Optimizing the energy envelope in the Internet of Things using predictive cubic splines

The paradigm of Internet of Things (IoT) is the result of rapid advances in the development of small low powered computing devices with wireless connectivity. This has given rise to a plethora of new applications, including, but not limited to monitoring, sensing, intrusion detection and others. Due to the nature of these applications, IoT devices are often burdened with receiving and transmitting a large volume of data that is time and delay sensitive. Furthermore, since most of these devices are battery powered, a data deluge often renders parts of the network depleted of energy, causing that part to go dark. In this work, we propose an energy optimization framework based on predictive cubic splines that anticipate a sudden increase in the bandwidth and minimize energy consumed by these devices while still maintaining an optimum degree of availability and reducing bottlenecks in the data flow.

[1]  Benjamin C. Lee,et al.  A framework for collaborative sensing and processing of mobile data streams: demo , 2016, MobiCom.

[2]  Chen-Khong Tham,et al.  A load balancing scheme for sensing and analytics on a mobile edge computing network , 2017, 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[3]  D. Schweikert.  An Interpolation Curve Using a Spline in Tension , 1966 .

[4]  Steven Pruess,et al.  Properties of splines in tension , 1976 .

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

[6]  Dimitrios Gunopulos,et al.  Misco: a MapReduce framework for mobile systems , 2010, PETRA '10.

[7]  J. Gregory,et al.  Piecewise rational quadratic interpola-tion to monotonic data , 1982 .

[8]  Peter R. Pietzuch,et al.  Network-aware stream query processing in mobile ad-hoc networks , 2015, MILCOM 2015 - 2015 IEEE Military Communications Conference.

[9]  Jian Li,et al.  Analytical modeling and mitigation techniques for the energy hole problem in sensor networks , 2007, Pervasive Mob. Comput..

[10]  Dimitrios Koutsonikolas,et al.  Power-throughput tradeoffs of 802.11n/ac in smartphones , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[11]  Steven Pruess,et al.  Alternatives to the exponential spline in tension , 1979 .

[12]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

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

[14]  George Wolberg,et al.  Digital image warping , 1990 .

[15]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[16]  J. Gregory,et al.  Shape Preserving Piecewise Rational Interpolation , 1985 .

[17]  Srikanth V. Krishnamurthy,et al.  Computing while charging: building a distributed computing infrastructure using smartphones , 2012, CoNEXT '12.

[18]  Ramesh Govindan,et al.  Online optimization of 802.11 mesh networks , 2009, CoNEXT '09.

[19]  Qigang Gao,et al.  An efficient ensemble classification method based on novel classifier selection technique , 2012, WIMS '12.

[20]  G. Nielson.  SOME PIECEWISE POLYNOMIAL ALTERNATIVES TO SPLINES UNDER TENSION , 1974 .

[21]  David F. McAllister,et al.  Algorithms for Computing Shape Preserving Spline Interpolations to Data , 1977 .

[22]  Henry Wolkowicz,et al.  Post-processing piecewise cubics for monotonicity , 1989 .

[23]  Ashutosh Sabharwal,et al.  An Axiomatic Theory of Fairness in Network Resource Allocation , 2009, 2010 Proceedings IEEE INFOCOM.

[24]  Li-Shiuan Peh,et al.  MobiStreams: A Reliable Distributed Stream Processing System for Mobile Devices , 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.

[25]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[26]  Shusen Yang,et al.  Distributed optimization in energy harvesting sensor networks with dynamic in-network data processing , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[27]  C. R. Deboor,et al.  A practical guide to splines , 1978 .