Providing healthcare services on-the-fly using multi-player cooperation game theory in Internet of Vehicles (IoV) environment

Abstract Internet of Vehicles (IoV) is a leading technology of the present era. It has gained huge attention with respect to its implementation in wide variety of domains ranging from traffic safety to infotainment applications. However, IoV can also be extended to healthcare domain, where the patients can be provided healthcare services on-the-fly. We extend this novel concept in this paper and refer it as “Healthcare services on-the-fly”. The concept of game theory has been used among the vehicles to access the healthcare services while traveling. The vehicles act as players in the game and tend to form and split coalitions to access these services. Learning automata (LA) act as the players for interaction with the environment and take appropriate actions based on reward and penalty. Apart from this, Virtual Machine (VM) scheduling algorithm for efficient utilization of resources at cloud level has also been formulated. A stochastic reward net (SRN)-based model is used to represent the coalition formation and splitting with respect to availability of resources at cloud level. The performance of the proposed scheme is evaluated using various performance evaluation metrics. The results obtained prove the effectiveness of the proposed scheme in comparison to the best, first, and random fit schemes.

[1]  Xuemin Shen,et al.  Connected Vehicles: Solutions and Challenges , 2014, IEEE Internet of Things Journal.

[2]  Arif Ghafoor,et al.  A distributed cloud architecture for mobile multimedia services , 2013, IEEE Network.

[3]  Joel J. P. C. Rodrigues,et al.  Intelligent Mobile Video Surveillance System as a Bayesian Coalition Game in Vehicular Sensor Networks: Learning Automata Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[4]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[5]  Joel J. P. C. Rodrigues,et al.  Bayesian Coalition Game for Contention-Aware Reliable Data Forwarding in Vehicular Mobile Cloud , 2015, Future Gener. Comput. Syst..

[6]  Mario Gerla,et al.  Vehicular networks and the future of the mobile internet , 2011, Comput. Networks.

[7]  Meikang Qiu,et al.  High reliable real-time bandwidth scheduling for virtual machines with hidden Markov predicting in telehealth platform , 2015, Future Gener. Comput. Syst..

[8]  Subhas C. Misra,et al.  An intelligent RFID-enabled authentication scheme for healthcare applications in vehicular mobile cloud , 2016, Peer-to-Peer Netw. Appl..

[9]  Vivian Martins Gomes,et al.  Studying Close Approaches for a Cloud of Particles Considering Atmospheric Drag , 2013 .

[10]  Albert Mo Kim Cheng,et al.  An auto-scaling mechanism for virtual resources to support mobile, pervasive, real-time healthcare applications in cloud computing , 2013, IEEE Network.

[11]  Kishor S. Trivedi,et al.  Stochastic Petri Net Models of Polling Systems , 1990, IEEE J. Sel. Areas Commun..

[12]  Saswati Mukherjee,et al.  A genetic algorithm based scheduler for cloud environment , 2013, 2013 4th International Conference on Computer and Communication Technology (ICCCT).

[13]  Djamal Zeghlache,et al.  Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[14]  Rajkumar Buyya,et al.  A survey on vehicular cloud computing , 2014, J. Netw. Comput. Appl..

[15]  Mario Gerla,et al.  Vehicular Cloud Computing , 2012, 2012 The 11th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[16]  Naveen K. Chilamkurti,et al.  Bayesian Coalition Game as-a-Service for Content Distribution in Internet of Vehicles , 2014, IEEE Internet of Things Journal.

[17]  Mohammad S. Obaidat,et al.  QoS-Guaranteed Bandwidth Shifting and Redistribution in Mobile Cloud Environment , 2014, IEEE Transactions on Cloud Computing.

[18]  Jongsung Kim,et al.  Probabilistic trust aware data replica placement strategy for online video streaming applications in vehicular delay tolerant networks , 2013, Math. Comput. Model..

[19]  Xiaojing Liu,et al.  A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing , 2013 .

[20]  Seema Bawa,et al.  A systematic review on routing protocols for Vehicular Ad Hoc Networks , 2014, Veh. Commun..

