Replication in Mobile Ad-hoc Network using Hopfield Network

Mobile Ad hoc Network (MANET) is a network comprises of mobile nodes connected without any centralized administration. Each node in the network has the ability to share a large number of objects with other nodes. The nodes are connected with other nodes to forward a message to other nodes until the search for node that desire meets. This paper represents a new replication method to recover the performance in distributed system. Objects are replicated on different nodes in the network to minimize the search for an object. It is observed that mobility of nodes predicted using SOM (Self Organizing Maps) technique provides less accuracy. This paper represents an algorithm that uses the network parameters as input condition to replicate objects into the nodes. The decision for each node will be taken by gathering the input conditions and feed to the Hopfield Neural Network. It is view that it will provide more accuracy and improve the performance

Temperature and relative humidity estimation and prediction in the tobacco drying process using artificial neural networks

Producción CientíficaThis paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.Centro para el Desarrollo Tecnológico Industrial (CDTI), proyecto "Mejora de la competitividad del sector del tabaco en Extremadura: nuevos procesos y productos" (under project IDI-20100986)Junta de Castilla y León, financiado por el Plan Regional de Proyectos de Investigación (proyecto VA034A10-2

Underwater Sensor Nodes and Networks

Sensor technology has matured enough to be used in any type of environment. The appearance of new physical sensors has increased the range of environmental parameters for gathering data. Because of the huge amount of unexploited resources in the ocean environment, there is a need of new research in the field of sensors and sensor networks. This special issue is focused on collecting recent advances on underwater sensors and underwater sensor networks in order to measure, monitor, surveillance of and control of underwater environments. On the one hand, from the sensor node perspective, we will see works related with the deployment of physical sensors, development of sensor nodes and transceivers for sensor nodes, sensor measurement analysis and several issues such as layer 1 and 2 protocols for underwater communication and sensor localization and positioning systems. On the other hand, from the sensor network perspective, we will see several architectures and protocols for underwater environments and analysis concerning sensor network measurements. Both sides will provide us a complete view of last scientific advances in this research field.Lloret, J. (2013). Underwater Sensor Nodes and Networks. Sensors. 13(9):11782-11796. doi:10.3390/s130911782S1178211796139Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2011). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, 5(12), 1682-1690. doi:10.1049/iet-com.2010.0654Martinez, J. J., Myers, J. R., Carlson, T. J., Deng, Z. D., Rohrer, J. S., Caviggia, K. A., … Weiland, M. A. (2011). Design and Implementation of an Underwater Sound Recording Device. Sensors, 11(9), 8519-8535. doi:10.3390/s110908519Ardid, M., Martínez-Mora, J. A., Bou-Cabo, M., Larosa, G., Adrián-Martínez, S., & Llorens, C. D. (2012). Acoustic Transmitters for Underwater Neutrino Telescopes. Sensors, 12(4), 4113-4132. doi:10.3390/s120404113Baronti, F., Fantechi, G., Roncella, R., & Saletti, R. (2012). Wireless Sensor Node for Surface Seawater Density Measurements. Sensors, 12(3), 2954-2968. doi:10.3390/s120302954Mànuel, A., Roset, X., Rio, J. D., Toma, D. M., Carreras, N., Panahi, S. S., … Cadena, J. (2012). Ocean Bottom Seismometer: Design and Test of a Measurement System for Marine Seismology. Sensors, 12(3), 3693-3719. doi:10.3390/s120303693Jollymore, A., Johnson, M. S., & Hawthorne, I. (2012). Submersible UV-Vis Spectroscopy for Quantifying Streamwater Organic Carbon Dynamics: Implementation and Challenges before and after Forest Harvest in a Headwater Stream. Sensors, 12(4), 3798-3813. doi:10.3390/s120403798Won, T.-H., & Park, S.