軟體缺陷根本原因是軟體錯誤的源頭，錯誤源頭若被移除可降低或刪除軟體缺陷。軟體錯誤是由軟體工程師在開發軟體過程中產生。軟體團隊管理者，例如專案經理，最關注的事情之一是誰產生軟體錯誤以及何時產生。在本文中，提出一個成本有效的方法(原因分析與行動優先法，Cause Analysis and Action Priority, CAAP)，允許軟體團隊決定軟體工程師的技能弱點與定義優先改善指標，此方法透過分析軟體缺陷的根本原因，有效地提供過程回饋，此方法包含正交缺陷原因分類、角色根本原因分類、漸進式改良行動。在實驗中，本文發展兩個系統並紀錄系統錯誤與提供原因分析，一個分析血壓與體脂肪可評估自我健康狀況的模糊推論系統，以及一個在無線感測環境可自行調校的室內定位系統，此二系統皆可進一步地運用在遠距健康照護環境中；透過軟體發展各階段的軟體測試與文件審查，蒐集此二系統的軟體錯誤，並進行缺陷根本原因之分析，用以驗證方法的實用性。實驗結果顯示軟體缺陷原因發生在軟體設計、程式實作、需求分析，模式分析與軟體佈署的比例分別為33.8%、30.6%、21.9%、10.7%、與3.0%；軟體缺陷主要大量出現在軟體設計與程式實作的階段，其中設計主要問題是例外處理與資料庫定義分別是設計階段問題中的40.5%與25.0%，是此軟體團隊主要的技能弱點；此發現可以幫助專案經理在成本考量情況下，有效地決定改善技能弱點的優先順序。

A root cause is a source of software defect, the removal of which reduces or eliminates the defect. Software engineers inject a root cause of a software defect during the development process. One of the main concerns of software team leaders, such as the project manager, is to determine who injected various root causes of defects into the software and the time these root causes were injected. In this paper, a cost–benefit scheme (Cause Analysis and Action Priority or CAAP) is presented. This scheme allows a software team to determine the weakness in skill and improve team capability. The scheme provides effective in-process feedback based on the causal analysis of software defects. The proposed CAAP scheme includes orthogonal root cause definitions, role-based root cause types, and gradational correction actions. In the experiment, two systems for defect causal analysis are developed. The first system is a fuzzy inference system for self-health estimation through blood pressure and body mass index; the system leverages physical vital signs through fuzzy logic technology for daily self-health status estimation. The second system is a self-adaptable indoor localization system; the close tracking algorithm-based system is designed by improving the received signal strength indication (RSSI)-based algorithms to locate moving objects in wireless sensor networks. Both systems can be further applied in out-of-hospital services in the health-care domain. The two healthcare systems are utilized to validate the efficiency of the proposed scheme. Results show that the root cause ratios are 33.8%, 30.6%, 21.9%, 10.7%, and 3.0% in design, implementation, analysis, business, and deployment, respectively. The defects in the projects mainly occurred during the design and implementation phases. Correction activities to enhance the skills of the designers, such as exception handling (40.5%) and DB/data schema (25.0%), are the top priorities that the software team must address. The findings can help the team leader determine methods to improve these weaknesses in the software team.

1. Joao A. Duraes and Henrique S. Madeira, "Emulation of Software Faults: A Field Data Study and a Practical Approach," IEEE Trans. Software Eng., vol. 32, no. 11, pp. 849-867, Nov., 2006.
2. Maggie Hamill and Katerina Goseva-Popstojanova, "Common Trends in Software Fault and Failure Data," IEEE Trans. Software Eng., vol. 35, no. 4, pp. 484-496, July/Aug., 2009.
3. N.E. Fenton and N. Ohlsson, "Quantitative Analysis of Faults and Failures in a Complex Software System," IEEE Trans. Software Eng., vol. 26, no. 8, pp. 797-814, Aug. 2000.
4. R. Krishnamoorthi and S. A. Sahaaya Arul Mary, "Requirement Based System Test Case Prioritization of New and Regression Test Cases," Int. J. Soft. Eng. Knowl. Eng., vol. 19, no. 3, pp. 453-475, May 2009.
