Elsevier

Ad Hoc Networks

Volume 13, Part B, February 2014, Pages 516-534
Ad Hoc Networks

WCCP: A congestion control protocol for wireless multimedia communication in sensor networks

https://doi.org/10.1016/j.adhoc.2013.10.006Get rights and content

Abstract

The growing interest in applications of Wireless Multimedia Sensor Networks (WMSNs) imposes new challenges on congestion control protocols in such networks. In this paper, we propose a new content-aware cross layer WMSN Congestion Control Protocol (WCCP) by considering the characteristics of multimedia content. WCCP employs a Source Congestion Avoidance Protocol (SCAP) in the source nodes, and a Receiver Congestion Control Protocol (RCCP) in the intermediate nodes. SCAP uses Group of Picture (GOP) size prediction to detect congestion in the network, and avoids congestion by adjusting the sending rate of source nodes and distribution of the departing packets from the source nodes. In addition, RCCP monitors the queue length of the intermediate nodes to detect congestion in both monitoring and event-driven traffics. Moreover, to improve the received video quality in base stations, WCCP keeps the I-frames and ignores the other less important frame types of compressed video, in the congestion situations. The proposed WCCP protocol is evaluated through simulations based on various performance metrics such as packet loss rate, frame loss rate, Peak Signal-to-Noise Ratio (PSNR), end-to-end delay, throughput, and energy consumption. The results show that WCCP significantly improves the network performance and the quality of received video in the sink nodes, and outperforms the existing state-of-the-art congestion control protocols.

Introduction

Due to rapid development of networked video sensors in recent years, there has been a growing demand in Wireless Multimedia Sensor Network (WMSN) applications such as multimedia surveillance, traffic monitoring, and real-time object tracking systems [1], [2]. The Wireless Multimedia Sensor Networks can be described as a group of connected wireless sensors that collect multimedia data (i.e. audio and video) along with scalar data from the environment and transmit them to a base station (sink node) [3]. Achieving higher video quality in base stations is an important objective in WMSNs. The main reason for low video quality in WMSN’s base station is bursty traffic which causes congestion in the network, and consequently a large number of lost packets. Furthermore, because of the small size of sensors and hence their limited battery lives, energy conservation is an important issue in WMSNs [4], [5].

There are significant number of research efforts in solving the congestion problem of sensor networks [6], [7], [8], [9]. Based on the different congestion detection and rate adjustment techniques, one can primarily classify these works into four major categories: (I) queue assisted protocols, (II) priority aware protocols, (III) topology formation protocols, and (IV) resource control protocols. The queue assisted protocols mostly concentrate on the queue length of the nodes and use a simple rate adjustment technique such as Additive Increase Multipartite Decrease (AIMD) to keep the queue length of nodes as low as possible [8], [10]. However, because of the simple nature of these protocols, they do not have efficient energy consumption. The priority aware protocols consider the different priority of nodes in congestion situations and try to provide equal service for nodes in the same priority class [11], [12], [13], [14]. The topology formation protocols adjust the input rate of congested nodes by forwarding some parts of traffic from other nodes or by activating or deactivating nodes near the congested area. However, changing network topology is not always practical, and it may result in lower performance for sparse networks [7], [15], [16], [17]. The resource control protocols increase the amount of resource consumption (duty cycle) in nodes near the congested area which by itself increases the energy consumption for these nodes. It may also make the interference problem more severe in the congested areas [6], [9].

While the above congestion control protocols have achieved high performance in scalar sensors (sensors which sense non-multimedia data such as temperature or humidity), they do not provide high multimedia quality (video and audio) in WMSNs. The main reason for low multimedia quality (specially video) in congestion control protocols is that they are not content aware. In other words, they treat multimedia packets similar to regular data packets, whereas in multimedia communication some packets are more important than other packets. For instance, in the case of video, packets which carry I-frames have the highest priority compared to the other frame types. Moreover, the rate adjustment techniques that are deployed in congestion control protocols, only try to adjust the output sending rate of source nodes without considering the distribution of inter-arrival packets (inter-arrival process of the packets) that can have a great impact on number of lost packets in WMSNs. Recently, several cross-layer studies are presented in the scope of designing efficient protocols for WMSNs [18], [19]. However, these protocols provide different congestion control techniques without analyzing or deploying any traffic model.

