.. Runtime-activities-of-our-proactive-approach, 96 5.10 Communication Structure (based on [122]) 97 5.11 Power consumption in transmit mode (CC2420) [128] 99 5.12 Summary of the different implementations of the business rule 102 5.13 Evaluation of Fire Potential Classification 105 5.14 Fire potential output table Implementation of the Predictive systems The number is relative to the reactive system, 5.16 Number of needed reconfigurations relative to the reactive system . . . . . . . 108 5.17 Power consumption relative to the reactive system . . . . . . . . . . . . . . . . 109 5.18 Number of hours when a fire detected by the node was waiting for transmission to the data collector, p.110

M. Alia, F. Eliassen, S. Hallsteinsen, and E. Stav, MADAM, Proceedings of the 2006 international workshop on Self-adaptation and self-managing systems , SEAMS '06, pp.96-96, 2006.
DOI : 10.1145/1137677.1137699

C. Alverson, Polling and statistical models can't predict the future. Personal Blog, 2012. Retrieved on 01 Dec, 2014.

J. Andersson, R. De-lemos, S. Malek, and D. Weyns, Modeling Dimensions of Self-Adaptive Software Systems, Software engineering for self-adaptive systems, pp.27-47, 2009.
DOI : 10.1007/978-3-540-32259-7_1

M. Arlitt and T. Jin, A workload characterization study of the 1998 World Cup Web site, IEEE Network, vol.14, issue.3, pp.30-37, 2000.
DOI : 10.1109/65.844498

R. Rafael, A. Aschoff, and . Zisman, Proactive adaptation of service composition, 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pp.1-10, 2012.

K. Ashton, That 'internet of things' thing, RFiD Journal, vol.22, pp.97-114, 2009.

K. J. Astrom and B. Wittenmark, Adaptive control, 1995.

K. Bache, University of California, Irvine, and Computer Sciences. (uci) machine learning repository, 2015.

D. Barstow, Artificial Intelligence and Software Engineering, Proceedings of the 9th international conference on Software Engineering, pp.200-211, 1987.
DOI : 10.1016/B978-0-934613-67-5.50020-4

R. Victor and . Basili, The experimental paradigm in software engineering, Experimental Software Engineering Issues: Critical Assessment and Future Directions, pp.1-12, 1993.

R. Victor, F. Basili, F. Shull, and . Lanubile, Building knowledge through families of experiments. Software Engineering, IEEE Transactions on, vol.25, issue.4, pp.456-473, 1999.

N. Bencomo, P. Sawyer, S. Gordon, P. Blair, and . Grace, Dynamically adaptive systems are product lines too: Using model-driven techniques to capture dynamic variability of adaptive systems, In SPLC, issue.2, pp.23-32, 2008.

R. Michael and . Berthold, Mixed fuzzy rule formation, International journal of approximate reasoning, vol.32, issue.2, pp.67-84, 2003.

R. Michael, C. Berthold, F. Borgelt, F. Höppner, and . Klawonn, Guide to Intelligent Data Analysis, 2010.

R. Michael, J. Berthold, and . Diamond, Constructive training of probabilistic neural networks, Neurocomputing, vol.19, issue.1, pp.167-183, 1998.

J. P. Bigus, D. A. Schlosnagle, J. R. Pilgrim, I. Mills, and Y. Diao, ABLE: A toolkit for building multiagent autonomic systems, IBM Systems Journal, vol.41, issue.3, pp.350-371, 2002.
DOI : 10.1147/sj.413.0350

G. Blair, N. Bencomo, and R. France, Models@ run.time, Computer, vol.42, issue.10, pp.22-27, 2009.
DOI : 10.1109/MC.2009.326
URL : https://hal.archives-ouvertes.fr/hal-00733338

W. Barry, R. Boehm, B. Madachy, and . Steece, Software cost estimation with Cocomo II with Cdrom, 2000.

G. Edward, P. Box, and G. C. Jenkins, Time series analysis: Forecasting and control, 1994.

G. Edward, P. Box, and G. Jenkins, Time Series Analysis, Forecasting and Control, 1970.

E. George, . Box, A. David, and . Pierce, Distribution of residual autocorrelations in autoregressive-integrated moving average time series models, Journal of the American statistical Association, vol.65, issue.332, pp.1509-1526, 1970.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

Y. Brun, G. D. , M. Serugendo, C. Gacek, H. Giese et al., Engineering selfadaptive systems through feedback loops, Software engineering for self-adaptive systems, pp.48-70, 2009.

