Energy-Aware Computation Offloading and Transmit Power Allocation in Ultradense IoT Networks

To meet the surging demands on network throughput and spectrum resources arising with billions of Internet-of-Things mobile devices (IMDs), ultradense networks are envisioned to be a promising technology, which gives rise to the so-called ultradense Internet-of-Things (IoT) networks. Meanwhile, with the constant emergence of new IoT applications, the conflict between computing-intensive applications and resource-constrained IMDs is increasingly prominent. By offloading computing-intensive tasks to the edge servers in close proximity, mobile-edge computing is expected as an effective solution to address this issue. However, computation offloading research in ultradense IoT networks is still scarce until now. Toward this end, we provide this paper to study the energy-aware task offloading problem with multiple edge servers in ultradense IoT networks, where diverse kinds of computation tasks are randomly requested by the IMDs and the computing resources at the edge servers change dynamically. An iterative searching-based task offloading scheme is proposed as our solution, which jointly optimizes task offloading, computational frequency scaling, and transmit power allocation. Extensive numerical results demonstrate the superior performance of conducting task offloading among multiple edge servers, and corroborate the advantages of our scheme over existing works which either fixed computational frequency and transmit power, or neglected the impact of the IMDs’ residual battery.

[1]  Henri E. Bal,et al.  SWAN-lake: opportunistic distributed sensing for Android smartphones , 2016 .

[2]  B. Golden,et al.  Solving the Maximum Cardinality Bin Packing Problem with a Weight Annealing-Based Algorithm , 2009 .

[3]  Nei Kato,et al.  On Minimizing Energy Consumption in FiWi Enhanced LTE-A HetNets , 2018, IEEE Transactions on Emerging Topics in Computing.

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

[5]  Ju Ren,et al.  Two Time-Scale Resource Management for Green Internet of Things Networks , 2019, IEEE Internet of Things Journal.

[6]  Xuemin Shen,et al.  RF Energy Harvesting and Transfer in Cognitive Radio Sensor Networks: Opportunities and Challenges , 2018, IEEE Communications Magazine.

[7]  Jun Xu,et al.  Narrowband Internet of Things: Evolutions, Technologies, and Open Issues , 2018, IEEE Internet of Things Journal.

[8]  Henri E. Bal,et al.  Kea: A Computation Offloading System for Smartphone Sensor Data , 2017, 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom).

[9]  Sergio Barbarossa,et al.  Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing , 2014, IEEE Transactions on Signal and Information Processing over Networks.

[10]  Ju Ren,et al.  Serving at the Edge: A Scalable IoT Architecture Based on Transparent Computing , 2017, IEEE Network.

[11]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[12]  Nei Kato,et al.  GT-QoSec: A Game-Theoretic Joint Optimization of QoS and Security for Differentiated Services in Next Generation Heterogeneous Networks , 2017, IEEE Transactions on Wireless Communications.

[13]  Simon Lacoste-Julien,et al.  Convergence Rate of Frank-Wolfe for Non-Convex Objectives , 2016, ArXiv.

[14]  Gang Qu,et al.  What is the limit of energy saving by dynamic voltage scaling? , 2001, IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281).

[15]  Thomas D. Burd,et al.  Processor design for portable systems , 1996, J. VLSI Signal Process..

[16]  Ju Ren,et al.  BOAT: A Block-Streaming App Execution Scheme for Lightweight IoT Devices , 2018, IEEE Internet of Things Journal.

[17]  Igor Bisio,et al.  Smart Probabilistic Fingerprinting for Indoor Localization over Fog Computing Platforms , 2016, 2016 5th IEEE International Conference on Cloud Networking (Cloudnet).

[18]  Jiajia Liu,et al.  Collaborative Computation Offloading for Multiaccess Edge Computing Over Fiber–Wireless Networks , 2018, IEEE Transactions on Vehicular Technology.

[19]  Igor Bisio,et al.  Context Awareness over Transient Clouds , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[20]  Yan Zhang,et al.  Mobile Edge Computing: A Survey , 2018, IEEE Internet of Things Journal.

[21]  Feng Zhao,et al.  Fine-grained energy profiling for power-aware application design , 2008, PERV.

[22]  Byung-Gon Chun,et al.  CloneCloud: elastic execution between mobile device and cloud , 2011, EuroSys '11.

[23]  Wei-Ho Chung,et al.  Enabling Low-Latency Applications in Fog-Radio Access Networks , 2017, IEEE Network.

[24]  Zhigang Chen,et al.  Utility-Optimal Resource Management and Allocation Algorithm for Energy Harvesting Cognitive Radio Sensor Networks , 2016, IEEE Journal on Selected Areas in Communications.

[25]  Weifa Liang,et al.  Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks , 2017, IEEE Transactions on Cloud Computing.