5. IEEE Standard Glossary of Software Engineering Terminology. IEEE, 1990.
6. P. Runeson, C. Andersson, T. Thelin, A. Andrews, and T. Berling, "What Do We Know about Defect Detection Methods?" IEEE Software, vol. 23, no.3, pp.82-90, May/June 2006.
7. Hai Hu, C.H. Jiang and K.Y. Cai, "An Improved Approach to Adaptive Testing," Int. J. Soft. Eng. Knowl. Eng., vol. 19, no. 5, pp. 679–705, August 2009.
8. N.E. Fenton and M. Neil, "A Critique of Software Defect Prediction Models," IEEE Trans. Software Eng., vol. 25, no. 5, pp. 675-689, Sept./Oct. 1999.
9. Ram Chillarege, "The marriage of Business Dynamics and Software Engineering," IEEE Software, vol. 19, no. 6, pp. 43-49, Nov./Dec., 2002.
10. Mika V. Mantyla and Casper Lassenius, "What Types of Defects Are Really Discovered in Code Reviews?" IEEE Trans. Software Eng., vol. 35, no. 3, pp.430-448, May/June, 2009.
11. Rober B. Grady, "Software Failure Analysis for High-Return Process Improvement Decisions," Hewlett-Packard Journal, article 2, pp. 1-12, August, 1996.
12. B. Regnell, P. Runeson, and T. Thelin, "Are the Perspectives Really Different? – Further Experimentation on Scenario-Based Reading of Requirements," Empirical Software Eng., vol. 5, no. 4, pp. 331-356, Dec. 2000.
13. Ram Chillarege, Inderpal S. Bhandari, Jarir K. Chaar, Michael J. Halliday, et. al., "Orthogonal Defect Classification-A Concept for In-Process Measurements," IEEE Trans. Software Eng., vol. 18, no. 11, pp. 943-955, Nov., 1992.
14. R. Chillarege, "Orthogonal Defect Classification," Handbook of Software Reliability Eng., chapter 9, IEEE CS Press, McGraw-Hill, 1995.
15. Daniel P. Siewiorek, Ram Chillarege,and Zbigniew T. Kalbarczyk, "Reflections on Industry Trends and Experimental Research in Dependability," IEEE trans. on Dependable and Secure Computing, vol. 1, no. 2, pp.109-127, April/June 2004.
16. Robyn R. Lutz and Ines Carmen Mikulski, "Empirical Analysis of Safety-Critical Anomalies During Operations," IEEE Trans. Software Eng., vol.30, no.3, pp. 172-180, March, 2004.
17. J. Zheng, L. Williams, N. Nagappan, W. Snipes, John P. Hudepohl, and Mladen A. Vouk, " On the Value of Static Analysis for Fault Detection in Software," IEEE Trans. Software Eng., vol.32, no.4, pp. 240-253, April, 2006.
18. R. R. Lutz and I. C. Mikulski, "Ongoing Requirements Discovery in High-Integrity Systems," IEEE Software, vol. 21, no.2, pp. 19-25, 2004.
19. J.C. Chen and S.J. Huang, "An Empirical Analysis of the Impact of Software Development Problem Factors on Software Maintainability," Journal of Systems and Software, vol. 82, no. 6, pp. 981–992, June 2009.
20. B.C.D. Anda, D.I.K. Sjoberg, and A. Mockus, "Variability and Reproducibility in Software Engineering: A Study of Four Companies that Developed the Same System," IEEE Trans. Software Eng., vol. 35, no. 3, pp.407-429, May/June 2009.
21. Shaoying Liu, Tetsuo Tamai, and Shin Nakajima "A Framework for Integrating Formal Specification, Review, and Testing to Enhance Software Reliability," Int. J. Soft. Eng. Knowl. Eng., vol. 21, no. 2, pp. 259-288, March 2011.
22. V.R. Basili, S. Green, O. Laitenberger, F. Lanubile, F. Shull, S. Sørumgård, M.V. Zelkowitz, "The Empirical Investigation of Perspective-Based Reading," Empirical Software Eng., vol. 1, no. 2, pp.133-164, 1996.
23. A.A. Porter, L.G. Votta, Jr., and V.R. Basili, "Comparing Detection Methods For Software Requirements Inspections: A Replicated Experiment," IEEE Trans. Software Eng., vol. 21, no. 6, pp.563-575, June 1995.
