Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Framework for cost-effective analytical modelling for sensory data over cloud environment

In order to offer sensory data as a service over the cloud, it is necessary to execute a cost-effective and yet precise data analytical logic within the sensing units. However, it is quite questionable as such forms of analytical operation are quite resource dependent which cannot be offered by the resource constraint sensory units. Therefore, the proposed paper introduces a novel approach of performing cost-effective data analytical method in order to extract knowledge from big data over the cloud. The proposed study uses a novel concept of the frequent pattern along with a tree-based approach in order to develop an analytical model for carrying out the mining operation in the large-scale sensor deployment over the cloud environment. Using a simulation-based approach over the mathematical model, the proposed model exhibit reduced mining duration, controlled energy dissipation, and highly optimized memory demands for all the resource constraint nodes

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

Particle Swarm Optimization for Interference Mitigation of Wireless Body Area Network: A Systematic Review

Wireless body area networks (WBAN) has now become an important technology in supporting services in the health sector and several other fields. Various surveys and research have been carried out massively on the use of swarm intelligent (SI) algorithms in various fields in the last ten years, but the use of SI in wireless body area networks (WBAN) in the last five years has not seen any significant progress. The aim of this research is to clarify and convince as well as to propose a answer to this problem, we have identified opportunities and topic trends using the particle swarm optimization (PSO) procedure as one of the swarm intelligence for optimizing wireless body area network interference mitigation performance. In this research, we analyzes primary studies collected using predefined exploration strings on online databases with the help of Publish or Perish and by the preferred reporting items for systematic reviews and meta-analysis (PRISMA) way. Articles were carefully selected for further analysis. It was found that very few researchers included optimization methods for swarm intelligence, especially PSO, in mitigating wireless body area network interference, whether for intra, inter, or cross-WBAN interference. This paper contributes to identifying the gap in using PSO for WBAN interference and also offers opportunities for using PSO both standalone and hybrid with other methods to further research on mitigating WBAN interference

