Sensor Technologies for Intelligent Transportation Systems

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment

On supporting university communities in indoor wayfinding: An inclusive design approach

Mobility can be defined as the ability of people to move, live and interact with the space. In this context, indoor mobility, in terms of indoor localization and wayfinding, is a relevant topic due to the challenges it presents, in comparison with outdoor mobility, where GPS is hardly exploited. Knowing how to move in an indoor environment can be crucial for people with disabilities, and in particular for blind users, but it can provide several advantages also to any person who is moving in an unfamiliar place. Following this line of thought, we employed an inclusive by design approach to implement and deploy a system that comprises an Internet of Things infrastructure and an accessible mobile application to provide wayfinding functions, targeting the University community. As a real word case study, we considered the University of Bologna, designing a system able to be deployed in buildings with different configurations and settings, considering also historical buildings. The final system has been evaluated in three different scenarios, considering three different target audiences (18 users in total): i. students with disabilities (i.e., visual and mobility impairments); ii. campus students; and iii. visitors and tourists. Results reveal that all the participants enjoyed the provided functions and the indoor localization strategy was fine enough to provide a good wayfinding experience

An Overview of Vehicular Communications

The transport sector is commonly subordinate to several issues, such as traffic congestion and accidents. Despite this, in recent years, it is also evolving with regard to cooperation between vehicles. The fundamental objective of this trend is to increase road safety, attempting to anticipate the circumstances of potential danger. Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies strive to give communication models that can be employed by vehicles in different application contexts. The resulting infrastructure is an ad-hoc mesh network whose nodes are not only vehicles but also all mobile devices equipped with wireless modules. The interaction between the multiple connected entities consists of information exchange through the adoption of suitable communication protocols. The main aim of the review carried out in this paper is to examine and assess the most relevant systems, applications, and communication protocols that will distinguish the future road infrastructures used by vehicles. The results of the investigation reveal the real benefits that technological cooperation can involve in road safety. Document type: Articl

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

This research received no external funding. The APC is funded by Prince Sultan UniversityThe authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique, and a comparison with basic classifiers. The results show that SLFN classification technique and the choice of Support Vector Machine and Synthetic Minority Oversampling Technique (SVM-SMOTE) with a ratio of 0.9 and the k value of 3 for k-means++ clustering technique give better results than other values and other classification techniques.Prince Sultan Universit

Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms

[EN] By offering low-latency and context-aware services, fog computing will have a peculiar role in the deployment of Internet of Things (IoT) applications for smart environments. Unlike the conventional remote cloud, for which consolidated architectures and deployment options exist, many design and implementation aspects remain open when considering the latest fog computing paradigm. In this paper, we focus on the problems of dynamically discovering the processing and storage resources distributed among fog nodes and, accordingly, orchestrating them for the provisioning of IoT services for smart environments. In particular, we show how these functionalities can be effectively supported by the revolutionary Named Data Networking (NDN) paradigm. Originally conceived to support named content delivery, NDN can be extended to request and provide named computation services, with NDN nodes acting as both content routers and in-network service executors. To substantiate our analysis, we present an NDN fog computing framework with focus on a smart campus scenario, where the execution of IoT services is dynamically orchestrated and performed by NDN nodes in a distributed fashion. A simulation campaign in ndnSIM, the reference network simulator of the NDN research community, is also presented to assess the performance of our proposal against state-of-the-art solutions. Results confirm the superiority of the proposal in terms of service provisioning time, paid at the expenses of a slightly higher amount of traffic exchanged among fog nodes.This research was partially funded by the Italian Government under grant PON ARS01_00836 for the COGITO (A COGnItive dynamic sysTem to allOw buildings to learn and adapt) PON Project.Amadeo, M.; Ruggeri, G.; Campolo, C.; Molinaro, A.; Loscri, V.; Tavares De Araujo Cesariny Calafate, CM. (2019). Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms. Future Internet. 11(11):1-21. https://doi.org/10.3390/fi11110222S1211111Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440. doi:10.1016/j.bushor.2015.03.008Cicirelli, F., Guerrieri, A., Spezzano, G., Vinci, A., Briante, O., Iera, A., & Ruggeri, G. (2018). Edge Computing and Social Internet of Things for Large-Scale Smart Environments Development. IEEE Internet of Things Journal, 5(4), 2557-2571. doi:10.1109/jiot.2017.2775739Chiang, M., & Zhang, T. (2016). Fog and IoT: An Overview of Research Opportunities. IEEE Internet of Things Journal, 3(6), 854-864. doi:10.1109/jiot.2016.2584538Openfog Consortiumhttp://www.openfogconsortium.org/Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., claffy, kc, Crowley, P., … Zhang, B. (2014). Named data networking. ACM SIGCOMM Computer Communication Review, 44(3), 66-73. doi:10.1145/2656877.2656887Amadeo, M., Ruggeri, G., Campolo, C., & Molinaro, A. (2019). IoT Services Allocation at the Edge via Named Data Networking: From Optimal Bounds to Practical Design. IEEE Transactions on Network and Service Management, 16(2), 661-674. doi:10.1109/tnsm.2019.2900274ndnSIM 2.0: A New Version of the NDN Simulator for NS-3https://www.researchgate.net/profile/Spyridon_Mastorakis/publication/281652451_ndnSIM_20_A_new_version_of_the_NDN_simulator_for_NS-3/links/5b196020a6fdcca67b63660d/ndnSIM-20-A-new-version-of-the-NDN-simulator-for-NS-3.pdfAhlgren, B., Dannewitz, C., Imbrenda, C., Kutscher, D., & Ohlman, B. (2012). A survey of information-centric networking. IEEE Communications Magazine, 50(7), 26-36. doi:10.1109/mcom.2012.6231276NFD Developer’s Guidehttps://named-data.net/wp-content/uploads/2016/03/ndn-0021-diff-5..6-nfd-developer-guide.pdfPiro, G., Amadeo, M., Boggia, G., Campolo, C., Grieco, L. A., Molinaro, A., & Ruggeri, G. (2019). Gazing into the Crystal Ball: When the Future Internet Meets the Mobile Clouds. IEEE Transactions on Cloud Computing, 7(1), 210-223. doi:10.1109/tcc.2016.2573307Zhang, G., Li, Y., & Lin, T. (2013). Caching in information centric networking: A survey. Computer Networks, 57(16), 3128-3141. doi:10.1016/j.comnet.2013.07.007Yi, C., Afanasyev, A., Moiseenko, I., Wang, L., Zhang, B., & Zhang, L. (2013). A case for stateful forwarding plane. Computer Communications, 36(7), 779-791. doi:10.1016/j.comcom.2013.01.005Amadeo, M., Briante, O., Campolo, C., Molinaro, A., & Ruggeri, G. (2016). Information-centric networking for M2M communications: Design and deployment. Computer Communications, 89-90, 105-116. doi:10.1016/j.comcom.2016.03.009Tourani, R., Misra, S., Mick, T., & Panwar, G. (2018). Security, Privacy, and Access Control in Information-Centric Networking: A Survey. IEEE Communications Surveys & Tutorials, 20(1), 566-600. doi:10.1109/comst.2017.2749508Ndn-ace: Access Control for Constrained Environments over Named Data Networkinghttp://new.named-data.net/wp-content/uploads/2015/12/ndn-0036-1-ndn-ace.pdfZhang, Z., Yu, Y., Zhang, H., Newberry, E., Mastorakis, S., Li, Y., … Zhang, L. (2018). An Overview of Security Support in Named Data Networking. IEEE Communications Magazine, 56(11), 62-68. doi:10.1109/mcom.2018.1701147Cisco White Paperhttps://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdfAazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying Fog Computing in Industrial Internet of Things and Industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674-4682. doi:10.1109/tii.2018.2855198Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., & Chen, S. (2016). Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures. IEEE Transactions on Vehicular Technology, 65(6), 3860-3873. doi:10.1109/tvt.2016.2532863Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., … Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98, 289-330. doi:10.1016/j.sysarc.2019.02.009Baktir, A. C., Ozgovde, A., & Ersoy, C. (2017). How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions. IEEE Communications Surveys & Tutorials, 19(4), 2359-2391. doi:10.1109/comst.2017.2717482Duan, Q., Yan, Y., & Vasilakos, A. V. (2012). A Survey on Service-Oriented Network Virtualization Toward Convergence of Networking and Cloud Computing. IEEE Transactions on Network and Service Management, 9(4), 373-392. doi:10.1109/tnsm.2012.113012.120310Amadeo, M., Campolo, C., & Molinaro, A. (2016). NDNe: Enhancing Named Data Networking to Support Cloudification at the Edge. IEEE Communications Letters, 20(11), 2264-2267. doi:10.1109/lcomm.2016.2597850Krol, M., Marxer, C., Grewe, D., Psaras, I., & Tschudin, C. (2018). Open Security Issues for Edge Named Function Environments. IEEE Communications Magazine, 56(11), 69-75. doi:10.1109/mcom.2018.170111711801-2:2017 Information Technology—Generic Cabling for Customer Premiseshttps://www.iso.org/standard/66183.htm