24. José C. Maldonado, Jeffrey Carver, Forrest Shull, Sandra Fabbri, Emerson Dória, Luciana Martimiano, Manoel Mendonça, and Victor Basili, "Perspective-Based Reading: A Replicated Experiment Focused on Individual Reviewer Effectiveness," Empirical Software Eng., vol. 11, no. 1, pp.119-142, 2006.
25. G.S. Walia and J.C. Carver., "A Systematic Literature Review to Identify and Classify Requirement Errors," Journal of Information and Software Technology, vol. 51, no. 7, pp.1087-1109, July 2009.
26. G.M. Schneider, et al., "An experimental study of fault detection in user requirements documents," ACM Trans. Softw. Eng. Methodol., vol. 1, pp. 188-204, 1992.
27. I.L. Margarido, J.P. Faria, R.M. Vidal, andM. Vieira, "Classification of Defect Types in Requirements Specifications: Literature Review, Proposal and Assessment," in Proceedings of the 6th Iberian Conference on Information Systems and Technologies, pp. 1-6, June 2011.
28. G.S. Walia and J.C. Carver, "Evaluating the Use of Requirement Error Abstraction and Classification Method for Preventing Errors during Artifact Creation: A Feasibility Study," in Proceedings of the IEEE 21st International Symposium on Software Reliability Engineering, pp, 81-90, Nov. 2010.
29. David .N. Card, "Learning from Our Mistakes with Defect Causal Analysis," IEEE Software, vol. 15, no. 1, pp.56-63, Jan/Feb 1998.
30. M. Leszak, D.E Perry, and D. Stoll, "Classification and Evaluation of Defects in a Project Retrospective," Journal of System and Software, vol. 61, no. 3, pp.173-187, 2002.
31. C.P. Chang and C.P. Chu, "Defect Prevention in Software Process: An Action-based Approach," Journal of Systems and Software, vol. 80, no.4, pp.559-570, April 2007.
32. R. Pooley, D. Senior, and D. Christie, "Collecting and Analyzing Web-based Project Metrics," IEEE Software, vol. 19, no. 1, pp.52-58, Jan/Feb 2002.
33. Ala Alwan, Timothy Armstrong, Melanie Cowan and Leanne Riley et al., “Noncommunicable Diseases Country Profiles 2011,” World Health Organization (WHO), Sep. 2011.
34. Ala Alwan, Tim Armstrong and Douglas Bettcher et al., “Global status report on noncommunicable diseases2010,” World Health Organization (WHO), April 2011.
35. Alwan A, Maclean DR et al., “Monitoring and surveillance of chronic noncommunicable diseases: progress and capacity in high-burden countries,” the Lancet, 376:1861–1868, Nov. 2010.
36. Colin Mathers, Gretchen Stevens and Maya Mascarenhas, “Global health risks: mortality and burden of disease attributable to selected major risks,” Geneva, World Health Organization (WHO), Oct. 2009.
37. Danaei G et al. “National, regional, and global trends in systolic blood pressure since 1980: systematic analysis of health examination surveys and epidemiological studies with 786 country-years and 5.4 million participants,” the Lancet, 377:568-577, Feb. 2011.
38. Finucane M.M. et al., “National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants,” the Lancet, 377:557-567, Feb. 2011.
39. Danaei G et al., “National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: Systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants,” the Lancet, 378:31-40, June 2011.
40. Ferlay J et al., “Estimates of worldwide burden of cancer in 2008: Globocan 2008,” International Journal of Cancer, 127:2893–2917, June 2010.
41. I. Korhonen, J. Parkka, and M. Van Gils, “Health monitoring in the home of the future-infrastructure and usage models for wearable sensors that measure health data in the daily environment of the users,” IEEE Eng. Med. Biol. Mag., vol. 22, no. 3, pp. 66-73, May/Jun. 2003.
42. Alwan M., Dalal S., Mack D., Kell S.W., Turner B., Leachtenauer J., and Felder R., “Impact of monitoring technology in assisted living: Outcome pilot,” IEEE Trans. Inf. Technol. Biomed., vol. 10, no. 1, pp. 192-198, Jan. 2006.
43. Merritt C.R., Nagle H.T., and Grant E., “Fabric-Based Active Electrode Design and Fabrication for Health Monitoring Clothing, ” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 2, pp. 274–280, March 2009.
