FIoT: A Framework for Self-Adaptive and Self-Organizing Internet of Things Applications. Nathalia Moraes do Nascimento nnascimento@inf.puc-rio.
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1 FIoT: A Framework for Self-Adaptive and Self-Organizing Internet of Things Applications Nathalia Moraes do Nascimento nnascimento@inf.puc-rio.br
2 Roadmap Motivation FIoT Main Idea Application Scenario Decentralized Car Traffic Control 2
3 MOTIVATION 3
4 Motivation Internet Of Things (IoT) Billions of things and events connected. Fonte: ReadWrite The Internet of Things represents an evolution in which objects are capable of interacting with other objects. [IBM; 2013] 4
5 Motivation On the last years, numerous IoT research and application projects have been done by universities or in industry Most of them are concentrating on operational technology Limit Internet traffic capacity Architecture Protocols 5
6 Motivation On this way, a lot of questions are still opened: How should these data be managed? How to define the best way to communicate all these things? How can the system use this information to make inferences over context? [Velloso, Raposo, Fuks; 2010] 6
7 Motivation Some researchers concentrating only on operational technologies may be a clear drawback Artificial Intelligence (AI) methods and techniques can help to accomplish important tasks from IoT environment: Interpreting the environment s state; To represent entities in the environment; To make inferences over the context in order to make decision; and Acting on the environment. 7
8 Motivation Multiagent Systems (MAS) are especially good to design real-world and social systems It s almost impossible to correctly determine the repertoire of behaviors on a MAS at design time. This complexity can be reduced or prevented by providing agents with learning capabilities (WEIß, 95) 8
9 Proposed Solution FIoT = MultiAgent Systems + Machine Learning + Internet of Things 9
10 Framework for Internet of Things FIOT 10
11 Main Idea Development of decentralized controllers for Internet of Things The application consists of the development of three kind of agents: God Agent; Adaptive Agent; Observer Agent. 11
12 Main Idea 12
13 Main Idea 13
14 FIoT: Core Devices Connection Detect devices Receive data from devices Send data for devices Communication among agents Control structure: sensor, make decisions based on a controller, act, evaluate decisions and adapt 14
15 FIoT: Flexible Points Different control modules: Neural Network; State Machine Different training process for control module: Learning Techniques Evaluate the results: how the environment changes have to be evaluated 15
16 A NeuroEvolutionary and MultiAgent Approach to Create a Decentralized and Adaptive Car Traffic Control using FIoT APPLICATION SCENARIO 16
17 Application Scenario Traffic Control Control Application for a Car Traffic Scenario Problem Scenario Elements: roads, lanes, cars Elements controlled by agents: only lanes Decision Control Neural Network Learning Algorithm Genetic Algorithm 17
18 Simulated Environment 18
19 Configuring FIoT to Traffic Control Application (a) (b) 02/09/
20 Quickly Background Review Neural Network Memory for associations y = f(x); x: inputs and weights colorsignal = f(microphone *W1, carrate *W2, previoussignal * W3) signalvoice = f(microphone *W4, carrate *W5, previoussignal * W6) Each weight represents how much each input gives contribution for outputs (the relevance of each one) Middle layer: association among inputs 20
21 Quickly Background Review Genetic Algorithm Unsupervised Learning Each individual: W1 W2 W3 W4 W5 W6 Fitness: How good is this neural controller with this sequence of weights? (How many cars concluded their route?) Get best individuals to generate new individuals (crossover and mutation). After 85 generations, use the best one. 21
22 Results The communication system among lanes, the influence of rate of cars on traffic light decision, nothing was specified before simulation. Norms of traffic, the lanes interactions emerged through evolutionary process. Neural Network: black box 22
23 Fitness Value Results Fitness Average Generations Normally, an abrupt change on this graph can indicate the emergence of a new behavior Fitness Average C D B E A Generation AVERAGE A: car rate makes negative influence. If there is a car, traffic light is put on red 23
24 Fitness Value Results Max Fitness Max Fitness - Linear Trend Generation BEST FITNESS 24
