Mixed-reality simulation of RFID systems using iterated learning

Cheng, Hong; Liu, Yu; Cheung, Shing-Chi; Zeng, Junfang; Tan, Jie; Yang, Yiping

doi:10.3233/rft-2012-026

Cited by 2 publications

(3 citation statements)

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“…For example, RFID systems, a typical kind of pervasive computing systems, are very difficult to implement because developers have to carefully consider, predict, and handle various environmental factors such as cross-coupling of neighboring RFID tags. Thus, in practice, these systems often contain many faults and are error-prone [13][14][15][16][17]48]. To help developers improve the dependability of pervasive computing systems, much research effort has been devoted to study possible faults in such systems and techniques for diagnosing such faults.…”

Section: Fault Detectionmentioning

confidence: 99%

“…The effect of these factors on radio waves cannot be precisely simulated. In [13], an effective technique was proposed to construct a mixed-reality simulator for pervasive computing systems (i.e., RFID systems) using iterated learning based on a support vector machine. In our previous work, we also used simulation to verify and detect faults in robot-car pervasive computing systems [7,47,49].…”

Section: Detecting Techniquesmentioning

confidence: 99%

“…Unfortunately, due to their peculiarity, many of these faults cannot be effectively detected by existing quality assurance techniques for traditional softwares. For example, many pervasive computing systems perceive environment using radio frequency identification (RFID) [13][14][15][16][17], a key enabling technology for Internet of Things. Many faults in such RFID systems can easily bypass existing quality assurance techniques [16,17], because these faults can be either very specific to environmental interactions or are caused by special programming paradigms of pervasive computing systems.…”

Section: Introductionmentioning

confidence: 99%

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A survey on dependability improvement techniques for pervasive computing systems

Yang

Liu

et al. 2015

Sci. China Inf. Sci.

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The goal of this survey is to summarize the state-of-the-art research results and identify research challenges of developing and deploying dependable pervasive computing systems. We discuss the factors that affect the system dependability and the studies conducted to improve it with respect to these factors. These studies were categorized according to their similarities and differences in hope of shedding some insight into future research. There are three categories: context management, fault detection, and uncertainty handling. These three categories of work address the three most difficult problems of pervasive computing systems. First, pervasive computing systems' perceived environments, which are also called their contexts, can vary intensively, and thus have a great impact on the systems' dependability. Second, it is challenging to guarantee the correctness of the systems' internal computations integrated with interactions with external environments for developers. Fault detection is then an important issue for improving dependability for these systems. Last but not least importantly, pervasive computing systems interact with their environments frequently. These interactions can be affected by many uncertainties, which can jeopardize the systems' dependability. After a discussion of these pieces of work, we present an outlook for its future research directions.

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