J. Buckley, T. Mens, M. Zenger, A. Rashid, and G. Kniesel, Towards a taxonomy of software change, Journal of Software Maintenance and Evolution: Research and Practice, vol.11, issue.5, pp.309-332, 2005.
DOI : 10.1002/smr.319

C. Cetina, J. Fons, and V. Pelechano, Applying Software Product Lines to Build Autonomic Pervasive Systems, 2008 12th International Software Product Line Conference, pp.117-126, 2008.
DOI : 10.1109/SPLC.2008.13

H. Betty, R. Cheng, H. De-lemos, P. Giese, J. Inverardi et al., Software engineering for self-adaptive systems: A research roadmap, Software engineering for self-adaptive systems, pp.1-26, 2009.

H. Betty, P. Cheng, N. Sawyer, J. Bencomo, and . Whittle, A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty, Model Driven Engineering Languages and Systems, pp.468-483, 2009.

D. Shang-wen-cheng, B. Garlan, and . Schmerl, Architecture-based selfadaptation in the presence of multiple objectives, Proceedings of the 2006 international workshop on Self-adaptation and self-managing systems, pp.2-8, 2006.

. Shang-wen-cheng, V. Vahe, D. Poladian, B. Garlan, and . Schmerl, Improving architecture-based self-adaptation through resource prediction, Software Engineering for Self-Adaptive Systems, pp.71-88, 2009.

D. Cooray, S. Malek, R. Roshandel, and D. Kilgore, RESISTing reliability degradation through proactive reconfiguration, Proceedings of the IEEE/ACM international conference on Automated software engineering, ASE '10, pp.83-92, 2010.
DOI : 10.1145/1858996.1859011

K. Da, M. Dalmau, and P. Roose, A survey of adaptation systems, International Journal on Internet and Distributed Computing Systems, vol.2, issue.1, pp.1-18, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00689773

E. Daubert, F. Fouquet, O. Barais, G. Nain, G. Sunye et al., A models@runtime framework for designing and managing Service-Based Applications, 2012 First International Workshop on European Software Services and Systems Research, Results and Challenges (S-Cube), pp.10-11, 2012.
DOI : 10.1109/S-Cube.2012.6225498

J. G. , D. Gooijer, and R. J. Hyndman, 25 years of time series forecasting, International journal of forecasting, vol.22, issue.3, pp.443-473, 2006.

H. Rogério-de-lemos, . Giese, A. Hausi, M. Müller, J. Shaw et al., Software engineering for self-adaptive systems: A second research roadmap, Software Engineering for Self-Adaptive Systems II, pp.1-32, 2013.

. Dmg and . Org, Pmml 4.2 -general structure, 2015. Accessed 15, 2015.

S. Dobson, S. Denazis, A. Fernández, D. Gaïti, E. Gelenbe et al., A survey of autonomic communications, ACM Transactions on Autonomous and Adaptive Systems, vol.1, issue.2, pp.223-259, 2006.
DOI : 10.1145/1186778.1186782

J. Enrique, M. Duarte-melo, and . Liu, Analysis of energy consumption and lifetime of heterogeneous wireless sensor networks, Global Telecommunications Conference, pp.21-25, 2002.

T. Dunning and E. Friedman, Time Series Databases New Ways to Store and Access Data, volume 1st Edit, 2014.

G. Edwards, S. Malek, and N. Medvidovic, Scenario-Driven Dynamic Analysis of Distributed Architectures, Fundamental Approaches to Software Engineering, pp.125-139, 2007.
DOI : 10.1007/978-3-540-71289-3_12

A. Elkhodary, N. Esfahani, and S. Malek, FUSION, Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering, FSE '10, pp.7-16, 2010.
DOI : 10.1145/1882291.1882296

N. Esfahani, E. Kouroshfar, and S. Malek, Taming uncertainty in self-adaptive software, Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering, SIGSOFT/FSE '11, pp.234-244, 2011.
DOI : 10.1145/2025113.2025147

N. Esfahani and S. Malek, Uncertainty in Self-Adaptive Software Systems, Software Engineering for Self-Adaptive Systems II, pp.214-238, 2013.
DOI : 10.1007/978-3-642-04425-0_36

S. Robert and . Fabry, How to design a system in which modules can be changed on the fly, ICSE'76: Proceedings of the 2nd International Conference on Software Engineering, pp.470-476, 1976.

A. Filieri, C. Ghezzi, A. Leva, and M. Maggio, Self-adaptive software meets control theory: A preliminary approach supporting reliability requirements, 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011), pp.283-292, 2011.
DOI : 10.1109/ASE.2011.6100064

A. Filieri, C. Ghezzi, A. Leva, and R. Ole, Reliability-driven dynamic binding via feedback control, 2012 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pp.43-52, 2012.
DOI : 10.1109/SEAMS.2012.6224390

F. Fleurey and A. Solberg, A Domain Specific Modeling Language Supporting Specification, Simulation and Execution of Dynamic Adaptive Systems, Model Driven Engineering Languages and Systems, pp.606-621, 2009.
DOI : 10.1109/MIC.2007.2

J. Floch, S. Hallsteinsen, E. Stav, F. Eliassen, K. Lund et al., Using architecture models for runtime adaptability. Software, IEEE, vol.23, issue.2, pp.62-70, 2006.

U. and F. Service, Modis active fire detections for conus (2010) -through 12, 2011.

S. Fortmann-roe, Accurately measuring model prediction error, 2012.

F. Fouquet, E. Daubert, N. Plouzeau, O. Barais, J. Bourcier et al., Dissemination of Reconfiguration Policies on Mesh Networks, Distributed Applications and Interoperable Systems, pp.16-30, 2012.
DOI : 10.1007/978-3-642-30823-9_2
URL : https://hal.archives-ouvertes.fr/hal-00688707

F. Fouquet, B. Morin, F. Fleurey, O. Barais, N. Plouzeau et al., A dynamic component model for cyber physical systems, Proceedings of the 15th ACM SIGSOFT symposium on Component Based Software Engineering, CBSE '12, pp.135-144, 2012.
DOI : 10.1145/2304736.2304759
URL : https://hal.archives-ouvertes.fr/hal-00713769

F. Fouquet, G. Nain, B. Morin, E. Daubert, O. Barais et al., An Eclipse Modelling Framework Alternative to Meet the Models@Runtime Requirements, 2012.
DOI : 10.1007/978-3-642-33666-9_7
URL : https://hal.archives-ouvertes.fr/hal-00714558

E. John and . Gaffney, Estimating the number of faults in code. Software Engineering, IEEE Transactions on, issue.4, pp.459-464, 1984.

J. Gama, Functional trees, Machine Learning, pp.219-250, 2004.

E. Carlos, . Garcia, M. David, M. Prett, and . Morari, Model predictive control: theory and practice?a survey, Automatica, vol.25, issue.3, pp.335-348, 1989.

