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CCNC-2018, 12-15 January, Las Vegas, USA

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The Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things Environments

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CCNC-2018, 12-15 January, Las Vegas, USA

  1. 1. Introduction Background Evaluation Methodology Results Conclusion The Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things Environments Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-Marganiec January 2018 Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 1 / 36
  2. 2. Introduction Background Evaluation Methodology Results Conclusion Introduction What do you think when we talk about Internet of Things? Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 2 / 36
  3. 3. Introduction Background Evaluation Methodology Results Conclusion Introduction What do you think when we talk about Internet of Things? Figure: Smart home Figure: Smart city Figure: Smart car Figure: Smart farm Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 3 / 36
  4. 4. Introduction Background Evaluation Methodology Results Conclusion Introduction Cloud of Things Development of unprecedented services Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 4 / 36
  5. 5. Introduction Background Evaluation Methodology Results Conclusion Introduction Cloud of Things Development of unprecedented services Models Data as a Service Sensor as a Service Sensing as a Service Big data as a Service Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 5 / 36
  6. 6. Introduction Background Evaluation Methodology Results Conclusion Motivation Figure: Solutions to provide sensor and sensor data as a service Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 6 / 36
  7. 7. Introduction Background Evaluation Methodology Results Conclusion Motivation Let’s take the Flamingo Las Vegas as example Figure: Restaurants near to Flamingo Las Vegas Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 7 / 36
  8. 8. Introduction Background Evaluation Methodology Results Conclusion Motivation Let’s take the Flamingo Las Vegas as example Figure: Restaurants near to Flamingo Las Vegas Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 8 / 36
  9. 9. Introduction Background Evaluation Methodology Results Conclusion Motivation Let’s take the Flamingo Las Vegas as example Figure: Restaurants near to Flamingo Las Vegas Static criteria Distance Price Dynamic criteria Service time in the last hour Quality of service in the past four hours Percentage of available tables in the last 10 minutes Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 9 / 36
  10. 10. Introduction Background Evaluation Methodology Results Conclusion Motivation Problem A resource(restaurant) could not be suitable to accomplish the requirements of other users Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 10 / 36
  11. 11. Introduction Background Evaluation Methodology Results Conclusion Motivation Problem A resource(restaurant) could not be suitable to accomplish the requirements of other users Resource discovery Context User constraints Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 11 / 36
  12. 12. Introduction Background Evaluation Methodology Results Conclusion Motivation Problem A resource(restaurant) could not be suitable to accomplish the requirements of other users Resource discovery Context User constraints Solution Multiple Criteria Decision Analysis Context Aware Computing Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 12 / 36
  13. 13. Introduction Background Evaluation Methodology Results Conclusion Related Works Figure: Related works focused on techniques Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 13 / 36
  14. 14. Introduction Background Evaluation Methodology Results Conclusion Related Works Gap These solutions are concerned just with the selection time of the algorithms. They not consider the quality of the proposed solution or the impact of user constraints in their quality. Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 14 / 36
  15. 15. Introduction Background Evaluation Methodology Results Conclusion Motivation Objective We propose a novel Elimination-Selection (E-S) algorithm to increase the quality of the proposed resources in a timmely manner. Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 15 / 36
  16. 16. Introduction Background Evaluation Methodology Results Conclusion Multiple-criteria decision-analysis algorithms Multiples Objective Optimization Different comparable units Assist the judgment of decision making Goals and criteria The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Vector normalisation Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 16 / 36
  17. 17. Introduction Background Evaluation Methodology Results Conclusion Multi-Objective optimization TOPSIS Normalize the available options Distance (min) Price(min) Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 17 / 36
  18. 18. Introduction Background Evaluation Methodology Results Conclusion Multi-Objective optimization TOPSIS Compute the Ideal Positive Solution and Ideal Negative Solution Distance (min) Price(min) Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 18 / 36
  19. 19. Introduction Background Evaluation Methodology Results Conclusion Multi-Objective optimization TOPSIS The solution which is closer to Ideal Positive Solution and farthest from Negative Solution is the best one Distance (min) Price(min) Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 19 / 36
  20. 20. Introduction Background Evaluation Methodology Results Conclusion Multi-Objective optimization Pareto Optimality Compare different sets of solutions through dominance. Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 20 / 36
  21. 21. Introduction Background Evaluation Methodology Results Conclusion Multi-Objective optimization Pareto Optimality Compare different sets of solutions through dominance. Condition 1 The x solution could not be worse than y for all objectives f(x) ≥ f(y), for all objectives Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 21 / 36
