Elsevier

Computer Networks

Volume 156, 19 June 2019, Pages 33-40
Computer Networks

Energy-efficient data collection in strip-based wireless sensor networks with optimal speed mobile data collectors

https://doi.org/10.1016/j.comnet.2019.03.019Get rights and content

Abstract

Energy consumption and network lifetime are the major concerns in wireless sensor networks (WSNs). In particular, WSNs use radios for communication, which are the major energy consumers. Due to frequent data forwarding process, the sensors near the sink especially in strip-based network deplete more energy, which causes energy hole problem and network lifetime reduction. In this work, as a first attempt, an energy efficient data collection process is proposed for strip-based WSNs to solve the aforementioned issues. More precisely, an optimal speed for the mobile data collector (MDC) with accurate data transmission range for each cluster is determined through an analytical approach. In addition, to reduce the energy usage of the sensor nodes, the transmission range of the sensors is adjusted automatically. With this proposed methodology, the network can avoid energy hole problem. Simulations are carried out to validate the proposed method, where it is shown that the network lifetime is significantly extended when compared to the existing methods.

Introduction

A set of homogeneous or heterogeneous tiny sensors are grouped together to perform a particular task called wireless sensor network (WSN) which can sense, process and perform short range communications [1], [2]. These sensors are spatially deployed over a particular domain for monitoring or measuring the environment changes periodically. Usually, sensors are operated with low powered battery and WSN lifetime is limited to it. The applications include target tracking, environment monitoring, space surveillance, battlefield surveillance, weather forecasting, etc., [3], [4], [5], [6]. The abilities of WSNs have been expanded significantly because of the micro-electro-mechanical systems and in advancements of the Internet of things (IoT).

Deployment of large number of sensors in the hostile environment creates faulty and unreliable networks due to limited battery resource. Every sensors consume energy for its data transmission and reception. The gathered data are forwarded or routed to the sink via multi-hop transmission [7], [8]. Hence, the sensors closer to sink dissipate their energy faster when compared to other sensors because of their frequent data forwarding activity. Hence, the existing network suffers a problem around the sink called energy hole problem [9], [10]. If energy hole problem occurs in the sensor network, no more data can be transferred to the sink. Consequently, other sensors cannot deliver their collected data to the sink. Thus, data loss occurs in the network and considerate energy is wasted by disconnected sensors, which opens the way to the premature death of sensors. Further, the energy hole and data loss issues degrade the network lifetime as well as the quality of service [11].

The strip-based WSNs are the network where the length of the network is larger than the height of the network. Some real-time applications for the strip-based network include bridges, pipelines, rivers, metros, roads, etc. Here, the sensors are arranged linearly and hence, the data transmission would follow many-to-one pattern. As a result, the number of data transmission to be handled by the nodes near the sink is huge which an causes energy hole problem. Most of the existing methodologies were applied only to spiral or circular network scenarios and it cannot be applied to strip-based network [12], [13], [14], [15], [16].

In the existing literature, several approaches have been proposed to improve the network lifetime, see [17], [18], [19], [20], [21], [22] and the references cited therein. It can be achieved via different techniques such as deployment, localization, data fusion, clustering, transmission range adjustment, routing and energy scheduling. Therefore, it is very essential and an important task to extend the network lifetime by reducing the energy consumption using the aforementioned techniques. Here, instead of forwarding the sensed data blindly to the network, the sensors are grouped together called clusters. Each cluster is assigned with a periodically changing cluster head (CH) and this process of grouping is called as clustering [23], [24]. In general, all the sensors need to perform the data transmission. By applying the clustering approach, most of the data forwarding can be done through CH and sinks [25]. Hence, clustering is one of the important methodologies to extend the network lifetime.

Radio is the main energy consumer in the sensor unit. Here, the energy consumption is determined by how far the data has to be transmitted during the communication over the wireless medium. As transmission distance plays an important role in the energy usage, proper adjustment of transmission range in the radio device maximizes the network lifetime [26], [27], [28].

