Ultra-Low Power IoT Traffic Monitoring System

Given the sizable anticipated proliferation of Internet of Things (IoT) devices, Forrester Research forecasts that the fleet management and transportation industry sectors will enjoy more growth than others. This may come as no surprise, since infrastructure (e.g., roadways, bridges, airports) is a prime candidate for sensor integration to provide real-time measurements and to support intelligent decisions. The predicted increase of deployed devices makes it difficult to calculate the amount of energy required for these functions. Current estimates suggest that 2 to 4% of worldwide carbon emissions can be attributed to the information and communication industry. This paper presents novel algorithms designed to optimize power consumption of an intelligent vehicle counter and classifier sensors. Each was based on an event-driven methodology wherein a control block orchestrates the work of different components and subsystems. System duty-cycle is reduced through several techniques, and a reinforcement learning algorithm is introduced to control the system power policy, according to the traffic environment. Battery life for a sensor supported by a 2300 mAh battery was extended from 48-hour, adopted all-on policy to more than 400 days when leveraging the algorithms and techniques presented in this work.

[1]  Elwin Chandra Monie,et al.  Hardware Architecture of Reinforcement Learning Scheme for Dynamic Power Management in Embedded Systems , 2007, EURASIP J. Embed. Syst..

[2]  Luca Benini,et al.  Dynamic power management using adaptive learning tree , 1999, 1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051).

[3]  Ying Bai,et al.  An ultra-wearable, wireless, low power ECG monitoring system , 2006, 2006 IEEE Biomedical Circuits and Systems Conference.

[4]  Walid Balid.  FULLY AUTONOMOUS SELF-POWERED INTELLIGENT WIRELESS SENSOR FOR REAL-TIME TRAFFIC SURVEILLANCE IN SMART CITIES , 2016 .

[5]  Chee-Pun Ooi,et al.  Power management of a wireless sensor node with solar energy harvesting technology , 2012 .

[6]  Didier Colle,et al.  Power consumption in telecommunication networks: overview and reduction strategies , 2011, IEEE Communications Magazine.

[7]  Mehrdad Nourani,et al.  Reducing leakage power in wearable medical devices using memory nap controller , 2014, 2014 IEEE Dallas Circuits and Systems Conference (DCAS).

[8]  Mihai T. Lazarescu,et al.  Design of a WSN Platform for Long-Term Environmental Monitoring for IoT Applications , 2013, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[9]  Shahriar Mirabbasi,et al.  Wireless Energy Harvesting for Internet of Things , 2014 .

[10]  Walid Balid,et al.  On the development of self-powered iot sensor for real-time traffic monitoring in smart cities , 2017, 2017 IEEE SENSORS.

[11]  Hazem H. Refai,et al.  Intelligent Vehicle Counting and Classification Sensor for Real-Time Traffic Surveillance , 2018, IEEE Transactions on Intelligent Transportation Systems.

[12]  Inwhee Joe,et al.  Energy management algorithm for solar-powered energy harvesting wireless sensor node for Internet of Things , 2016, IET Commun..

[13]  Gonzalo Mateos,et al.  Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges , 2015, 2015 IEEE International Conference on Services Computing.

[14]  David Atienza,et al.  Work-in-progress: a machine learning-based approach for power and thermal management of next-generation video coding on MPSoCs , 2017, 2017 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[15]  Mohamed tamer alamiri.  IOT SYSTEMS FOR TRAVEL TIME ESTIMATION , 2017 .

[16]  Cécile Belleudy,et al.  Power Management in Real Time Embedded Systems through Online and Adaptive Interplay of DPM and DVFS Policies , 2010, 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing.