We have reviewed what has been researched in MANET and WSN and how they drive the progress of CPS.Table 4 shows a qualitative comparison of them. While the society of WSN focuses more on the designs of sensing, event-handling, data-retrieving, communication, and coverage issues, the society of CPS focuses more on the development of cross-domain intelligence from multiple WSNs and the interactions between the virtual world and the physical world. A CPS application may bridge multiple remote WSNs and take actuation actions. We have seen a lot of successful vehicle- and mobile phone-based CPS services. Data from such applications is also expected to be continuous streaming data at a very large volume, so storing, processing, and interpreting these data in a real-time manner is essential. Important factors to the
Table 4
A qualitative comparison of MANET, WSN, and CPS.
Networks/features MANET WSN CPS
Network formation Random deployment ✓ ✓ ✓
Dynamic topology ✓ ✓
Internet-supported networking ✓ ✓
Time-varying deployment ✓
Interconnection among multiple networks ✓
Communication pattern Query-response flows ✓ ✓ ✓
Arbitrary communication flows ✓ ✓
Cross-network communication flows ✓
Power management Opportunistic sleep ✓ ✓
Multiple sleep modes of nodes ✓
Power management techniques for both sensors and central servers ✓
Network connectivity and coverage Connectivity ✓ ✓ ✓
Coverage ✓ ✓
Heterogeneous coverage and coverage ✓
Knowledge mining Data mining and database management ✓ ✓
Multi-domain data sources ✓ ✓
Data privacy and security ✓
Quality of services Networking QoS ✓ ✓ ✓
Multiple data resolution ✓ ✓
Table 5
Important factors in designing different CPS applications.
Applications/factors Health care Navigation and rescue ITS Social networking and gaming
Network formation ✓
Data gathering ✓ ✓ ✓ ✓
Query and reply ✓ ✓ ✓ ✓
Sensing coverage ✓
Network connectivity ✓ ✓
Node mobility ✓ ✓
Heterogeneous networks ✓ ✓ ✓ ✓
Knowledge discovery ✓ ✓ ✓ ✓
Security and privacy ✓ ✓
success of CPS include management of cross-domain sensing data, embedded and mobile sensing technologies, elastic computing/storaging technologies, and privacy and security designs. We have also reviewed platforms for health care, navigation and rescue services, ITS, and social networking and gaming, and pointed out the challenges in theses systems.
InTable 5, we summarize important design factors in different applications. Through these reviews, we hope to stimulate more technological development and progress for future CPS applications.
Acknowledgements
Y.-C. Tseng’s research is co-sponsored by MoE ATU Plan, by NSC grants 97-3114-E-009-001, 97-2221-E-009-142-MY3, 98-2219-E-009-019, and 98-2219-E-009-005, 99-2218-E-009-005, by ITRI, Taiwan, by III, Taiwan, by D-Link, and by Intel.
References
[1] I. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Commun. Mag. 40 (8) (2002) 102–114.
[2] S. Basagni, M. Conti, S. Giordano, I. Stojmenovic, Mobile Ad Hoc Networking, John Wiley and Sons, Inc., 2004.
[3] Terrestrial ecology observing systems, center for embedded networked sensing.http://research.cens.ucla.edu/.
[4] S. Upadhyayula, V. Annamalai, S.K.S. Gupta, A low-latency and energy-efficient algorithm for convergecast in wireless sensor networks, in: Global Telecommunications Conf., 2003, pp. 3525–3530.
[5] C. Intanagonwiwat, R. Govindan, D. Estrin, Directed diffusion: a scalable and robust communication paradigm for sensor networks, in: MobiCom, 2000, pp. 56–67.
[6] L. Han, S. Potter, G. Beckett, G. Pringle, S. Welch, S.-H. Koo, G. Wickler, A. Usmani, J.L. Torero, A. Tate, FireGrid: an e-infrastructure for next-generation emergency response support, J. Parallel Distrib. Comput. 70 (11) (2010) 1128–1141.
[7] H.-C. Lee, A. Banerjee, Y.-M. Fang, B.-J. Lee, C.-T. King, Design of a multifunctional wireless sensor for in-situ monitoring of debris flows, IEEE Trans.
Mob. Comput. 59 (11) (2010) 2958–2967.
[8] X. Wang, G. Xing, Y. Zhang, C. Lu, R. Pless, C. Gill, Integrated coverage and connectivity configuration in wireless sensor networks, in: Int’l Conf.
Embedded Networked Sensor Systems, 2003, pp. 28–39.
[9] M. Ma, Y. Yang, SenCar: an energy-efficient data gathering mechanism for large-scale multihop sensor networks, IEEE Trans. Parallel Distrib. Syst.
18 (10) (2007) 1476–1488.
[10] G. Xing, T. Wang, Z. Xie, W. Jia, Rendezvous planning in wireless sensor networks with mobile elements, IEEE Trans. Mob. Comput. 7 (12) (2008) 1430–1443.