Smart FRP Composite Sandwich Bridge Decks in Cold Regions

INE/AUTC 12.0

Real-time early warning system design for pluvial flash floods - A review

[EN] Pluvial flash floods in urban areas are becoming increasingly frequent due to climate change and human actions, negatively impacting the life, work, production and infrastructure of a population. Pluvial flooding occurs when intense rainfall overflows the limits of urban drainage and water accumulation causes hazardous flash floods. Although flash floods are hard to predict given their rapid formation, Early Warning Systems (EWS) are used to minimize casualties. We performed a systematic review to define the basic structure of an EWS for rain flash floods. The structure of the review is as follows: first, Section 2 describes the most important factors that affect the intensity of pluvial flash floods during rainfall events. Section 3 defines the key elements and actors involved in an effective EWS. Section 4 reviews different EWS architectures for pluvial flash floods implemented worldwide. It was identified that the reviewed projects did not follow guidelines to design early warning systems, neglecting important aspects that must be taken into account in their implementation. Therefore, this manuscript proposes a basic structure for an effective EWS for pluvial flash floods that guarantees the forecasting process and alerts dissemination during rainfall events.Administrative Department of Science, Technology and Innovation of the presidency of the Republic of Colombia (COLCIENCIAS) #728.Acosta-Coll, M.; Ballester Merelo, FJ.; Martínez Peiró, MA.; De La Hoz-Franco, E. (2018). Real-time early warning system design for pluvial flash floods - A review. Sensors. 18(7). https://doi.org/10.3390/s18072255S187Kundzewicz, Z. W. (2002). Non-structural Flood Protection and Sustainability. Water International, 27(1), 3-13. doi:10.1080/02508060208686972Singh, P., Sinha, V. S. P., Vijhani, A., & Pahuja, N. (2018). Vulnerability assessment of urban road network from urban flood. International Journal of Disaster Risk Reduction, 28, 237-250. doi:10.1016/j.ijdrr.2018.03.017Birkmann, J., & von Teichman, K. (2010). Integrating disaster risk reduction and climate change adaptation: key challenges—scales, knowledge, and norms. Sustainability Science, 5(2), 171-184. doi:10.1007/s11625-010-0108-yEmerging Challenges for Early Warning Systems in context of Climate Change and Urbanizationhttp://www.preventionweb.net/ files/15689_ewsincontextofccandurbanization.pdfChaumillon, E., Bertin, X., Fortunato, A. B., Bajo, M., Schneider, J.-L., Dezileau, L., … Pedreros, R. (2017). Storm-induced marine flooding: Lessons from a multidisciplinary approach. Earth-Science Reviews, 165, 151-184. doi:10.1016/j.earscirev.2016.12.005Alfieri, L., Cohen, S., Galantowicz, J., Schumann, G. J.-P., Trigg, M. A., Zsoter, E., … Salamon, P. (2018). A global network for operational flood risk reduction. Environmental Science & Policy, 84, 149-158. doi:10.1016/j.envsci.2018.03.014Maggioni, V., & Massari, C. (2018). On the performance of satellite precipitation products in riverine flood modeling: A review. Journal of Hydrology, 558, 214-224. doi:10.1016/j.jhydrol.2018.01.039Jiang, Y., Zevenbergen, C., & Ma, Y. (2018). Urban pluvial flooding and stormwater management: A contemporary review of China’s challenges and «sponge cities» strategy. Environmental Science & Policy, 80, 132-143. doi:10.1016/j.envsci.2017.11.016Veldhuis, J. A. E. (2011). How the choice of flood damage metrics influences urban flood risk assessment. Journal of Flood Risk Management, 4(4), 281-287. doi:10.1111/j.1753-318x.2011.01112.xGlobal Approach to Address Flash Floodshttp://www.hrc-lab.org/publicbenefit/downloads/wmo-flashflood.pdfChen, Y., Zhou, H., Zhang, H., Du, G., & Zhou, J. (2015). Urban flood risk warning under rapid urbanization. Environmental Research, 139, 3-10. doi:10.1016/j.envres.2015.02.028Guerreiro, S., Glenis, V., Dawson, R., & Kilsby, C. (2017). Pluvial Flooding in European Cities—A Continental Approach to Urban Flood Modelling. Water, 9(4), 296. doi:10.3390/w9040296Bhattarai, R., Yoshimura, K., Seto, S., Nakamura, S., & Oki, T. (2016). Statistical model for economic damage from pluvial floods in Japan using rainfall data and socioeconomic parameters. Natural Hazards and Earth System Sciences, 16(5), 1063-1077. doi:10.5194/nhess-16-1063-2016Acosta-Coll, M., Ballester-Merelo, F., & Martínez-Peiró, M. (2018). Early warning system for detection of urban pluvial flooding