AsTAR: Sustainable Energy Harvesting for the Internet of Things through Adaptive Task Scheduling

2022 ◽  
Vol 18 (1) ◽  
pp. 1-34
Fan Yang ◽  
Ashok Samraj Thangarajan ◽  
Gowri Sankar Ramachandran ◽  
Wouter Joosen ◽  
Danny Hughes

Battery-free Internet-of-Things devices equipped with energy harvesting hold the promise of extended operational lifetime, reduced maintenance costs, and lower environmental impact. Despite this clear potential, it remains complex to develop applications that deliver sustainable operation in the face of variable energy availability and dynamic energy demands. This article aims to reduce this complexity by introducing AsTAR, an energy-aware task scheduler that automatically adapts task execution rates to match available environmental energy. AsTAR enables the developer to prioritize tasks based upon their importance, energy consumption, or a weighted combination thereof. In contrast to prior approaches, AsTAR is autonomous and self-adaptive, requiring no a priori modeling of the environment or hardware platforms. We evaluate AsTAR based on its capability to efficiently deliver sustainable operation for multiple tasks on heterogeneous platforms under dynamic environmental conditions. Our evaluation shows that (1) comparing to conventional approaches, AsTAR guarantees Sustainability by maintaining a user-defined optimum level of charge, and (2) AsTAR reacts quickly to environmental and platform changes, and achieves Efficiency by allocating all the surplus resources following the developer-specified task priorities. (3) Last, the benefits of AsTAR are achieved with minimal performance overhead in terms of memory, computation, and energy.

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Yichuan Wang ◽  
Han Yu ◽  
Xinhong Hei ◽  
Binbin Bai ◽  
Wenjiang Ji

Internet of Things (IoT) is the development and extension of computer, Internet, and mobile communication network and other related technologies, and in the new era of development, it increasingly shows its important role. To play the role of the Internet of Things, it is especially important to strengthen the network communication information security system construction, which is an important foundation for the Internet of Things business relying on Internet technology. Therefore, the communication protocol between IoT devices is a point that cannot be ignored, especially in recent years; the emergence of a large number of botnet and malicious communication has seriously threatened the communication security between connected devices. Therefore, it is necessary to identify these unknown protocols by reverse analysis. Although the development of protocol analysis technology has been quite mature, it is impossible to identify and analyze the unknown protocols of pure bitstreams with zero a priori knowledge using existing protocol analysis tools. In this paper, we make improvements to the existing protocol analysis algorithm, summarize and learn from the experience and knowledge of our predecessors, improve the algorithm ideas based on the Apriori algorithm idea, and perform feature string finding under the idea of composite features of CFI (Combined Frequent Items) algorithm. The advantages of existing algorithm ideas are combined together to finally propose a more efficient OFS (Optimal Feature Strings) algorithm with better performance in the face of bitstream protocol feature extraction problems.

Olfa Kanoun ◽  
Thomas Keutel ◽  
Christian Viehweger ◽  
Xinming Zhao ◽  
Sonia Bradai ◽  

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