Situation awareness via Internet of things and in-network data processing

International Journal of Distributed Sensor Networks

Volumn 13, Issue 1, 2017, Pages 1-21

  Ehala, Johannes ^a   Kaugerand, Jaanus ^a   Pahtma, Raido ^a   Astapov, Sergei ^a   Riid, Andri ^a   Tomson, Timo ^a   Preden, Jürgo Sören ^a   Mõtus, Leo ^a  

^a TALLINN UNIVERSITY OF TECHNOLOGY   (Estonia)

Author keywords

Data validity; Distributed data aggregation; Distributed data fusion; Edge computing; In sensor computation; Internet of things; Middleware; Situation awareness; Wireless sensor networks

Indexed keywords

EDGE COMPUTING; IN SITU PROCESSING; INTERNET OF THINGS; MIDDLEWARE; MILITARY OPERATIONS; NETWORK SECURITY; SENSOR NODES; SIGNAL PROCESSING; WIRELESS SENSOR NETWORKS;

DATA VALIDITY; DISTRIBUTED DATA; DISTRIBUTED DATA FUSION; DISTRIBUTED SENSOR NETWORKS; IN-NETWORK DATA AGGREGATION; INTELLIGENCE , SURVEILLANCE AND RECONNAISSANCES; SERVICE-ORIENTED MIDDLEWARE; SITUATION AWARENESS;

SENSOR DATA FUSION;

EID: 85010341391     PISSN: 15501329     EISSN: 15501477     Source Type: Journal    
DOI: 10.1177/1550147716686578     Document Type: Article

References (50)