Recently, researchers found a new idea to avoid the conventional multi-hop transmission over the fixed sinks and used a mobile device or robot which travels over the network to collect the data from the sensors or cluster heads [29], [30], [31]. MDC is used because of two main reasons: (1) MDC can collect data over spatially separated regions and (2) minimal energy consumption requirement. In general, MDC aims to optimize the node energy consumption, network reliability and data latency [12]. Moreover, the conventional data forwarding mechanism for a longer distance from the sensor can be avoided [32]. The suggested approach provides the balanced network and increases the network lifetime significantly because of effective data collection.

Authors in [33] did a comparative study on various topologies and data collection/dissemination schemes of sink mobility. In [34], the authors proposed optimization-based mobile data collector for general WSN. But these works consider corona network model where they cannot be applied for strip based network model. In [35], authors have considered strip-based WSN with static sink. Though the work maximizes the network lifetime by adjusting the communication range, the energy hole problem is not solved completely.

Most of the existing works considers multi path transmission model and consumes more energy due to longer travel distance of data causing energy hole problem. So, mobile data collectors were deployed to alleviate this issue. But still operating the MDC at optimal speed for data collection is not explored. Also, many data transmission protocols adjust the sensor transmission distance in the closer area of the sinks [36], but optimal speed MDC with transmission range adjustment is one of the areas to be well explored.

This work is focused on using MDC for data collection in strip-based network to optimize the energy consumption of the node and to enhance the network lifetime. To the endeavor of our knowledge from the literature, the proposed approach is the first attempt for strip-based WSN with the integrated implementation of clustering, accurate transmission range adjustment of nodes and velocity adjustment of MDC. The proposed approach has the following phases: Firstly, the network is partitioned into number of equal sized clusters and a CH is elected for each cluster based on the LEACH protocol [37]. Periodically the CH is rotated to maintain the energy balance in the network. Secondly, the transmission ranges of cluster members are adjusted in accordance with CH to reduce the energy consumption for intra cluster communication. Thirdly, MDC is enforced for effective data collection from the CH in WSN. Speed of the MDC has to be operated for a particular cluster depends on the size of cluster, the data transmission rate and the amount of data to be transmitted by CH to MDC. Lastly, CH transmission ranges are adjusted to reduce the energy consumption and to increase the network lifetime. Therefore, the proposed approach can prevent the network from energy hole problem and can avoid data loss.

The main contributions of this paper can be summarized as follows:

  • MDC-based data collection approach with optimal speed and dynamic transmission range adjustment is proposed in WSNs as a first attempt to extend the network lifetime.

  • The speed of MDC depends on the length of the cluster and the number of packets to be transmitted by the cluster. So, the proposed variable speed MDC-based scheme increases the network lifetime significantly and avoids the energy hole problem and packet loss.

  • In this work, a novel heuristic algorithm is proposed for data collection with MDC, which helps in construction of energy efficient strip-based WSN.

  • It is revealed from the numerical simulations that the proposed approach considerably improves the network lifetime than the existing approaches.

Section snippets

Network model

Stationary sensor nodes are deployed uniformly in linear fashion. Generally, in strip-based network L >  > H, where L is length and H is the height of the strip as shown in Fig. 1. In this work, the sink acts as MDC which traverses over the network to collect the data. If all the nodes communicate directly with the sink, the total energy consumption will be huge. In order to avoid that, the network is divided into n number of clusters denoted as C1,C2,C3,,Cn and each Ci has a cluster head CHi

Transmission scheme based on accurate distances

In the considered network, two types of communications are performed, which are: (1) intra-cluster communication, i.e. cluster members transmit data to its CH which adopts single hop communication since li < d0; and (2) communication between CH and MDC, which also adopts single hop. Theoretical energy depletion calculation is given as follows:

Performance evaluation via numerical simulation

In this section, the obtained results are evaluated with different network sizes. The proposed work is compared over the existing linear network models such as accurate-distances-based transmission scheme (ADTS) [35], energy efficient geographic routing protocols (EEGR) [39], energy-balanced data gathering protocol (EBDG) [39] and direct transmission scheme (DT) [39]. The parameters’ values for the simulations are listed in Table 1.

