A Comprehensive Analysis of Congestion Control Models in Wireless Sensor Networks

A Comprehensive Analysis of Congestion Control Models in Wireless Sensor Networks

Sangeetha Ganesan (CEG, Anna University, Chennai, India), Vijayalakshmi Muthuswamy (CEG, Anna University, Chennai, India), Ganapathy Sannasi (VIT University, Chennai, India) and Kannan Arputharaj (VIT University, Vellore, India)
DOI: 10.4018/978-1-7998-2454-1.ch057

Abstract

Congestion control is an important factor for performance improvement in wireless sensor networks (WSNs). Congestion occurs due to various reasons including a variation in the data rate between incoming and outgoing links, buffer size, flooding attacks and multiple inputs and minimum output capability. Various outcomes of congestion in sensor networks include immense packet loss or packet drop, fast energy depletion, unfairness across the network, reduced node performance and increased delay in packet delivery. Hence, there is an extreme need to check channel congestion in order to enhance the performance with better congestion management. The job of choosing a suitable congestion control technique is a challenging task for the network designer. In this article, the authors traverse through the underlying conceptual ideas on congestion control schemes which come under six unique models. This article highlights a survey on the existing works done so far on congestion control domains in sensor networks. A comparative analysis based on Quality of Service parameters has been discussed.
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1. Introduction

A Wireless Sensor Networks (WSN) consists of a network of sensor nodes (Akyilidiz, 2002) that cooperatively senses and manages the surrounding, interfacing the interaction between humans or devices and the environment. Wireless sensor networks have been widely used for excelling in areas of military, healthcare, agriculture and navigation of vehicles. However, there is a major limitation in the amount of energy needed for the activities such as sensing, processing and communication in the sensor nodes. Another challenge in the design of sensor network applications is the provision of reliable services and continuous availability.

A WSN (Yick, 2008) comprises of different types of sensors scattered randomly across the sensor field. The information segregated from these sensors reach the destination called sink node, either by using a single or a multi-hop protocol for traffic flows. Congestion in a sensor network occurs mainly in between two nodes along the path between nodes to sink or vice versa between a sink to node, when the buffer capacity of the sensor node overflows. Thus, a congestion state can occur either in an upstream or a downstream manner. One possible solution to these problems is a cross-layer design which can be kindled that needs a joint venture of distributed signal or data processing, medium access control and data communication protocols to reduce and walk through congestion in sensor networks.

In this paper, we categorize several congestion control models depending on their major functioning in the network. First classification is done based on techniques that focus mainly on congestion control in Elastic (Jin,2010; Ren, 2011; Tao, 2010) and Inelastic Traffic flows (Akan, 2005, Rahman, 2008; Rezaee, 2013; Wang, 2007; Yaakob, 2016). The second classification is related to Event-to-Sink (Antoniou, 2013; Iyer, 2005; Munir, 2007; Yin, 2009) and Sink-to-Event (Chena, 2006; Fang, 2010; Li, 2014; Karenos, 2010) flow directional congestion control techniques. Third classification is related to Source dependent (Huang, 2011; Kumar, 2008; Vuran, 2010) and Network dependent (Chen, 2009; Hussain, 2008; Misra, 2009; Liajun, 2013) congestion control. The Source based techniques are majorly related to the contributions of source side congestion management.

The Network based congestion control mechanisms are related to the actions taken by the sensor network and its layers to alleviate congestion. Fourth classification relies on the congestion control mechanisms focusing Rate management (Chen, 2015; Gungor, 2008; Kang, 2007; Karenos, 2005; Lee, 2010; Raza, 2010; Sergiou, 2014) and Resource variations (Gholipour, 2015; Hull, 2004; Kafi, 2016; Monowar, 2012; Zawodiok, 2007) . Fifth classification includes congestion management in similar and different traffic flows, namely Homogeneous (Stai, 2014) and Heterogeneous (Iqbal, 2007; Alkylidiz, 2002; Yick, 2008; Arianpoo, 2017) traffic flows. The last classification includes the congestion control techniques related to Hop-by-Hop (Al-Saggaf, 2016; Chand, 2015; Jayakumari, 2014; Sayyad, 2015) and Multi-hop data (Ben-Othman, 2010; Betzler, 2016; Iqbal, 2007; Sergiou, 2013) transmission in sensor networks.

The scope of this paper is to present a survey of different congestion control protocols in WSNs classified under various models. The uniqueness of this paper compared to previous works includes its compatibility in bringing a detailed discussion of congestion control protocols with aspects such as performance analysis, network stack and real-life applications. A wider comparison is made between the six congestion control models with their own merits and demerits.

The remaining part of the paper is structured as follows: Section II presents various congestion mitigation models in detail. A consolidated performance analysis is given for the existing mechanisms to congestion alleviation in Section III. Section IV explains about the selection of an optimal technique under each model grouping. Section V gives an outline of recent devised techniques to check congestion in WSNs. Certain possible open research issues to mitigate congestion in WSNs are mentioned in Section VI.

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