IoT Node Elimination and Selection for Completing Tasks in Opportunistic Networks: A Fuzzy Logic Approach

In this work, we implement two Fuzzy-Based Systems: Node Elimination System (NES) and Node Selection System (NSS) for IoT node elimination and selection in OppNets. We use three input parameters for NES: Node’s Distance to Event (NDE), Node’s Battery Level (NBL), Node’s Free Buffer Space (NFBS) and four input parameters for NSS: Node’s Number of Past Encounters (NNPE), Node’s Unique Encounters (NUE), Node Inter Contact Time (NICT), Node Contact Duration (NCD). The output parameter is IoT Node Selection Possibility (NSP). The results show that the proposed systems make a proper elimination and selection decision for IoT nodes in OppNets.

[1]  Isaac Woungang,et al.  HBPR: History Based Prediction for Routing in Infrastructure-less Opportunistic Networks , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[2]  Fatos Xhafa,et al.  A comparison study for two fuzzy-based systems: improving reliability and security of JXTA-overlay P2P platform , 2016, Soft Comput..

[3]  M. Grabisch.  The application of fuzzy integrals in multicriteria decision making , 1996 .

[4]  Nobuo Funabiki,et al.  Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system , 2017, Int. J. Space Based Situated Comput..

[5]  Leonard Barolli,et al.  A multi-modal simulation system for wireless sensor networks: a comparison study considering stationary and mobile sink and event , 2015, J. Ambient Intell. Humaniz. Comput..

[6]  Leonard Barolli,et al.  FACS-MP: A fuzzy admission control system with many priorities for wireless cellular networks and its performance evaluation , 2015, J. High Speed Networks.

[7]  Leonard Barolli,et al.  A Fuzzy-Based CAC Scheme for Cellular Networks Considering Security , 2014, 2014 17th International Conference on Network-Based Information Systems.

[8]  Leonard Barolli,et al.  A CAC Scheme Based on Fuzzy Logic for Cellular Networks Considering Security and Priority Parameters , 2014, 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications.

[9]  Fatos Xhafa,et al.  Trustworthiness in P2P: performance behaviour of two fuzzy-based systems for JXTA-overlay platform , 2014, Soft Comput..

[10]  Leonard Barolli,et al.  A comparison study of two fuzzy-based systems for selection of actor node in wireless sensor actor networks , 2015, J. Ambient Intell. Humaniz. Comput..

[11]  Leonard Barolli,et al.  Integrating Wireless Cellular and Ad-Hoc Networks Using Fuzzy Logic Considering Node Mobility and Security , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops.

[12]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[13]  Panwit Tuwanut,et al.  A survey on internet of things architecture, protocols, possible applications, security, privacy, real-world implementation and future trends , 2015, 2015 IEEE 16th International Conference on Communication Technology (ICCT).

[14]  Leonard Barolli,et al.  An Integrated System for Wireless Cellular and Ad-Hoc Networks Using Fuzzy Logic , 2014, 2014 International Conference on Intelligent Networking and Collaborative Systems.

[15]  Fatos Xhafa,et al.  A Fuzzy-Based System for Peer Reliability in JXTA-Overlay P2P Considering Number of Interactions , 2013, 2013 16th International Conference on Network-Based Information Systems.

[16]  J. Mendel.  Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[17]  Fatos Xhafa,et al.  Goodput and PDR analysis of AODV, OLSR and DYMO protocols for vehicular networks using CAVENET , 2011, Int. J. Grid Util. Comput..

[18]  Leonard Barolli,et al.  FBMIS: A Fuzzy-Based Multi-interface System for Cellular and Ad Hoc Networks , 2014, 2014 IEEE 28th International Conference on Advanced Information Networking and Applications.