Abstract
Healthcare applications supported by the Internet of Things enable personalized monitoring of a patient in everyday settings. Such applications often consist of battery-powered sensors coupled to smart gateways at the edge layer. Smart gateways offer several local computing and storage services (e.g., data aggregation, compression, local decision making), and also provide an opportunity for implementing local closed-loop optimization of different parameters of the sensor layer, particularly energy consumption. To implement efficient optimization methods, information regarding the context and state of patients need to be considered to find opportunities to adjust energy to demanded accuracy. Edge-assisted optimization can manage energy consumption of the sensor layer but may also adversely affect the quality of sensed data, which could compromise the reliable detection of health deterioration risk factors. In this article, we propose two approaches: myopic and Markov decision processes (MDPs)—to consider both energy constraints and risk factor requirements for achieving a twofold goal: energy savings while satisfying accuracy requirements of abnormality detection in a patient’s vital signs. Vital signs, including heart rate, respiration rate, and oxygen saturation, are extracted from a photoplethysmogram signal and errors of extracted features are compared to a ground truth that is modeled as a Gaussian distribution. We control the sensor’s sensing energy to minimize the power consumption while meeting a desired level of satisfactory detection performance. We present experimental results on realistic case studies using a reconfigurable photoplethysmogram sensor in an IoT system, and show that compared to nonadaptive methods, myopic reduces an average of 16.9% in sensing energy consumption with the maximum probability of abnormality misdetection on the order of 0.17 in a 24-hour health monitoring system. In addition, over 4 weeks of monitoring, we demonstrate that our MDP policy can extend the battery life on average of more than 2x while fulfilling the same average probability of misdetection compared to the myopic method. We illustrate results comparing myopic, MDP, and nonadaptive methods to monitor 14 subjects over 1 month.
- Farshad Firouzi, Amir M. Rahmani, Kunal Mankodiya, Mustafa Badaroglu, Geoff V. Merrett, P. Wong, and Bahar Farahani. 2018. Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics. Future Generation Computer Systems 78, Pt. 2 (2018), 583--586.Google Scholar
- Riitta Mieronkoski, Iman Azimi, Amir M. Rahmani, Riku Aantaa, Virpi Terävä, Pasi Liljeberg, and Sanna Salanterä. 2017. The Internet of Things for basic nursing—A scoping review. International Journal of Nursing Studies 69 (2017), 78--90.Google Scholar
- S. M. Riazul Islam, Daehan Kwak, M. D. Humaun Kabir, Mahmud Hossain, and Kyung-Sup Kwak. 2015. The Internet of Things for health care: A comprehensive survey. IEEE Access 3 (2015), 678--708.Google Scholar
- A. M. Rahmani, P. Liljeberg, J.-S. Preden, and A. Jantsch (Eds.). 2017. Fog Computing in the Internet of Things: Intelligence at the Edge. Springer.Google Scholar
- Delaram Amiri, Arman Anzanpour, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg, Nikil Dutt, and Marco Levorato. 2019. Optimizing energy in wearable devices using fog computing. In Fog Computing: Theory and Practice. Wiley. arXiv:1907.11989.Google Scholar
- Amir M. Rahmani, Tuan Nguyen Gia, Behailu Negash, Arman Anzanpour, Iman Azimi, Mingzhe Jiang, and Pasi Liljeberg. 2018. Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Future Generation Computer Systems 78 (2018), 641--658.Google Scholar
Digital Library