[11] J. Lee, D.-K. Jung, Y. Kim, Y.-W. Lee, Y.-M. Kim, Smart grid solutions, services, and business models focused on telco, in: IEEE/IFIP Network Operations and Management Symp. Workshops, 2010, pp. 323–326.
[12] E. Miluzzo, N.D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi, S.B. Eisenman, X. Zheng, A.T. Campbell, Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application, in: Int’l Conf. Embedded Networked Sensor Systems, 2008, pp. 337–350.
[13] H. Ahmadi, N. Pham, R. Ganti, T. Abdelzaher, S. Nath, J. Han, Privacy-aware regression modeling of participatory sensing data, in: Int’l Conf. Embedded Networked Sensor Systems, 2010, pp. 99–112.
[14] I. Constandache, X. Bao, M. Azizyan, R.R. Choudhury, Did you see Bob?: Human localization using mobile phones, in: MobiCom, 2010, pp. 149–160.
[15] E.A. Lee, Cyber physical systems: design challenges, in: Int’l Symp. on Object/Component/Service-Oriented Real-Time Distributed Computing, 2008.
[16] Motes, smart dust sensors, wireless sensor networks.http://www.xbow.com/.
[17] Texas Instruments CC2431.http://www.ti.com/.
[18] Jennic JN5121.http://www.jennic.com/.
[19] ZigBee specification version 2006, ZigBee document 064112.
[20] IEEE standard for information technology - telecommunications and information exchange between systems - local and metropolitan area networks specific requirements part 15.4: wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs), 2003
[21] F. Cuomo, E. Cipollone, A. Abbagnale, Performance analysis of IEEE 802.15.4 wireless sensor networks: an insight into the topology formation process, Comput. Netw. 53 (18) (2009) 3057–3075.
[22] P. Baronti, P. Pillai, V.W. Chook, S. Chessa, A. Gotta, Y.F. Hu, Wireless sensor networks: a survey on the state of the art and the 802.15.4 and ZigBee standards, Comput. Commun. 30 (7) (2007) 1655–1695.
[23] M.R. Garey, D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W.H. Freeman, 1979.
[24] A. Czumaj, W.-B. Strothmann, Bounded degree spanning trees, in: European Symp. Algorithms, 1997.
[25] J. Konemann, R. Ravi, A matter of degree: Improved approximation algorithms for degree-bounded minimum spanning trees, in: ACM Symp. Theory of Computing, 2000.
[26] J. Konemann, A. Levin, A. Sinha, Approximating the degree-bounded minimum diameter spanning tree problem, Algorithmica 41 (2) (2004) 117–129.
[27] M.-S. Pan, C.-H. Tsai, Y.-C. Tseng, The orphan problem in ZigBee wireless networks, IEEE Trans. Mob. Comput. 8 (11) (2009) 1573–1584.
[28] G. Bhatti, G. Yue, A structured addressing scheme for wireless multi-hop networks, Technical Report, Mitsubishi Electric Research Labs Cambridge, MA, 2005.
[29] E. Ould-Ahmed-Vall, D.M. Blough, B.S. Heck, G.F. Riley, Distributed unique global ID assignment for sensor networks, in: Int’l Conf. Mobile Ad Hoc and Sensor Systems, 2005.
[30] M.-S. Pan, H.-W. Fang, Y.-C. Liu, Y.-C. Tseng, Address assignment and routing schemes for ZigBee-based long-thin wireless sensor networks, in:
Vehicular Tech. Conf., 2008, pp. 173–177.
[31] C. Wan, S. Eisenman, A. Campbell, J. Crowcroft, Siphon: overload traffic management using multi-radio virtual sinks in sensor networks, in: Int’l Conf.
Embedded Networked Sensor Systems, 2005, pp. 116–129.
[32] Y. Yu, B. Krishnamachari, V.K. Prasanna, Energy-latency tradeoffs for data gathering in wireless sensor networks, in: INFOCOM, 2004.
[33] G. Lu, B. Krishnamachari, C.S. Raghavendra, An adaptive energy-efficient and low-latency MAC for data gathering in wireless sensor networks, in:
Int’l Parallel and Distributed Processing Symp., 2004.
[34] B. Hohlt, L. Doherty, E. Brewer, Flexible power scheduling for sensor networks, in: Int’l Symp. Information Processing in Sensor Networks, 2004, pp.
205–214.
[35] A. Keshavarzian, H. Lee, L. Venkatraman, Wakeup scheduling in wireless sensor networks, in: Int’l Symp. Mobile Ad Hoc Networking and Computing, 2006, pp. 322–333.
[36] S. Gandham, Y. Zhang, Q. Huang, Distributed minimal time convergecast scheduling in wireless sensor networks, in: Int’l Conf. Distributed Computing Systems, 2006.