hazard levels in an ungauged basin. Natural Hazards, 92(2), 1237-1265. doi:10.1007/s11069-018-3249-4Yin, J., Ye, M., Yin, Z., & Xu, S. (2014). A review of advances in urban flood risk analysis over China. Stochastic Environmental Research and Risk Assessment, 29(3), 1063-1070. doi:10.1007/s00477-014-0939-7Azam, M., Kim, H. S., & Maeng, S. J. (2017). Development of flood alert application in Mushim stream watershed Korea. International Journal of Disaster Risk Reduction, 21, 11-26. doi:10.1016/j.ijdrr.2016.11.008Creutin, J. D., Borga, M., Gruntfest, E., Lutoff, C., Zoccatelli, D., & Ruin, I. (2013). A space and time framework for analyzing human anticipation of flash floods. Journal of Hydrology, 482, 14-24. doi:10.1016/j.jhydrol.2012.11.009Yin, J., Yu, D., Yin, Z., Liu, M., & He, Q. (2016). Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai, China. Journal of Hydrology, 537, 138-145. doi:10.1016/j.jhydrol.2016.03.037UNISDR Terminology on Disaster Risk Reductionhttps://www.unisdr.org/we/inform/publications/657Einfalt, T., Hatzfeld, F., Wagner, A., Seltmann, J., Castro, D., & Frerichs, S. (2009). URBAS: forecasting and management of flash floods in urban areas. Urban Water Journal, 6(5), 369-374. doi:10.1080/15730620902934819Lam, R. P. K., Leung, L. P., Balsari, S., Hsiao, K., Newnham, E., Patrick, K., … Leaning, J. (2017). Urban disaster preparedness of Hong Kong residents: A territory-wide survey. International Journal of Disaster Risk Reduction, 23, 62-69. doi:10.1016/j.ijdrr.2017.04.008Bouwer, L. M., Papyrakis, E., Poussin, J., Pfurtscheller, C., & Thieken, A. H. (2014). The Costing of Measures for Natural Hazard Mitigation in Europe. Natural Hazards Review, 15(4), 04014010. doi:10.1061/(asce)nh.1527-6996.0000133Praskievicz, S., & Chang, H. (2009). A review of hydrological modelling of basin-scale climate change and urban development impacts. Progress in Physical Geography: Earth and Environment, 33(5), 650-671. doi:10.1177/0309133309348098Hunt, A., & Watkiss, P. (2010). Climate change impacts and adaptation in cities: a review of the literature. Climatic Change, 104(1), 13-49. doi:10.1007/s10584-010-9975-6Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J., Nicholls, N., Peduzzi, P., … Sherstyukov, B. (2013). Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59(1), 1-28. doi:10.1080/02626667.2013.857411You, Q., Kang, S., Aguilar, E., Pepin, N., Flügel, W.-A., Yan, Y., … Huang, J. (2010). Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961–2003. Climate Dynamics, 36(11-12), 2399-2417. doi:10.1007/s00382-009-0735-0Miller, J. D., & Hutchins, M. (2017). The impacts of urbanisation and climate change on urban flooding and urban water quality: A review of the evidence concerning the United Kingdom. Journal of Hydrology: Regional Studies, 12, 345-362. doi:10.1016/j.ejrh.2017.06.006Borga, M., Anagnostou, E. N., Blöschl, G., & Creutin, J.-D. (2011). Flash flood forecasting, warning and risk management: the HYDRATE project. Environmental Science & Policy, 14(7), 834-844. doi:10.1016/j.envsci.2011.05.017Grillakis, M. G., Koutroulis, A. G., Komma, J., Tsanis, I. K., Wagner, W., & Blöschl, G. (2016). Initial soil moisture effects on flash flood generation – A comparison between basins of contrasting hydro-climatic conditions. Journal of Hydrology, 541, 206-217. doi:10.1016/j.jhydrol.2016.03.007Zhang, J., Yu, Z., Yu, T., Si, J., Feng, Q., & Cao, S. (2018). Transforming flash floods into resources in arid China. Land Use Policy, 76, 746-753. doi:10.1016/j.landusepol.2018.03.002Spiekermann, R., Kienberger, S., Norton, J., Briones, F., & Weichselgartner, J. (2015). The Disaster-Knowledge Matrix – Reframing and evaluating the knowledge challenges in disaster risk reduction. International Journal of Disaster Risk Reduction, 13, 96-108. doi:10.1016/j.ijdrr.2015.05.002Weichselgartner, J., & Pigeon, P. (2015). The Role of Knowledge in Disaster Risk Reduction. International Journal of Disaster Risk Science, 6(2), 107-116. doi:10.1007/s13753-015-0052-7Hunt, D. P. (2003). The concept of knowledge and how to measure it. Journal of Intellectual Capital, 4(1), 100-113. doi:10.1108/14691930310455414Strengthening Capacities for Disaster Risk Reduction, A Primerhttps://www.preventionweb.net/files/globalplatform/entry_bg_paper~strengtheningcapacityfordrraprimerfullreport.pdfSurjan, A., Sharma, A., & Shaw, R. (2011). Chapter 2 Understanding Urban Resilience. Community, Environment and Disaster Risk Management, 