44. Brink M., Muller C. H., and Schierz C., “Contact-free measurement of heart rate, respiration rate, and body movements during sleep,” Behav. Res. Methods, vol. 38, no. 2, pp. 511-521, Aug. 2006.
45. Mack D.C., Patrie J.T., Suratt P.M., Felder R.A., and Alwan M., “Development and Preliminary Validation of Heart Rate and Breathing Rate Detection Using a passive, Ballistocardiography-Based Sleep Monitoring System,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 1, pp. 111-120, Jan. 2009.
46. P. Magni and R. Bellazzi, “A stochastic model to assess the variability of blood glucose time series in diabetic patients self-monitoring,” IEEE Trans. Biomed. Eng., vol. 53, no.6, pp.977-985, June 2006.
47. Tsipouras MG, Voglis C and Fotiadis DI, “A framework for fuzzy expert system creation--application to cardiovascular diseases,” IEEE Trans Biomed Eng., vol. 54, no. 11, pp. 2089-2105, Nov. 2007.
48. Mohamed A. El-Rashidy, Taha E. Taha, Nabil M. A. Ayad, and Hoda S. Sroor, “An Intelligent Model for Automated Heart Disease Diagnosis,” Journal of Computer Science and Engineering, vol. 4, no. 2, pp. 1-10, Dec. 2010.
49. Latha P., and R. Subramanian, “Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm,” International Journal of Biological and Medical Sciences, vol. 3, no. 3, pp. 157-160, 2008.
50. L.D. Lascio, A. Gisolfi, A. Allbunia, G. Galardi, and F. Meschi, “A fuzzy-based methodology for the analysis of diabetic neuropathy,” Fuzzy Sets Syst., vol. 129, no.2, pp. 203-228, July 2002.
51. Beatrice Fidele, Jayrani Cheeneebash, Ashvin Gopaul, and Smita S.D. Goorah, “Artificial neural network as a clinical decision supporting tool to predict cardiovascular disease,” Trends Appl Sci Res., vol. 4, no. 1, pp. 36-46, 2009.
52. H. Kahramanli and N. Allahverdi, “Design of a Hybrid System for the Diabetes and Heart Diseases,” Expert Systems with Applications, vol. 35, no. 1-2, pp. 82-89, 2008.
53. P. Grant, “A new Aapproach to Diabetic Control: Fuzzy Logic and Insulinpump Technology,” Medical Engineering & Physics, vol. 29, no. 7, pp. 824-827, 2007.
54. C.S. Lee, M.H. Wang, and H. Hagras, “A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 2, pp. 374-395, April 2010.
55. Jantzen J., Foundations of Fuzzy Control, John Wiley and Sons Ltd., April 2007.
56. Ross T. J., Fuzzy Logic with Engineering Applications, McGraw Hill Int. Ed., 1997.
57. K. Y. Seo, G. K. Park, C. S. Lee, and M. H. Wang, "Ontology-based fuzzy support agent for ship steering control," Expert Systems with Applications, vol. 36, no. 1, pp. 755-765, Jan. 2009.
58. C. S. Lee and M. H. Wang, "Ontological fuzzy agent for electrocardiogram application," Expert Systems with Applications, vol. 35, no. 3, pp. 1223-1236, Oct. 2008.
59. C. S. Lee, Y. C. Chang, and M. H. Wang, "Ontological recommendation multi-agent for Tainan city travel," Expert Systems with Applications, vol. 36, no. 3, pp. 6740-6753, Apr. 2009.
60. Zadeh L. A., “Outline of a New Approach to the Analysis of the Ccomplex Systems and Decision Processes,” IEEE Trans. Systems, Man and Cybernetics, vol. SMC-3, pp. 28-44, Jan 1973.
61. Saade J. J., “A Unfying Approach to Defuzzification and Comparison of the outputs of Fuzzy Controllers,” IEEE Transactions on Fuzzy Systems, vol. 4, no. 3, pp. 227-237, Aug. 1996.
62. Hayashi K. and Otsubo A., “Simulator for Studies of Fuzzy Control Methods”, Fuzzy Sets and Systems, Vol. 93, pp. 137—144, 1998.
63. Guillaume S., “Designing Fuzzy Inference Systems From Data: An Interpretability Oriented Review”, IEEE Transaction on Fuzzy Systems, Vol. 9, No. 3 , pp. 426-443, 2001.
