research-article

Idea: A System for Efficient Failure Management in Smart IoT Environments

Authors:

Palanivel A. Kodeswaran,

Mudhakar SrivatsaAuthors Info & Claims

MobiSys '16: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services

Pages 43 - 56

https://doi.org/10.1145/2906388.2906406

Published: 20 June 2016 Publication History

Abstract

IoT enabled smart environments are expected to proliferate significantly in the near future, particularly in the context of monitoring services for wellness living, patient healthcare and elderly care. Timely maintenance of failed sensors is of critical importance in such deployments to ensure minimal disruption to monitoring services. However, maintenance of large and geographically spread deployments can be a significant challenge. We present Idea that significantly increases the vtime-before-repair for a smart home deployment, thereby reducing the maintenance overhead. Specifically, our approach leverages the facts that (a) there is inherent sensor redundancy when combinations of sensors monitor activities of daily living (ADLs) in smart environments, and (b) the impact of each sensor failure depends on the activities being monitored and the functional redundancy afforded by rest of the heterogeneous sensors available for detecting the activities. Consequently, Idea identifies homes that need to be fixed based on expected degradation in ADL detection performance, and optimizes maintenance scheduling accordingly. We demonstrate that our approach leads to 3--40 times fewer maintenance personnel than a scheme in which failed sensors are fixed without considering their impact.

References

[1]

Telehealth and self-monitoring in the elder-care markets: A global analysis. http://blog.bccresearch.com/telehealth-and-self-monitoring-in-the-elder-care-markets-a-global-analysis-0.

[2]

V Rialle, C Ollivet, C Guigui, and C Hervé. What do family caregivers of alzheimer's disease patients desire in smart home technologies' Methods of information in medicine, 47:63--69, 2008.

[3]

G et. al. Acampora. A survey on ambient intelligence in healthcare. Proceedings of the IEEE, 101(12):2470--2494, 2013.

[4]

S Robben, M Pol, and B Krose. Longitudinal ambient sensor monitoring for functional health assessments: a case study. In UbiComp, 2014.

Digital Library

[5]

G. LeBellego, N. Noury, G. Virone, M. Mousseau, and J. Demongeot. A model for the measurement of patient activity in a hospital suite. Information Technology in Biomedicine, IEEE, 10:92--99, 2006.

Digital Library

[6]

Basma M. Mohammad El-Basioni, Sherine Mohamed Abd El-Kader, and Hussein S. Eissa. Independent living for persons with disabilities and elderly people using smart home technology. International Journal of Application or Innovation in Engineering & Management, 2014.

[7]

Timothy W. Hnat, Vijay Srinivasan, Jiakang Lu, Tamim I. Sookoor, Raymond Dawson, John Stankovic, and Kamin Whitehouse. The hitchhiker's guide to successful residential sensing deployments. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, SenSys '11, pages 232--245, New York, NY, USA, 2011. ACM.

Digital Library

[8]

Shouzhi Huang and Xuezeng Zhao. Redundant nodes elimination in wireless sensor networks. In Information Computing and Applications, pages 48--58. Springer, 2013.

[9]

Chi-Fu Huang and Yu-Chee Tseng. The coverage problem in a wireless sensor network. In Proceedings of the 2Nd ACM International Conference on Wireless Sensor Networks and Applications, WSNA '03, pages 115--121, New York, NY, USA, 2003. ACM.

Digital Library

[10]

Di Tian and Nicolas D. Georganas. A coverage-preserving node scheduling scheme for large wireless sensor networks. In Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, pages 32--41. ACM Press, 2002.

Digital Library

[11]

Mihaela Cardei, My T Thai, Yingshu Li, and Weili Wu. Energy-efficient target coverage in wireless sensor networks. In INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE, volume 3, pages 1976--1984. IEEE, 2005.

[12]

Habib M. Ammari and Sajal K. Das. Fast track article: Scheduling protocols for homogeneous and heterogeneous k-covered wireless sensor networks. Pervasive Mob. Comput., 7(1):79--97, February 2011.

Digital Library

[13]

Honghai Zhang and Jennifer C Hou. Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc & Sensor Wireless Networks, 1(1--2):89--124, 2005.