17-45. doi:10.1108/s2040-7262(2011)0000006008Fakhruddin, S. H. M., Kawasaki, A., & Babel, M. S. (2015). Community responses to flood early warning system: Case study in Kaijuri Union, Bangladesh. International Journal of Disaster Risk Reduction, 14, 323-331. doi:10.1016/j.ijdrr.2015.08.004Balis, B., Kasztelnik, M., Bubak, M., Bartynski, T., Gubała, T., Nowakowski, P., & Broekhuijsen, J. (2011). The UrbanFlood Common Information Space for Early Warning Systems. Procedia Computer Science, 4, 96-105. doi:10.1016/j.procs.2011.04.011Krzhizhanovskaya, V. V., Shirshov, G. S., Melnikova, N. B., Belleman, R. G., Rusadi, F. I., Broekhuijsen, B. J., … Meijer, R. J. (2011). Flood early warning system: design, implementation and computational modules. Procedia Computer Science, 4, 106-115. doi:10.1016/j.procs.2011.04.012Chang, C. L., & Lin, T.-C. (2015). The role of organizational culture in the knowledge management process. Journal of Knowledge Management, 19(3), 433-455. doi:10.1108/jkm-08-2014-0353MARK, O., WEESAKUL, S., APIRUMANEKUL, C., AROONNET, S., & DJORDJEVIC, S. (2004). Potential and limitations of 1D modelling of urban flooding. Journal of Hydrology, 299(3-4), 284-299. doi:10.1016/s0022-1694(04)00373-7Henonin, J., Russo, B., Mark, O., & Gourbesville, P. (2013). Real-time urban flood forecasting and modelling – a state of the art. Journal of Hydroinformatics, 15(3), 717-736. doi:10.2166/hydro.2013.132Mayhorn, C. B., & McLaughlin, A. C. (2014). Warning the world of extreme events: A global perspective on risk communication for natural and technological disaster. Safety Science, 61, 43-50. doi:10.1016/j.ssci.2012.04.014Cools, J., Innocenti, D., & O’Brien, S. (2016). Lessons from flood early warning systems. Environmental Science & Policy, 58, 117-122. doi:10.1016/j.envsci.2016.01.006Plate, E. J. (2007). Early warning and flood forecasting for large rivers with the lower Mekong as example. Journal of Hydro-environment Research, 1(2), 80-94. doi:10.1016/j.jher.2007.10.002Altay, N., & Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475-493. doi:10.1016/j.ejor.2005.05.016Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., & Pappenberger, F. (2013). GloFAS – global ensemble streamflow forecasting and flood early warning. Hydrology and Earth System Sciences, 17(3), 1161-1175. doi:10.5194/hess-17-1161-2013Morss, R. E., Mulder, K. J., Lazo, J. K., & Demuth, J. L. (2016). How do people perceive, understand, and anticipate responding to flash flood risks and warnings? Results from a public survey in Boulder, Colorado, USA. Journal of Hydrology, 541, 649-664. doi:10.1016/j.jhydrol.2015.11.047Cama-Pinto, A., Acosta-Coll, M., Piñeres-Espitia, G., Caicedo-Ortiz, J., Zamora-Musa, R., & Sepulveda-Ojeda, J. (2016). Diseño de una red de sensores inalámbricos para la monitorización de inundaciones repentinas en la ciudad de Barranquilla, Colombia. Ingeniare. Revista chilena de ingeniería, 24(4), 581-599. doi:10.4067/s0718-33052016000400005Espitia, G. P. (2014). Plataformas tecnológicas aplicadas al monitoreo climático. Prospectiva, 11(2), 78. doi:10.15665/rp.v11i2.42Caicedo Ortiz, J. G. (2015). Modelo de despliegue de una WSN para la medición de las variables climáticas que causan fuertes precipitaciones. Prospectiva, 13(1), 106. doi:10.15665/rp.v13i1.365Marshall, J. S., & Palmer, W. M. K. (1948). THE DISTRIBUTION OF RAINDROPS WITH SIZE. Journal of Meteorology, 5(4), 165-166. doi:10.1175/1520-0469(1948)0052.0.co;2Liquid-Level Monitoring Using a Pressure Sensorhttp://www.ti.com/lit/an/snaa127/snaa127.pdfUltrasonic Transmitters vshttps://www.flo-corp.com/wp-content/uploads/2017/01/LTT1_UltrasonicTransmitters_GuidedWaveRadar_LevelMeasurement_whitepaper.pdfPanda, K. G., Agrawal, D., Nshimiyimana, A., & Hossain, A. (2016). Effects of environment on accuracy of ultrasonic sensor operates in millimetre range. Perspectives in Science, 8, 574-576. doi:10.1016/j.pisc.2016.06.024Saad, C., Mostafa, B., Ahmadi, E., & Abderrahmane, H. (2014). Comparative Performance Analysis of Wireless Communication Protocols for Intelligent Sensors and Their Applications. International Journal of Advanced Computer Science and Applications, 5(4). doi:10.14569/ijacsa.2014.050413FloodCitiSense: Early Warning Service for Urban Pluvial Floods for and by Citizens and City Authoritieshttp://www.iiasa.ac.at/web/home/research/researchPrograms/EcosystemsServicesandManagement/FloodCitiSense.htmlParker, D. J. (2017). Flood Warning Systems and Their Performance. Oxford Research Encyclopedia of Natural Hazard Science. doi:10.1093/acrefore/9780199389407.013.8

