Weighted Adaptive Opportunistic Scheduling Framework for Smartphone Sensor Data Collection in IoT = Weighted Adaptive Opportunistic Scheduling Framework for Smartphone Sensor Data Collection in IoT

다국어 초록 (Multilingual Abstract)

Smartphones are important platforms because of their sophisticated computation, communication, and sensing capabilities, which enable a variety of applications in the Internet of Things (IoT) systems. Moreover, advancements in hardware have enabled sensors on smartphones such as environmental and chemical sensors that make sensor data collection readily accessible for a wide range of applications. However, dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users that vary throughout the day, which greatly affects the efficacy of sensor data collection. Therefore, it is necessary to consider phone users mobility patterns to design data collection schedules that can reduce the loss of sensor data. In this paper, we propose a mobility-based weighted adaptive opportunistic scheduling framework that can adaptively adjust to the dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users and provide prioritized scheduling based on various application scenarios, such as velocity, region of interest, and sensor type. The performance of the proposed framework is compared with other scheduling frameworks in various heterogeneous smartphone user mobility scenarios. Simulation results show that the proposed scheduling improves the transmission rate by 8 percent and can also improve the collection of higher-priority sensor data compared with other scheduling approaches.

참고문헌 (Reference)

1 W. C. Y. Lee, "Wireless and Cellular Telecommunication" McGraw-Hill 2006

2 L. Atzori, "The Internet of Things: A survey" 54 (54): 2787-2805, 2010

3 S. M. R. Islam, "The Internet of Things for health care : A comprehensive survey" 3 : 678-708, 2015

4 L. Fenghua, "Smartphones for sensing" 61 (61): 190-201, 2016

5 A. Corradi, "Smartphones as smart cities sensors: Mcs scheduling in the participant project" 222-228, 2015

6 A. Alkhelaiwi, "Scheduling crowdsensing data to smart city applications in the cloud" 395-401, 2016

7 W. H. Tranter, "Principles of Communication Systems Simulation with Wireless Applications" Prentice-Hall Professional Technical Reference 2004

8 Wikipedia, "Preferred walking speed"

9 G. M. Viswanathan, "Optimizing the success of random searches" 401 : 911-914, 1999

10 A. Zhalgasbekova, "Opportunistic data collection for IoT-based indoor air quality monitoring" Springer International Publishing 53-65, 2017

11 I. Rhee, "On the levy-walk nature of human mobility" 19 (19): 630-643, 2011

12 J. Li, "Nanotechnology-based cell-all phone-sensors for extended network chemical sensing" 1-4, 2012

13 S. M. Mousavi, "Mobisim: A framework for simulation of mobility models in mobile ad-hoc networks" 82-82, 2007

14 "Mobisim"

15 S. H. Ali, "Mobility assisted opportunistic scheduling for downlink transmissions in cellular data networks" 2 : 1213-1218, 2005

16 W. Z. Khan, "Mobile phone sensing systems : A survey" 15 (15): 402-427, 2013

17 G. Choudhary, "Internet of Things: A survey on architecture, technologies, protocols and challenges" 1-8, 2016

18 A. Zanella, "Internet of Things for smart cities" 1 (1): 22-32, 2014

19 A. Al-Fuqaha, "Internet of Things : A survey on enabling technologies, protocols, and applications" 17 (17): 2347-2376, 2015

20 M. B. Shah, "Human mobility based stable clustering for data aggregation in single-hop cell phone based wireless sensor network" 427-434, 2011

21 S. V. George, "Heterogeneous user mobility based scheduling scheme and transmission mode selection in downlink LTE/LTE-a systems" 78-83, 2015

22 S. B. Eisenman, "Halo : Managing node rendezvous in opportunistic sensor networks" Springer Berlin Heidelberg 273-287, 2010

23 Z. Ma, "Experimental evaluation of mobile phone sensors" 1-8, 2013

24 S. Aram, "Environment sensing using smartphone" 1-4, 2012

25 A. Capponi, "Energy efficient data collection in opportunistic mobile crowdsensing architectures for smart cities" 307-312, 2017

26 M. B. Shah, "Efficient scheduling algorithm for query processing in opportunistic sensor network under human mobility model" 401-405, 2014

27 B. Birand, "Dynamic graph properties of mobile networks under levy walk mobility" 292-301, 2011

28 Thejaswini M, "Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing" 9 (9): 721-730, 2016

29 A. Anton, "Chemical sensors integrated with mobile phones for remote medical diagnostics: State-of-the-art and beyond" 234-237, 2014

30 Q. Ou, "Application of Internet of Things in smart grid power transmission" 96-100, 2012

31 T. M. Bojan, "An Internet of Things based intelligent transportation system" 174-179, 2014

32 S. H. Shah, "A survey: Internet of Things (IoT) technologies, applications and challenges" 381-385, 2016

33 S. Nalbandian, "A survey on Internet of Things: Applications and challenges" 165-169, 2015

34 M. B. Shah, "A realistic weighted clustering algorithm for data gathering in single hop cell phone-based sensor network" 1253-1257, 2011

35 T. Qiu, "A data emergency-aware scheduling scheme for Internet of Things in smart cities" 1-1, 2017

36 S. E. Odongo, "A broadcast scheme for vehicle-to-pedestrian safety message dissemination" 13 (13): 1-19, 2017