[37] H. Choi, J. Wang, E.A. Hughes, Scheduling for information gathering on sensor network, Wirel. Netw. 15 (2009) 127–140.
[38] IEEE standard for information technology - telecommunications and information exchange between systems - local and metropolitan area networks specific requirements part 15.4: wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs)(revision of IEEE Std 802.15.4-2003), 2006
[39] Y.-C. Tseng, M.-S. Pan, Quick convergecast in ZigBee beacon-enabled tree-based wireless sensor networks, Comput. Commun. 31 (5) (2008) 999–1011.
[40] W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, in: Hawaii Int’l Conf. on System Sciences, 2000.
[41] W.B. Heinzelman, A.P. Chandrakasan, H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks, IEEE Wirel.
Commun. 1 (4) (2002) 660–670.
[42] L. Zhao, X. Hong, Q. Liang, Energy-efficient self-organization for wireless sensor networks: a fully distributed approach, in: Global Telecommunications Conf., 2004, pp. 2728–2732.
[43] O. Younis, S. Fahmy, HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks, IEEE Trans. Mob. Comput. 3 (4) (2004) 366–379.
[44] Z. Zhang, M. Ma, Y. Yang, Energy-efficient multihop polling in clusters of two-layered heterogeneous sensor networks, IEEE Trans. Comput. 57 (2) (2008) 231–245.
[45] K. Xu, H. Hassanein, G. Takahara, Q. Wang, Relay node deployment strategies in heterogeneous wireless sensor networks, IEEE Trans. Mob. Comput.
9 (2) (2010) 145–159.
[46] H.J. Choe, P. Ghosh, S.K. Das, QoS-aware data reporting control in cluster-based wireless sensor networks, Comput. Commun. 33 (11) (2010) 1244–1254.
[47] R. Cristescu, B. Beferull-Lozano, M. Vetterli, R. Wattenhofer, Network correlated data gathering with explicit communication: NP-completeness and algorithms, IEEE/ACM Trans. Netw. 14 (1) (2006) 41–54.
[48] K. Yuen, B. Liang, B. Li, A distributed framework for correlated data gathering in sensor networks, IEEE Trans. Veh. Technol. 57 (1) (2007) 578–593.
[49] K.-W. Fan, S. Liu, P. Sinha, Structure-free data aggregation in sensor networks, IEEE Trans. Mob. Comput. 6 (8) (2007) 929–942.
[50] E. Fasolo, M. Rossi, J. Widmer, M. Zorzi, In-network aggregation techniques for wireless sensor networks: a survey, IEEE Wirel. Commun. 14 (2) (2007) 70–87.
[51] K. Akkaya, M. Younis, A survey on routing protocols for wireless sensor networks, Ad Hoc Networks 3 (3) (2005) 325–349.
[52] A. Marcucci, M. Nati, C. Petrioli, A. Vitaletti, Directed diffusion light: low overhead data dissemination in wireless sensor networks, in: Vehicular Tech. Conf., 2005, pp. 2538–2545.
[53] M. Chen, T. Kwon, Y. Choi, Energy-efficient differentiated directed diffusion (EDDD) in wireless sensor networks, Comput. Commun. 29 (2) (2006) 231–245.
[54] K. Casey, R. Neelisetti, A. Lim, RTDD: a real-time communication protocol for directed diffusion, in: Wireless Communications and Networking Conf., 2008, pp. 2852–2857.
[55] S. Ratnasamy, B. Karp, L. Yin, F. Yu, D. Estrin, R. Govindan, S. Shenker, GHT: a geographic hash-table for data-centric storage, in: ACM Int’l Workshop Wireless Sensor Networks and Their Applications, 2002, pp. 78–87.
[56] B. Greenstein, D. Estrin, R. Govindan, S. Ratnasamy, S. Shenker, DIFS: a distributed index for features in sensor networks, Ad Hoc Networks 1 (2–3) (2003) 333–349.
[57] X. Li, Y.J. Kim, R. Govindan, W. Hong, Multi-dimensional range queries in sensor networks, in: Int’l Conf. Embedded Networked Sensor Systems, 2003, pp. 63–75.
[58] D. Ganesan, B. Greenstein, D. Estrin, J. Heidemann, R. Govindan, Multiresolution storage and search in sensor networks, ACM Trans. Storage 1 (3) (2005) 277–315.
[59] Y.-C. Wang, Y.-Y. Hsieh, Y.-C. Tseng, Multiresolution spatial and temporal coding in a wireless sensor network for long-term monitoring applications, IEEE Trans. Comput. 58 (6) (2009) 827–838.
[60] C.-F. Huang, Y.-C. Tseng, The coverage problem in a wireless sensor network, Mobile Netw. Appl. 10 (4) (2005) 519–528.
[61] C.-F. Huang, Y.-C. Tseng, H.-L. Wu, Distributed protocols for ensuring both coverage and connectivity of a wireless sensor network, ACM Trans. Sens.