  22. 22. Introduction Background Evaluation Methodology Results Conclusion Multi-Objective optimization Pareto Optimality Compare different sets of solutions through dominance. Condition 1 The x solution could not be worse than y for all objectives f(x) ≥ f(y), for all objectives Condition 2 The x solution must be better than y at list for one objective f(x) > f(y), at least for one objective Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 22 / 36
  23. 23. Introduction Background Evaluation Methodology Results Conclusion Multi-Objective optimization Pareto Optimality Compare different sets of solutions through dominance. Condition 1 The x solution could not be worse than y for all objectives f(x) ≥ f(y), for all objectives Condition 2 The x solution must be better than y at list for one objective f(x) > f(y), at least for one objective Fast Non Dominated Sort [2] Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 23 / 36
  24. 24. Introduction Background Evaluation Methodology Results Conclusion Elimination Sort Algorithm Table: List of variable used in the algorithm Variable Description Q Available options N Number of options to be selected SR Search rate value S Selected options Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 24 / 36
  25. 25. Introduction Background Evaluation Methodology Results Conclusion Evaluation Workflow Table: Approximately weather conditions required to seed a corn crop based on Doblas-Reyes et al. [3] and Rosen- zweig et al. [5] al. Factor Value Temperature (t) 59 ºF <t <91.4 ºF Humidity (h) h >50 % Sky coverage (sc) sc >50 % Wind (w) w <20 km/h Precipitation (p) 0 mm <p <50 mm Table: Objectives functions to seed a corn crop Context property Objective Function Temperature max(−0.01777778t2 + 10.512t − 1552.9364) Humidity max(h) Sky coverage max(sc) Wind min(w) Precipitation max(0.0016p2 + 0.08p) Datetime (dt) max(dt) Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 25 / 36
  26. 26. Introduction Background Evaluation Methodology Results Conclusion Evaluation Workflow Figure: Evaluation Workflow. Adapted from Nunes et al. [4] Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 26 / 36
  27. 27. Introduction Background Evaluation Methodology Results Conclusion Factors and levels Table: Factors and levels used in the experiment Factor Level Number of Resource Descriptions 209,555 MCDA Method TOPSIS and E-S Number of Selected Sensors 2095 Number of User Constraint 2, 4 and 6 Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 27 / 36
  28. 28. Introduction Background Evaluation Methodology Results Conclusion Metric of Evaluation Overall nondominated vector generation metric Shows the number of the proposed MCDA solutions as a proportion of the number of solutions in the Pareto front [1] ONVGRfront(n) = MCDAfront(n) Paretofront(n) Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 28 / 36
  29. 29. Introduction Background Evaluation Methodology Results Conclusion First Phase 2 Criteria 4 Criteria 6 Criteria 01000020000300004000050000 #ofResources # of Criteria 46755 14489 14394 Figure: Number of resources considering the conditions to seed a corn crop Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 29 / 36
  30. 30. Introduction Background Evaluation Methodology Results Conclusion Quality q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 10 20 30 40 50 60 70 0.00.20.40.60.81.0 ONVGR # Fronts Algorithms Topsis E−S Optimal Figure: ONVGR value for 2 criteria Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 30 / 36
  31. 31. Introduction Background Evaluation Methodology Results Conclusion Quality q q q q q 2 4 6 8 0.00.20.40.60.81.0 ONVGR # Fronts Algorithms Topsis E−S Optimal Figure: ONVGR value for 4 criteria Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 31 / 36
  32. 32. Introduction Background Evaluation Methodology Results Conclusion Quality q q 1 2 3 4 5 0.00.20.40.60.81.0 ONVGR # Fronts Algorithms Topsis E−S Optimal Figure: ONVGR value for 6 criteria Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 32 / 36
  33. 33. Introduction Background Evaluation Methodology Results Conclusion Time TOPSIS E−S Optimal TOPSIS E−S Optimal TOPSIS E−S Optimal 0200400600 Time(s) 2 Criteria 4 Criteria 6 Criteria Algorithm # of Criteria Figure: Time to select the resources regarding the conditions Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 33 / 36
  34. 34. Introduction Background Evaluation Methodology Results Conclusion Conclusion General Conclusion The number of criteria impacts directly on the number of available resources in the query phase and the ONVGR value E-S algorithm conclusion E-S algorithm show the ONVGR metric value at least equal than TOPSIS algorithm E-S algorithm present an acceptable tradeoff between quality and time Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 34 / 36
  35. 35. Introduction Background Evaluation Methodology Results Conclusion End Thank you! Questions? lhnunes@icmc.usp.br Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 35 / 36
  36. 36. Introduction Background Evaluation Methodology Results Conclusion References I Y. Collette and P. Siarry. Multiobjective Optimization. Springer Berlin Heidelberg, 2004. K. Deb. Multi-objective optimization using evolutionary algorithms. John Wiley & Sons, Chichester New York, 2001. F. Doblas-Reyes, A. Garcia, J. Hansen, L. Mariani, A. Nain, K. Ramesh, L. Rathore, and R. Venkataraman. Weather and climate forecasts for agriculture. Guide to agricultural, meteorological practices, page 57, 2003. L. H. Nunes, J. C. Estrella, C. Perera, S. Reiff-Marganiec, and A. N. Delbem. Multi-criteria iot resource discovery: A comparative analysis. Software: Practice and Experience, -(-):–, 2016. In print. C. Rosenzweig, A. Iglesias, X. Yang, P. R. Epstein, and E. Chivian. Climate change and extreme weather events; implications for food production, plant diseases, and pests. Global change & human health, 2(2):90–104, 2001. Luiz H. Nunes, Júlio C. Estrella, Alexandre C. B. Delbem, Charith Perera and Stephan Reiff-MarganiecThe Elimination-Selection Based Algorithm for Efficient Resource Discovery in Internet of Things EnvironmentsJanuary 2018 36 / 36

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