A REAL-TIME TRAFFIC CONDITION ASSESSMENT AND PREDICTION FRAMEWORK USING VEHICLE-INFRASTRUCTURE INTEGRATION (VII) WITH COMPUTATIONAL INTELLIGENCE

This research developed a real-time traffic condition assessment and prediction framework using Vehicle-Infrastructure Integration (VII) with computational intelligence to improve the existing traffic surveillance system. Due to the prohibited expenses and complexity involved for the field experiment of such a system, this study adopted state-of-the-art simulation tools as an efficient alternative. This work developed an integrated traffic and communication simulation platform to facilitate the design and evaluation of a wide range of online traffic surveillance and management system in both traffic and communication domain. Using the integrated simulator, the author evaluated the performance of different combination of communication medium and architecture. This evaluation led to the development of a hybrid VII framework exemplified by hierarchical architecture, which is expected to eliminate single point failures, enhance scalability and easy integration of control functions for traffic condition assessment and prediction. In the proposed VII framework, the vehicle on-board equipments and roadside units (RSUs) work collaboratively, based on an intelligent paradigm known as \u27Support Vector Machine (SVM),\u27 to determine the occurrence and characteristics of an incident with the kinetics data generated by vehicles. In addition to incident detection, this research also integrated the computational intelligence paradigm called \u27Support Vector Regression (SVR)\u27 within the hybrid VII framework for improving the travel time prediction capabilities, and supporting on-line leaning functions to improve its performance over time. Two simulation models that fully implemented the functionalities of real-time traffic surveillance were developed on calibrated and validated simulation network for study sites in Greenville and Spartanburg, South Carolina. The simulation models\u27 encouraging performance on traffic condition assessment and prediction justifies further research on field experiment of such a system to address various research issues in the areas covered by this work, such as availability and accuracy of vehicle kinetic and maneuver data, reliability of wireless communication, maintenance of RSUs and wireless repeaters. The impact of this research will provide a reliable alternative to traditional traffic sensors to assess and predict the condition of the transportation system. The integrated simulation methodology and open source software will provide a tool for design and evaluation of any real-time traffic surveillance and management systems. Additionally, the developed VII simulation models will be made available for use by future researchers and designers of other similar VII systems. Future implementation of the research in the private and public sector will result in new VII related equipment in vehicles, greater control of traffic loading, faster incident detection, improved safety, mitigated congestion, and reduced emissions and fuel consumption

Underground Mining Monitoring and Communication Systems based on ZigBee and GIS

ZigBee as a wireless sensor network (WSN) was developed for underground mine monitoring and communication systems. The radio wave attenuations between ZigBee nodes were investigated to measure underground communication distances. Various sensor node arrangements of ZigBee topologies were evaluated. A system integration of a WSN-assisted GIS for underground mining monitoring and communication from a surface office was proposed. The controllable and uncontrollable parameters of underground environments were assessed to establish a reliable ZigBee network

Developing a Digital Twin at Building and City Levels: A Case Study of West Cambridge Campus

A digital twin (DT) refers to a digital replica of physical assets, processes, and systems. DTs integrate artificial intelligence, machine learning, and data analytics to create living digital simulation models that are able to learn and update from multiple sources as well as represent and predict the current and future conditions of physical counterparts. However, current activities related to DTs are still at an early stage with respect to buildings and other infrastructure assets from an architectural and engineering/construction point of view. Less attention has been paid to the operation and maintenance (O&M) phase, which is the longest time span in the asset life cycle. A systematic and clear architecture verified with practical use cases for constructing a DT would be the foremost step for effective operation and maintenance of buildings and cities. According to current research about multitier architectures, this paper presents a system architecture for DTs that is specifically designed at both the building and city levels. Based on this architecture, a DT demonstrator of the West Cambridge site of the University of Cambridge in the UK was developed that integrates heterogeneous data sources, supports effective data querying and analysis, supports decision-making processes in O&M management, and further bridges the gap between human relationships with buildings/cities. This paper aims at going through the whole process of developing DTs in building and city levels from the technical perspective and sharing lessons learned and challenges involved in developing DTs in real practices. Through developing this DT demonstrator, the results provide a clear roadmap and present particular DT research efforts for asset management practitioners, policymakers, and researchers to promote the implementation and development of DT at the building and city levels