Netw. 3 (1) (2007).
[62] H.M. Ammari, S.K. Das, Integrated coverage and connectivity in wireless sensor networks: a two-dimensional percolation problem, IEEE Trans.
Comput. 57 (10) (2008) 1423–1433.
[63] H.M. Ammari, S.K. Das, Critical density for coverage and connectivity in three-dimensional wireless sensor networks using continuum percolation, IEEE Trans. Parallel Distrib. Syst. 20 (6) (2009) 872–885.
[64] S. Slijepcevic, M. Potkonjak, Power efficient organization of wireless sensor networks, in: Int’l Conf. Communications, 2001, pp. 472–476.
[65] S.K. Das, A.K. Datta, M.G. Potop-Butucaru, R. Patel, A. Yamazaki, Self-stabilizing minimum connected covers of query regions in sensor networks, Wireless Communications and Mobile Computing (2009).
[66] F. Ye, G. Zhong, J. Cheng, S. Lu, L. Zhang, PEAS: a robust energy conserving protocol for long-lived sensor networks, in: Int’l Conf. Distributed Computing Systems, 2003, pp. 28–37.
[67] W. Choi, S.K. Das, CROSS: a probabilistic constrained random sensor selection scheme in wireless sensor networks, Perform. Eval. 66 (12) (2009) 754–772.
[68] T. Yan, T. He, J.A. Stankovic, Differentiated surveillance for sensor networks, in: Int’l Conf. Embedded Networked Sensor Systems, 2003, pp. 51–62.
[69] C.-F. Huang, L.-C. Lo, Y.-C. Tseng, W.-T. Chen, Decentralized energy-conserving and coverage-preserving protocols for wireless sensor networks, ACM Trans. Sens. Netw. 2 (2) (2006) 182–187.
[70] Y. Zou, K. Chakrabarty, Sensor deployment and target localization in distributed sensor networks, ACM Trans. Embedded Comput. Syst. 3 (1) (2004) 61–91.
[71] N. Heo, P.K. Varshney, Energy-efficient deployment of intelligent mobile sensor networks, IEEE Trans. Syst. Man Cybern. A 35 (1) (2005) 78–92.
[72] N. Aitsaadi, N. Achir, K. Boussetta, B. Gavish, A gradient approach for differentiated wireless sensor network deployment, in: IFIP Wireless Days Conf., 2008, pp. 1–5.
[73] G. Wang, G. Cao, T.F.L. Porta, Movement-assisted sensor deployment, IEEE Trans. Mob. Comput. 5 (6) (2006) 640–652.
[74] Y.-C. Wang, C.-C. Hu, Y.-C. Tseng, Efficient placement and dispatch of sensors in a wireless sensor network, IEEE Trans. Mob. Comput. 7 (2) (2008) 262–274.
[75] Y.-C. Wang, Y.-C. Tseng, Distributed deployment schemes for mobile wireless sensor networks to ensure multilevel coverage, IEEE Trans. Parallel Distrib. Syst. 19 (9) (2008) 1280–1294.
[76] G. Tan, S.A. Jarvis, A.-M. Kermarrec, Connectivity-guaranteed and obstacle-adaptive deployment schemes for mobile sensor networks, IEEE Trans.
Mob. Comput. 8 (6) (2009) 836–848.
[77] S. Zhou, M.-Y. Wu, W. Shu, Finding optimal placements for mobile sensors: wireless sensor network topology adjustment, in: IEEE Circuits and Systems Symp. Emerging Technologies: Frontiers of Mobile and Wireless Communications, 2004, pp. 529–532.
[78] P. Basu, J. Redi, Movement control algorithms for realization of fault-tolerant ad hoc robot networks, IEEE Netw. 18 (4) (2004) 36–44.
[79] G. Wang, G. Cao, P. Berman, T.F.L. Porta, Bidding protocols for deploying mobile sensors, IEEE Trans. Mob. Comput. 6 (5) (2007) 515–528.
[80] Z. Butler, D. Rus, Event-based motion control for mobile-sensor networks, IEEE Pervasive Comput. 2 (4) (2003) 34–42.
[81] A.A. Abbasi, M. Younis, K. Akkaya, Movement-assisted connectivity restoration in wireless sensor and actor networks, IEEE Trans. Parallel Distrib.
Syst. 20 (9) (2009) 1366–1379.
[82] G. Wang, G. Cao, T.L. Porta, W. Zhang, Sensor relocation in mobile sensor networks, in: INFOCOM, 2005, pp. 2302–2312.
[83] A. Verma, H. Sawant, J. Tan, Selection and navigation of mobile sensor nodes using a sensor network, Pervasive Mob. Comput. 2 (1) (2006) 65–84.
