QoS-Aware Dispersed Dynamic Mapping of Virtual Sensors in Sensor-Cloud

In this paper, we study the problem of dynamic mapping of virtual sensors in sensor-cloud for provisioning high quality of Sensors-as-a-Service (Se-aaS) in the presence of multiple sensor-owners and heterogeneous sensor nodes. We divide this problem into two subproblems optimal dispersed node selection and optimal data-rate distribution, and analyze that these problems are NP-complete. Hence, we propose a game theory-based online scheme, named QADMAP, to solve these two problems in polynomial time. For the optimal node selection problem, we design a dynamic coalition-formation game-based online scheme, while maximizing the dispersion index of the selected nodes. On the other hand, we propose an evolutionary game theory-based scheme for distributing the data-rate requirements of the services among the selected nodes, optimally. As per our knowledge, none of the existing works on dynamic mapping of virtual sensors considers the stochastic behavior of sensor-cloud for provisioning Se-aaS. From simulations, we observe that, using QADMAP, the energy consumption of the network reduces by 29.88-31.73%, thereby improving the QoS in terms of service availability by 11% and increasing the profit of the SCSP by 3.63-9.82% compared to the existing benchmark schemes.

[1]  Narendra Singh Raghuwanshi,et al.  Dynamic Duty Scheduling for Green Sensor-Cloud Applications , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[2]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[3]  Mohammad S. Obaidat,et al.  Connectivity and coverage in machine-type communications , 2017, 2017 IEEE International Conference on Communications (ICC).

[4]  George K. Karagiannidis,et al.  Realizing 5G vision through Cloud RAN: technologies, challenges, and trends , 2018, EURASIP J. Wirel. Commun. Netw..

[5]  Mohammad S. Obaidat,et al.  On Theoretical Modeling of Sensor Cloud: A Paradigm Shift From Wireless Sensor Network , 2017, IEEE Systems Journal.

[6]  Luci Pirmez,et al.  An efficient heuristic for selecting active nodes in wireless sensor networks , 2006, Comput. Networks.

[7]  Matteo Cesana,et al.  On optimal resource allocation in virtual sensor networks , 2016, Ad Hoc Networks.

[8]  Albert Y. Zomaya,et al.  Olympus: The Cloud of Sensors , 2015, IEEE Cloud Computing.

[9]  Sudip Misra,et al.  Optimal composition of a virtual sensor for efficient virtualization within sensor-cloud , 2015, 2015 IEEE International Conference on Communications (ICC).

[10]  João M. F. Xavier,et al.  Sensor Selection for Event Detection in Wireless Sensor Networks , 2010, IEEE Transactions on Signal Processing.

[11]  Sanjay Kumar Madria,et al.  Risk Assessment in a Sensor Cloud Framework Using Attack Graphs , 2017, IEEE Transactions on Services Computing.

[12]  Sudip Misra,et al.  Distributed topology management for wireless multimedia sensor networks: exploiting connectivity and cooperation , 2015, Int. J. Commun. Syst..

[13]  Thomas Lagkas,et al.  Network Protocols, Schemes, and Mechanisms for Internet of Things (IoT): Features, Open Challenges, and Trends , 2018, Wirel. Commun. Mob. Comput..

[14]  Sudip Misra,et al.  Cache-enabled sensor-cloud: The economic facet , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[15]  Sudip Misra,et al.  Dynamic Trust Enforcing Pricing Scheme for Sensors-as-a-Service in Sensor-Cloud Infrastructure , 2021, 2021 IEEE World Congress on Services (SERVICES).

[16]  Sudip Misra,et al.  DIVISOR: Dynamic virtual sensor formation for overlapping region in IoT-based sensor-cloud , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[17]  Ioannis D. Moscholios,et al.  Towards Distributed Data Management in Fog Computing , 2018, Wirel. Commun. Mob. Comput..

[18]  Narendra Singh Raghuwanshi,et al.  DVSP: Dynamic Virtual Sensor Provisioning in Sensor–Cloud-Based Internet of Things , 2019, IEEE Internet of Things Journal.

[19]  Ramesh Govindan,et al.  RCRT: Rate-controlled reliable transport protocol for wireless sensor networks , 2010, TOSN.

[20]  Samiran Chattopadhyay,et al.  Optimal gateway selection in sensor-cloud framework for health monitoring , 2014, IET Wirel. Sens. Syst..

[21]  Albert Y. Zomaya,et al.  Zeus: A resource allocation algorithm for the cloud of sensors , 2019, Future Gener. Comput. Syst..

[22]  Sudip Misra,et al.  Dynamic Optimal Pricing for Heterogeneous Service-Oriented Architecture of Sensor-Cloud Infrastructure , 2017, IEEE Transactions on Services Computing.

[23]  Schahram Dustdar,et al.  Optimizing Elastic IoT Application Deployments , 2018, IEEE Transactions on Services Computing.

[24]  Richard E. Wendell,et al.  Location Theory, Dominance, and Convexity , 1973, Oper. Res..

[25]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[26]  Madoka Yuriyama,et al.  A New Model of Accelerating Service Innovation with Sensor-Cloud Infrastructure , 2011, 2011 Annual SRII Global Conference.

[27]  Sungwook Kim.  An Effective Sensor Cloud Control Scheme Based on a Two-Stage Game Approach , 2018, IEEE Access.

[28]  Nileshsingh V. Thakur,et al.  LOAD BALANCING BASED APPROACH TO IMPROVE LIFETIME OF WIRELESS SENSOR NETWORK , 2012 .

[29]  Sudip Misra,et al.  Energy-efficient data transmission in sensor-cloud , 2015, 2015 Applications and Innovations in Mobile Computing (AIMoC).