In this paper we introduce a two-stage WMSN Congestion Control Protocol (WCCP) as follows.

  • The Source Congestion Avoidance Protocol (SCAP) is deployed in source and is responsible for predicting congestion using a proposed Group of Picture (GOP) size prediction method. Moreover, the SCAP is responsible for adjusting the distribution of the leaving packets along with the sending rate of the source nodes using the proposed traffic model. To the best of our knowledge, it is the first time in WMSN area that a protocol adjusts the distribution of inter-arrival packets to gain a better video quality.

  • The Receiver Congestion Control Protocol (RCCP) is deployed in intermediate nodes and detects congestion occurrence and informs the source nodes about the congestion. RCCP uses a proposed queueing model to detect congestion in intermediate nodes and informs the SCAP protocol in source part about the congestion.

The performance evaluation of the proposed mechanism is carried out by comparing its performance against the state of the art protocols such as XLP [6], PCCP [13], CCF [12], and other classic congestion control protocols. We show the importance of considering the contents of data in multimedia communication, and the affects of using model-based approaches in adjusting the output rate of source nodes. The key contributions of this paper are as follows:

  • 1.

    A two-stage protocol (WCCP) is proposed to control congestion in WMSNs; SCAP in the source nodes to avoid congestion, and RCCP in intermediate nodes to detect and control congestion.

  • 2.

    A traffic model is proposed using the inter-arrival process of the packets to adjust the sending rate of the source nodes.

  • 3.

    An intermediate node’s queueing model is proposed using the MMPP queueing model and is used to detect congestion in the receiver nodes.

  • 4.

    The content of data in transmission is taken into account in WCCP protocol to gain higher video quality in the base station (we preserve the I-frames which are the most important frames in the multimedia communications).

  • 5.

    A GOP size prediction method is proposed to predict congestion (this method is also applicable to other works such as peer to peer networks, or wireless networks).

The rest of the paper is organized as follows. Related work is presented in Section 2. The WMSN source traffic model and intermediate queueing models are presented in Section 3. Section 4 introduces the proposed protocol. Section 5 provides performance evaluation, and the concluding remarks are presented in Section 6.

Section snippets

Literature review

Recently, several studies have been performed on developing efficient congestion control protocols for WMSNs [6], [20]. A typical congestion control protocol includes three phases: congestion detection in order to detect congestion in nodes, congestion notification to inform other nodes about the congestion, and rate adjustment to mitigate the congestion problem. Based on the different congestion detection and rate adjustment techniques, we have primarily categorized the congestion control

WMSN source traffic model and intermediate nodes queueing model

Modeling network characteristics leads to more accurate decision in congestion situations. In this section, we propose a source traffic model and a queueing model for intermediate nodes to adjust the transmission distribution of packets and to detect congestion in intermediate nodes, respectively.

WCCP: WMSN Congestion Control Protocol

WCCP is a two-part protocol. In the source-part, it uses SCAP to adjust the sending rate and distribution of the leaving packets (refer to Fig. 3). The goal of SCAP is to begin congestion avoidance from the source node. SCAP uses GOP size prediction method to predict congestion occurrence and adjusts the sending rate and the distributions of leaving packets from the source by considering WMSN traffic model. In the receiver-part

Performance evaluation

We analyzed the performance of the congestion control protocol in terms of packet loss, frame loss, Peak Signal-to-Noise Ratio (PSNR), throughput, delay and energy consumption. We used the NS-2 simulator [26] to evaluate the performance of the WCCP protocol. Moreover, we used the Evalvid [27] to enable video transmission simulation in NS-2.