[84] Y.-C. Wang, W.-C. Peng, Y.-C. Tseng, Energy-balanced dispatch of mobile sensors in a hybrid wireless sensor network, IEEE Trans. Parallel Distrib.
Syst. 21 (12) (2010) 1836–1850.
[85] P. Ballal, A. Trivedi, F. Lewis, Deadlock avoidance policy in mobile wireless sensor networks with free choice resource routing, Int. J. Adv. Rob. Syst.
5 (3) (2008) 279–290.
[86] W. Zhao, M.H. Ammar, Message ferrying: proactive routing in highly-partitioned wireless ad hoc networks, in: IEEE Workshop Future Trends of Distributed Computing Systems, 2003, pp. 308–314.
[87] W. Zhao, M. Ammar, E. Zegura, Controlling the mobility of multiple data transport ferries in a delay-tolerant network, in: INFOCOM, 2005, pp.
1407–1418.
[88] B. Yuan, M. Orlowska, S. Sadiq, On the optimal robot routing problem in wireless sensor networks, IEEE Trans. Knowl. Data Eng. 19 (9) (2007) 1252–1261.
[89] M.M.B. Tariq, M. Ammar, E. Zegura, Message ferry route design for sparse ad hoc networks with mobile nodes, in: Int’l Symp. Mobile Ad Hoc Networking and Computing, 2006, pp. 37–48.
[90] E.C.-H. Ngai, J. Liu, M.R. Lyu, An adaptive delay-minimized route design for wireless sensor–actuator networks, IEEE Trans. Veh. Technol. 58 (9) (2009) 5083–5094.
[91] G. Xing, T. Wang, W. Jia, M. Li, Rendezvous design algorithms for wireless sensor networks with a mobile base station, in: Int’l Symp. Mobile Ad Hoc Networking and Computing, 2008, pp. 231–240.
[92] M. Ma, Y. Yang, Data gathering in wireless sensor networks with mobile collectors, in: Int’l Parallel and Distributed Processing Symp., 2008, pp. 1–9.
[93] M. Zhao, M. Ma, Y. Yang, Mobile data gathering with space-division multiple access in wireless sensor networks, in: INFOCOM, 2008, pp. 1283–1291.
[94] J. Rao, T. Wu, S. Biswas, Network-assisted sink navigation protocols for data harvesting in sensor networks, in: Wireless Communications and Networking Conf., 2008, pp. 2887–2892.
[95] J. Rao, S. Biswas, Joint routing and navigation protocols for data harvesting in sensor networks, in: Int’l Conf. Mobile Ad Hoc and Sensor Systems, 2008, pp. 143–152.
[96] H.M. Ammari, S.K. Das, Promoting heterogeneity, mobility, and energy-aware Voronoi diagram in wireless sensor networks, IEEE Trans. Parallel Distrib. Syst. 19 (7) (2008) 995–1008.
[97] V.L. Narasimhan, A.A. Arvind, K. Bever, Greenhouse asset management using wireless sensor–actuator networks, in: The Int’l Conf. on Mobile Ubiquitous Computing, Systems, Services and Technologies, 2007, pp. 9–14.
[98] T. Melodia, D. Pompili, I.F. Akyildiz, A communication architecture for mobile wireless sensor and actor networks, in: Sensor and Ad Hoc Communications and Networks Conf., 2006, pp. 109–118.
[99] T. Melodia, D. Pompili, V.C. Gungor, I.F. Akyildiz, A distributed coordination framework for wireless sensor and actor networks, in: Int’l Symp. Mobile Ad Hoc Networking and Computing, 2005, pp. 99–110.
[100] I.F. Akyildiz, I.H. Kasimoglu, Wireless sensor and actor networks: research challenges, Ad Hoc Networks 2 (4) (2004) 351–367.
[101] E. Jovanov, A. Milenkovic, C. Otto, P.C. de Groen, A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation, J. Neuroeng. Rehabil. 2 (6) (2005).
[102] C.C.Y. Poon, Y.-T. Zhang, S.-D. Bao, A novel biometrics method to secure wireless body area sensor networks for telemedicine and M-health, IEEE Commun. Mag. 44 (4) (2006) 73–81.
[103] T. Taleb, D. Bottazzi, M. Guizani, H. Nait-Charif, ANGELAH: a framework for assisting elders at home, IEEE J. Sel. Areas Commun. 27 (4) (2009) 480–494.
[104] Q. Li, M.D. Rosa, D. Rus, Distributed algorithm for guiding navigation across a sensor network, in: Int’l Symp. Mobile Ad Hoc Networking and Computing, 2003, pp. 313–325.
[105] Y.-C. Tseng, M.-S. Pan, Y.-Y. Tsai, A distributed emergency navigation algorithm for wireless sensor networks, IEEE Comput. 39 (7) (2006) 55–62.
[106] M.-S. Pan, C.-H. Tsai, Y.-C. Tseng, Emergency guiding and monitoring applications in indoor 3D environments by wireless sensor networks, Int. J.
