Analyzing ant colony optimization based routing protocol against the hole problem for enhancing user's connectivity experience

Investigates the issue of holes in Pervasive Wireless Sensor Networks (PWSNs).We study the capability of an ACO-based routing protocol, named BIOSARP.To perform an analysis by involving flagged based feedback mechanism in BIOSARP.BIOSARP is further compared with ODVA protocol.BIOSARP can self-adapt to faults appearing in PWSN. This paper investigates the ant colony optimization (ACO) based routing protocol against holes (or voids) to address user's connectivity via Pervasive Wireless Sensor Networks (PWSNs). A hole is an area that has no active sensors, which makes a connection between one side of the network and the other side impossible. To avoid such holes, prior works detected them only when packets reached nodes near the hole, called dead-ends. In this case, the packets need to be rerouted, which results in additional communication cost. The ant colony optimization (ACO) approach is known to be suitable for dynamic environments, which makes it a good choice to deal with the hole problem. We study the capability of an ACO-based routing protocol, called the biologically inspired secure autonomous routing protocol (BIOSARP), for resolving this issue. Because of its routing criteria, BIOSARP does not try to detect the holes after their appearance, but rather avoids them. Network simulator 2 (ns-2) is utilized to perform an analysis by adopting a flag-based feedback mechanism in BIOSARP and is further compared with on-demand routing with the void avoidance (ODVA) protocol in terms of the delivery ratio and energy consumption. Findings clearly demonstrate that BIOSARP can efficiently maintain the network prior to any possible hole problems, by switching data forwarding to the most optimal neighboring node. Thus, it can self-adapt to faults appearing in PWSN and efficiently maintains the network communication.

[1]  Deborah Estrin,et al.  Directed diffusion: a scalable and robust communication paradigm for sensor networks , 2000, MobiCom '00.

[2]  Kashif Saleem,et al.  An Intelligent Information Security Mechanism for the Network Layer of WSN: BIOSARP , 2011, CISIS.

[3]  Maria Luisa Villani,et al.  Ant Colony Optimization for Deadlock Detection in Concurrent Systems , 2011, 2011 IEEE 35th Annual Computer Software and Applications Conference.

[4]  Deborah Estrin,et al.  Statistical model of lossy links in wireless sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[5]  Matthias Jarke,et al.  The future of e-learning: a shift to knowledge networking and social software , 2007, Int. J. Knowl. Learn..

[6]  Michael S. Hsiao,et al.  An ant colony optimization technique for abstraction-guided state justification , 2009, 2009 International Test Conference.

[7]  Lei Wang,et al.  Network coding-based multipath routing for energy efficiency in wireless sensor networks , 2012, EURASIP J. Wirel. Commun. Netw..

[8]  Norsheila Fisal,et al.  Cross layer based biological inspired self-organized routing protocol for wireless sensor network , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.

[9]  Mohamed Aissani,et al.  Repellent voids for improving geographical routing efficiency in wireless sensor networks , 2013, Int. J. Commun. Networks Distributed Syst..

[10]  David E. Culler,et al.  Telos: enabling ultra-low power wireless research , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[11]  Selcuk Okdem,et al.  Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip , 2009, Sensors.

[12]  Chenyang Lu,et al.  SPEED: a stateless protocol for real-time communication in sensor networks , 2003, 23rd International Conference on Distributed Computing Systems, 2003. Proceedings..

[13]  Jiuhui Pan,et al.  Entropy-based metrics in swarm clustering , 2009 .

[14]  Sunil Kumar,et al.  Ubiquitous Computing for Remote Cardiac Patient Monitoring: A Survey , 2008, International journal of telemedicine and applications.

[15]  Mehmet A. Orgun,et al.  Efficient Random Key Based Encryption System for Data Packet Confidentiality in WSNs , 2013, 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications.

[16]  Kashif Saleem,et al.  Enhanced Ant Colony algorithm for self-optimized data assured routing in wireless sensor networks , 2012, 2012 18th IEEE International Conference on Networks (ICON).

[17]  Basit Shahzad,et al.  Utilizing Technology in Education Environment: A Case Study , 2013, 2013 10th International Conference on Information Technology: New Generations.

[18]  Abdelhamid Mellouk,et al.  A novel approach for void avoidance in wireless sensor networks , 2010 .

[19]  Kashif Saleem,et al.  Energy efficient information assured routing based on hybrid optimization algorithm for WSNs , 2013, 2013 10th International Conference on Information Technology: New Generations.

[20]  Ying Zhang,et al.  Improvements on Ant Routing for Sensor Networks , 2004, ANTS Workshop.

[21]  Nancy Alonistioti,et al.  A lightweight framework for prediction-based resource management in future wireless networks , 2012, EURASIP J. Wirel. Commun. Netw..

[22]  Basit Shahzad,et al.  Maximization of Tweet's viewership with respect to time , 2014, 2014 World Symposium on Computer Applications & Research (WSCAR).

[23]  Jun Li,et al.  Flexible-Segmentation-Jumping Strategy to Reduce User-Perceived Latency for Video on Demand , 2011, Appl. Comput. Intell. Soft Comput..

[24]  Md. Akhtaruzzaman Adnan,et al.  Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey , 2013, Sensors.

[25]  Kashif Saleem,et al.  Empirical Studies of Bio-Inspired Self-Organized Secure Autonomous Routing Protocol , 2014, IEEE Sensors Journal.

[26]  Joseph A. Paradiso,et al.  Identifying and facilitating social interaction with a wearable wireless sensor network , 2010, Personal and Ubiquitous Computing.

[27]  Govind Sharma,et al.  An Efficient Prevention of Black Hole Problem in AODV Routing Protocol in MANET , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[28]  A. El Saddik,et al.  Ant Colony-Based Reinforcement Learning Algorithm for Routing in Wireless Sensor Networks , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[29]  Yue Wu,et al.  Evolutionary Computation and Its Applications in Neural and Fuzzy Systems , 2011, Appl. Comput. Intell. Soft Comput..

[30]  Norsheila Fisal,et al.  Secure real-time routing protocol with load distribution in wireless sensor networks , 2011, Secur. Commun. Networks.

[31]  Hassan I. Mathkour,et al.  Role of Effective Facilitator: FAST Made Effective , 2009, 2009 International Conference on Management and Service Science.

[32]  Tiande Guo,et al.  An improved ant-based routing protocol in Wireless Sensor Networks , 2006 .

[33]  Anis Koubaa,et al.  Challenges and trends in wireless ubiquitous computing systems , 2011, Personal and Ubiquitous Computing.

[34]  Sanjay Jha,et al.  The holes problem in wireless sensor networks: a survey , 2005, MOCO.

[35]  R. Sumathi,et al.  An Energy Efficient On-Demand Routing by Avoiding Voids in Wireless Sensor Network , 2012 .

[36]  Saudi Arabia,et al.  Software Risk Management and Avoidance Strategy , 2011 .