In wireless sensor networks (WSNs), the sensor nodes that locate near often sense the similar data, however, transmitting the repeated or redundant data often cause unnecessary energy consumption. Aiming at this point, this paper firstly proposes a gridbased spatial correlation clustering (GSCC) method which clusters the sensor nodes according to data correlation. According to GSCC, in the same cluster the member nodes have high similarity. Based on GSCC, then this paper proposes a spatial correlation clustering approximation framework (SCCAF). SCCAF can largely save networks' energy by which the cluster head estimates the data of its member nodes provided that approximation value is in the allowable error range. Experiments prove that not only SCCAF based on GSCC method can prolong the lifetime of the sensor networks compared with LEACH but also SCCAF guarantees more accuracy than CASA (clustering-based approximate scheme for data aggregation) which is a previous approximation scheme.

A clustering approximation mechanism based on data spatial correlation in wireless sensor networks

Objective: The main objective of this paper is to construct a distributed clustering algorithm based upon spatial data correlation among sensor nodes and perform data accuracy for each distributed cluster at their respective cluster head node. Design Procedure/Approach: We investigate that due to deployment of high density of sensor nodes in the sensor field, spatial data are highly correlated among sensor nodes in spatial domain. Based on high data correlation among sensor nodes, we propose a non -overlapping irregular distributed clustering algorithm with different sizes to collect most accurate or precise data at the cluster head node for each respective distributed cluster. To collect the most accurate data at the cluster head node for each distributed cluster in sensor field, we propose a Data accuracy model and compare the results with Information accuracy model. Finding: Simulation results shows that our propose Data accuracy model collects more accurate data and gives better performance than Information accuracy model at the cluster head node for each respective distributed cluster in our propose distributed clustering algorithm.Morover there exist a optimal cluster of sensor nodes which is adequate to perform approximately the same data accuracy achieve by a cluster. Practical Implementation: Measuring humidity and moisture content in an agricultural field, measuring temperature in physical environment. Inventive /Novel Idea: A distributed clustering algorithm is proposed based on spatial data correlation among sensor nodes with Data accuracy model.

Data Accuracy Model for Distributed Clustering Algorithm based on Spatial Data Correlation in Wireless Sensor Networks

Wireless Sensor Networks have a wide range of applications including environmental monitoring. These networks consist of wireless sensor nodes which are densely deployed to provide a wider coverage area. The dense deployment of the sensor node provides spatial correlation in the network. In this paper an efficient data gathering approach is implemented by combining the dual prediction and clustering algorithm. Clustering algorithm based on spatial correlation is used to cluster the sensor nodes. Then within the cluster, the nodes send their data to the sink using the Normalized Least Mean Square dual prediction algorithm. Simulation results show that the proposed algorithm reduces the average energy consumption of the network. .

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An energy efficient spatial correlation based data gathering algorithm for wireless sensor networks

The redundancy of sensing data in wireless sensor networks (WSNs) gives rise to longer transmission delays and more energy consumption. In this paper, we focus on the energy-efficient data redundancy elimination and compression with the objective of recovering the original data. To balance aggregation load of a large-scale WSN, we propose a novel energy-efficient dynamic clustering algorithm by utilizing spatial correlation, which can achieve a distributed compressive data aggregation in each cluster head. Furthermore, we propose a distributed fast data compression approach based on eliminable lifting wavelet to reduce the amount of raw data. Also, it offers high fidelity recovery for the raw data. Extensive experimental results demonstrate that our clustering method based on data correlation clustering (CDSC) for data aggregation outperforms other methods on prolonging network lifetime and reducing the amount of data transmitted. In particular, our data compression aggregation algorithm can achieve 98.4% recovery accuracy when the compression ratio equals 1.3333.

Lifting Wavelet Compression Based Data Aggregation in Big Data Wireless Sensor Networks

The main objective of this paper is to reduce the number of sensor nodes by estimating a trade off between data accuracy and energy consumption for selecting nodes in probabilistic approach in a distributed network. Design Procedure/Approach: Observed data are highly correlated among sensor nodes in the spatial domain due to deployment of high density of sensor nodes. These sensor nodes form non-overlapping distributed clusters due to high data correlation among them. We develop a probabilistic model for each distributed cluster to perform data accuracy and energy consumption model in the network. Finally we find a trade off between data accuracy and energy consumption model to select an optimal number of sensor nodes in each distributed cluster. We also compare the performance for our data accuracy estimation model with information accuracy model for each distributed cluster in the network. Practical Implementation: Measuring temperature in physical environment and measuring moisture content in agricultural field. Inventive /Novel Idea: Optimal node selection in probabilistic approach using the trade of between data accuracy and energy consumption in a cluster-based distributed network.

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Energy Aware Node Selection for Cluster-based Data Accuracy Estimation in Wireless Sensor Networks

In this paper, spatial data correlations are exploited to group sensor nodes into clusters of high data aggregation efficiency. The problem of selecting the set of cluster heads is defined as the weighted connected dominating set problem. Then centralized and distributed algorithms are developed to select the cluster heads. Simulation results demonstrate the effectiveness and efficiency of the designed algorithms.

Spatial Data Correlation Based Clustering Algorithms for Wireless Sensor Networks

In densely deployed wireless sensor networks, spatial data correlations are introduced by the observations of multiple spatially proximal sensor nodes on a same phenomenon or event. These correlations bring significant potential advantages for the development of efficient strategies for reducing energy consumption. In this paper, spatial data correlations are exploited to group sensor nodes into clusters of high data aggregation efficiency. We define the problem of selecting the set of cluster heads as the weighted connected dominating set problem. Then we develop a set of centralized and distributed algorithms to select the cluster heads. Simulation results demonstrate the effectiveness and efficiency of the designed algorithms.

Clustering algorithms for wireless sensor networks using spatial data correlation

How to collect data energy-efficiently from sensor nodes is an important research issue in wireless sensor networks. In this paper, spatial correlation of sensor data is exploited to address the problem of collecting data from sensor nodes. In our approach, a spatial model is maintained by a cluster head and a replica of the model is maintained the base station. The base station uses the replica model to estimate the actual measurement data of the sensor node in usual time, and an actual measurement datum is sent to the base station only when the error of the modelpsilas corresponding estimation exceeds allowable error bound. In such a way, energy can be saved by reducing the transmission of actual measurement data. Experimental results show the effectiveness of our approach.

Sheng Fang

Papers

Spatial Data Correlation Based Clustering Algorithms for Wireless Sensor Networks

Clustering algorithms for wireless sensor networks using spatial data correlation

Data collecting in wireless sensor networks using spatial models