In this paper, we consider a large-scale heterogeneous mobile edge computing system, where each device’s mean computing task arrival rate, mean service rate, mean energy consumption, and mean offloading latency are drawn from different bounded continuous probability distributions to reflect the diverse compute-intensive applications, mobile devices with different computing capabilities and battery efficiencies, and different types of wireless access networks (e.g., 4G/5G cellular networks, WiFi). We consider a class of distributed threshold-based randomized offloading policies and develop a threshold update algorithm based on its computational load, average offloading latency, average energy consumption, and edge server processing time, depending on the server utilization. We show that there always exists a unique Mean-Field Nash Equilibrium (MFNE) in the large-system limit when the task processing times of mobile devices follow an exponential distribution. This is achieved by carefully partitioning the space of mean arrival rates to account for the discrete structure of each device’s optimal threshold. Moreover, we show that our proposed threshold update algorithm converges to the MFNE. Finally, we perform simulations to corroborate our theoretical results and demonstrate that our proposed algorithm still performs well in more general setups based on the collected real-world data and outperforms the well-known probabilistic offloading policy.
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Edge-Assisted Sensor Control in Healthcare IoT
The Internet of Things is a key enabler of mobile
health-care applications. However, the inherent constraints of
mobile devices, such as limited availability of energy, can impair
their ability to produce accurate data and, in turn, degrade the
output of algorithms processing them in real-time to evaluate the
patient’s state. This paper presents an edge-assisted framework,
where models and control generated by an edge server inform
the sensing parameters of mobile sensors. The objective is to
maximize the probability that anomalies in the collected signals
are detected over extensive periods of time under battery-imposed
constraints. Although the proposed concept is general, the control
framework is made specific to a use-case where vital signs –
heart rate, respiration rate and oxygen saturation – are extracted
from a Photoplethysmogram (PPG) signal to detect anomalies
in real-time. Experimental results show a 16.9% reduction in
sensing energy consumption in comparison to a constant energy
consumption with the maximum misdetection probability of 0.17
in a 24-hour health monitoring system.
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- Award ID(s):
- 1702950
- NSF-PAR ID:
- 10091623
- Date Published:
- Journal Name:
- 2018 IEEE Global Communications Conference (GLOBECOM)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GLOCOM.2018.8647457
