Energy Optimization in Association-Free Fog-IoT Networks

Minimizing energy consumption while providing quality of service (QoS) is of paramount importance for energy-constrained networks, such as Internet of Things (IoT). The emergence of fog computing in IoT has the great potential to reduce the energy consumption of IoT nodes, which also known as terminal nodes (TNs), and also minimizing the task delays. However, this in general comes at the price of the higher energy consumption of fog nodes (FNs). This paper aims to study the energy consumption tradeoff between TNs and FNs in the Fog-IoT system. To this end, we first propose a new protocol, where TNs immediately broadcast their data with the certain transmission rate to potentially all FNs without the need of prior determination which FN to associate with. Upon receiving the requests, FNs determine whether to process the requests locally or forward them to the cloud center, depending on whether the FNs are overloaded or not. By considering the fading channels between TNs and FNs, we mathematically characterize the energy consumption tradeoff between TNs and FNs by varying the broadcasting rate of TNs.

[1]  Kaibin Huang,et al.  Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management , 2018, IEEE Transactions on Wireless Communications.

[2]  Supeng Leng,et al.  Energy-Efficient Transmission Schemes for Cooperative Wireless Powered Cellular Networks , 2019, IEEE Transactions on Green Communications and Networking.

[3]  Yang Yang,et al.  DEBTS: Delay Energy Balanced Task Scheduling in Homogeneous Fog Networks , 2018, IEEE Internet of Things Journal.

[4]  Nei Kato,et al.  A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues , 2017, IEEE Communications Surveys & Tutorials.

[5]  Nei Kato,et al.  Optimal Edge Resource Allocation in IoT-Based Smart Cities , 2019, IEEE Network.

[6]  Duy Trong Ngo,et al.  A Distributed Energy-Harvesting-Aware Routing Algorithm for Heterogeneous IoT Networks , 2018, IEEE Transactions on Green Communications and Networking.

[7]  Abbas Jamalipour,et al.  Enabling interference-aware and energy-efficient coexistence of multiple wireless body area networks with unknown dynamics , 2016, IEEE Access.

[8]  Tansu Alpcan,et al.  Fog Computing May Help to Save Energy in Cloud Computing , 2016, IEEE Journal on Selected Areas in Communications.

[9]  Abbas Jamalipour,et al.  Distributed Inter-BS Cooperation Aided Energy Efficient Load Balancing for Cellular Networks , 2013, IEEE Transactions on Wireless Communications.

[10]  Zening Liu,et al.  Minimization of Weighted Bandwidth and Computation Resources of Fog Servers under Per-Task Delay Constraint , 2018, 2018 IEEE International Conference on Communications (ICC).

[11]  Ming-Tuo Zhou,et al.  FEMTO: Fair and Energy-Minimized Task Offloading for Fog-Enabled IoT Networks , 2019, IEEE Internet of Things Journal.

[12]  Daniele Tarchi,et al.  An Energy and Delay-Efficient Partial Offloading Technique for Fog Computing Architectures , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[13]  Dongwoo Kim,et al.  Probability of SNR Gain by Dual-Hop Relaying over Single-Hop Transmission in SISO Rayleigh Fading Channels , 2008, IEEE Communications Letters.

[14]  Ananthi Govindasamy,et al.  Outage probability analysis of multiple input multiple output ad-hoc networks with random topology , 2015, IET Signal Process..

[15]  Tie Qiu,et al.  Survey on fog computing: architecture, key technologies, applications and open issues , 2017, J. Netw. Comput. Appl..

[16]  Hao Liang,et al.  Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption , 2016, IEEE Internet of Things Journal.

[17]  Riti Gour,et al.  On Reducing IoT Service Delay via Fog Offloading , 2018, IEEE Internet of Things Journal.

[18]  Lei Shu,et al.  Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges , 2018, IEEE Communications Surveys & Tutorials.

[19]  Lyes Khoukhi,et al.  Multi-Tier Fog Architecture: A New Delay-Tolerant Network for IoT Data Processing , 2018, 2018 IEEE International Conference on Communications (ICC).

[20]  C. Siva Ram Murthy,et al.  Discrete Breathing: An Energy Efficient Resource Scheduling for Future Wireless Networks , 2019, IEEE Transactions on Green Communications and Networking.

[21]  Daniele Tarchi,et al.  Centralized and Distributed Architectures for Energy and Delay Efficient Fog Network-Based Edge Computing Services , 2019, IEEE Transactions on Green Communications and Networking.

[22]  Tapani Ristaniemi,et al.  Energy Efficient Optimization for Computation Offloading in Fog Computing System , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[23]  Harpreet S. Dhillon,et al.  Joint Energy and SINR Coverage in Spatially Clustered RF-Powered IoT Network , 2018, IEEE Transactions on Green Communications and Networking.

[24]  Roch H. Glitho,et al.  A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges , 2017, IEEE Communications Surveys & Tutorials.

[25]  Sayantan Choudhury,et al.  Information Transmission Over Fading Channels , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[26]  Jie Zhang,et al.  Mobile-Edge Computation Offloading for Ultradense IoT Networks , 2018, IEEE Internet of Things Journal.

[27]  Haibin Zhang,et al.  Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[28]  Abbas Jamalipour,et al.  Energy Consumption Tradeoff for Association-Free Fog-IoT , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).