- Sina Shahhosseini, Iman Azimi, Arman Anzanpour, Axel Jantsch, Pasi Liljeberg, Nikil Dutt, and Amir M. Rahmani. 2019. Dynamic computation migration at the edge: Is there an optimal choice? In Proceedings of the 2019 Great Lakes Symposium on VLSI (GLSVLSI ’19). ACM, New York, NY, 519--524.Google Scholar
- Toshiyo Tamura, Yuka Maeda, Masaki Sekine, and Masaki Yoshida. 2014. Wearable photoplethysmographic sensors—Past and present. Electronics 3, 2 (2014), 282--302.Google Scholar
Cross Ref
- Delaram Amiri, Arman Anzanpour, Iman Azimi, Marco Levorato, Amir M. Rahmani, Pasi Liljeberg, and Nikil Dutt. 2018. Edge-assisted sensor control in healthcare IoT. In Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM’18). IEEE, Los Alamitos, CA, 1--6.Google Scholar
- Emad Kasaeyan Naeini, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg, and Nikil Dutt. 2019. A real-time PPG quality assessment approach for healthcare Internet-of-Things. Procedia Computer Science 151 (2019), 551--558.Google Scholar
Digital Library
- Kun Wang, Yihui Wang, Yanfei Sun, Song Guo, and Jinsong Wu. 2016. Green industrial Internet of Things architecture: An energy-efficient perspective. IEEE Communications Magazine 54, 12 (2016), 48--54.Google Scholar
Cross Ref
- Chunsheng Zhu, Victor C. M. Leung, Laurence T. Yang, and Lei Shu. 2015. Collaborative location-based sleep scheduling for wireless sensor networks integrated with mobile cloud computing. IEEE Transactions on Computers 64, 7 (2015), 1844--1856.Google Scholar
Cross Ref
- Dayong Ye and Minjie Zhang. 2018. A self-adaptive sleep/wake-up scheduling approach for wireless sensor networks. IEEE Transactions on Cybernetics 48, 3 (2018), 979--992.Google Scholar
Cross Ref
- Yanwen Wang, Hainan Chen, Xiaoling Wu, and Lei Shu. 2016. An energy-efficient SDN based sleep scheduling algorithm for WSNs. Journal of Network and Computer Applications 59 (2016), 39--45.Google Scholar
Digital Library
- Jae-Han Jeon, Hee-Jung Byun, and Jong-Tae Lim. 2013. Joint contention and sleep control for lifetime maximization in wireless sensor networks. IEEE Communications Letters 17, 2 (2013), 269--272.Google Scholar
Cross Ref
- Sarder Fakhrul Abedin, Md Golam Rabiul Alam, Rim Haw, and Choong Seon Hong. 2015. A system model for energy efficient green-IoT network. In Proceedings of the 2015 International Conference on Information Networking (ICOIN’15). IEEE, Los Alamitos, CA, 177--182.Google Scholar
- Ovidiu Vermesan, Peter Friess, Patrick Guillemin, Sergio Gusmeroli, Harald Sundmaeker, Alessandro Bassi, Ignacio Soler Jubert, et al. 2011. Internet of Things strategic research roadmap. Internet of Things—Global Technological and Societal Trends 1, 2011 (2011), 9--52.Google Scholar
- Navroop Kaur and Sandeep K. Sood. 2017. An energy-efficient architecture for the Internet of Things (IoT). IEEE Systems Journal 11, 2 (2017), 796--805.Google Scholar
Cross Ref
- Caglar Tunc and Nail Akar. 2017. Markov fluid queue model of an energy harvesting IoT device with adaptive sensing. Performance Evaluation 111 (2017), 1--16.Google Scholar
Digital Library
- Lin-Huang Chang, Tsung-Han Lee, Shu-Jan Chen, and Cheng-Yen Liao. 2013. Energy-efficient oriented routing algorithm in wireless sensor networks. In Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC’13). IEEE, Los Alamitos, CA, 3813--3818.Google Scholar
Digital Library
- Gitanjali Pradhan, Rajni Gupta, and Suparna Biswasz. 2018. Study and simulation of WBAN MAC protocols for emergency data traffic in healthcare. In Proceedings of the 2018 5th International Conference on Emerging Applications of Information Technology (EAIT’18). IEEE, Los Alamitos, CA, 1--4.Google Scholar
