Abstract
Resource allocation and offloading in green Internet of Things (IoT) relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.
- [1] . 2019. The green Internet of Things (G-IoT). Wireless Communications and Mobile Computing 2019.Google Scholar
Digital Library
- [2] . 2019. Greening Internet of Things for greener and smarter cities: A survey and future prospects. Telecommunication Systems 72, 4 (2019), 609–632.Google Scholar
Digital Library
- [3] . 2020. Hybrid seismic-electrical data acquisition station based on cloud technology and green IoT. IEEE Access, 1–1.Google Scholar
- [4] . 2020. Energy analysis of Internet of Things data mining algorithm for smart green communication networks. Computer Communications 152, (2020), 223–231.Google Scholar
Cross Ref
- [5] . 2018. Internet of things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems 86, 614–628.Google Scholar
Digital Library
- [6] . 2019. Resource allocation mechanisms and approaches on the Internet of Things. Cluster Computing 22, 4 (2019), 1253–1282.Google Scholar
Digital Library
- [7] . 2019. Transformation-based processing of typed resources for multimedia sources in the IoT environment. Wireless Networks 2019.Google Scholar
Digital Library
- [8] . 2019. An efficient anonymous mutual authentication technique for providing secure communication in mobile cloud computing for smart city applications. Sustainable Cities and Society 49, 101522.Google Scholar
Cross Ref
- [9] . 2020. A new block-based reinforcement learning approach for distributed resource allocation in clustered IoT networks. IEEE Transactions on Vehicular Technology, 1–1.Google Scholar
- [10] . 2020. Concurrent service access and management framework for usercentric future Internet of Things in smart cities. Complex & Intelligent Systems. Google Scholar
Cross Ref
- [11] . 2017. Optimizing M2M communications and quality of services in the IoT for sustainable smart cities. IEEE Transactions on Sustainable Computing 3, 1 (2017), 4–15.Google Scholar
Cross Ref
- [12] . 2020. UAV-assisted wireless powered Internet of Things: Joint trajectory optimization and resource allocation. Ad Hoc Networks 98, 102052, 2020.Google Scholar
Digital Library
- [13] . 2020. Energy efficient for UAV-enabled mobile edge computing networks: Intelligent task prediction and offloading. Computer Communications 150, (2020), 556–562.Google Scholar
Digital Library
- [14] . 2020. FairEdge: A fairness-oriented task offloading scheme for IoT applications in mobile cloudlet networks. IEEE Access 8, (2020), 13516–13526.Google Scholar
Cross Ref
- [15] . 2020. A multi-device multi-tasks management and orchestration architecture for the design of enterprise IoT applications. Future Generation Computer Systems 106, (2020), 482–500.Google Scholar
Digital Library
- [16] . 2020. A two-tier bipartite graph task allocation approach based on fuzzy clustering in cloud–fog environment. Future Generation Computer Systems 103, (2020), 79–90.Google Scholar
Digital Library
- [17] . 2019. Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT. Future Generation Computer Systems 97, (2019), 50–60.Google Scholar
Digital Library
- [18] . 2019. Edge-assisted stream scheduling scheme for the green-communication-based IoT. IEEE Internet of Things Journal 6, 4 (2019), 7282–7292.Google Scholar
Cross Ref
- [19] . 2019. Energy and cost aware scheduling with batch processing for instance-intensive IoT workflows in clouds. Future Generation Computer Systems 101, (2019), 39–50.Google Scholar
Digital Library
- [20] . 2019. Energy and time efficient task offloading and resource allocation on the generic IoT-fog-cloud architecture. Peer-to-Peer Networking and Applications 2019.Google Scholar
- [21] . 2019. Energy-efficient and delay-guaranteed workload allocation in IoT-edge-cloud computing systems. IEEE Access 7, (2019), 78685–78697.Google Scholar
Cross Ref
- [22] . 2019. Energy-efficient orchestration in wireless powered Internet of Things infrastructures. IEEE Transactions on Green Communications and Networking 3, 2 (2019), 317–328.Google Scholar
Cross Ref
- [23] . 2020. Green fog planning for optimal Internet-of-Thing task scheduling. IEEE Access 8, (2020), 1224–1234.Google Scholar
Cross Ref
- [24] . 2019. Parallel offloading in green and sustainable mobile edge computing for delay-constrained IoT system. IEEE Transactions on Vehicular Technology 68, 12 (2019), 12202–12214.Google Scholar
Cross Ref
- [25] . 2018. SEES: A scalable and energy-efficient scheme for green IoT-based heterogeneous wireless nodes. Journal of Ambient Intelligence and Humanized Computing 10, 4 (2018), 1571–1596.Google Scholar
Cross Ref
- [26] . 2020. Sensor placement and resource allocation for energy harvesting IoT networks. Digital Signal Processing 102659.Google Scholar
Cross Ref
- [27] . 2020. The redundant energy consumption laxity based algorithm to perform computation processes for IoT services. Internet of Things 100165.Google Scholar
Cross Ref
- [28] . 2019. Towards an optimal resource management for IoT based green and sustainable smart cities. Journal of Cleaner Production 220, (2019), 1167–1179.Google Scholar
Cross Ref
- [29] . 2019. Two time-scale resource management for green Internet of Things networks. IEEE Internet of Things Journal 6, 1 (2019), 545–556.Google Scholar
Cross Ref
- [30] . 2020. Workload allocation in IoT-fog-cloud architecture using a multi-objective genetic algorithm. Journal of Grid Computing 2020.Google Scholar
Cross Ref
Index Terms
Optimal Energy-Centric Resource Allocation and Offloading Scheme for Green Internet of Things Using Machine Learning
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