-J. (2012). Design and Implementation of an Omni-Directional Underwater Acoustic Micro-Modem Based on a Low-Power Micro-Controller Unit. Sensors, 12(2), 2309-2323. doi:10.3390/s120202309Sánchez, A., Blanc, S., Yuste, P., Perles, A., & Serrano, J. J. (2012). An Ultra-Low Power and Flexible Acoustic Modem Design to Develop Energy-Efficient Underwater Sensor Networks. Sensors, 12(6), 6837-6856. doi:10.3390/s120606837Shin, S.-Y., & Park, S.-H. (2011). A Cost Effective Block Framing Scheme for Underwater Communication. Sensors, 11(12), 11717-11735. doi:10.3390/s111211717Kim, Y., & Park, S.-H. (2011). A Query Result Merging Scheme for Providing Energy Efficiency in Underwater Sensor Networks. Sensors, 11(12), 11833-11855. doi:10.3390/s111211833Llor, J., & Malumbres, M. P. (2012). Underwater Wireless Sensor Networks: How Do Acoustic Propagation Models Impact the Performance of Higher-Level Protocols? Sensors, 12(2), 1312-1335. doi:10.3390/s120201312Zhang, G., Hovem, J. M., & Dong, H. (2012). Experimental Assessment of Different Receiver Structures for Underwater Acoustic Communications over Multipath Channels. Sensors, 12(2), 2118-2135. doi:10.3390/s120202118Ramezani, H., & Leus, G. (2012). Ranging in an Underwater Medium with Multiple Isogradient Sound Speed Profile Layers. Sensors, 12(3), 2996-3017. doi:10.3390/s120302996Lloret, J., Sendra, S., Ardid, M., & Rodrigues, J. J. P. C. (2012). Underwater Wireless Sensor Communications in the 2.4 GHz ISM Frequency Band. Sensors, 12(4), 4237-4264. doi:10.3390/s120404237Gao, M., Foh, C. H., & Cai, J. (2012). On the Selection of Transmission Range in Underwater Acoustic Sensor Networks. Sensors, 12(4), 4715-4729. doi:10.3390/s120404715Gómez, J. V., Sandnes, F. E., & Fernández, B. (2012). Sunlight Intensity Based Global Positioning System for Near-Surface Underwater Sensors. Sensors, 12(2), 1930-1949. doi:10.3390/s120201930Han, G., Jiang, J., Shu, L., Xu, Y., & Wang, F. (2012). Localization Algorithms of Underwater Wireless Sensor Networks: A Survey. Sensors, 12(2), 2026-2061. doi:10.3390/s120202026Moradi, M., Rezazadeh, J., & Ismail, A. S. (2012). A Reverse Localization Scheme for Underwater Acoustic Sensor Networks. Sensors, 12(4), 4352-4380. doi:10.3390/s120404352Lee, S., & Kim, K. (2012). Localization with a Mobile Beacon in Underwater Acoustic Sensor Networks. Sensors, 12(5), 5486-5501. doi:10.3390/s120505486Mohamed, N., Jawhar, I., Al-Jaroodi, J., & Zhang, L. (2011). Sensor Network Architectures for Monitoring Underwater Pipelines. Sensors, 11(11), 10738-10764. doi:10.3390/s111110738Macias, E., Suarez, A., Chiti, F., Sacco, A., & Fantacci, R. (2011). A Hierarchical Communication Architecture for Oceanic Surveillance Applications. Sensors, 11(12), 11343-11356. doi:10.3390/s111211343Zhang, S., Yu, J., Zhang, A., Yang, L., & Shu, Y. (2012). Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation. Sensors, 12(1), 373-390. doi:10.3390/s120100373Climent, S., Capella, J. V., Meratnia, N., & Serrano, J. J. (2012). Underwater Sensor Networks: A New Energy Efficient and Robust Architecture. Sensors, 12(1), 704-731. doi:10.3390/s120100704Min, H., Cho, Y., & Heo, J. (2012). Enhancing the Reliability of Head Nodes in Underwater Sensor Networks. Sensors, 12(2), 1194-1210. doi:10.3390/s120201194Yoon, S., Azad, A. K., Oh, H., & Kim, S. (2012). AURP: An AUV-Aided Underwater Routing Protocol for Underwater Acoustic Sensor Networks. Sensors, 12(2), 1827-1845. doi:10.3390/s120201827Caiti, A., Calabrò, V., Dini, G., Lo Duca, A., & Munafò, A. (2012). Secure Cooperation of Autonomous Mobile Sensors Using an Underwater Acoustic Network. Sensors, 12(2), 1967-1989. doi:10.3390/s120201967Wu, H., Chen, M., & Guan, X. (2012). A Network Coding Based Routing Protocol for Underwater Sensor Networks. Sensors, 12(4), 4559-4577. doi:10.3390/s120404559Navarro, G., Huertas, I. E., Costas, E., Flecha, S., Díez-Minguito, M., Caballero, I., … Ruiz, J. (2012). Use of a Real-Time Remote Monitoring Network (RTRM) to Characterize the Guadalquivir Estuary (Spain). Sensors, 12(2), 1398-1421. doi:10.3390/s120201398Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., & Hernández, L. (2012). Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors. Sensors, 12(2), 1468-1481. doi:10.3390/s12020146