1. Preden J, Kaugerand J, Suurjaak E, et al. Data to decision: pushing situational information needs to the edge of the network. In: Proceedings of the IEEE international multi-disciplinary conference on cognitive methods in situation awareness and decision, Orlando, FL, 9-12 March 2015, pp.158-164. New York: IEEE.
2. Srisooksai T, Keamarungsi K, Lamsrichan P, et al. Practical data compression in wireless sensor networks: a survey. J Netw Comput Appl 2012; 35(1): 37-59.
3. Mayk I, Regli W, Nguyen D, et al. Net-centric information and knowledge management and dissemination for data-to-decision C2 applications using intelligent agents and service-oriented architectures. In: Proceedings of the military communications conference (MILCOM 2011), Baltimore, MD, 7-10 November 2011, pp.1568-1573. New York: IEEE.
4. Preden JS, Tammemae K, Jantsch A, et al. The benefits of self-awareness and attention in fog and mist computing. Computer 2015; 48(7): 37-45.
5. Corke P, Wark T, Jurdak R, et al. Environmental wireless sensor networks. P IEEE 2010; 98(11): 1903-1917.
6. Dehwah AH, Mousa M and Claudel CG. Lessons learned on solar powered wireless sensor network deployments in urban, desert environments. Ad Hoc Netw 2015; 28: 52-67.
7. Shen Y, Yang P, Zhang P, et al. Development of a multi-type wireless sensor network for the large-scale structure of the national stadium in China. Int J Distrib Sens N 2013; 2013: 16pp.
8. Jo H, Sim SH, Mechitov KA, et al. Hybrid wireless smart sensor network for full-scale structural health monitoring of a cable-stayed bridge. In: Proceedings of the SPIE7981 sensors and smart structures technologies for civil, mechanical, and aerospace systems, San Diego, California, USA, 06 March 2011, vol. 7981, p. 798105.
9. Yoon I, Noh DK, Lee D, et al. Reliable wildfire monitoring with sparsely deployed wireless sensor networks. In: Proceedings of the 26th international conference on advanced information networking and applications (AINA), Fukuoka, Japan, 26-29 March 2012, pp.460-466. New York: IEEE.
10. Ramesh MV. Design, development, and deployment of a wireless sensor network for detection of landslides. Ad Hoc Netw 2014; 13: 2-18.
11. Liu G, Tan R, Zhou R, et al. Volcanic earthquake timing using wireless sensor networks. In: Proceedings of the 12th international conference on information processing in sensor networks, Philadelphia, PA, 8-11 April 2013, pp.91-102. New York: ACM.
12. Salvadori F, De Campos M, Sausen PS, et al. Monitoring in industrial systems using wireless sensor network with dynamic power management. IEEE T Instrum Meas 2009; 58(9): 3104-3111.
13. Lim A, Yang Q, Casey K, et al. Real-time target tracking with CPA algorithm in wireless sensor networks. In: Proceedings of the 5th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks, San Francisco, 16-20 June 2008, pp.305-313. New York: IEEE.
14. Wittenburg G, Dziengel N, Wartenburger C, et al. A system for distributed event detection in wireless sensor networks. In: Proceedings of the 9th ACM/IEEE international conference on information processing in sensor networks, Stockholm, 12-16 April 2010, pp.94-104. New York: ACM.
15. Gao T, Massey T, Selavo L, et al. The advanced health and disaster aid network: a light-weight wireless medical system for triage. IEEE T Biomed Circuits Syst 2007; 1(3): 203-216.
16. Egner AI, Luu D, den Hartog J, et al. An authorization service for collaborative situation awareness. In: Proceedings of the sixth ACM conference on data and application security and privacy, New Orleans, LA, 9-11 March 2016, pp.136-138. New York: ACM.
17. Turkmen F, den Hartog J, Ranise S, et al. Analysis of XACML policies with SMT. Berlin; Heidelberg: Springer, 2015, pp.115-134.
18. Motus L, Meriste M and Preden J. Towards middleware based situation awareness. In: Proceedings of the 2009 IEEE military communications conference, Boston, MA, 18-21 October 2009, pp.1-7. New York: IEEE.
19. Motus L, Meriste M, Preden J, et al. Self-aware architecture to support partial control of emergent behavior. In: Proceedings of the 7th international conference on system of systems engineering (SoSE), Genova, 16-19 July 2012, pp.422-427. New York: IEEE.
20. Preden J, Llinas J, Rogova G, et al. On-line data validation in distributed data fusion. In: Proceedings of the SPIE8742 ground/air multisensor interoperability, integration, and networking for persistent ISR IV, Baltimore, MD, 22 May 2013. Bellingham, WA: SPIE.
21. Winkler M, Tuchs KD, Hughes K, et al. Theoretical and practical aspects of military wireless sensor networks. J Telecommun Inf Technol 2008; 2: 37-45.
22. Kopetz H. Real-time systems: design principles for distributed embedded applications. Berlin: Springer, 2011.
23. Zhao G. Wireless sensor networks for industrial process monitoring and control: a survey. Netw Protocol Algorithm 2011; 3(1): 46-63.
24. Peng AS, Moen DM, Spinks JA, et al. Reliable data aggregation and dissemination framework in tactical network architecture. In Proceedings of the 2010 military communications conference, San Jose, CA, 31 October-3 November 2010, pp.569-574. New York: IEEE.
25. Pham T, Cirincione GH, Verma D, et al. Intelligence, surveillance, and reconnaissance fusion for coalition operations. In: Proceedings of the 11th international conference on information fusion, Cologne, 30 June-3 July 2008, pp.1-8. New York: IEEE.
