ContextAiDe: End-to-End Architecture for Mobile Crowd-sensing Applications

被引:1
|
作者
Pore, Madhurima [1 ]
Chakati, Vinaya [1 ]
Banerjee, Ayan [1 ]
Gupta, Sandeep K. S. [1 ]
机构
[1] Arizona State Univ, IMPACT Lab, 517 BYE, Tempe, AZ 85281 USA
关键词
Mobile crowd-sensing; context aware; edge computing; middleware; MODEL;
D O I
10.1145/3301444
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Mobile crowd-sensing (MCS) enables development of context-aware applications by mining relevant information from a large set of devices selected in an ad hoc manner. For example, MCS has been used for real-time monitoring such as Vehicle ad hoc Networks-based traffic updates as well as offline data mining and tagging for future use in applications with location-based services. However, MCS could be potentially used for much more demanding applications such as real-time perpetrator tracking by online mining of images from nearby mobile users. A recent example is tracking the miscreant responsible for the Boston bombing. We present a new design approach for tracking using MCS for such complex processing in real time. Since MCS applications assume an unreliable underlying computational platform, most typically sample size for recruited devices is guided by concerns such as fault tolerance and reliability of information. As the real-time requirements get stricter coupled with increasing complexity of data-mining approaches, the communication and computation overheads can impose a very tight constraint on the sample size of devices needed for realizing real-time operation. This results in trade-off in acquiring context-relevant data and resource usage incurred while the real-time operation requirements get updated dynamically. Such effects have not been properly studied and optimized to enable real-time MCS applications such as perpetrator tracking. In this article, we propose ContextAiDe architecture, a combination of API, middleware, and optimization engine. The key innovation in ContextAiDe is context-optimized recruitment for execution of computation-and communication-heavy MCS applications in edge environment. ContextAiDe uses a notion of two types of contexts, exact (hard constraints), which have to be satisfied, and preferred (soft constraints), which may be satisfied to a certain degree. By adjusting the preferred contexts, ContextAiDe can optimize the operational overheads to enable real-time operation. ContextAiDe provides an API to specify contexts requirements and the code of MCS app, offload execution environment, a middleware that enables context-optimized and a fault-tolerant distributed execution. ContextAiDe evaluation using a real-time perpetrator tracking application shows reduced energy consumption of 37.8%, decrease in data transfer of 24.8%, and 43% less time compared to existing strategy. In spite of a small increase in the minimum distance from the perpetrator, iterations of optimization tracks the perpetrator successfully. Proactively learning the context and using stochastic optimization strategy minimizes the performance degradation caused due to uncertainty (<20%) in usage-dependent contexts.
引用
收藏
页数:23
相关论文
共 50 条
  • [41] Crowd Counting Using End-to-End Semantic Image Segmentation
    Khan, Khalil
    Khan, Rehan Ullah
    Albattah, Waleed
    Nayab, Durre
    Qamar, Ali Mustafa
    Habib, Shabana
    Islam, Muhammad
    ELECTRONICS, 2021, 10 (11)
  • [42] Blockchain-based decentralized model for mobile crowd-sensing
    Hao, Deran
    Wang, En
    Liu, Wenbin
    Yang, Yilin
    Yang, Yongjian
    Yang, Funing
    CCF TRANSACTIONS ON PERVASIVE COMPUTING AND INTERACTION, 2024, 6 (01) : 68 - 81
  • [43] On the impact of selective data acquisition in mobile crowd-sensing performance
    Dasari, Venkat Surya
    Pouryazdan, Maryam
    Kantarci, Burak
    2018 IEEE CANADIAN CONFERENCE ON ELECTRICAL & COMPUTER ENGINEERING (CCECE), 2018,
  • [44] Blockchain-based decentralized model for mobile crowd-sensing
    Deran Hao
    En Wang
    Wenbin Liu
    Yilin Yang
    Yongjian Yang
    Funing Yang
    CCF Transactions on Pervasive Computing and Interaction, 2024, 6 : 68 - 81
  • [45] Compressive sensing based data quality improvement for crowd-sensing applications
    Cheng, Long
    Niu, Jianwei
    Kong, Linghe
    Luo, Chengwen
    Gu, Yu
    He, Wenbo
    Das, Sajal K.
    JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2017, 77 : 123 - 134
  • [46] Functional Architecture of End-to-End Reconfigurable Systems
    Moessner, Klaus
    Luo, Jesse
    Mohyeldin, Eliman
    Grandblaise, David
    Kloeck, Clemens
    Martoyo, Ihan
    Sallent, Oriol
    Demestichas, P.
    Dimitrakopoulos, G.
    Tsagkaris, K.
    Olaziregi, N.
    2006 IEEE 63RD VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-6, 2006, : 196 - +
  • [47] An end-to-end architecture for advanced multimedia broadcast
    Caprioglio, M
    Planterose, T
    Morel, C
    2001 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5: E-SYSTEMS AND E-MAN FOR CYBERNETICS IN CYBERSPACE, 2002, : 3217 - 3222
  • [48] End-to-End Architecture for Adaptive Communication Systems
    Boufidis, Z.
    Alonistioti, N.
    Stamatelatos, M.
    Vogler, J.
    Luecking, U.
    Kloeck, C.
    Grandblaise, D.
    Bourse, D.
    2006 IEEE 64TH VEHICULAR TECHNOLOGY CONFERENCE, VOLS 1-6, 2006, : 3027 - +
  • [49] An End-to-End Generative Architecture for Paraphrase Generation
    Yang, Qian
    Huo, Zhouyuan
    Shen, Dinghan
    Chen, Yong
    Wang, Wenlin
    Wang, Guoyin
    Carin, Lawrence
    2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019): PROCEEDINGS OF THE CONFERENCE, 2019, : 3132 - 3142
  • [50] End-to-End Communication Architecture for Smart Grids
    Sauter, Thilo
    Lobashov, Maksim
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2011, 58 (04) : 1218 - 1228