- Daphney-Stavroula Zois, Marco Levorato, and Urbashi Mitra. 2012. A POMDP framework for heterogeneous sensor selection in wireless body area networks. In Proceedings of IEEE INFOCOM 2012. IEEE, Los Alamitos, CA, 2611--2615.Google Scholar
- D.-S. Zois, M. Levorato, and U. Mitra. 2014. Active classification for POMDPs: A Kalman-like state estimator. IEEE Transactions on Signal Processing 62, 23 (2014), 6209--6224.Google Scholar
- Daphney-Stavroula Zois, Marco Levorato, and Urbashi Mitra. 2013. Energy-efficient, heterogeneous sensor selection for physical activity detection in wireless body area networks. IEEE Transactions on Signal Processing 61, 7 (2013), 1581--1594.Google Scholar
Digital Library
- Peter H. Charlton, Timothy Bonnici, Lionel Tarassenko, David A. Clifton, Richard Beale, and Peter J. Watkinson. 2016. An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram. Physiological Measurement 37, 4 (2016), 610.Google Scholar
Cross Ref
- Marco A. F. Pimentel, Peter H. Charlton, and David A. Clifton. 2015. Probabilistic estimation of respiratory rate from wearable sensors. In Wearable Electronics Sensors. Springer, 241--262.Google Scholar
- Walter Karlen, Srinivas Raman, J. Mark Ansermino, and Guy A. Dumont. 2013. Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Transactions on Biomedical Engineering 60, 7 (2013), 1946--1953.Google Scholar
Cross Ref
- Ainara Garde, Walter Karlen, J. Mark Ansermino, and Guy A. Dumont. 2014. Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram. PLoS One 9, 1 (2014), e86427.Google Scholar
- L.-G. Lindberg, H. Ugnell, and P. Å. Öberg. 1992. Monitoring of respiratory and heart rates using a fibre-optic sensor. Medical and Biological Engineering and Computing 30, 5 (1992), 533--537.Google Scholar
- Maxim Integrated. 2018. High-Sensitivity Pulse Oximeter and Heart-Rate Sensor for Wearable Health. Retrieved November 1, 2018 from https://www.maximintegrated.com/en/products/sensors/MAX30102.html.Google Scholar
- Garmin. 2018. Vivosmart HR | Activity Tracker. Retrieved November 1, 2018 from https://buy.garmin.com/en-US/US/p/531166.Google Scholar
- C. Tudor-Locke, S. B. Sisson, T. Collova, S. M. Lee, and P. Swan. 2005. Pedometer-determined step count guidelines for classifying walking intensity in a young ostensibly healthy population. Canadian Journal of Applied Physiology 30, 6 (2005), 666--76.Google Scholar
Cross Ref
- Catrine Tudor-Locke, Sarah M. Camhi, Claudia Leonardi, William D. Johnson, Peter T. Katzmarzyk, Conrad P. Earnest, and Timothy S. Church. 2011. Patterns of adult stepping cadence in the 2005--2006 NHANES. Preventive Medicine 53, 3 (2011), 178--181.Google Scholar
Index Terms
Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems
Recommendations
Energy-efficient and Reliable Wearable Internet-of-Things through Fog-Assisted Dynamic Goal Management
AbstractManagement of energy dissipation and battery life is a challenge in health monitoring wearables. Low-quality data collection, non-reliable monitoring process, and missing important health events are consequences of single-goal fixed-policy ...
Energy efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease
AbstractBlood glucose plays an important role in maintaining body’s activities. For example, brain only uses glucose as its energy source. However, when blood glucose level is abnormal, it causes some serious consequences. For instance, low-...
Making Sense of Sleep Sensors: How Sleep Sensing Technologies Support and Undermine Sleep Health
CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing SystemsSleep is an important aspect of our health, but it is difficult for people to track manually because it is an unconscious activity. The ability to sense sleep has aimed to lower the barriers of tracking sleep. Although sleep sensors are widely available,...






Comments