Sens. Netw. 1 (1–2) (2006) 2–10.
[107] M. Li, Y. Liu, J. Wang, Z. Yang, Sensor network navigation without locations, in: INFOCOM, 2009, pp. 2419–2427.
[108] C.-H. Lo, W.-C. Peng, C.-W. Chen, T.-Y. Lin, C.-S. Lin, CarWeb: a traffic data collection platform, in: Int’l Conf. Mobile Data Management, 2008, pp.
221–222.
[109] P. Mohan, V.N. Padmanabhan, R. Ramjee, Nericell: rich monitoring of road and traffic conditions using mobile smartphones, in: Int’l Conf. Embedded Networked Sensor Systems, 2008, pp. 323–336.
[110] A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, J. Eriksson, Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones, in: Int’l Conf. Embedded Networked Sensor Systems, 2009, pp. 85–98.
[111] A. Thiagarajan, J. Biagioni, T. Gerlich, J. Eriksson, Cooperative transit tracking using smart-phones, in: Int’l Conf. Embedded Networked Sensor Systems, 2010, pp. 85–98.
[112] S.B. Eisenman, E. Miluzzo, N.D. Lane, R.A. Peterson, G.S. Ahn, A.T. Campbell, The BikeNet mobile sensing system for cyclist experience mapping, in:
Int’l Conf. Embedded Networked Sensor Systems, 2007, pp. 87–101.
[113] S. Mathur, T. Jin, N. Kasturirangan, J. Chandrashekharan, W. Xue, M. Gruteser, W. Trappe, ParkNet: drive-by sensing of road-side parking statistics, in: Int’l Conf. on Mobile Systems, Applications, and Services, 2010, pp. 123–136.
[114] K.A.S. Al-Khateeb, J.A. Johari, W.F. Al-Khateeb, Dynamic traffic light sequence algorithm using RFID, J. Comput. Sci. 4 (7) (2008) 517–524.
[115] M.-C. Chiu, S.-P. Chang, Y.-C. Chang, H.-H. Chu, C.C.-H. Chen, F.-H. Hsiao, J.-C. Ko, Playful bottle: a mobile social persuasion system to motivate healthy water intake, in: Int’l Conf. Ubiquitous computing, 2009, pp. 185–194.
[116] S. Gaonkar, J. Li, R.R. Choudhury, Micro-Blog: sharing and querying content through mobile phones and social participation, in: Int’l Conf. on Mobile Systems, Applications, and Services, 2008, pp. 174–186.
[117] C.-H. Wu, Y.-T. Chang, Y.-C. Tseng, Multi-screen cyber–physical video game: an integration with body-area inertial sensor networks, in: Int’l Conf.
Pervasive Computing and Communications, 2010, pp. 832–834.
[118] F.-J. Wu, C.-S. Huang, Y.-C. Tseng, My Tai-Chi Book: a virtual–physical social network platform, in: Int’l Symp. Information Processing in Sensor Networks, 2010, pp. 428–429.
[119] J. Brutovsky, D. Novak, Low-cost motivated rehabilitation system for post-operation exercises, in: Engineering in Medicine and Biology Society, 2006, pp. 6663–6666.
[120] J.-H. Huang, S. Amjad, S. Mishra, CenWits: a sensor-based loosely coupled search and rescue system using witnesses, in: Int’l Conf. Embedded Networked Sensor Systems, 2005, pp. 180–191.
[121] G. Kantor, S. Singh, R. Peterson, D. Rus, A. Das, V. Kumar, G. Pereira, J. Spletzer, Distributed search and rescue with robot and sensor teams, Field Serv.
Robot. 24 (2006) 529–538.
[122] The harvard robobees project.http://robobees.seas.harvard.edu.
[123] W.-T. Chen, P.-Y. Chen, C.-H. Wu, C.-F. Huang, A load-balanced guiding navigation protocol in wireless sensor networks, in: Global Telecommunications Conf., 2008, pp. 1–6.
[124] N. Dimakis, A. Filippoupolitis, E. Gelenbe, Distributed building evacuation simulator for smart emergency management, Comput. J. 53 (9) (2010) 1384–1400.
[125] S. Li, A. Zhan, X. Wu, G. Chen, ERN: emergence rescue navigation with wireless sensor networks, in: Int’l Conf. Parallel and Distributed Systems, 2009, pp. 361–368.
[126] Intelligent transportation systems.http://www.its.dot.gov/.
[127] California center for innovative transportation.http://www.calccit.org/.
[128] California Partners for Advanced Transit and Highways (PATH).http://www.path.berkeley.edu/.
[129] Streetline.http://www.streetlinenetworks.com.
[130] Vehiclesense.http://www.vehiclesense.com.
[131] On-board diagnostics (OBD).http://www.epa.gov/otaq/regs/im/obd/index.htm.