An intelligent surveillance platform for large metropolitan areas with dense sensor deployment

Producción CientíficaThis paper presents an intelligent surveillance platform based on the usage of large numbers of inexpensive sensors designed and developed inside the European Eureka Celtic project HuSIMS. With the aim of maximizing the number of deployable units while keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is based on the usage of inexpensive visual sensors which apply efficient motion detection and tracking algorithms to transform the video signal in a set of motion parameters. In order to automate the analysis of the myriad of data streams generated by the visual sensors, the platform’s control center includes an alarm detection engine which comprises three components applying three different Artificial Intelligence strategies in parallel. These strategies are generic, domain-independent approaches which are able to operate in several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The architecture is completed with a versatile communication network which facilitates data collection from the visual sensors and alarm and video stream distribution towards the emergency teams. The resulting surveillance system is extremely suitable for its deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap visual sensors and autonomous alarm detection facilitate dense sensor network deployments for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies. 6(3):1385-1408. doi:10.3390/en6031385S1385140863Booklets European Comission. Your Guide to the Lisbon Treaty 2009http://ec.europa.eu/publications/booklets/others/84/en.pdfHernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Communications Magazine, 51(1), 106-113. doi:10.1109/mcom.2013.6400446http://ec.europa.eu/energy/technology/set_plan/set_plan_en.htmFUTURED—Spanish Technological Platform for Energy Grids Home Pagehttp://www.futured.es/European Technology Platform for Electricity Networks of the Future—SmartGrids ETP Home Pagehttp://www.smartgrids.eu/Kim, H.-M., Kinoshita, T., & Shin, M.-C. (2010). A Multiagent System for Autonomous Operation of Islanded Microgrids Based on a Power Market Environment. Energies, 3(12), 1972-1990. doi:10.3390/en3121972Kim, H.-M., Lim, Y., & Kinoshita, T. (2012). An Intelligent Multiagent System for Autonomous Microgrid Operation. Energies, 5(9), 3347-3362. doi:10.3390/en5093347Xiao, Z., Li, T., Huang, M., Shi, J., Yang, J., Yu, J., & Wu, W. (2010). Hierarchical MAS Based Control Strategy for Microgrid. Energies, 3(9), 1622-1638. doi:10.3390/en3091622Hong, Y.-Y., & Chou, J.-H. (2012). Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks. Energies, 5(7), 2578-2593. doi:10.3390/en5072578Douglas, A. P., Breipohl, A. M., Lee, F. N., & Adapa, R. (1998). The impacts of temperature forecast uncertainty on Bayesian load forecasting. IEEE Transactions on Power Systems, 13(4), 1507-1513. doi:10.1109/59.736298Sadownik, R., & Barbosa, E. P. (1999). Short-term forecasting of industrial electricity consumption in Brazil. Journal of Forecasting, 18(3), 215-224. doi:10.1002/(sici)1099-131x(199905)18:33.0.co;2-bHuang, S. R. (1997). Short-term load forecasting using threshold autoregressive models. IEE Proceedings - Generation, Transmission and Distribution, 144(5), 477. doi:10.1049/ip-gtd:19971144Infield, D. G., & Hill, D. C. (1998). Optimal smoothing for trend removal in short term electricity demand forecasting. IEEE Transactions on Power Systems, 13(3), 1115-1120. doi:10.1109/59.709108Sargunaraj, S., Sen Gupta, D. P., & Devi, S. (1997). Short-term load forecasting for demand side management. IEE Proceedings - Generation, Transmission and Distribution, 144(1), 68. doi:10.1049/ip-gtd:19970599Hong-Tzer Yang, & Chao-Ming Huang. (1998). A new short-term load forecasting approach using self-organizing fuzzy ARMAX models. IEEE Transactions on Power Systems, 13(1), 217-225. doi:10.1109/59.651639Hong-Tzer Yang, Chao-Ming Huang, & Ching-Lien Huang. (1996). Identification of ARMAX model for short term load forecasting: an evolutionary programming approach. IEEE Transactions on Power Systems, 11(1), 403-408. doi:10.1109/59.486125Yu, Z. (1996). A temperature match based optimization method for daily load prediction considering DLC effect. IEEE Transactions on Power Systems, 11(2), 728-733. doi:10.1109/59.496146Charytoniuk, W., Chen, M. S., & Van Olinda, P. (1998). Nonparametric regression based short-term load forecasting. IEEE Transactions on Power Systems, 