64. Mamdami E. H. and Assilina S., “An Experirnalment in Llinguistic Synthesis with a Fuzzy Logic Controller”, International Journal of Man-Machine Studies, vol. 7, no. 1, pp.1-13, 1975.
65. C. S. Lee and M. H. Wang, "Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition," Expert Systems with Applications, vol. 33, no. 3, pp. 606-619, Oct. 2007.
66. K. Guney and N. Sarikaya, “Comparision of Mamdani and Sugeno Fuzzy Interence System Models for Resonant Frequency Calculation of Rectangular Microstrip Anatennas,” Progress In Electromagnetics Research B, vol. 12, pp. 81-104, 2009.
67. Arshdeep Kaur and Amrit Kaur, “Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System,” International Journal of Soft Computing and Engineering, vol. 2, no. 2, pp. 323-325, May 2012.
68. Giovanni Acampora and Vincenzo Loia, “Using FML and Fuzzy Technology in Adaptive Ambient Intelligence Environments,” International Journal of Computational Intelligence Research, vol. 1, no. 2, pp.171-182, 2005.
69. Giovanni Acampora and Vincenzo Loia, “Fuzzy Control Interoperability and Scalability for Adaptive Domotic Framwork,” IEEE Transactions on Industrial Informatics, vol. 1, no. 2, pp.97-111, May 2005.
70. Thanh Tho Quan, Siu Cheung Hui and Fong, A.C.M, “Automatic Fuzzy Ontology Generation for Semantic Help-Desk Support,” IEEE Transactions on Industrial Informatics, vol. 2, no. 3, pp. 155-164, May 2006.
71. Erin-Ee-Lin Lau, Boon-Giin Lee, Seung-Chul Lee, and Wan-Young Chung, “Enhanced RSSI-Based High Accuracy Real-Time User Location Tracking System for Indoor and Outdoor Environments,” International Journal on Smart Sensing and Intelligent Systems, Vol. 1, No. 2, June 2008, pp. 534-548.
72. Youngjune Gwon, Ravi Jain, and Toshiro Kawahara, “Robust Indoor Location Estimation of Stationary and Mobile Users,” in Proc. of the IEEE INFOCOM’04, March 2004, pp. 1032-1043.
73. Masashi Sugano, Tomonori Kawazoe, Yoshikazu Ohta, and Masayuki Murata, “Indoor Localization System Using RSSI Measurement of Wireless Sensor Network Based on ZigBee Standard,” in Proc. of Wireless Sensor Networks 2006 (WSN 2006), Canada, July 2006, pp.1-6.
74. Stefano Tennina, Marco Di Renzo, Fabio Graziosi and Fortunato Santucci, “Locating ZigBee Nodes Using the TI’s CC2431 Location Engine: A Testbed Platform and New Solutions for Positioning Estimation of WSNs in Dynamic Indoor Environments,” in Proc. of the First ACM International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments (MELT 2008), Sep. 2008, pp. 37-42.
75. Hyo-Sung Ahn and Wonpil Yu, “Environmental-Adaptive RSSI-Based Indoor Localization,” IEEE Trans. on Automation Science and Engineering, Vol. 6, No. 4, October 2009, pp. 626-633.
76. Allen Ka, and Lun Miu, “Design and Implementation of an Indoor Mobile Navigation System,” Master thesis of CS at MIT 2002.
77. P. Bahl and V.N. Padmanabhan, “RADAR: An In-Building RF-based User Location and Tracking System,” in Proc. of the IEEE INFOCOM2000, March 2000, pp. 775-784.
78. Angela Song-Ie Noh, Woong Jae Lee, and Jin Young Ye, “Comparison of the mechanisms of the ZigBee’s indoor localization algorithm,” in Proc. of the Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2008), pp.13-18, Aug., 2008, pp. 13-18.
79. Qingming Yao, Fei-Yue Wang, Hui Gao, Kunfeng Wang, and Hongxia Zhao, “Location Estimation in ZigBee Network Based on Fingerprinting,” in Proc. IEEE International Conference on Vehicular Electronics and Safety, Dec 2007, pp. 1-6.
80. Shashank Tadakamadla, "Indoor local positioning system for zigbee based on RSSI", M.Sc. Thesis report, Mid Sweden University, 2006.
