Adaptive radio and transmission power selection for Internet of Things

Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various Internet of Things (IoT) applications. However, none of the existing technologies delivers optimal performance across all critical quality of service (QoS) dimensions under varying environmental conditions. Using a single wireless technology therefore cannot meet the demands of varying workloads or changing environmental conditions. This problem is exacerbated with the increasing interest in placing embedded devices on the user's body or other mobile objects in mobile IoT applications. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS, an adaptive radio and transmission power selection system, which makes available multiple wireless technologies at runtime and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability.

[1]  Erich M. Nahum,et al.  Design, implementation, and evaluation of energy-aware multi-path TCP , 2015, CoNEXT.

[2]  Minghua Chen,et al.  Energy efficient multipath TCP for mobile devices , 2014, MobiHoc '14.

[3]  Roger Wattenhofer,et al.  Does topology control reduce interference? , 2004, MobiHoc '04.

[4]  Arun Venkataramani,et al.  Energy consumption in mobile phones: a measurement study and implications for network applications , 2009, IMC '09.

[5]  Raghupathy Sivakumar,et al.  On effectively exploiting multiple wireless interfaces in mobile hosts , 2009, CoNEXT '09.

[6]  Ning Ding,et al.  Smartphone Energy Drain in the Wild , 2015, SIGMETRICS.

[7]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[8]  Kamin Whitehouse,et al.  Towards Stable Network Performance in Wireless Sensor Networks , 2009, 2009 30th IEEE Real-Time Systems Symposium.

[9]  David E. Culler,et al.  System Design for a Synergistic, Low Power Mote/BLE Embedded Platform , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[10]  Johannes O. Royset,et al.  Engineering Decisions under Risk Averseness , 2015 .

[11]  Feng Qian,et al.  An in-depth understanding of multipath TCP on mobile devices: measurement and system design , 2016, MobiCom.

[12]  Insik Shin,et al.  GreenBag: Energy-Efficient Bandwidth Aggregation for Real-Time Streaming in Heterogeneous Mobile Wireless Networks , 2013, 2013 IEEE 34th Real-Time Systems Symposium.

[13]  Chenyang Lu,et al.  Practical control of transmission power for Wireless Sensor Networks , 2012, 2012 20th IEEE International Conference on Network Protocols (ICNP).

[14]  Feng Qian,et al.  A close examination of performance and power characteristics of 4G LTE networks , 2012, MobiSys '12.

[15]  Ramachandran Ramjee,et al.  Bartendr: a practical approach to energy-aware cellular data scheduling , 2010, MobiCom.

[16]  Yan Gao,et al.  Topology Control for Maintaining Network Connectivity and Maximizing Network Capacity under the Physical Model , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[17]  Chenyang Lu,et al.  Robust topology control for indoor wireless sensor networks , 2008, SenSys '08.

[18]  Cheng-Hsin Hsu,et al.  MultiNets: A system for real-time switching between multiple network interfaces on mobile devices , 2014, ACM Trans. Embed. Comput. Syst..

[19]  Lei Yang,et al.  Accurate online power estimation and automatic battery behavior based power model generation for smartphones , 2010, 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[20]  Sang Hyuk Son,et al.  ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks , 2016, TOSN.

[21]  Fengyuan Xu,et al.  V-edge: Fast Self-constructive Power Modeling of Smartphones Based on Battery Voltage Dynamics , 2013, NSDI.

[22]  Chenyang Lu,et al.  Self-Adapting MAC Layer for Wireless Sensor Networks , 2013, 2013 IEEE 34th Real-Time Systems Symposium.

[23]  R. Rockafellar,et al.  Conditional Value-at-Risk for General Loss Distributions , 2001 .

[24]  Jeong‐Soo Park.  Optimal Latin-hypercube designs for computer experiments , 1994 .

[25]  Ιωάννης Μανώλης,et al.  Οδηγός για το Raspberry Pi 3 Model B , 2017 .

[26]  M. Jünger,et al.  50 Years of Integer Programming 1958-2008 - From the Early Years to the State-of-the-Art , 2010 .

[27]  Khaled A. Harras,et al.  Bandwidth Aggregation Techniques in Heterogeneous Multi-homed Devices: A Survey , 2015, Comput. Networks.

[28]  Ning Ding,et al.  Characterizing and modeling the impact of wireless signal strength on smartphone battery drain , 2013, SIGMETRICS '13.

[29]  Ben Y. Zhao,et al.  Energy and Performance of Smartphone Radio Bundling in Outdoor Environments , 2015, WWW.