[14]

Yong Gao, Kui Wu, and Fulu Li. Analysis on the redundancy of wireless sensor networks. In IN PROC. 2ND ACM INTL. WORKSHOP ON WIRELESS SENSOR NETWORKS AND APPLICATIONS (WSNA, pages 108--114. ACM Press, 2003.

Digital Library

[15]

Rajagopal Iyengar. Low-coordination topologies for redundancy in sensor networks. In In ACM MobiHoc, pages 332--342, 2005.

Digital Library

[16]

Benchmark datasets; datasets for activity recognitions. https://sites.google.com/site/tim0306/datasets.

[17]

Wsu casas dataset. http://ailab.wsu.edu/casas/datasets/.

[18]

K.D. Feuz, D.J. Cook, C. Rosasco, K. Robertson, and M. Schmitter-Edgecombe. Automated detection of activity transitions for prompting. Human-Machine Systems, IEEE Transactions, 2014.

[19]

Prafulla Nath Dawadi, Diane Joyce Cook, and Maureen Schmitter-Edgecombe. Automated clinical assessment from smart home-based behavior data. IEEE Journal of Biomedical and Health Informatics, 2015.

[20]

Tim van Kasteren, Athanasios Noulas, Gwenn Englebienne, and Ben Kröse. Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp '08, pages 1--9, New York, NY, USA, 2008. ACM.

Digital Library

[21]

Parisa Rashidi, Diane J. Cook, Lawrence B. Holder, and Maureen Schmitter-Edgecombe. Discovering activities to recognize and track in a smart environment. IEEE Trans. Knowl. Data Eng., 23(4):527--539, 2011.

Digital Library

[22]

Aaron S. Crandall and Diane J. Cook. Using a hidden markov model for resident identification. In Sixth International Conference on Intelligent Environments, IE 2010, Kuala Lumpur, Malaysia, July 19-21, 2010, pages 74--79, 2010.

Digital Library

[23]

TLM Van Kasteren, Gwenn Englebienne, and Ben JA Kröse. Activity recognition using semi-markov models on real world smart home datasets. Journal of ambient intelligence and smart environments, 2(3):311--325, 2010.

Digital Library

[24]

Emmanuel Munguia Tapia, Stephen S Intille, and Kent Larson. Activity recognition in the home using simple and ubiquitous sensors. Springer, 2004.

[25]

T. van Kasteren and B. Krose. Bayesian activity recognition in residence for elders. In 3rd IET International Conference on Intelligent Environments (IE 07), pages 209--212, 2010.

[26]

L Liao, D Fox, and H Kautz. Location-based activity recognition using relational markov networks. In Proc. Int. Joint Conf. Artif. Intell., 2005.

Digital Library

[27]

Krasimira Kapitanova, Enamul Hoque, John A. Stankovic, Kamin Whitehouse, and Sang H. Son. Being smart about failures: Assessing repairs in smart homes. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp '12, pages 51--60, New York, NY, USA, 2012. ACM.

Digital Library

[28]

Sirajum Munir, John Stankovic, et al. Failuresense: Detecting sensor failure using electrical appliances in the home. In Mobile Ad Hoc and Sensor Systems (MASS), 2014 IEEE 11th International Conference on, pages 73--81. IEEE, 2014.

Digital Library

[29]

Hazewinkel Michiel ed. Weibull distribution. Encyclopedia of Mathematics, 2001.

[30]

Curt Ronniger. Reliability analyses with weibull. http://www.crgraph.com/Weibull.pdf, 2012.

[31]

Department of Defense USA. Electronic reliability design handbook. "http://reliabilityanalytics.com/reliability_engineering_library/MIL-HDBK-338-V1_Electronic_Reliability_Design_Handbook_15_Oct_1984/MIL-HDBK-338-V1_Electronic_Reliability_Design_Handbook_15_Oct_1984_pp_2.htm", 1984.

[32]

Seymour Morris. Online redundancy calculator for effective failure rate of n active redundant units, with m required for success (with repair), assuming either exponential or Weibull failure distributions. http://reliabilityanalyticstoolkit.appspot.com/active_redundancy_integrate, 2010.

