A sustainable data gathering technique based on nature inspired optimization in WSNs

https://doi.org/10.1016/j.suscom.2019.100354Get rights and content

Highlights

  • A novel approach for cluster head selection using nature inspired optimization method for both homogeneous and heterogeneous WSNs is proposed.

  • A chain based data gathering and transmission process for intra and inter-cluster communication is proposed.

  • A data aggregation process is also discussed for removing redundant data. It helps in decreasing the transmission cost and overhead.

  • The performance of the proposed methods is evaluated with the state of the art protocols by considering the different performance matrices.

  • The proposed protocols variants is developed for unattended hostile application viz. forest fire detection.

Abstract

Proficient clustering method helps in decreasing the battery consumption of the resources in wireless sensor networks (WSNs). Election of an appropriate sensor for Cluster Head (CH) can be an effective way to increase the efficiency of the clustering process. In the last two decades, the number of clustering methods have been proposed. However, most of the methods are suffering from uneven variation in the number of CH, irregular energy consumption by nodes, transmission of the redundant data, and an unequal load on the CHs. This paper resolves these problems by proposing a sustainable data gathering technique based on nature inspired optimization for both homogeneous and heterogeneous networks. It considers a fitness function by integrating four fitness parameters namely: energy efficiency, cluster node density, average distance of sensors to the CH, and distance from CH to Base Station (BS). This method considers a chain-based data gathering and transmission process for intra and inter-cluster communication. A data aggregation process is also introduced for removing the redundant data which helps in decreasing the transmission cost and overhead of networks. The performance of the proposed method is evaluated against the state-of-the-art protocols by considering the different performance matrices like network lifetime, stability period, total energy consumption, throughput, number of CHs etc. The experimental results show the network lifetime and throughput of GSA-DEEC, GSA-DEEC-CA, and GSA-DEEC-CA-DA are increased by 08.37%, 39.36%, & 44.72% and 18.77%, 49.53%, & 77.29% in respect of the DEEC for 100 J network energy in case of tier-3 heterogeneity, respectively.

Introduction

Over the last few decades, there has been a significant growth in academia as well as industry in the field of wireless sensor networks (WSNs) due to the advancement in the sensor's abilities like sensing power, computation, and communication capabilities [1]. WSNs possess different characteristics like scalability in the existing networks, easy to use, energy constraints of the nodes, self-organizing capability for node failure, both types of heterogeneity and homogeneity nodes, and ability to monitor harsh environment. In WSN, various sensors are spread in target area for capturing the physical surroundings of the atmosphere that have small size, low processing power, less storage and low energy abilities. These sensor nodes have the ability to sense the target and make an infrastructure-less wireless communication among them and base station (BS). Sensors accumulate the data from the target region and forward it to the BS directly or with the assistance of other sensors. The BS is connected to the server or other sensor networks with the help of wired/wireless links. Thus, the information is further sent to the end user via internet. The BS is supposed to be reliable and is capable of performing any operation [2].

WSNs are being used in various applications such as forest fire detection, armed surveillance, landslide recognition, natural disaster deterrence, data classification, health care, structural health, data centre, and water quality monitoring, industrial and consumer applications, monitoring environmental such as humidity, wind, temperature, sound, pollution levels etc., and so on. Based on above discussed applications, we can categorize the wireless sensor node deployment into two categories: deterministic and non-deterministic [3]. In deterministic deployments, sensor nodes are placed manually at the selected locations where the deployment area is physically accessible such as city sense monitoring, soil monitoring, etc. On the other hand, in non-deterministic deployments sensor nodes are installed into physically unapproachable regions using other ways like sensors are dropped from an aircraft, e.g., battlefield surveillance and landslide detection etc. The non-deterministic deployment is also known as random deployment [[2], [3], [4]].

The WSNs may be classified as homogeneous or heterogeneous depending on the energy tiers of nodes in the network. In the homogeneous networks, all the sensors equipped with identical level of initial energy whereas in case of heterogeneous networks, it is not. The heterogeneous nodes have distributed into some groups based on the energy level. The energy levels are also known as tires. Apart from energy heterogeneity, there exist computational and link heterogeneity [5] in WSNs. In computational heterogeneity, the sensor nodes may have dissimilar microprocessor and other computational resources viz-a-viz the normal nodes. In link heterogeneity, sensor nodes have wide-ranging bandwidth and transmission heterogeneity viz-a-viz the normal nodes.