25 Results Best Individual of Generation 4 C: Priority is given for Lanes with higher rate of cars 25
26 Results E: Best individual. Lanes that don t have intersection with other lanes adopted the strategy to keep the signal on green Most important factor to decide about traffic light is rate of cars, but is not the only one: if traffic light is red and/or the closest lane emits signal zero, the chance of lane to set traffic light on green is higher. 26
27 Conclusion The main goal of FIoT is to reduce the complexity of development of IoT applications Investigate the use of learning techniques to model complex systems based on MAS The expected is to easily introduce these concepts on real situation or a most advanced simulated applications Contribution of MAS for IoT 27
28 References Accenture. Digital Industry 4.0. Disponível em Acesso em março de Annes, R. APLICAÇÕES DE SISTEMAS MULTIAGENTES E APRENDIZAGEM AUTOMÁTICA NO PROCESSAMENTO DA LINGUAGEM NATURAL. Disponível em Acesso em março de Costa, A.D., Nunes, C., Silva, V.T., Fonseca, B., Lucena, C.J.P., Jaaf+t: a framework to implement self-adaptive agents that apply selftest, in: Proceedings of the 2010 ACM Symposium on Applied Computing, ACM, New York, NY, USA. pp D. Floreano, P. Du rr, C. Mattiussi. Neuroevolution: from architectures to learning, in proceeding of the Evolutionary Intelligence, F. Bellifemine, A. Poggi, G. Rimassa. JADE A FIPA-compliant agent framework. Available on< Access in September FARSA. Framework for Autonomous Robotics Simulation and Analysis. Available on < Access in September Frevo. FRamework for EVOlutionary design. Available on < I. Fehérvári and W. Elmenreich. Evolutionary Methods in Self-organizing System Design. International Conference on Genetic and Evolutionary Methods A. Sobe, I. Fehervari, W. Elmenreich. FREVO: A Tool for Evolving and Evaluating Selforganizing Systems. In the proceedings of the Internacional Conference on Self-Adaptive and Self-Organizing Systems, Gardelli, L. Designing Self-Organising environments with agents and artifacts: A simulation-driven approach. International Journal of Agent-Oriented Software Engineering, 2(2). In Press Gardelli, L.; Mirko, V.; Casadei, M.; Omicini, A. Designing Self-organising MAS Environments: The Collective Sort Case. International Workshop on Environments for MultiAgent System (E4MAS), 2007.a. G. Di Marzo Serugendo, M.-P. Gleizes, A. Karageorgos. Self-Organisation in MAS, Knowledge Engineering Review 20(2): , Cambridge University Press, G. Di Marzo Serugendo, J. Fitzgerald, N. Guelfi. A Generic Framework for Designing and Implementing Self-Adaptive and Self-Organising Systems. COMPUTING SCIENCE, University of Newcastle, 2007.
29 References G. Di Marzo Serugendo, M. Kelly. Decentralised Car Traffic Control using Message Propagation Optimized with a Genetic Algorithm, in: Proceeding of the Evolutionary Computation, IBM. Internet of Things. Available on Access in December Jakob, M. AgentPolis: Towards a Platform for Fully Agent-based Modeling of Multi-Modal Transportation(Demonstration), in Proceedings of the AAMAS, Nolfi S., Floreano D. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. Cambridge, MA: MIT Press/Bradford Books, JADE HomePage. Integrating JADE and Jess. Available on < Access in September JNEAT HomePahe. Framework for NeuroEvolution (NEAT JAVA). Available on < Access in September Neto, B.F.S. JAAF: Implementando Agentes Auto-Adaptativos Orientados a Serviços. Dissertação de Mestrado Departamento de Informática PUC-RIO, Neural Network Research Group. NeuroEvolution. Available on Access in September R. Miikkulainen, E. Feasley, L. Johnson, I. Karpov, P. Rajagopalan, A. Rawal, W. Tansey. Multiagent Learning through Neuroevolution, in Proceedings of IEEE WCCI, SIMON S. HAYKIN. Redes Neurais. ReadWrite. How The Internet Of Things Will Transform Everything - According To IT Experts. Available on Spril Telefonica. Smart Cities for a better world. Available on Access in December V. I. Gorodetskii. SelfOrganization and Multiagent Systems: II. Applications and the Development Technology. JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATIONAL Vol. 51 No Velloso, E.; Raposo, A.; Fuks, H. Web of Things: The Collaborative Interaction Designer Point of View. Proceedings of Web Science Brasil 2010, Workshop of the Brazilian Institute for Web Science Research. Read more: WEIß, G. ; SEN, S. Adaptation and Learning in Multi-Agent. Springer, 1995.
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