From the Internet of Things to the web of things-enabling by sensing as-A service

© 2016 IEEE. Sensing as a Service (SenaaS) is emerging as a prominent element in the middleware linking together the Internet of Things (IoT) and the Web of Things (WoT) layers of future ubiquitous systems. An architecture framework is discussed in this paper whereby things are abstracted into services via embedded sensors which expose a thing as a service. The architecture acts as a blueprint to guide software architects realizing WoT applications. Web-enabled things are eventually appended into Web platforms such as Social Web platforms to drive data and services that are exposed by these things to interact with both other things and people, in order to materialize further the future social Web of Things. Research directions are discussed to illustrate the integration of SenaaS into the proposed WoT architectural framework

Cognitive computing meets the internet of things

Abstract: This paper discusses the blend of cognitive computing with the Internet-of-Things that should result into developing cognitive things. Today’s things are confined into a data-supplier role, which deprives them from being the technology of choice for smart applications development. Cognitive computing is about reasoning, learning, explaining, acting, etc. In this paper, cognitive things’ features include functional and non-functional restrictions along with a 3 stage operation cycle that takes into account these restrictions during reasoning, adaptation, and learning. Some implementation details about cognitive things are included in this paper based on a water pipe case-study

Building sustainable parking lots with the Web of Things

Peak-time traffic woes create considerable amount of stress and environmental pollution resulting in an economic loss. Research innovations in areas such as the Web of Things are able to curtail some of these issues by creating scalable and sustainable environments like parking lots, which provide motorists with access to convenient parking spots. We present a scalable parking lot network infrastructure that exposes parking management operations through a judicious mashup of physical things\u27 services within a parking lot. Our system uses service-oriented architecture, allowing motorists to reserve parking spots in advance. In doing so, our proposed system leverages the use of HTTP and Wi-Fi for the Web enablement and interoperability of things within a parking spot and elevates it as a Smart Parking Spot on the Web. Our suggested semantic Web-based structure for representing things makes it possible to query physical things\u27 states and services depending on their capabilities and other relevant parking-related parameters. Our performance evaluation reveals that a maximum of 40 % time is saved to find parking spots and also 40 % reduction in air pollution is observed. © 2013 Springer-Verlag London

Adopting Open Source IT Certification in Higher Education: Lessons from the Field

This paper suggests areas of good practice and considerations based upon the experience of embedding an open source information technology (IT) certification into a UK higher education program. Academically, open source is used as a vehicle for teaching general academic skills and values, but also as a collection of marketable skills. IT certification is used to further develop and signpost these skills to employers. This paper critically reviews literature in the fields of open source software in education and IT certification. A case study then discusses the methods used to embed such certification at Birmingham City University in the UK. Key barriers are reviewed along with a summary of lessons learned for the benefit of those considering similar actions

Cognitive Computing Meets the Internet of Things

This paper discusses the blend of cognitive computing with the Internet-of-Things that should result into developing cognitive things. Today\u27s things are confined into a data-supplier role, which d ..

Towards an approach for weaving preferences into web services operation

Existing approaches on Web services privacy dominate solutions from a users’ perspective, giving little consideration to the preferences of Web service providers. The integration of service providers’ preferences into Web services’ operations is discussed in this paper. A Web service provider indicates peer Web services that it could interact with as well as the data that they could exchange with. We focus on Privacy and (trust) Partnership preferences based on which, we develop a Specification for Privacy and Partnership Preferences (S3P). This specification suggests a list of exceptional actions to deploy at run-time when these preferences are not met. An integration model of these preferences into Web services design is illustrated throughout a running scenario, and an implementation framework proves the S3P concept.Zakaria Maamar, Quan Z. Sheng, Yacine Atif, Sujith Samuel Mathew, Khouloud Boukad

Building Trust in E-Commerce

A network of Internet-based intermediaries that guarantee delivery and payment in e-commerce could help bolster consumer and merchant confidence

Conversational learning integration in technology enhanced classrooms

Today’s college students have grown up with technology. These digital natives typically gravitate toward group activities in technology embedded social contexts. However, despite this multidimensional evolution, little has changed in conventional classrooms where they build their education experience. We investigate learning models in a classroom environment which still remains the main driver of education today. We describe a conversational learning model based on group activities which involve multi-party conversations. We implement this model in a technology-enhanced studio-classroom to “visualize” conversations which otherwise would remain abstract to learners. Teachers are empowered with instructional patterns to guide their changing role in this novel classroom environment. Based on standard assessment indicators, we conduct an experimental analysis which results show interesting tradeoffs of learning performance that favor the proposed conversational learning approach compared to those obtained from conventional instruction