[21]  Jong Hyuk Park,et al.  ALCA: agent learning–based clustering algorithm in vehicular ad hoc networks , 2012, Personal and Ubiquitous Computing.

[22]  Joel J. P. C. Rodrigues,et al.  An intelligent approach for building a secure decentralized public key infrastructure in VANET , 2015, J. Comput. Syst. Sci..

[23]  Yuan Zhao,et al.  When mobile terminals meet the cloud: computation offloading as the bridge , 2013, IEEE Network.

[24]  Rajkumar Buyya,et al.  Bandwidth‐aware divisible task scheduling for cloud computing , 2014, Softw. Pract. Exp..

[25]  Naveen K. Chilamkurti,et al.  Bayesian coalition game for the internet of things: an ambient intelligence-based evaluation , 2015, IEEE Communications Magazine.

[26]  Xiaofei Wang,et al.  Cloud-enabled wireless body area networks for pervasive healthcare , 2013, IEEE Network.

[27]  Mohammad S. Obaidat,et al.  Networks of learning automata for the vehicular environment: a performance analysis study , 2014, IEEE Wireless Communications.

[28]  Seema Bawa,et al.  QoS-Aware Data Dissemination for Dense Urban Regions in Vehicular Ad Hoc Networks , 2015, Mob. Networks Appl..

[29]  Dusit Niyato,et al.  Coalition-Based Cooperative Packet Delivery under Uncertainty: A Dynamic Bayesian Coalitional Game , 2013, IEEE Transactions on Mobile Computing.

[30]  Kishor S. Trivedi,et al.  Dependability modeling using Petri-nets , 1995 .

[31]  Mohammad S. Obaidat,et al.  Collaborative Learning Automata-Based Routing for Rescue Operations in Dense Urban Regions Using Vehicular Sensor Networks , 2015, IEEE Systems Journal.

[32]  Giovanni Pau,et al.  On the Effectiveness of an Opportunistic Traffic Management System for Vehicular Networks , 2011, IEEE Transactions on Intelligent Transportation Systems.

[33]  Joel J. P. C. Rodrigues,et al.  Clustering in vehicular ad hoc networks: Taxonomy, challenges and solutions , 2014, Veh. Commun..

[34]  Muhammad Shiraz,et al.  A study on virtual machine deployment for application outsourcing in mobile cloud computing , 2012, The Journal of Supercomputing.

[35]  M. A. L. Thathachar,et al.  Networks of Learning Automata , 2004 .

[36]  Neeraj Kumar,et al.  Peer-to-Peer Cooperative Caching for Data Dissemination in Urban Vehicular Communications , 2014, IEEE Systems Journal.

[37]  Kishor S. Trivedi,et al.  Performability Evaluation of Grid Environments Using Stochastic Reward Nets , 2015, IEEE Transactions on Dependable and Secure Computing.

[38]  Tarik Taleb,et al.  Follow me cloud: interworking federated clouds and distributed mobile networks , 2013, IEEE Network.

[39]  Paulo F. Ribeiro,et al.  A Game Theory Strategy to Integrate Distributed Agent-Based Functions in Smart Grids , 2013, IEEE Transactions on Smart Grid.

[40]  Der-Jiunn Deng,et al.  LA-EEHSC: Learning automata-based energy efficient heterogeneous selective clustering for wireless sensor networks , 2014, J. Netw. Comput. Appl..

[41]  Naveen K. Chilamkurti,et al.  Learning Automata-based Opportunistic Data Aggregation and Forwarding scheme for alert generation in Vehicular Ad Hoc Networks , 2014, Comput. Commun..

[42]  Mohammad S. Obaidat,et al.  Coalition Games for Spatio-Temporal Big Data in Internet of Vehicles Environment: A Comparative Analysis , 2015, IEEE Internet of Things Journal.

[43]  Hui Li,et al.  Toward a unified elastic computing platform for smartphones with cloud support , 2013, IEEE Network.