13(3), 725-730. doi:10.1109/59.708572Taylor, J. W., & Majithia, S. (2000). Using combined forecasts with changing weights for electricity demand profiling. Journal of the Operational Research Society, 51(1), 72-82. doi:10.1057/palgrave.jors.2600856Ramanathan, R., Engle, R., Granger, C. W. J., Vahid-Araghi, F., & Brace, C. (1997). Short-run forecasts of electricity loads and peaks. International Journal of Forecasting, 13(2), 161-174. doi:10.1016/s0169-2070(97)00015-0Soliman, S. A., Persaud, S., El-Nagar, K., & El-Hawary, M. E. (1997). Application of least absolute value parameter estimation based on linear programming to short-term load forecasting. International Journal of Electrical Power & Energy Systems, 19(3), 209-216. doi:10.1016/s0142-0615(96)00048-8Hyde, O., & Hodnett, P. F. (1997). An adaptable automated procedure for short-term electricity load forecasting. IEEE Transactions on Power Systems, 12(1), 84-94. doi:10.1109/59.574927Ku-Long Ho, Yuan-Yih Hsu, Chuan-Fu Chen, Tzong-En Lee, Chih-Chien Liang, Tsau-Shin Lai, & Kung-Keng Chen. (1990). Short term load forecasting of Taiwan power system using a knowledge-based expert system. IEEE Transactions on Power Systems, 5(4), 1214-1221. doi:10.1109/59.99372Rahman, S., & Hazim, O. (1993). A generalized knowledge-based short-term load-forecasting technique. IEEE Transactions on Power Systems, 8(2), 508-514. doi:10.1109/59.260833Mori, H., & Kobayashi, H. (1996). Optimal fuzzy inference for short-term load forecasting. IEEE Transactions on Power Systems, 11(1), 390-396. doi:10.1109/59.486123Bakirtzis, A. G., Theocharis, J. B., Kiartzis, S. J., & Satsios, K. J. (1995). Short term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 10(3), 1518-1524. doi:10.1109/59.466494Papadakis, S. E., Theocharis, J. B., Kiartzis, S. J., & Bakirtzis, A. G. (1998). A novel approach to short-term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 13(2), 480-492. doi:10.1109/59.667372Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179-211. doi:10.1207/s15516709cog1402_1Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7(2-3), 195-225. doi:10.1007/bf00114844Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480. doi:10.1109/5.58325Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., & Hernández, L. (2012). Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors. Sensors, 12(2), 1468-1481. doi:10.3390/s120201468Martínez-Martínez, V., Baladrón, C., Gomez-Gil, J., Ruiz-Ruiz, G., Navas-Gracia, L. M., Aguiar, J. M., & Carro, B. (2012). Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks. Sensors, 12(10), 14004-14021. doi:10.3390/s121014004Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E., & Damborg, M. J. (1991). Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 6(2), 442-449. doi:10.1109/59.76685Ho, K. L., Hsu, Y.-Y., & Yang, C.-C. (1992). Short term load forecasting using a multilayer neural network with an adaptive learning algorithm. IEEE Transactions on Power Systems, 7(1), 141-149. doi:10.1109/59.141697Drezga, I., & Rahman, S. (1998). Input variable selection for ANN-based short-term load forecasting. IEEE Transactions on Power Systems, 13(4), 1238-1244. doi:10.1109/59.736244Drezga, I., & Rahman, S. (1999). Short-term load forecasting with local ANN predictors. IEEE Transactions on Power Systems, 14(3), 844-850. doi:10.1109/59.780894Lee, K. Y., Cha, Y. T., & Park, J. H. (1992). 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IEEE Transactions on Power Systems, 13(4), 1386-1391. doi:10.1109/59.736281Senjyu, T., Mandal, P., Uezato, K., & Funabashi, T. (2005). Next Day Load Curve Forecasting Using Hybrid Correction Method. IEEE Transactions on Power Systems, 20(1), 102-109. doi:10.1109/tpwrs.2004.831256Hsu, Y.-Y., & Yang, C.-C. (1991). Design of artificial neural networks for short-term load forecasting. Part 2: Multilayer feedforward networks for peak load and valley load forecasting. IEE Proceedings C Generation, Transmission and Distribution, 138(5), 414. doi:10.1049/ip-c.1991.0052Rejc, M., & Pantos, M. (2011). Short-Term Transmission-Loss Forecast for the Slovenian Transmission Power System Based on a Fuzzy-Logic Decision Approach. IEEE Transactions on Power Systems, 26(3), 1511-1521. doi:10.1109/tpwrs.2010.2096829Wang, Y., Xia, Q., & Kang, C. (2011). Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting. 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Sistema de vídeo vigilancia semántico basado en movimiento. Aplicación a la seguridad y control de tráfico