D. Garlan, A 10-year perspective on software engineering self-adaptive systems (keynote), 2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pp.2-2, 2013.
DOI : 10.1109/SEAMS.2013.6595486

D. Garlan, . Shang-wen, A. Cheng, B. Huang, P. Schmerl et al., Rainbow: architecture-based self-adaptation with reusable infrastructure, Computer, vol.37, issue.10, pp.3746-54, 2004.
DOI : 10.1109/MC.2004.175

D. Garlan, . Shang-wen, B. Cheng, and . Schmerl, Increasing System Dependability through Architecture-Based Self-Repair, Architecting dependable systems, pp.61-89, 2003.
DOI : 10.1007/3-540-45177-3_3

D. Garlan and B. Schmerl, Model-based adaptation for self-healing systems, Proceedings of the first workshop on Self-healing systems , WOSS '02, pp.27-32, 2002.
DOI : 10.1145/582128.582134

C. John, . Georgas, N. Richard, and . Taylor, Policy-based self-adaptive architectures: a feasibility study in the robotics domain, Proceedings of the 2008 international workshop on Software engineering for adaptive and self-managing systems, pp.105-112, 2008.

J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, Internet of Things (IoT): A vision, architectural elements, and future directions, Future Generation Computer Systems, vol.29, issue.7, pp.1645-1660, 2013.
DOI : 10.1016/j.future.2013.01.010

N. Venkat, R. Gudivada, . Baeza-yates, V. Vijay, and . Raghavan, Big data: Promises and problems, Computer, issue.3, pp.20-23, 2015.

]. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann et al., The WEKA data mining software, ACM SIGKDD Explorations Newsletter, vol.11, issue.1, pp.10-18, 2009.
DOI : 10.1145/1656274.1656278

D. Han, Q. Yang, and J. Xing, Extending uml for the modeling of fuzzy self-adaptive software systems The 26th Chinese, Control and Decision Conference CCDC), pp.2400-2406, 2014.

M. Harman, The role of Artificial Intelligence in Software Engineering, 2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE), pp.1-6, 2012.
DOI : 10.1109/RAISE.2012.6227961

W. Rabiner-heinzelman, A. Chandrakasan, and H. Balakrishnan, Energyefficient communication protocol for wireless microsensor networks, System sciences Proceedings of the 33rd annual Hawaii international conference on, p.10, 2000.

N. Nikolas-roman-herbst, S. Huber, E. Kounev, and . Amrehn, Selfadaptive workload classification and forecasting for proactive resource provisioning, Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering, pp.187-198, 2013.

J. Hielscher, R. Kazhamiakin, A. Metzger, and M. Pistore, A Framework for Proactive Self-adaptation of Service-Based Applications Based on Online Testing, 2008.
DOI : 10.1007/978-3-540-89897-9_11

P. Horn, Autonomic computing: Ibm's perspective on the state of information technology, 2001.

C. Markus, J. A. Huebscher, and . Mccann, A survey of autonomic computing?degrees, models, and applications, ACM Computing Surveys (CSUR), vol.40, issue.3, p.7, 2008.

J. Rob, Y. Hyndman, and . Khandakar, Automatic time series for forecasting: the forecast package for r, 2007.

R. Nicholas and . Jennings, On agent-based software engineering, Artificial intelligence, vol.117, issue.2, pp.277-296, 2000.

C. Paul and . Jorgensen, Software testing: a craftsman's approach, 2013.

L. Pack-kaelbling, L. Michael, . Littman, W. Andrew, and . Moore, Reinforcement learning: A survey. arXiv preprint cs, 1996.

C. Kyo, . Kang, G. Sholom, . Cohen, A. James et al., Feature-oriented domain analysis (foda) feasibility study, 1990.

. Joost-pieter-katoen, S. Ivan, E. Zapreev, H. Moritz-hahn, . Hermanns et al., The ins and outs of the probabilistic model checker mrmc. Performance evaluation, pp.90-104, 2011.

O. Jeffrey, . Kephart, M. David, and . Chess, The vision of autonomic computing, Computer, vol.36, issue.1, pp.41-50, 2003.

D. Kim and S. Park, Reinforcement learning-based dynamic adaptation planning method for architecture-based self-managed software, Software Engineering for Adaptive and Self-Managing Systems, 2009. SEAMS'09. ICSE Workshop on, pp.76-85, 2009.

A. Barbara, S. L. Kitchenham, . Pfleeger, M. Lesley, . Pickard et al., Preliminary guidelines for empirical research in software engineering. Software Engineering, IEEE Transactions on, vol.28, issue.8, pp.721-734, 2002.

