History and buffer rule based (forward chaining/data driven) intelligent system for storage level big data congestion handling in smart opportunistic network

Delay tolerant networks (DTN’s) is the most growing application of wireless multi-hop networking under the umbrella of research done so far in sensor networks. Numerous challenges have to be faced by such networks; because of disconnection in terms of intermittent community, long delays etc in network due to drastic mobility. An intermediate node therefore interested to take custody of the transmission till subsequent notable appropriate is located toward destination. This study specializes on this key issue how selection as best custodian node in terms of storage capacity as mobile devices have limited storage capacity for the transmission to raise delivery of the packets with less drop rate. In this research a history and buffer based totally intelligent approach based expert gadgets has been added and validated and compared with existing MAXPROP protocol. Simulation outcomes shows proposed technique outperforms MAXPROP in overall over node, network and buffer level.

[1]  Michele Zorzi,et al.  Using Bayesian Networks for Cognitive Control of Multi-hop Wireless Networks , 2010, 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE.

[2]  Karl Andersson,et al.  A web based belief rule based expert system to predict flood , 2015, iiWAS.

[3]  Leonard Barolli,et al.  Implementation and performance evaluation of two fuzzy-based systems for selection of IoT devices in opportunistic networks , 2019, J. Ambient Intell. Humaniz. Comput..

[4]  Jörg Ott,et al.  The ONE simulator for DTN protocol evaluation , 2009, SIMUTools 2009.

[5]  Nirvana Meratnia,et al.  Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks , 2009, AIAI Workshops.

[6]  Bambang Soelistijanto,et al.  Transfer Reliability and Congestion Control Strategies in Opportunistic Networks: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[7]  Samy S. Abu Naser,et al.  Rule Based System for Diagnosing Wireless Connection Problems Using SL5 Object , 2016 .

[8]  Leonard Barolli,et al.  A multi-objectives based technique for optimized routing in opportunistic networks , 2018, J. Ambient Intell. Humaniz. Comput..

[9]  Wen-Hua Ju,et al.  LOCATION ESTIMATION IN WIRELESS NETWORKS: A BAYESIAN APPROACH , 2006 .

[10]  V. Malleswara Rao,et al.  Prediction of Effective Mobile Wireless Network Data Profiling Using Data Mining Approaches , 2013 .

[11]  S. Subburam,et al.  Predictive congestion control mechanism for MANET , 2012 .

[12]  Sharda Patel,et al.  Congestion Avoidance in Mobile Ad-Hoc Networks, through Cooperative AODV Routing , 2014 .

[13]  M Sangeetha,et al.  Genetic optimization of hybrid clustering algorithm in mobile wireless sensor networks , 2018 .

[14]  Bandana Sharma.  Effective Flow Count Mechanism for Congestion Control Avoidance , 2014 .

[15]  Gürsel Serpen,et al.  AI-WSN: Adaptive and Intelligent Wireless Sensor Network , 2013, Complex Adaptive Systems.

[16]  S. Garba,et al.  Congestion Control Strategies on Integrated Routing Protocol for the Opportunistic Network: A Comparative Study and Performance Analysis , 2015 .

[17]  Milena Radenkovic,et al.  Framework for utility driven congestion control in delay tolerant opportunistic networks , 2011, 2011 7th International Wireless Communications and Mobile Computing Conference.

[18]  Jörg Ott,et al.  Adaptive routing in mobile opportunistic networks , 2010, MSWIM '10.

[19]  Deepika Kukreja,et al.  Supernode routing: a grid-based message passing scheme for sparse opportunistic networks , 2019, J. Ambient Intell. Humaniz. Comput..

[20]  Huang,et al.  [IEEE 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008) - Gino-wan, Okinawa, Japan (2008.03.25-2008.03.28)] 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008) - A Survey , 2008 .

[21]  Daru Pan,et al.  A comprehensive-integrated buffer management strategy for opportunistic networks , 2013, EURASIP J. Wirel. Commun. Netw..

[22]  Jaime Lloret,et al.  Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.