A Survey of Smart Classroom Literature

Recently, there has been a substantial amount of research on smart classrooms, encompassing a number of areas, including Information and Communication Technology, Machine Learning, Sensor Networks, Cloud Computing, and Hardware. Smart classroom research has been quickly implemented to enhance education systems, resulting in higher engagement and empowerment of students, educators, and administrators. Despite decades of using emerging technology to improve teaching practices, critics often point out that methods miss adequate theoretical and technical foundations. As a result, there have been a number of conflicting reviews on different perspectives of smart classrooms. For a realistic smart classroom approach, a piecemeal implementation is insufficient. This survey contributes to the current literature by presenting a comprehensive analysis of various disciplines using a standard terminology and taxonomy. This multi-field study reveals new research possibilities and problems that must be tackled in order to integrate interdisciplinary works in a synergic manner. Our analysis shows that smart classroom is a rapidly developing research area that complements a number of emerging technologies. Moreover, this paper also describes the co-occurrence network of technological keywords using VOSviewer for an in-depth analysis

NFV orchestration on intermittently available SUAV platforms: challenges and hurdles

Proceeding of: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS MiSARN 2019: Mission-Oriented Wireless Sensor, UAV and Robot Networking), 29 April-2 May 2019 Paris, FranceIn this paper, we analyze the main challenges and issues related with the orchestration of Virtualized Network Functions (VNFs) on Small Unmanned Aerial Vehicles (SUAVs). Our analysis considers a reference scenario where a number of SUAVs are deployed over a delimited geographic area and provide a mobile cloud environment that supports the deployment of functionalities using Network Functions Virtualization (NFV) technologies. The orchestration of services in this reference scenario presents different challenges, due to the constrained capacity and limited lifetime of battery-powered SUAVs, the intermittent availability of network communications, and the need to consider enhanced policies for the allocation of virtual functions to SUAVs. Finally, we perform a first exploratory evaluation of the identified challenges and issues, using a well-known and widely adopted virtualized infrastructure manager, i.e., OpenStack.This article has been partially supported by the European H2020 5GRANGE project (grant agreement 777137), and by the 5GCity project (TEC2016-76795- C6-3-R) funded by the Spanish Ministry of Economy and Competitiveness

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).This work is funded by FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

The survey on Near Field Communication

PubMed ID: 26057043Near Field Communication (NFC) is an emerging short-range wireless communication technology that offers great and varied promise in services such as payment, ticketing, gaming, crowd sourcing, voting, navigation, and many others. NFC technology enables the integration of services from a wide range of applications into one single smartphone. NFC technology has emerged recently, and consequently not much academic data are available yet, although the number of academic research studies carried out in the past two years has already surpassed the total number of the prior works combined. This paper presents the concept of NFC technology in a holistic approach from different perspectives, including hardware improvement and optimization, communication essentials and standards, applications, secure elements, privacy and security, usability analysis, and ecosystem and business issues. Further research opportunities in terms of the academic and business points of view are also explored and discussed at the end of each section. This comprehensive survey will be a valuable guide for researchers and academicians, as well as for business in the NFC technology and ecosystem.Publisher's Versio