[33]

Ehsan Nazerfard, Parisa Rashidi, and Diane J. Cook. Using association rule mining to discover temporal relations of daily activities. In Proceedings of the 9th International Conference on Toward Useful Services for Elderly and People with Disabilities: Smart Homes and Health Telematics, ICOST'11, pages 49--56, Berlin, Heidelberg, 2011. Springer-Verlag.

Digital Library

[34]

P. Kodeswaran, R. Kokku, M. Mallick, and S. Sen. Demultiplexing Activities of Daily Living in IoT enabled Smarthomes (to appear). In IEEE Infocom, 2016.

[35]

Nirmalya Roy, Archan Misra, and Diane J. Cook. Infrastructure-assisted smartphone-based ADL recognition in multi-inhabitant smart environments. In 2013 IEEE International Conference on Pervasive Computing and Communications, PerCom 2013, San Diego, CA, USA, March 18-22, 2013, pages 38--46, 2013.

[36]

Cplex optimizer. http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/.

[37]

P Kodeswaran and S Sen. Intellihome: Activity detection in smart homes on IBM BlueMix. http://intellihome.eu-gb.mybluemix.net/.

[38]

S. Mitra, S. Ranu, V. Kolar, A. Telang, A. Bhattacharya, R. Kokku, and S. Raghavan. Trajectory aware macro-cell planning for mobile users. In Computer Communications (INFOCOM), 2015 IEEE Conference on, pages 792--800, April 2015.

[39]

Maximo:ibm enterprise asset management. http://www-03.ibm.com/software/products/en/maximoassetmanagement.

[40]

Sap--enterprise asset management. http://go.sap.com/solution/lob/asset-management.html.

[41]

Seungwoo Kang, Jinwon Lee, Hyukjae Jang, Hyonik Lee, Youngki Lee, Souneil Park, Taiwoo Park, and Junehwa Song. Seemon: Scalable and energy-efficient context monitoring framework for sensor-rich mobile environments. In Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, MobiSys '08, pages 267--280, New York, NY, USA, 2008. ACM.

Digital Library

[42]

Seungwoo Kang, Youngki Lee, Chulhong Min, Younghyun Ju, Taiwoo Park, Jinwon Lee, Yunseok Rhee, and Junehwa Song. Orchestrator: An active resource orchestration framework for mobile context monitoring in sensor-rich mobile environments. In PerCom, 2010.

[43]

Suman Nath. Ace: Exploiting correlation for energy-efficient and continuous context sensing. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, MobiSys '12, pages 29--42, New York, NY, USA, 2012. ACM.

Digital Library

[44]

Jessica Staddon, Dirk Balfanz, and Glenn Durfee. Efficient tracing of failed nodes in sensor networks. In Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, WSNA '02, pages 122--130, New York, NY, USA, 2002. ACM.

Digital Library

[45]

Sapon Tanachaiwiwat, Pinalkumar Dave, Rohan Bhindwale, and Ahmed Helmy. Secure locations: routing on trust and isolating compromised sensors in location-aware sensor networks. In Ian F. Akyildiz, Deborah Estrin, David E. Culler, and Mani B. Srivastava, editors, SenSys, pages 324--325. ACM, 2003.

Digital Library

[46]

S. Rost and H. Balakrishnan. Memento: A health monitoring system for wireless sensor networks. In Sensor and Ad Hoc Communications and Networks, 2006. SECON '06. 2006 3rd Annual IEEE Communications Society on, volume 2, pages 575--584, Sept 2006.

[47]

Lilia Paradis and Qi Han. A survey of fault management in wireless sensor networks. Journal of Network and Systems Management, 15(2):171--190, 2007.

Digital Library

[48]

Nithya Ramanathan, Thomas Schoellhammer, Eddie Kohler, Kamin Whitehouse, Thomas Harmon, and Deborah Estrin. Suelo: Human-assisted sensing for exploratory soil monitoring studies. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys '09, pages 197--210, New York, NY, USA, 2009. ACM.

