Development of a Modified Token Based Congestion Control Scheme with Adaptive Forwarding for Opportunistic Network

Development of a Modified Token-Based Congestion Control Scheme with Adaptive Forwarding for Opportunistic Network.

ABSTRACT

This research presents the development of a modified token-based congestion control scheme with an adaptive forwarding mechanism (mTBCC) algorithm for addressing congestion problems in opportunistic networks (OppNets).

The algorithm addresses the limitations associated with the standard token-based congestion control (TBCC) in terms of its ability to redirect the traffic from more congested nodes of OppNet to congestion-free nodes without necessarily compromising computational time.

This is because the TBCC has the tendency to drop a significant number of messages when a node is full (overflow) in order to control congestion. OppNet was modeled using ONE simulator with Eclipse, which is a java based programming language.

The node density was controlled by varying the greatest connected component (GCC) expressed in percentage and the corresponding results were used to evaluate the performance of the proposed approach using (dropped messages and network transit time) as performance metrics.

The results showed a reduction in dropped messages and network transit time across all scenarios considered. With a queue size of 10(QS-10), TBCC had 38592 messages, and mTBCC has 36037 messages, yielding an average improvement of 13.91%, a queue size of 20 (QS-20).

TBCC had 30330 messages and mTBCC had 27845 messages resulting in an average improvement of 10.78%, a queue size of 30(QS-30) TBCC produced 28356 messages and mTBCC yielded 26767 messages resulting in an average improvement of 5.68% and at queue size of 40(QS-40).

TBCC had 23150 messages while mTBCC had 22197 messages, providing an average improvement of 4.22% respectively for dropped messages.

In addition, at 0.5GCC, TBCC had 29401.70 times and mTBCC had 27151.41 times, producing an average improvement of 8.34%, at 0.6GCC. TBCC produced 16319.29 times and mTBCC had 15966.42 times, resulting in an average improvement of 2.19%, at 0.7GCC, TBCC yielded 13178.21 times and mTBCC produced 12581.01 times, resulting in an average improvement of 4.61%, and at 0.8GCC.

TBCC had 12333.55 times and mTBCC had 11453.23 times, yielding an average improvement of 7.63% for network transit time.

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TABLE OF CONTENT

CHAPTER ONE: INTRODUCTION
1.1 Background 1
1.2 Problem statement 3
1.3 Motivation 3
1.4 Aim and objectives 4
1.5 Significant of Research 4
1.6 Scope of the Research 5
ix
1.7 Dissertation outline 5
CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction 6
2.2 Review of fundamental concepts 6
2.2.1 Delay tolerant networks 6
2.2.1.1 Delay tolerant network architecture 7
2.2.1.2 Bundle protocol 8
2.2.2 Delay tolerant networks routing protocols 9
2.2.2.1 Flooding strategies 10
2.2.2.2 Forwarding strategies 11
2.2.3 Delay tolerant networks congestion control schemes 13
2.2.3.1 Congestion detection 14
2.2.3.2 Congestion control 14
2.2.4 Opportunistic networks 15
2.2.5 Token-based congestion control 18
2.2.6 Adaptive forwarding strategy 19
2.2.7 Opportunistic network environment (ONE) simulator 20
2.2.8 Research performance metrics 21
2.3 Review of similar works 22
CHAPTER THREE: METHODS
3.1 Introduction 32
3.2 Methodology 32
3.3 Replication of the token-based congestion control 33
3.3.1 Initializing token-based parameter 33
3.3.2 Token-based congestion control scenario settings 34
3.4 Development of a modified token-based congestion control 35
3.4.1 Modified token-based congestion control scenario settings 37
3.5 Installation and configuration 38
3.6 Opportunistic network modeling 38
3.7 Simulation model 39
3.8 Prophet routing protocol 39
3.9 Visualization 39
3.10 Validation of the congestion control strategies 40
3.11 Performance evaluation 41
3.11.1 Percentage improvement 42
3.11.1 Relevant equations 42
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1 Introduction 43
4.2 Results of the token-based congestion control strategy 43
4.3 Results of the modified token-based congestion strategy 49
4.4 Comparison of the results 55
4.4.1 Comparison of mTBCC and TBCC performance for dropped message 55
4.4.1.1 Dropped message percentage improvement 58
4.4.2 Comparison of mTBCC and TBCC performance for NTT 60
4.4.2.1 NTT percentage improvement 63
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
5.1 Summary 65
5.2 Conclusion 65
5.3 Limitation 65
5.4 Significant contribution 66
5.5 Recommendations for further work 66
REFERENCES 67

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INTRODUCTION

This section consists of two subsections. The first section addresses the fundamental concepts critical to the research. The other part of the review focus on similar works of different researchers who worked in this domain. The works are critically studied to establish the ground to bring in the contribution of this work, which is aimed to achieve.

Review of Fundamental Concepts

This section introduces the fundamental concepts pertinent to the research, which includes delay tolerant networks, delay-tolerant network routing protocols, opportunistic networks, congestion control scheme, and opportunistic network environment (ONE) simulator.

Delay Tolerant Networks

Delay tolerant networks (DTNs) represent a full division of wireless networks which requires minimum to no infrastructures and has the potential to support network functionality experiencing frequent and long-lasting partition.

DTNs are opted to tackle scenarios such as heterogeneity of standards, intermittent connectivity between adjacent nodes, lack of connected end-to-end paths as well as excessive-high delay and data error rates.

The accessibility of mobile nodes in stressed environments can immensely affect their resources, which include a central processing unit (CPU), memory, and network capacity (SuvarnaPatil & Chillerge, 2014).

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REFERENCES

Akestoridis, D.-G., Papanikos N., & Papapetrou E. (2014). Exploiting social preferences for congestion control in opportunistic networks. Paper presented at the IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2014
Akyildiz, I. F., Su W., Sankarasubramaniam Y., & Cayirci E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102-114.
An, Y., Huang J., Song H., & Wang J. (2012). A Congestion Level based end-to-end acknowledgement mechanism for Delay Tolerant Networks. Paper presented at the IEEE Global Communications Conference (GLOBECOM), 2012.
Balasubramanian, A., Levine B., & Venkataramani A. (2007). DTN routing as a resource allocation problem. Paper presented at the ACM SIGCOMM computer communication review.
Burleigh, S., Hooke A., Torgerson L., Fall K., Cerf V., Durst B., Scott K., & Weiss H. (2003). Delay-tolerant networking: an approach to interplanetary internet. IEEE Communications Magazine, 41(6), 128-136.