IEEE 802.15.4 is the most common MAC and physical layer protocol for WMSN [28]. Therefore, the NS-2 simulator was setup to use IEEE 802.15.4 standard in MAC

Conclusion

In this paper, a new content-aware cross layer protocol (WCCP) was proposed to minimize the packet loss in WMSNs by considering the traffic characteristics, inter-arrival pattern of packets, and video packets priority. WCCP employs intermediate nodes queueing, and source traffic model to detect and remedy the congestion. A two part scheme was introduced in WCCP. In the source part, SCAP, uses a GOP size prediction method to predict future sending rate and congestion occurrences. It, then

Shahin Mahdizadeh Aghdam received his M.Sc. degree with honors in Communication and Computer Networks from Sharif University of Technology in October 2011, under supervision of Prof. Mohammad Khansari. He received his B.Sc. degree in Information Technology from Nabi Akram University in 2009. He was a Research Assistant at Digital Media Lab (DML), Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. His current research interests include wireless multimedia sensor

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    Shahin Mahdizadeh Aghdam received his M.Sc. degree with honors in Communication and Computer Networks from Sharif University of Technology in October 2011, under supervision of Prof. Mohammad Khansari. He received his B.Sc. degree in Information Technology from Nabi Akram University in 2009. He was a Research Assistant at Digital Media Lab (DML), Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. His current research interests include wireless multimedia sensor networks, adaptive and cross-layer protocol design for wireless sensor networks, and stochastic modeling and performance optimization in wireless multimedia networks.

    Mohammad Khansari received his B.Sc., M.Sc., and Ph.D. degrees in Computer Engineering from Sharif University of Technology, Tehran, Iran, in 1996, 1999, and 2008, respectively. Formerly, he has been the director, member of steering committee, and head of technical committee of the Iran Free/Open Source Software national project, and a faculty member and head of Information Technology department in Iran Telecommunication Research Center, Tehran, Iran. He is currently a faculty of New Sciences and Technologies at University of Tehran. His current research interests include network science and complex networks, wireless multimedia/health sensor networks, Peer-to-Peer multimedia networks, and Free/Open Source Software.

    Hamid R. Rabiee (SM’07) received his BS (1987) With Great Distinction and MS (1989) degrees in Electrical Engineering from CSULB, his EEE (1993) degree in Electrical and Computer Engineering from USC, and his Ph.D (1996) in Electrical and Computer Engineering from Purdue University, West Lafayette, USA. From 1993 to 1996 he was a Member of Technical Staff at AT&T Bell Laboratories. From 1996 to 1999 he worked as a Senior Software Engineer at Intel Corporation. He was also with PSU, OGI and OSU universities as an adjunct professor of Electrical and Computer Engineering from 1996–2000. Since September 2000, he has joined Sharif University of Technology, Tehran, Iran. He is the founder of Sharif University Advanced Information and Communication Technology Research Center (AICT), Advanced Technologies Incubator (SATI), Digital Media Laboratory (DML), and Mobile Value Added Services Laboratory (VASL). He is currently a Professor of Computer Engineering at Sharif University of Technology, and the Director of AICT, DML, and VASL. He has been the initiator and director of international and national level projects in the context of UNDP International Open Source Network (IOSN), and Iran National ICT Development Plan. He has received numerous awards and honors for his industrial, scientific and academic contributions. He has acted as chairman in a number of national and international conferences, and holds three patents

    Mostafa Salehi received his B.Sc. (2005) degree in Computer Engineering from Iran University of Science and Technology, his M.Sc. (2008) degree in Computer Architecture from Amirkabir University of Technology, and Ph.D. (2012) degree in Computer Engineering from Sharif University of Technology. On February 2013, he joined the Faculty of New Sciences and Technologies at University of Tehran as an assistant professor. He was also involved in a number of large scale network design and consulting projects in the telecom industry. His research interests include multimedia networks, and complex networks

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