Digital Library

[49]

Saurabh Ganeriwal, Laura K. Balzano, and Mani B. Srivastava. Reputation-based framework for high integrity sensor networks. ACM Trans. Sen. Netw., 4(3):15:1--15:37, June 2008.

Digital Library

[50]

N. Roy, A. Misra, and D. Cook. Infrastructure-assisted smartphone-based adl recognition in multi-inhabitant smart environments. In Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on, pages 38--46, March 2013.

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Sunar SShirani PMajumdar SBrown JKim YKim JKoushanfar FRasmussen K(2024)On Continuously Verifying Device-level Functional Integrity by Monitoring Correlated Smart Home DevicesProceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3643833.3656132(219-230)Online publication date: 27-May-2024
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Idea: A System for Efficient Failure Management in Smart IoT Environments

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cover image ACM Conferences

MobiSys '16: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services

June 2016

440 pages

ISBN:9781450342698

DOI:10.1145/2906388

General Chairs:
Rajesh Balan
Singapore Management University
,
Archan Misra
Singapore Management University
,
Program Chairs:
Sharad Agarwal
Microsoft
,
Cecilia Mascolo
University of Cambridge

Copyright © 2016 ACM.

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Published: 20 June 2016

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MobiSys'16: The 14th Annual International Conference on Mobile Systems, Applications, and Services

June 26 - 30, 2016

Singapore, Singapore

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MobiSys '16 Paper Acceptance Rate 31 of 197 submissions, 16%;

Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

Wang CHe WPierson TKotz D(2024)Moat: Adaptive Inside/Outside Detection System for Smart HomesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997518:4(1-31)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699751
Sunar SShirani PMajumdar SBrown JKim YKim JKoushanfar FRasmussen K(2024)On Continuously Verifying Device-level Functional Integrity by Monitoring Correlated Smart Home DevicesProceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3643833.3656132(219-230)Online publication date: 27-May-2024
https://dl.acm.org/doi/10.1145/3643833.3656132
Wang GIvanov NChen BWang QNguyen TYan Q(2023)Graph Learning for Interactive Threat Detection in Heterogeneous Smart Home Rule DataProceedings of the ACM on Management of Data10.1145/35889561:1(1-27)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588956
Wang JLi ZSun MYuan BLui J(2023)IoT Anomaly Detection Via Device Interaction Graph2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)10.1109/DSN58367.2023.00053(494-507)Online publication date: Jun-2023
https://doi.org/10.1109/DSN58367.2023.00053
Guittoum AAïssaoui FBolle SBoyer FDe Palma N(2023)Solving the IoT Cascading Failure Dilemma Using a Semantic Multi-agent SystemThe Semantic Web – ISWC 202310.1007/978-3-031-47243-5_18(325-344)Online publication date: 27-Oct-2023
https://doi.org/10.1007/978-3-031-47243-5_18
Correia RSousa CCarneiro D(2023)IoT Data Ness: From Streaming to Added ValueHybrid Intelligent Systems10.1007/978-3-031-27409-1_64(703-713)Online publication date: 25-May-2023
https://doi.org/10.1007/978-3-031-27409-1_64
Jung HKim WSeo HLee YGummeson JLee SGao JXing G(2022)Simultaneous Sporadic Sensor Anomaly Detection for Smart HomesProceedings of the 20th ACM Conference on Embedded Networked Sensor Systems10.1145/3560905.3567767(1061-1066)Online publication date: 6-Nov-2022
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Norris MCelik ZVenkatesh PZhao SMcDaniel PSivasubramaniam ATan G(2022)IoTRepair: Flexible Fault Handling in Diverse IoT DeploymentsACM Transactions on Internet of Things10.1145/35321943:3(1-33)Online publication date: 19-Jul-2022
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Hirmer PHirmer P(2022)Model Based Approaches to the Internet of ThingsModel-Based Approaches to the Internet of Things10.1007/978-3-031-18884-8_5(31-117)Online publication date: 19-Sep-2022
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Olorunnife KLee KKua J(2021)Automatic Failure Recovery for Container-Based IoT Edge ApplicationsElectronics10.3390/electronics1023304710:23(3047)Online publication date: 6-Dec-2021
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