Hållbar, smart parkeringskoordinering för uppkopplade och autonoma fordon

Traffic induced by parking-spot seekers is a growing challenge and constitutes a considerable portion of the traffic in city centers. New opportunities to solve this problem are emerging by connected vehicles and infrastructure. For instance, ultrasonic and magnetic sensors are already mounted on the ceiling of many parking lots to detect the availability of a parking spot. These sensors can provide parking spot availability information in real-time. Further, traffic-aware smart sensors which can detect the movement of individual vehicles are also available in many city and highway areas. This report suggests an algorithm for a cloud-based parking service that exploits these streams of data to choose the best parking lot in a given parking area. The parking seeking problem is subject to a range of criteria that may include user, municipality and parking operator preferences. Users may have some preferences with respect to walking distance to destination. Municipalities prefer to spread the traffic to reduce congestion in the urban core. Parking operators seek to maximize parking lot utilization in order to increase the revenue on real-estate investments. To solve this problem, an optimization algorithm based on multicriteria decision making process is used. The proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot. The algorithm features three modular sections: • First, a search process is triggered to identify the expected arrival time periods to all parking lots in the targeted parking area. This process utilizes smart pole data streams reporting congestion rates across the targeted parking area. • Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state transition matrix, showing the transformation of available spots in a parking lot over short periods of time. • Finally, this matrix is projected against similar future seasonal periods to predict the actual vacancy of a parking lot at the arrival time.Trafik som består av sökande efter parkeringsplats är ett växande problem och utgör en avsevärd del av trafiken i stadskärnor. Nya möjligheter att lösa detta problem kommer dock finnas när fordonen är uppkopplade till infrastrukturen. Till exempel, ultraljudsensorer och magnetiska sensorer är redan monterade i taket på många parkeringshus för att detektera tillgängligheten av en parkeringsplats, och dessa sensorer kan ge information om lediga parkeringsplatser i realtid. Vidare, smarta sensorer för trafikmätning, som också kan se förflyttning av enskilda fordon, finns redan i många städer och på motorvägar. Denna rapport föreslår en algoritm till en molnbaserad parkeringstjänst som använder ovanstående typ av data för att välja den bästa parkeringen i ett visst parkeringsområde. Lösningen på problemet att välja bästa parkering omfattar en rad kriterier som kan inkludera preferenser från användare, kommuner och parkeringsbolag. Användare kan till exempel ha vissa preferenser med avseende på gångavstånd till destinationen, kommuner kan föredra att sprida trafiken för att minska trafikstockningarna i stadskärnan och parkeringsbolag försöker maximera parkeringars utnyttjandegrad för att öka avkastningen på fastighetsinvesteringar. För att lösa detta parkeringsproblem användes en optimeringsalgoritm baserad på en beslutsprocess med flera kriterier. Den föreslagna SmartPark algoritmen använder en diskret Markov-kedjemodell för att prognosticera det framtida tillståndet för en parkeringsplats. Algoritmen innehåller tre modulära delar: • Först används en sökprocess för att identifiera de förväntade ankomsttiderna på alla parkeringsplatser i det önskade parkeringsområdet. Denna process använder data från smarta stolpar som mäter trafik och trängsel inom parkeringsområdet. • Sedan görs en prediktiv analys med hjälp av sammanställda historiska data över tidigare parkeringsanvändning för att skapa en matris som visar förändringen av tillgängliga platser på en parkeringsplats över kortare tidsperioder. • Till sist används matrisen tillsammans med data om säsongsvariationer för att prediktera ledigheten av en parkeringsplats vid tiden för den beräknade ankomsten

Towards automatic discovery and assessment of vulnerability severity in cyber-physical systems

Despite their wide proliferation, complex cyber–physical systems (CPSs) are subject to cybersecurity vulnerabilities and potential attacks. Vulnerability assessment for such complex systems are challenging, partly due to the discrepancy among mechanisms used to evaluate their cyber-security weakness levels. Several sources do report these weaknesses like the National Vulnerability Database (NVD), as well as manufacturer websites besides other security scanning advisories such as Cyber Emergency Response Team (CERT) and Shodan databases. However, these multiple sources are found to face inconsistency issues, especially in terms of vulnerability severity scores. We advocate an artificial intelligence based approach to streamline the computation of vulnerability severity magnitudes. This approach decreases the error rate induced by manual calculation processes, that are traditionally used in cybersecurity analysis. Popular repositories such as NVD and SecurityFocus are employed to validate the proposed approach, assisted with a query method to retrieve vulnerability instances. In doing so, we report discovered correlations among reported vulnerability scores to infer consistent magnitude values of vulnerability instances. The method is applied to a case study featuring a CPS application to illustrate the automation of the proposed vulnerability scoring mechanism, used to mitigate cybersecurity weaknesses.CC BY 4.0This research has been supported in part by EU ISF (Internal Security Fund) in the context of Project Grant #A431.678/2016.</p