A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks

Abstract

In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison.

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

“…Based on the results, the proposed scheme improved data collection reliability, reduced data collection time, and also reduced the overall energy consumption. Li et al proposed a novel data collection scheme to help gather sensed data in an energy-efficient and load-balanced way [33]. The authors compared the proposed scheme with existing schemes used for data collection.…”

Big Data Collection in Large-Scale Wireless Sensor Networks

“…However, the problem of battery depletion of the sensor nodes lying at a jump from the sinks remains. Sensor nodes of this architecture, the choice of one sink compared to another, remain a challenge in this kind of architecture [85].…”

“…The disadvantage is the depletion of the energy resources of the nodes close to the base station because all traffic toward the latter obligatorily passes by them. Hierarchical routing. This approach is based on the formation of clusters (common areas) [36,85,100]. The principle is to route the data collected by each node of the cluster to its head of area, the Cluster Head (CH), which, after processing their common parts, forwards them to the next destination (If the CH cannot directly reach the station, basic information is routed to the next zone leader).…”

An energy-efficient data prediction and processing approach for the internet of things and sensing based applications

“…The in-network data approach is used at an intermediate node, mostly called aggregator or Cluster-Head (CH), and aims to a find correlation between neighboring nodes so as to transfer valuable data to the sink [14][15][16][17][18][19][20][21][22][23]35]. The authors in [14] propose a structure fidelity data collection (SFDC) technique dedicated for cluster-based periodic applications in WSNs.…”

“…We take the sensor node 1 and we present its correlation with neighbouring nodes for temperature, humidity and light conditions. The results show that the temperature sensor in node 1 has correlation with the set of temperature sensors [2,13,24,26,27,30,31,33,43] while the humidity and light sensors in node 1 have correlation with [13,24,26,27,28,29,30,31,33] and [2,3,15,21,22,23,26,32,33,35,36,41,43,46] respectively. Thus, we can deduce the following: 1) the sensors in the same node do not have the same number of correlated sensors; the light sensor has more correlations than temperature and humidity sensors.…”