[26]  Tiago Gomes,et al.  A 6LoWPAN Accelerator for Internet of Things Endpoint Devices , 2018, IEEE Internet of Things Journal.

[27]  Igor Bisio,et al.  Context-awareness over transient cloud in D2D networks: energy performance analysis and evaluation , 2017, Trans. Emerg. Telecommun. Technol..

[28]  Min Chen,et al.  Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network , 2018, IEEE Journal on Selected Areas in Communications.

[29]  Nei Kato,et al.  New Perspectives on Future Smart FiWi Networks: Scalability, Reliability, and Energy Efficiency , 2016, IEEE Communications Surveys & Tutorials.

[30]  Jiajia Liu,et al.  Coordinated Multipoint-Based Uplink Transmission in Internet of Things Powered by Energy Harvesting , 2018, IEEE Internet of Things Journal.

[31]  Lars Thiele,et al.  Coordinated multipoint: Concepts, performance, and field trial results , 2011, IEEE Communications Magazine.

[32]  Yueming Cai,et al.  Stochastic computation offloading game for mobile cloud computing , 2016, 2016 IEEE/CIC International Conference on Communications in China (ICCC).

[33]  Nei Kato,et al.  Energy Consumption Minimization for FiWi Enhanced LTE-A HetNets with UE Connection Constraint , 2016, IEEE Communications Magazine.

[34]  Ryu Miura,et al.  On A Novel Adaptive UAV-Mounted Cloudlet-Aided Recommendation System for LBSNs , 2019, IEEE Transactions on Emerging Topics in Computing.

[35]  Xuemin Shen,et al.  Energy-Sustainable Traffic Steering for 5G Mobile Networks , 2017, IEEE Communications Magazine.

[36]  Li Zhou,et al.  Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks , 2018, IEEE Internet of Things Journal.

[37]  Qianbin Chen,et al.  Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing , 2017, IEEE Transactions on Vehicular Technology.

[38]  Nei Kato,et al.  Hybrid Method for Minimizing Service Delay in Edge Cloud Computing Through VM Migration and Transmission Power Control , 2017, IEEE Transactions on Computers.

[39]  Henri E. Bal,et al.  Cowbird: A Flexible Cloud-Based Framework for Combining Smartphone Sensors and IoT , 2017, 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud).

[40]  Khaled Ben Letaief,et al.  Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices , 2016, IEEE Journal on Selected Areas in Communications.

[41]  Yao Zheng,et al.  A Feedback Control-Based Crowd Dynamics Management in IoT System , 2017, IEEE Internet of Things Journal.

[42]  Yueming Cai,et al.  Stochastic Game-Theoretic Spectrum Access in Distributed and Dynamic Environment , 2015, IEEE Transactions on Vehicular Technology.

[43]  Zhigang Chen,et al.  Energy-Harvesting-Aided Spectrum Sensing and Data Transmission in Heterogeneous Cognitive Radio Sensor Network , 2016, IEEE Transactions on Vehicular Technology.

[44]  Tapani Ristaniemi,et al.  Multiobjective Optimization for Computation Offloading in Fog Computing , 2018, IEEE Internet of Things Journal.

[45]  Issa M. Khalil,et al.  Online Auction of Cloud Resources in Support of the Internet of Things , 2017, IEEE Internet of Things Journal.

[46]  Shaolei Ren,et al.  Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing , 2017, IEEE Transactions on Cognitive Communications and Networking.

[47]  Antonio Iera,et al.  Edge Computing and Social Internet of Things for Large-Scale Smart Environments Development , 2018, IEEE Internet of Things Journal.

[48]  Jun Guo,et al.  Mobile Edge Computing Empowered Energy Efficient Task Offloading in 5G , 2018, IEEE Transactions on Vehicular Technology.

[49]  John A. Stankovic,et al.  Research Directions for the Internet of Things , 2014, IEEE Internet of Things Journal.

[50]  Jiajia Liu,et al.  Collaborative Mobile Edge Computation Offloading for IoT over Fiber-Wireless Networks , 2018, IEEE Network.

[51]  Ju Ren,et al.  Delay-Optimal Proactive Service Framework for Block-Stream as a Service , 2018, IEEE Wireless Communications Letters.

[52]  Enzo Mingozzi,et al.  Edge-Centric Distributed Discovery and Access in the Internet of Things , 2018, IEEE Internet of Things Journal.

[53]  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.

[54]  Ha H. Nguyen,et al.  Fast Global Optimal Power Allocation in Wireless Networks by Local D.C. Programming , 2012, IEEE Transactions on Wireless Communications.

[55]  Amr M. Youssef,et al.  Ultra-Dense Networks: A Survey , 2016, IEEE Communications Surveys & Tutorials.