Conclusion

In this paper, an energy efficient data collection model with MDC has been considered for linear WSNs. Specifically, a novel algorithm has been developed for finding the optimal speed of MDC to collect the data based on the amount of data to be transmitted and the length of the cluster. In addition to that, the communication range of the nodes has been adjusted to improve the network lifetime further. Thus, by operating the MDC at optimal speed and adjusting the communication range of the

Acknowledgment

The work of first author was supported by the University Grant Commission (UGC), Government of India [UGC NET-JRF].

R. Vishnuvarthan received his Bachelor’s degree in computer science and engineering from Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, in 2013, and Master of Engineering from Anna University Regional Campus, Coimbatore in 2016. Currently, he is working towards his Ph.D. degree in Department of Electronics and Communication Engineering, Anna University, Regional Campus, Coimbatore-641 046, India. His current research interests include Wireless Sensor Networks and Adaptive

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    R. Vishnuvarthan received his Bachelor’s degree in computer science and engineering from Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, in 2013, and Master of Engineering from Anna University Regional Campus, Coimbatore in 2016. Currently, he is working towards his Ph.D. degree in Department of Electronics and Communication Engineering, Anna University, Regional Campus, Coimbatore-641 046, India. His current research interests include Wireless Sensor Networks and Adaptive Learning.

    R. Sakthivel received the B.Sc., M.Sc., M.Phil., and Ph.D. degrees in mathematics from Bharathiar University, Coimbatore, India, in 1992, 1994, 1996, and 1999, respectively. He was a Lecturer with the Department of Mathematics, Sri Krishna College of Engineering and Technology, Coimbatore, from 2000 to 2001. From 2001 to 2003, he was a Post-Doctoral Fellow with the Department of Mathematics, Inha University, Incheon, South Korea. He was a Visiting Fellow with the Max Planck Institute, Magdeburg, Germany, in 2002. From 2003 to 2005, he was a Japan Society for the Promotion of Science Fellow with the Department of Systems Innovation and Informatics, Kyushu Institute of Technology, Kitakyushu, Japan. He was a Research Professor with the Department of Mathematics, Yonsei University, Seoul, South Korea, until 2006. He was a Post-Doctoral Fellow (Brain Pool Program) with the Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, South Korea, from 2006 to 2008. He was an Assistant Professor and an Associate Professor with the Department of Mathematics, Sungkyunkwan University, Suwon, South Korea, from 2008 to 2013. From 2013 to 2016, he was a Professor at the Department of Mathematics, Sri Ramakrishna Institute of Technology, India. He is currently a Professor with the Department of Applied Mathematics, Bharathiar University, Coimbatore, India. He has published over 250 research papers in reputed science citation index journals. His current research interests include systems and control theory, optimization techniques, and nonlinear dynamics. He has been on the Editorial Board of international journals, including the IEEE Access, the Journal of the Franklin Institute, Neurocomputing, Advances in Difference equations, and the Journal of Electrical Engineering and Technology.

    V. Bhanumathi received the B.E degree in electronics and communication engineering from Madras University, M.E. degree in communication systems and Ph.D in Information and Communication Engineering from Anna University, Chennai. She is currently working as an assistant professor in the Department of Electronics and Communication Engineering, Anna University, Regional Campus, Coimbatore. She has published her works in various International Journals and conferences. Her areas of interest are Wireless Communication, VLSI Design, Network Security, and Digital Communication.

    K. Muralitharan received the B.Sc. degree in electronics and M.C.A. degree in computer science from Bharathiar University, Coimbatore, Tamilnadu, India, in 2001 and 2004, respectively. He was a Senior Software Engineer in Nilgiri Networks Pvt Ltd., [TeNet group (I.I.T Madras)], Tamilnadu, India from 2004 to 2009 and worked as a Senior Engineering Research Fellow (SERF) in the Department of Computer Applications, Anna university of Technology, Coimbatore, India from 2009 to 2012. He received his Ph.D. degree from Anna University, Chennai, India in 2016. His current research interests include smart grid networks, wireless sensor networks and optimization techniques.

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