Se realiza el diseño y la arquitectura de un sistema de videovigilancia semántico orientado al control de tráfico. A partir de los datos provenientes de una red de sensores visuales inteligentes y basándose en el conocimiento definido en una ontología, el sistema automáticamente detecta e identifica las alarmas ocurridas en la escena. Este trabajo se ha desarrollado dentro del proyecto Europeo Celtic HuSIMS.Teoría de la Señal y Comunicaciones e Ingenieria TelemáticaMáster en Investigación en Tecnologías de la Información y las Comunicacione

Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors

This paper presents a proposal for an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors. Due to the specific conditions under which these sensors operate, it is not uncommon for them to fail, and maintenance operations are difficult and costly. Therefore, completion and prediction of the missing data can greatly improve the quality of the underwater datasets. A performance study using real data is presented to validate the approach, concluding that the proposed architecture is able to provide very low errors. The numbers show as well that the solution is especially suitable for cases where large portions of data are missing, while in situations where the missing values are isolated the improvement over other simple interpolation methods is limited

Caracterización semántica de espacios: Sistema de Videovigilancia Inteligente en Smart Cities

Esta Tesis Doctoral, realizada dentro del proyecto europeo HuSIMS - Human Situation Monitoring System, presenta una metodología inteligente para la caracterización de escenarios capaz de detectar e identificar situaciones anómalas analizando el movimiento de los objetos. El sistema está diseñado para reducir al mínimo el procesamiento y la transmisión de vídeo permitiendo el despliegue de un gran número de cámaras y sensores, y por lo tanto adecuada para Smart Cities. Se propone un enfoque en tres etapas. Primero, la detección de objetos en movimiento en las propias cámaras, utilizando algorítmica sencilla, evitando el envío de datos de vídeo. Segundo, la construcción de un modelo de las zonas de las escenas utilizando los parámetros de movimiento identificados previamente. Y tercero, la realización de razonado semántico sobre el modelo de rutas y los parámetros de los objetos de la escena actual para identificar las alarmas reconociendo la naturaleza de los eventosDepartamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic

ICWIM8 - 8th Conference on Weigh-in-Motion - Book of proceedings

ICWIM8, 8th International Conference on Weigh-in-Motion, PRAGUE, TCHÈQUE, RÉPUBLIQUE, 20-/05/2019 - 24/05/2019The conference addresses the broad range of topics related to on-road and in-vehicle WIM technology, its research, installation and operation and use of mass data across variable end-uses. Innovative technologies and experiences of WIM system implementation are presented. Application of WIM data to infrastructure, mainly bridges and pavements, is among the main topics. However, the most demanding application is now WIM for enforcement, and the greatest challenge is WIM for direct enforcement. Most of the countries and road authorities should ensure a full compliance of heavy vehicle weights and dimensions with the current regulations. Another challenging objective is to extend the lifetimes of existing road assets, despite of increasing heavy vehicle loads and flow, and without compromising with the structural safety. Fair competition and road charging also require accurately monitoring commercial vehicle weights by WIM. WIM contributes to a global ITS (Intelligent Transport System) providing useful data on heavy good vehicles to implement Performance Based Standards (PBS) and Intelligent Access Programme (IAP, Australia) or Smart Infrastructure Access Programme (SIAP). The conference reports the latest research and developments since the last conference in 2016, from all around the World. More than 150 delegates from 33 countries and all continents are attending ICWIM8, mixing academics, end users, decision makers and WIM vendors. An industrial exhibition is organized jointly with the conference