A Fuzzy-Based System for Selection of IoT Devices in Opportunistic Networks Considering IoT Device Contact Duration, Storage and Remaining Energy

The OppNets are a subclass of delay-tolerant networks where communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The Internet of Things (IoT) is the network of devices, vehicles, buildings and other items embedded with software, electronics, sensors and network connectivity that enables these objects to collect and exchange data. There are different issues for these networks. One of them is the selection of IoT devices in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT device selection in opportunistic networks. For our system, we use three input parameters: IoT Contact Duration (IDCD), IoT Device Storage (IDST) and IoT Device Remaining Energy (IDRE). The output parameter is IoT Device Selection Decision (IDSD). The simulation results show that the proposed system makes a proper selection decison of IoT-devices in opportunistic networks. The IoT device selection is increased up to 19% and 53% by increasing IDCD and IDRE respectively.

[1]  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.

[2]  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.

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

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

[5]  Fatos Xhafa,et al.  Improving reliability of JXTA-Overlay P2P platform: A comparison study for two fuzzy-based systems , 2015, J. High Speed Networks.

[6]  Deepak Kumar Sharma,et al.  KNNR:K-nearest neighbour classification based routing protocol for opportunistic networks , 2017, 2017 Tenth International Conference on Contemporary Computing (IC3).

[7]  Damla Turgut,et al.  APAWSAN: Actor positioning for aerial wireless sensor and actor networks , 2011, 2011 IEEE 36th Conference on Local Computer Networks.

[8]  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.

[9]  Damla Turgut,et al.  Local positioning for environmental monitoring in wireless sensor and actor networks , 2010, IEEE Local Computer Network Conference.

[10]  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).

[11]  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..

[12]  Dario Pompili,et al.  Communication and Coordination in Wireless Sensor and Actor Networks , 2007, IEEE Transactions on Mobile Computing.

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

[14]  Mario Gerla,et al.  Contact Duration-Aware Routing in Delay Tolerant Networks , 2017, 2017 International Conference on Networking, Architecture, and Storage (NAS).

[15]  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..

[16]  Robert Simon,et al.  The Impact of Intercontact Time within Opportunistic Networks : Protocol Implications and Mobility Models , 2009 .

[17]  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).

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

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

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

[21]  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..

[22]  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.

[23]  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.

[24]  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..

[25]  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.