[132] Controller area network (CAN-Bus).http://www.gaw.ru/data/Interface/CAN_BUS.PDF.
[133] IEEE 1609-Family of Standards for Wireless Access in Vehicular Environments (WAVE).http://vii.path.berkeley.edu/1609_wave/.
[134] ETC Systems.http://www.go-etc.jp/english/index.html.
[135] L.-W. Chen, Y.-H. Peng, Y.-C. Tseng, An infrastructure-less framework for preventing rear-end collisions by vehicular sensor networks, IEEE Commun.
Lett. 15 (3) (2011) 358–360.
[136] F. Ye, M. Adams, S. Roy, V2V wireless communication protocol for rear-end collision avoidance on highways, in: Int’l Conf. Communications, 2008, pp. 375–379.
[137] D. Vlasic, R. Adelsberger, G. Vannucc, J. Barnwell, M. Gross, W. Matusik, J. Popović, Practical motion capture in everyday surroundings, ACM Trans.
Graph. 26 (3) (2007).
[138] G. Zhou, J. Lu, C.-Y. Wan, M. Yarvis, J. Stankovic, BodyQoS: adaptive and radio-agnostic QoS for body sensor networks, in: INFOCOM, 2008, pp.
565–573.
[139] L. Cheng, S. Hailes, Z. Cheng, F.-Y. Fan, D. Hang, Y. Yang, Compressing inertial motion data in wireless sensing systems—an initial experiment, in: Int’l Workshop on Wearable and Implantable Body Sensor Networks, 2008, pp. 293–296.
[140] Wikimapia.http://wikimapia.org.
[141] Tinyos.http://www.tinyos.net/.
[142] A. Dunkels, Full TCP/IP for 8-bit architectures, in: Int’l Conf. on Mobile Systems, Applications, and Services, 2003, pp. 85–98.
[143] G. Montenegro, N. Kushalnagar, J. Hui, D. Culler, Transmission of IPv6 packets over IEEE 802.15.4 networks, RFC 4944 (Proposed Standard), 2007.
[144] J.W. Hui, D.E. Culler, IP is dead, long live IP for wireless sensor networks, in: Int’l Conf. Embedded Networked Sensor Systems, 2008, pp. 15–28.
[145] J.-H. Hauer, V. Handziski, A. Wolisz, Experimental study of the impact of WLAN interference on IEEE 802.15.4 body area networks, in: European Conf.
on Wireless Sensor Networks, 2009, pp. 17–32.
[146] R. Musaloiu-E, A. Terzis, Minimising the effect of WiFi interference in 802.15.4 wireless sensor networks, Int. J. Sens. Netw. 3 (1) (2007) 43–54.
[147] T.H. Kim, J.Y. Ha, S. Choi, W.H. Kwon, Virtual channel management for densely deployed IEEE 802.15.4 LR-WPANs, in: Int’l Conf. Pervasive Computing and Communications, 2006, pp. 103–115.
[148] C. Won, J.-H. Youn, H. Ali, H. Sharif, J. Deogun, Adaptive radio channel allocation for supporting coexistence of 802.15.4 and 802.11b, in: Vehicular Tech. Conf., 2005, pp. 2522–2526.
[149] C.-J.M. Liang, N.B. Priyantha, J. Liu, A. Terzis, Surviving Wi-Fi interference in low power ZigBee networks, in: Int’l Conf. Embedded Networked Sensor Systems, 2010, pp. 309–322.
[150] F. Xia, L. Ma, J. Dong, Y. Sun, Network QoS management in cyber–physical systems, in: Int’l Conf. Embedded Software and Systems Symp., 2008, pp.
302–307.
[151] J. Sonnek, J. Greensky, R. Reutiman, A. Chandra, Starling: minimizing communication overhead in virtualized computing platforms using decentralized affinity-aware migration, in: Int’l Conf. Parallel Processing, 2010, pp. 228–237.
[152] C. Yang, C. Yen, C. Tan, S.R. Madden, Osprey: implementing MapReduce-style fault tolerance in a shared-nothing distributed database, in: Int’l Conf.
Data Engineering, 2010, pp. 657–668.
[153] M. Addlesee, R. Curwen, S. Hodges, J. Newman, P. Steggles, A. Ward, A. Hopper, Implementing a sentient computing system, IEEE Comput. 34 (8) (2001) 50–56.
[154] N.B. Priyantha, A.K.L. Miu, H. Balakrishnan, S.J. Teller, The cricket compass for context-aware mobile applications, in: MobiCom, 2001, pp. 1–14.
[155] X. Chai, Q. Yang, Reducing the calibration effort for probabilistic indoor location estimation, IEEE Trans. Mob. Comput. 6 (6) (2007) 649–662.
[156] S.-P. Kuo, Y.-C. Tseng, A scrambling method for fingerprint positioning based on temporal diversity and spatial dependency, IEEE Trans. Knowl. Data Eng. 20 (5) (2008) 678–684.