81. C. Gentile and L. Klein-Berndt, “Robust Location Using System Dynamics and Motion Constraints”, in Proc. of the 2004 IEEE International Conference on Communications, vol. 3, June 2004, pp. 1360-1364.
82. System–on–chip for 2.4 GHz ZigBee / IEEE 802.15.4 with location engine, Datasheet, Texas Instruments, http://focus.ti.com/lit/ds/symlink/cc2431.pdf, July 2007.
83. K. Aamodt., CC2431 Location Engine. Application Note AN042, Texas Instruments.
84. IEEE 802.15 WPAN™ Task Group 4, IEEE 802.15.4-2006, ZigBee Specification Version r13, ZigBee Alliance, San Ramon, CA, USA, Dec. 2006.
85. Yu-Tso Chen, Chi-Lu Yang, Yeim-Kuan Chang, Chih-Ping Chu, ”A RSSI-based Algorithm for Indoor Localization Using ZigBee in Wireless Sensor Network,” in Proc. of the 15th International Conference on Distributed Multimedia Systems (DMS 2009), Sep. 2009, pp. 70-75.
86. Paolo Barsocchi, Stefano Lenzi, Stefano Chessa and Gaetano Giunta, “Virtual Calibration for RSSI-based Indoor Localization with IEEE 802.15.4,” in Proc. of International IEEE International Conference on Communications, Dresden, Germany, June 2009, pp. 1-5.
87. Chengqun Wang, Jiming Chen and Youxian Sun, “Sensor network localization using kernel spectral regression,” Wireless Communications and Mobile Computing, Published Online, June 2009.
88. Chi-Lu Yang, Yeim-Kuan Chang, Ching-Pao Chang and Chih-Ping Chu, "A Personalized Service Recommendation System in a Home-care Environment," in Proc. of the 15th International Conference on Distributed Multimedia Systems (DMS 2009), Sep. 2009, pp 76-81.
89. Yue Wang, Sol Lederer and Jie Gao, “Connectivity-based Sensor Network Localization with Incremental Delaunay Refinement Method,” in Proc. of the IEEE INFOCOM’09, April 2009, pp. 2401-2409.
90. T. Eren, D. K. Goldenberg, W. Whiteley, Y. R. Yang, A. S. Morse, B. D. O. Anderson and P. N. Belhumeur, “Rigidity, computation, and randomization in network localization,” in Proc. of the IEEE INFOCOM’04, volume 4, March 2004, pp. 2673-2684.
91. D. K. Goldenberg, P. Bihler, M. Cao, J. Fang, B. D. O. Anderson, A. S. Morse, and Y. R. Yang, “Localization in sparse networks using sweeps,” in Proc. of the ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 2006), pp. 110–121.
92. D. Goldenberg, A. Krishnamurthy, W. Maness, Y. R. Yang, A. Young, A. S. Morse, A. Savvides, and B. Anderson, “Network localization in partially localizable networks,” in Proc. of the IEEE INFOCOM’05, volume 1, March 2005, pp. 313–326.
93. D. Moore, J. Leonard, D. Rus, and S. Teller, “Robust distributed network localization with noisy range measurements,” in Proc. of the 2nd international conference on Embedded networked sensor systems (SenSys’04), Nov. 2004, pp. 50–61.
94. Won-Suk Jang and M. J. Skibniewski, “A Wireless Network System for Automated Tracking of Construction Materials on Project Sites,” Journal of civil engineering and management, Vol. 14 No. 1, 2008, pp. 11-19.
95. T.-T. Do, D. Kim, T. S. Lopez, H. Kim, S. Hong, M.-L. Pham, K. Lee, and S. Park, “An Evolvable Operating System for Wireless Sensor Networks,” International Journal of Software Engineering and Knowledge Engineering (IJSEKE), Vol. 15, No. 2005, pp. 265-270.
96. J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler and K. Pister, “System Architecture Directions for Networked Sensors”, in Proc. of the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2000), Nov. 2000, pp. 93-104.
97. Hu F, Jiang M and Xiao Y., “Low-cost wireless sensor networks for remote cardiac patients monitoring applications,” Wireless Communications and Mobile Computing, Vol. 8, No. 2, May 2008, pp. 513-529.