Finding an optimal energy-efficient policy that is adaptable to underlying edge devices while meeting deadlines for tasks has always been challenging. This research studies generalized systems with multi-task, multi-deadline scenarios with reinforcement learning-based DVFS for energy saving for periodic soft real-time applications on edge devices. This work addresses the limitation of previous work that models a periodic system as a single task and single-deadline scenario, which is too simplified to cope with complex situations. The method encodes time series data in the Linux kernel into information that is easy to interpret for reinforcement learning, allowing the system to generate DVFS policies to adapt system patterns based on the general workload. For encoding, we present two different methods for comparison. Both methods use only one performance counter: system utilization, and the kernel only needs minimal information from the userspace. Our method is implemented on Jetson Nano Board (2GB) and is tested with three fixed multitask workloads, which are three, five, and eight tasks in the workload, respectively. For randomness and generalization, we also designed a random workload generator to build different multitask workloads to test. Based on the test results, our method could save 3%-10% power compared to Linux built-in governors.

Energy measurement of computer devices, which are widely used in the Internet of Things (IoT),is an important yet challenging task. Most of these IoT devices lack ready-to-use hardware or software for power measurement. In this paper, we propose an easy-to-use approach to derive a software-based energy estimation model with external low-end power meters based on data-driven analysis. Our solution is demonstrated with a Jetson Nano board and Ruideng UM25C USB power meter. Various machine learning methods combined with our smart data collection & profiling method and physical measurement are explored. Periodic Long-duration measurements are utilized in the experiments to derive and validate power models, allowing more accurate power readings from the low-end power meter. Benchmarks were used to evaluate the derived software-power model for the Jetson Nano board and Raspberry Pi. The results show that 92% accuracy can be achieved by the software-based power estimation compared to measurement. A kernel module that can collect running traces of utilization and frequencies needed is developed, together with the power model derived, for power prediction for programs running in a real environment. Our cost-effective method facilitates accurate instantaneous power estimation, which low-end power meters cannot directly provide.

Internet of Things (IoT) is a technology that allows ordinary physical devices to collect, process, and share data with other physical devices and systems over the Internet. It provides pervasively connected infrastructures to support innovative applications and services that can automate otherwise intensely laborious manual effort. Edge computing (EC) complements the powerful centralized cloud servers by providing powerful computation capability close to the data source, minimizing communication latency, and securing data privacy. The energy consumption problem has continued to receive much attention from the IoT community in applying various techniques to reduce energy consumption while still meeting the computational demand. In this article, we propose an application-deadline-aware data offloading scheme using deep reinforcement learning and dynamic voltage and frequency scaling (DVFS) in an EC environment to reduce the energy consumption of IoT devices. The proposed scheme learns the optimal data distribution policies and local computation DVFS frequency scaling by interacting with the system environment and learning the behavior of the device, network, and edge servers. The proposed scheme was tested on multiple EC environments with different IoT devices. Experimental results show that this scheme can reduce energy consumption while achieving the IoT application and services timing and computational goals. The proposed scheme has substantial energy savings when compared with the native Linux governors.

Wireless body area networks (WBANs) are widely used in telemedicine, which can be utilized for real-time patients monitoring and home health-care. The sensor nodes in WBANs collect the client’s physiological data and transmit it to the medical center. However, the clients’ personal information is sensitive and there are many security threats in the extra-body communication. Therefore, the security and privacy of client’s physiological data need to be ensured. Many authentication protocols for WBANs have been proposed in recent years. However, the existing protocols fail to consider the key update phase. In this paper, we propose an efficient authenticated key agreement scheme for WBANs and add the key update phase to enhance the security of the proposed scheme. In addition, session keys are generated during the registration phase and kept secretly, thus reducing computation cost in the authentication phase. The performance analysis demonstrates that our scheme is more efficient than the currently popular related schemes.

Evapotranspiration plays an important role in the surface-atmosphere interaction. Remote sensing has long been identified as a technology capable of monitoring ET. But spatial scale problem has a great effect on the accuracy of the ET retrieval by satellite. The objective of this paper is to reduce the uncertainty produced by spatial scale problem based on the spatial characteristic of HJ-1B data. Firstly, the temperature is sharpened to 30m spatial resolutions associated with visible-near-infrared bands. Then net radiation, soil heat flux and sensible heat flux of sub pixel are calculated at 30m resolution using one-source energy balance model. Finally, the fluxes are averaged to 300m resolution and latent heat flux is computed as residual of surface energy balance. The result shows that Temperature sharpening and flux aggregation (TSFA) method reduces the uncertainty produced by spatial scale problem, and it can capture the land surface heterogeneities and associated uncertainties in a way.