An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks
Wan et al. Hum. Cent. Comput. Inf. Sci.
An energy‑efficient sleep scheduling mechanism with similarity measure for wireless sensor networks
Runze Wan 2
Naixue Xiong 1
Nguyen The Loc 0
0 Faculty of Information Technology, Hanoi National University of Education , Hanoi , Viet Nam
1 Department of Mathematics and Computer Science, Northeastern State University , Tahlequah, OK , USA
2 Hubei Co-Innovation Center of Information Technology Service for Elementary Education, Hubei University of Education , Wuhan , China
In wireless sensor networks, the high density of node's distribution will result in transmission collision and energy dissipation of redundant data. To resolve the above problems, an energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks (ESSM) is proposed, which will schedule the sensors into the active or sleep mode to reduce energy consumption effectively. Firstly, the optimal competition radius is estimated to organize the all sensor nodes into several clusters to balance energy consumption. Secondly, according to the data collected by member nodes, a fuzzy matrix can be obtained to measure the similarity degree, and the correlation function based on fuzzy theory can be defined to divide the sensor nodes into different categories. Next, the redundant nodes will be selected to put into sleep state in the next round under the premise of ensuring the data integrity of the whole network. Simulations and results show that our method can achieve better performances both in proper distribution of clusters and improving the energy efficiency of the networks with prerequisite of guaranteeing the data accuracy.
Wireless sensor networks; Load balance; Sleep scheduling; Energy-efficient
Introduction
In recent years, there has been a great deal of interest researches in wireless sensor
networks (WSNs), which involves the maturing techniques of integrated circuitry, micro
electro mechanical systems (MEMS) and digital signal processing [
1
]. WSNs usually can
be composed of hundreds or thousands of sensor nodes, each one of which is capable
of sensing its environment, performing simple computations, and communicating to its
neighbors [
2
]. Due to the sensing behavior on a larger geographical region and sending
their readings (raw data) to the sink, the sensor nodes with limited energy being supplied
from built-in battery will die gradually. Apparently, the power saving of sensor nodes is
vital to prolong the lifetime of the entire network.
In order to gather information more efficiently, hierarchical cluster-based structure is
introduced into the applications of WSNs [
3
]. In such scenario, the sensor nodes can be
partitioned into a number of small groups called clusters. Each cluster is composed of a
coordinator as cluster head (CH), which is responsible for managing the entire cluster
and forwarding the data to the base station (BS). By rotating cluster-heads selection
periodically, the node’s energy consumption over the network can be balanced. In case of
dense-deployment, the readings being collected by sensor nodes in the adjacent regions
may demonstrate the features with spatial and temporal correlations. Despite of
providing a fault-tolerant mechanism for data aggregation, the redundant data will result in
superfluous data transmission, and it will lead to collisions and
undesired energy depletion to affect the network lifetime [
4
]. In view of the recognizable targets, the
evolutionary algorithms are proposed to arrange the nodes with similar monitoring results into
the same cluster as far as possible. The amount of data that the CHs communicate with
the BS is maximally compressed by the fusion of similar information. Finally, the sensor
nodes with spatial-correlation can be organized as much as possible in a cluster.
Therefore, it can not only improve the accuracy of the data in the monitoring area, but also
reduce the transmission cost of the CH. However, this kind of methods has high
complexity and long time consuming [
5, 6
].
In this paper, we propose an energy-efficient sleep scheduling mechanism with
similarity measure for WSNs (ESSM), which will schedule the sensors into active or sleep
mode to reduce energy consumption effectively. Firstly, the optimal competition radius
is estimated to organize the all sensor nodes into several clusters to balance energy
consumption. Secondly, according to the data collected by member nodes, a fuzzy matrix
can be obtained to measure the similarity degree, and the correlation function based on
fuzzy theory can be defined to divide the sensor nodes into different categories. Next,
the redundant nodes will be selected to put into sleep state in the next round under the
premise of ensuring the data integrity of the whole network.
The rest of this paper is organized as follows: In “Related work” section, we briefly
introduce related work. We describe the assumptions and explain the details of our
method (...truncated)