26. McMahon A and Farrell S. Delay- and disruption-tolerant networking. IEEE Internet Comput 2009; 13(6): 82-87.
27. Mohamed N and Al-Jaroodi J. A survey on service-oriented middleware for wireless sensor networks. Serv Oriented Comput Appl 2011; 5(2): 71-85.
28. Damarla T and Kirkpatrick Alberts WC. Helicopter tracking using acoustic arrays. In: Proceedings of the 17th international conference on information fusion (FUSION), Salamanca, 7-10 July 2014, pp.1-7. New York: IEEE.
29. Maddumabandara A, Leung H and Liu M. Experimental evaluation of indoor localization using wireless sensor networks. IEEE Sens J 2015; 15(9): 5228-5237.
30. Astapov S, Ehala J and Preden JS. Collective acoustic localization in a network of dual channel low power devices. In: Proceedings of the 21st international conference on mixed design of integrated circuits systems (MIXDES), Lublin, 19-21 June 2014, pp.430-435. New York: IEEE.
31. Czuni L and Varga PZ. Lightweight acoustic detection of logging in wireless sensor networks. In: Proceedings of the international conference on digital information, networking, and wireless communications (DINWC), 24-26 June 2014, pp.120-125. Hong Kong: Society of Digital Information and Wireless Communication.
32. Boulmaiz A, Messadeg D, Doghmane N, et al. Robust acoustic bird recognition for habitat monitoring with wireless sensor networks. Int J Speech Tech 2016; 19(3): 631-645.
33. Salomons EL and Havinga PJ. A survey on the feasibility of sound classification on wireless sensor nodes. Sensors 2015; 15(4): 7462-7498.
34. Rajagopalan R and Varshney PK. Data-aggregation techniques in sensor networks: a survey. IEEE Commun Surveys Tutorials 2006; 8(4): 48-63.
35. Bahrepour M, Meratnia N and Havinga P. Sensor fusion-based event detection in wireless sensor networks. In: Proceedings of the 6th annual international conference on mobile and ubiquitous systems: computing, networking and services, MobiQuitous, Toronto, ON, Canada, 13-16 July 2009, pp.1-8. New York: IEEE.
36. Lai S, Cao J and Fan X. TED: efficient type-based composite event detection for wireless sensor network. In: Proceedings of the 2011 international conference on distributed computing in sensor systems and workshops (DCOSS), Barcelona, 27-29 June 2011, pp.1-8. New York: IEEE.
37. Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th international conference on pattern recognition, Cambridge, 26 August 2004, pp.28-31. Washington, DC: IEEE Computer Society.
38. Suzuki S and Abe K. Topological structural analysis of digitized binary images by border following. Comput Vis Graph Image Process 1985; 30(1): 32-46.
39. Sklansky J. Finding the convex hull of a simple polygon. Pattern Recogn Lett 1982; 1(2): 79-83.
40. Rosten E and Drummond T. Fusing points and lines for high performance tracking. In: Proceedings of the 10th international conference on computer vision, Beijing, China, 17-20 October 2005, pp.1508-1515. New York: IEEE.
41. Bouguet JY. Pyramidal implementation of the lucas kanade feature tracker description of the algorithm. New York, USA: Microprocessor Research Labs, Intel Corporation, 2000.
42. Lucas BD and Kanade T. An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th international joint conference on artificial intelligence. Vancouver, BC, Canada, 24-28 August 1981, pp.674-679. San Francisco, CA: Morgan Kaufmann Publishers.
43. Lucas BD. Generalized image matching by the method of differences. PhD Thesis, Carnegie Mellon University, Pittsburgh, PA, 1985.
44. Riid A and Preden JS. A combined method for error and complexity reduction in fuzzy rule-based classification. In: Proceedings of the 2015 IEEE international conference on fuzzy systems, Istanbul, 2-5 August 2015, pp.1-6. New York: IEEE.
45. Astapov S and Riid A. A multistage procedure of mobile vehicle acoustic identification for single-sensor embedded device. Int J Electron Telecommun 2013; 59(2): 151-160.
46. Astapov S, Riid A, Preden JS, et al. Industrial process monitoring by multi-channel acoustic signal analysis. In: Proceedings of the 2014 14th biennial baltic electronic conference (BEC), Tallinn, 6-8 October 2014, pp.209-212. New York: IEEE.
47. Preden JS, Motus L, Llinas J, et al. Improvised explosive devices in asymmetric conflicts: multisource data fusion for providing situational information. In: Gorod A, White BE, Ireland V, et al. (eds) Case studies in system of systems, enterprise systems, and complex systems engineering. London: Taylor & Francis, 2014, pp.407-443.
48. Preden JS, Motus L, Pahtma R, et al. Data exchange for shared situation awareness. In: Proceedings of the 2012 IEEE international multi-disciplinary conference on cognitive methods in situation awareness and decision support (CogSIMA), New Orleans, LA, 6-8 March 2012, pp.198-201. New York: IEEE.
49. Kaugerand J, Preden J, Suurjaak E, et al. A system of systems solution for perimeter control: combining unmanned aerial system with unattended ground sensor network. In: Proceedings of the 2015 9th annual IEEE international systems conference (SysCon), Vancouver, BC, Canada, 13-16 April 2015, pp.317-323. New York: IEEE.
50. Rodd MG and Motus L. Timing analysis of real-time software. Amsterdam: Elsevier, 1994.