[157] J.J. Pan, J.T. Kwok, Q. Yang, Y. Chen, Multidimensional vector regression for accurate and low-cost location estimation in pervasive computing, IEEE Trans. Knowl. Data Eng. 18 (9) (2006) 1181–1193.
[158] E. Foxlin, Pedestrian tracking with shoe-mounted inertial sensors, IEEE Comput. Graph. Appl. 25 (6) (2005) 38–46.
[159] O. Woodman, R. Harle, Pedestrian localisation for indoor environments, in: Int’l Conf. Ubiquitous computing, 2008, pp. 114–123.
[160] M. Azizyan, I. Constandache, R.R. Choudhury, SurroundSense: mobile phone localization via ambience fingerprinting, in: MobiCom, 2009, pp.
261–272.
[161] Brickhouse security.http://www.brickhousesecurity.com/gps-tracking-system.html.
[162] Liveview.http://www.liveviewgps.com/.
[163] S. Guha, K. Plarre, D. Lissner, S. Mitra, B. Krishna, AutoWitness: locating and tracking stolen property while tolerating GPS and radio outages, in: Int’l Conf. Embedded Networked Sensor Systems, 2010, pp. 29–42.
[164] L.E. Cordova-Lopez, A. Mason, J.D. Cullen, Online vehicle and atmospheric pollution monitoring using gis and wireless sensor networks, in: Int’l Conf.
Embedded Networked Sensor Systems, 2007, pp. 87–101.
[165] S.-C. Hu, Y.-C. Wang, C.-Y. Huang, Y.-C. Tseng, A vehicular wireless sensor network for CO2monitoring, in: IEEE Sensors Conf., 2009, pp. 1498–1501.
[166] U. Lee, B. Zhou, M. Gerla, E. Magistretti, P. Bellavista, A. Corradi, Mobeyes: smart mobs for urban monitoring with a vehicular sensor network, IEEE Wirel. Commun. 13 (5) (2006) 52–57.
[167] Y. Sun, B. McMillin, X.F. Liu, D. Cape, Verifying noninterference in a cyber–physical system—the advanced electric power grid, in: Int’l Conf. Quality Software, 2007, pp. 363–369.
[168] A.R. Metke, R.L. Ekl, Security technology for smart grid networks, IEEE Trans. Smart Grid 1 (1) (2010) 99–107.
[169] A. Krause, E. Horvitz, A. Kansal, F. Zhao, Toward community sensing, in: Int’l Symp. Information Processing in Sensor Networks, 2008, pp. 481–492.
[170] R.K. Ganti, N. Pham, Y.-E. Tsai, T.F. Abdelzaher, PoolView: stream privacy for grassroots participatory sensing, in: Int’l Conf. Embedded Networked Sensor Systems, 2008, pp. 281–294.
[171] N. Pham, R.K. Ganti, Y.S. Uddin, S. Nath, T. Abdelzaher, Privacy-preserving reconstruction of multidimensional data maps in vehicular participatory sensing, in: European Conf. on Wireless Sensor Networks, 2010, pp. 114–130.
Fang-Jing Wu received the B.S. degree in Mathematics form the Fu Jen Catholic University and the M.S. degree in Computer Science and Information Engineering from the National Chiao-Tung University, Taiwan, in 2001 and 2004, respectively. She was a research assistant in the Department of Communication Engineering, National Chiao-Tung University, Taiwan, in 2004. She is currently pursuing Ph.D. in the Department of Computer Science, National Chiao-Tung University, Taiwan. Her current research interests are primarily in pervasive computing and wireless sensor networks.
Yu-Fen Kao received her B.B.A. degree from National Taiwan University in 1986; M.P.A. degree from the University of Texas at Austin in 1992; and Ph.D. degree in Management Science from National Chiao Tung University in 2008. She is currently an assistant professor in the Department of Information Management in the Chung Hua University, Taiwan. Her main research interests include resource allocation strategies and web-based consumer behaviors.
Yu-Chee Tseng got his Ph.D. in Computer and Information Science from the Ohio State University in January of 1994. He is/was Professor (2000-present), Chairman (2005–2009), and Associate Dean (2007-present), Department of Computer Science, National Chiao-Tung University, Taiwan, and Chair Professor, Chung Yuan Christian University (2006–2010).
Dr. Tseng received Outstanding Research Award (National Science Council, 2001, 2003 and 2009), Best Paper Award (Int’l Conf.
on Parallel Processing, 2003), Elite I. T. Award (2004), and Distinguished Alumnus Award (Ohio State University, 2005), and Y. Z.
Hsu Scientific Paper Award (2009). His research interests include mobile computing, wireless communication, and parallel and
Hsu Scientific Paper Award (2009). His research interests include mobile computing, wireless communication, and parallel and