NACK, and the loss of a large amount of transmission data caused by network congestion is avoided. When data losing or transmission errors occur in a data transmission process, the sensor nodes carry out selective and preferential retransmission of the data, and reliable and efficient transmission of the data is guaranteed under the condition of not increasing network loads.

Outlier detection based fault tolerant data aggregation for wireless sensor networks

Abstract: Data aggregation protocols are essential for wireless sensor networks to prolong network lifetime by reducing energy consumption of sensor nodes. For mission critical wireless sensor networks, however, not only the energy consumption of sensor nodes but also the correctness of the data aggregation results is critical. This paper presents a fault tolerant data aggregation scheme that eliminates the false data sent by malfunctioning and/or compromised sensor nodes. To conserve energy while eliminating false data, an in-network outlier detection technique that is based on Locality Sensitive Hashing (LSH) scheme is used. The simulation results show that the proposed scheme is able to reduce the number of false data transmissions thereby increasing the data aggregation accuracy.

An approach to data Aggregation in wireless sensor network using Voronoi fuzzy clustering algorithm

Abstract: Data collected through any sensor are needed to be processed for gaining some useful information. Wireless Sensor Network (WSN) is a subclass of sensor node which is evolving as an astonishing technique in wireless communication technology for monitoring large application domains such as weather forecasting, military surveillance, medical diagnosis, fire detection alarming systems, etc. Each sensor will not be able to process itself due to the primary issue of energy (battery power) curb in WSN. Still many investigators desire to find a solution to improve the lifespan of WSN. The best way is to select an optimum head node for data aggregation to reduce the energy of data transmission for the reason that energy required for computing is more than for data transmission. This prototype shifts the attention from the outmoded addresscentric approaches to data-centric approach. Data centric techniques like data aggregation via energy efficient fuzzy clustering algorithm based on Voronoi diagram is proposed in this paper. The proposed novel algorithm is a combination of Voronoi and modified Fuzzy C-Means clustering algorithm called as Voronoi Fuzzy (VF) algorithm.Cluster head (CH) for VF clustering algorithm is nominated by considering node's residual energy, distance between CH and its neighbor’s sensor node and Quality of service. Furthermore, data aggregation is employed in each cluster’s CH to reduce the amount of data transmission which effectively extends the network lifetime. Simulation result reveals that this method achieves agreeable performance in extending the network lifetime compared to the existing ones.

Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics

Abstract: The restricted energy of nodes is one of the most important challenges in wireless sensor networks. Since data transmissions among nodes consume most of the nodes' energy, thus, minimizing the unnecessary transmissions reduces the consumed energy. One of the sources of this problem is the redundancy of raw data that can be eliminated at the aggregation points. As a result, data aggregation can be considered as an effective strategy to tackle the mentioned issue and to optimize the communication energy consumption. In this paper, the sensor nodes are organized in a tree structure, and the data aggregation are done in intermediate nodes at the junction of tree branches. One of the main characteristics of tree protocols is reduction of energy consumption through optimizing the structure of a data aggregation tree. For this, this paper proposes to apply a swarm intelligent algorithm named river formation dynamics. The simulation results show that the proposed algorithm outperforms in comparison to the famous ant colony optimization algorithm in terms of network lifetime. Simulations show that the proposed algorithm makes nearly 4% and 50% improvement in lifetime of wireless sensor networks than ant colony optimization and shortest path routing, respectively.