K. Knime, open source, open for innovation, 2015.

J. Kramer and J. Magee, Self-Managed Systems: an Architectural Challenge, Future of Software Engineering (FOSE '07), pp.259-268, 2007.
DOI : 10.1109/FOSE.2007.19

M. Kwiatkowska, G. Norman, and D. Parker, PRISM, ACM SIGMETRICS Performance Evaluation Review, vol.36, issue.4, pp.40-45, 2009.
DOI : 10.1145/1530873.1530882
URL : https://hal.archives-ouvertes.fr/hal-00740727

P. Leitner, A. Michlmayr, F. Rosenberg, and S. Dustdar, Monitoring, Prediction and Prevention of SLA Violations in Composite Services, 2010 IEEE International Conference on Web Services, pp.369-376, 2010.
DOI : 10.1109/ICWS.2010.21

K. Luckow, S. Corina, B. Matthew, A. Dwyer, W. Filieri et al., Exact and approximate probabilistic symbolic execution for nondeterministic programs, Proceedings of the 29th ACM/IEEE international conference on Automated software engineering, ASE '14, pp.575-586, 2014.
DOI : 10.1145/2642937.2643011

A. Michail and A. Ephremides, Energy efficient routing for connectionoriented traffic in ad-hoc wireless networks, Personal, Indoor and Mobile Radio Communications, pp.762-766, 2000.

S. Ryszard, I. Michalski, A. Bratko, and . Bratko, Machine learning and data mining; methods and applications, 1998.

M. Tom and . Mitchell, Machine learning, 1997.

M. Tom and . Mitchell, Machine learning and data mining, Communications of the ACM, vol.42, issue.11, pp.30-36, 1999.

B. Morin, Leveraging Models from Design-time to Runtime to Support Dynamic Variability, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00538548

B. Morin, O. Barais, G. Nain, and J. Jezequel, Taming Dynamically Adaptive Systems using models and aspects, 2009 IEEE 31st International Conference on Software Engineering, pp.122-132, 2009.
DOI : 10.1109/ICSE.2009.5070514

D. Narayanan, Operating system support for mobile interactive applications, 2002.

S. Kumpati, . Narendra, A. Osvaldo, and . Driollet, Adaptive control using multiple models, switching, and tuning, Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE, pp.159-164, 2000.

C. Nyce and A. Cpcu, Predictive analytics white paper. American Institute for CPCU. Insurance Institute of America, pp.9-10, 2007.

H. Oldenkamp, Probabilistic model checking: A comparison of tools, 2007.

P. Oreizy, M. Michael, . Gorlick, N. Richard, D. Taylor et al., An architecture-based approach to self-adaptive software, IEEE Intelligent Systems, vol.14, issue.3, pp.54-62, 1999.
DOI : 10.1109/5254.769885

I. D. , P. Anaya, V. Simko, J. Bourcier, N. Plouzeau et al., A prediction-driven adaptation approach for self-adaptive sensor networks, 9th International Symposium on Software Engineering for Adaptive and Self- Managing Systems, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00983046

T. Patikirikorala and A. Colman, Feedback controllers in the cloud, 2011.

T. Patikirikorala, A. Colman, L. Han, and . Wang, Tech-report: Multi-model driven framework to implement self-managing control systems for qos management, 2010.

T. Patikirikorala, A. C. Han, and L. Wang, A multi-model framework to implement self-managing control systems for QoS management, Proceeding of the 6th international symposium on Software engineering for adaptive and self-managing systems, SEAMS '11, pp.218-227, 2011.
DOI : 10.1145/1988008.1988040

T. Patikirikorala, A. C. Han, and L. Wang, A systematic survey on the design of self-adaptive software systems using control engineering approaches, 2012 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pp.33-42, 2012.
DOI : 10.1109/SEAMS.2012.6224389

B. Peters, The big data gold rush Forbes Magazine Retrieved on 28 Nov, 2012.

J. Platt, Fast training of support vector machines using sequential minimal optimization Advances in kernel methods?support vector learning, 1999.

