2017 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computed, Scalable Computing &Amp; Commun 2017
DOI: 10.1109/uic-atc.2017.8397495
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Cited by 33 publications
(20 citation statements)
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“…However, recent advances in mobile technologies enable us to obtain additional contextual information that can be used to determine if an individual is at a specific place, even in the absence of direct location data. Therefore, the work in this paper builds upon [13], where the battery charging status of smartphones and sleep data recorded by wearables (such as Fitbits) have been used to fill the gaps in location traces. However, in this paper, we also consider users' activity levels, i.e., step counts, which is commonly recorded by devices such as Fitbits.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…However, recent advances in mobile technologies enable us to obtain additional contextual information that can be used to determine if an individual is at a specific place, even in the absence of direct location data. Therefore, the work in this paper builds upon [13], where the battery charging status of smartphones and sleep data recorded by wearables (such as Fitbits) have been used to fill the gaps in location traces. However, in this paper, we also consider users' activity levels, i.e., step counts, which is commonly recorded by devices such as Fitbits.…”
Section: Related Workmentioning
confidence: 99%
“…For example, modern mobile devices can provide various types of data describing device status and usage, such as the current battery charge level, charging status, and screen/display mode. Various types of physiological data (e.g., heart rate and calorie burn) and behavioral data (e.g., different types of physical activity, step count, and sleep status) can also be obtained from different types of mobile sensors embedded in modern wearables (e.g., devices such as Fitbit, Apple Watch, and Microsoft Band) [8], [12], [13]. Therefore, the approach proposed in this paper proposes to augment a user's location traces using several other types of data, specifically: step counts (wearable), sleep data (wearable), and battery charging status (smartphone).…”
Section: Introductionmentioning
confidence: 99%
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“…Require: the matrix to be processed by the dispatching algorithm Ensure: scheduling map obtained by algorithms (1) function Matrix_process (mat) (2) if mat can be handled by the computing resources at hand then (3) processing the mat with the WOLF-PHC-based dispatch algorithm (4) return scheduling map obtained by the algorithm (5) else (6) Divide the matrix mat into two smaller ones mat 1 and mat 2 (7) map 1 � Matrix_process mat 1 (8) map 2 � Matrix_process mat 2 (9) get the result map by merging the map 1 and map 2 (10) end if (11) return map (12) end function ALGORITHM 2: Matrix process.…”
Section: Dispatch Processmentioning
confidence: 99%
“…With the rapid development of wireless communication technology and the Internet of ings (IoT), collecting the trajectory records of mobile objects becomes simple and fast, which makes intelligent transportation possible [5,8]. Various devices embedded with GPS are ubiquitous in our lives, such as smartphones [9,10], private cars [11,12], and public transport [13]. Location information can be obtained more easily, and a large number of trajectory data are collected every day.…”
Section: Introductionmentioning
confidence: 99%