As discussed above, the sensor nodes have some limitations due to its small size, low processing power, less storage, low energy abilities, and being unattained after deployment. Consequently, the major concerns are to design sustainable protocol that can increase the network lifetime. Clustering plays a dynamic part in designing sustainable protocols where a huge number of nodes are associated to each other for collecting the data and its transmission. The effective selection of cluster heads, size of the cluster, intra cluster communication between the sensor nodes and CH, inter cluster communication between the CH and base station, and data aggregation for removing the redundant data are the major concerns required to handle completely. Thus, designing of appropriate sustainable clustering-based protocol can address these concerns and helps in maintaining the sustainability of the networks by prolonging the network lifetime. In this work, we try to resolves these problems by proposing a sustainable data gathering technique based on nature inspired optimization for both homogeneous and heterogeneous networks. The main contributions of the proposed work are listed below:

  • A novel approach for cluster head selection using nature inspired optimization method for both homogeneous and heterogeneous WSNs is proposed. We formulate a fitness function by integrating four fitness parameters i.e., energy efficiency of the node, cluster node density, average distance of sensors to the Cluster Head (CH) with in its sensing range, and distance from CH to Base Station. This method improves clustering procedure by searching higher residual energy nodes with minimum data transmission distance.

  • Additionally, a chain-based data gathering and transmission process for intra and inter-cluster communication is proposed. To the best of my knowledge, it is the first ever considered in the nature inspired optimization method for both homogeneous and heterogeneous WSNs

  • Further, a data aggregation process is introduced for removing the redundant data. It helps in reducing the transmission cost and overhead of the networks.

  • Furthermore, a novel homogeneous and heterogeneous network energy model is proposed which can define any tire of heterogeneity. The proposed network energy supports all type of network deployment.

  • The performance of the proposed methods is evaluated with the state of the art protocols by considering the different performance matrices like lifetime of the network in terms of rounds, stability period in terms of first node dead, total energy consumption per round, throughput, number of CHs per round etc.

The rest of the paper is organized as follows. In Section 2, the literature review is discussed. Section 3 discusses the system models. Section 4 discusses the nature inspired optimization method for cluster head selection with different fitness parameters, data gathering process, and data aggregation process and Section 5 discusses the results and discussions. Finally, the paper is concluded and future works are mentioned in Section 6.

Section snippets

Literature review

In the past two decades, a lot of clustering techniques are proposed for load dissemination between the sensors by considering two kind of WSNs i.e., homogeneous and heterogeneous WSNs. The load dissemination among the sensors provides a solution for the energy constraints issue of the WSNs. Low energy adaptive clustering hierarchy (LEACH) is the very first distributed clustering protocol which has two phase implementations namely: setup phase and steady state phase [6]. In the first phase,

System model

In this section, we first discuss the assumptions made for the proposed network and then the network and radio energy dissipation model are discussed. The basic assumptions are given as follows:

  • All the sensors are stationary and have unique ID, and deployed randomly in the monitoring area.

  • Initially, energies of the sensors depend on the tier of heterogeneity because nodes can be homogeneous or heterogeneous in nature.

  • All the sensors have symmetric links, similar capabilities, and limited

Proposed work: gravitational search algorithm- deterministic energy-efficient clustering (GSA-DEEC) protocols

This section introduces the proposed work by covering different steps of nature inspired optimization method for cluster head selection i.e., fitness parameters for cluster formation, cluster formation process, chain based data gathering and transmission process for intra and inter-cluster communication, and data aggregation procedure. In the next subsection, we first discuss the basic overview of the Gravitational Search Algorithm.

Simulation results and discussions

In this section, the simulation results of the GSA-DEEC schemes are compared with the existing DEEC protocol [8] and hetDEEC-3 [9]. The deterministic energy efficient clustering (DEEC) selects cluster heads using ratio of residual energy of each node and average energy of the network [8]. It does not use extra energy of higher level nodes efficiently as energy of the nodes is randomly allocated from a given energy interval. Thus, it may not be feasible to design such network. The first proposed

Conclusion and future works

In this paper, a sustainable data gathering technique based on nature inspired optimization for prolonging the network lifetime in WSNs is proposed. The GSA-DEEC technique considered both homogeneous and heterogeneous WSNs and these networks consist of GSA-DEEC, GSA-DEEC-CA, GSA-DEEC-CA-DA, and hetDEEC-3 protocols. These protocols used GSA based clustering in DEEC for cluster head election procedure and chaining approach with data aggregation for efficient data collection. It provides

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