V. Poladian, D. Garlan, M. Shaw, M. Satyanarayanan, B. Schmerl et al., Leveraging Resource Prediction for Anticipatory Dynamic Configuration, First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007), pp.214-223, 2007.
DOI : 10.1109/SASO.2007.35

V. Vahe and . Poladyan, Tailoring configuration to user's tasks under uncertainty, 2008.

M. Riedmiller and H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, IEEE International Conference on Neural Networks, pp.586-591, 1993.
DOI : 10.1109/ICNN.1993.298623

R. Rouvoy, P. Barone, Y. Ding, F. Eliassen, S. Hallsteinsen et al., MUSIC, Proceedings of the 1st workshop on Mobile middleware embracing the personal communication device, MobMid '08, pp.164-182, 2009.
DOI : 10.1145/1462689.1462697

M. Salehie and L. Tahvildari, Self-adaptive software, ACM Transactions on Autonomous and Adaptive Systems, vol.4, issue.2, pp.1-42, 2009.
DOI : 10.1145/1516533.1516538

E. Robert, Y. Schapire, and . Freund, Boosting: Foundations and algorithms, 2012.

H. Robert, . Shumway, S. David, and . Stoffer, Time series analysis and its applications: with R examples, 2010.

E. Siegel, Predictive analytics: the power to predict who will click, buy, lie, or die, 2013.

R. Development and C. Team, R: A language and environment for statistical computing. r foundation for statistical computing, 2012.

G. Tesauro, Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies, IEEE Internet Computing, vol.11, issue.1, pp.22-30, 2007.
DOI : 10.1109/MIC.2007.21

G. Tesauro, M. David, . Chess, E. William, R. Walsh et al., A multi-agent systems approach to autonomic computing, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp.464-471, 2004.

G. Tesauro, K. Nicholas, R. Jong, M. N. Das, and . Bennani, A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation, 2006 IEEE International Conference on Autonomic Computing, pp.65-73, 2006.
DOI : 10.1109/ICAC.2006.1662383

M. Gabriela and C. Torres, Energy consumption in wireless sensor networks using gsp, 2006.

A. Van-lamsweerde, Requirements engineering, Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering, SIGSOFT '08/FSE-16, pp.238-249, 2008.
DOI : 10.1145/1453101.1453133

H. Van-vliet, H. V. Vliet, and J. V. Vliet, Software engineering: principles and practice, 1993.

L. Wang, Model predictive control system design and implementation using MATLAB R ?, 2009.

Q. Wang, M. Hempstead, and W. Yang, A Realistic Power Consumption Model for Wireless Sensor Network Devices, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, pp.286-295, 2006.
DOI : 10.1109/SAHCN.2006.288433

M. Weiser, The Computer for the 21st Century, Scientific American, vol.265, issue.3, pp.94-104, 1991.
DOI : 10.1038/scientificamerican0991-94

J. Rob and . Hyndman, with contributions from Slava Razbash and Drew Schmidt. forecast: Forecasting functions for time series and linear models, 2015.

R. Wolski, T. Neil, J. Spring, and . Hayes, The network weather service: a distributed resource performance forecasting service for metacomputing, Future Generation Computer Systems, vol.15, issue.5-6, pp.757-768, 1999.
DOI : 10.1016/S0167-739X(99)00025-4

J. Wu and S. Coggeshall, Foundations of Predictive Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series), 2012.

Q. Zhang, M. F. Zhani, S. Zhang, Q. Zhu, R. Boutaba et al., Dynamic energy-aware capacity provisioning for cloud computing environments, Proceedings of the 9th international conference on Autonomic computing, ICAC '12, pp.145-154, 2012.
DOI : 10.1145/2371536.2371562

J. Zhu, On the power efficiency and optimal transmission range of wireless sensor nodes, 2009 IEEE International Conference on Electro/Information Technology, pp.277-281, 2009.
DOI : 10.1109/EIT.2009.5189626