Conceptualising Green Awareness as Moderator in Technology Acceptance Model for Green IS/IT

Green Information System/Technology adoption is one of the key solutions sought by organisations, policy makers and governments to promote sustainability and deal with environmental issues. Surprisingly, in the research discipline of management information systems measuring the intention of decision maker to adopt Green IS/IT is ignored while only a few studies address the issue of Green IS/IT adoption. But these studies are mostly done in organisational manner and consistently lack to conceptualise the role of Green Awareness or environmental literacy of the end user that may play the role of the facilitator to such adoption models and can significantly moderate the relationship of users' cognitive and behavioural intention factors in decision making process of adopting Green IS/IT. To fill this gap in the Green IS/IT literature, this paper conceptualise the role of Green Awareness as a facilitator by incorporating a subjective green awareness rating scale as a moderator in Technology Acceptance Model. This paper contributes to the existing knowledge in the science of information systems, mapping users' intention to adopt Green IS/IT and sustainability by conceptualising green awareness rating scale for users and a theoretical framework of incorporating the scale in Technology Acceptances model to map its role as a moderator

Towards an adaptive SOA-based QoS & Demand-Response Provisioning Architecture for the Smart Grid

Dynamic selection of services and by extension of service providers are vital in today’s liberalized market of energy. On the other hand it is equally important for Service Providers to spot the one QoS Module that offers the best QoS level in a given cost. Type of service, response time, throughput, availability and cost, consist a basic set of attributes that should be taken into consideration when building a concrete Grid network. In the proposed QoS architecture Prosumers request services based on the aforementioned set of attributes. The Prosumer requests the service through the QoS Module. It is then the QoS Module that seeks the Service Provider that best fits the needs of the client. The aforementioned approach is well supplemented with a data analytics/machine learning architecture to further enrich the provisioning aspect this work is bringing to the Smart Grid market of energy

Building an adaptive E-learning system

© 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Research in adaptive learning is mainly focused on improving learners' learning achievements based mainly on personalization information, such as learning style, cognitive style or learning achievement. In this paper, an innovative adaptive learning approach is proposed based upon two main sources of personalization information that is, learning behaviour and personal learning style. To determine the initial learning styles of the learner, an initial assigned test is employed in our approach. In order to more precisely reflect the learning behaviours of each learner, the interactions and learning results of each learner are thoroughly recorded and in depth analysed, based on advanced machine learning techniques, when adjusting the subject materials. Based on this rather innovative approach, an adaptive learning prototype system has been developed

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D

Towards a Security Enabled and SOA-based QoS (for the Smart Grid) Architecture

QoS and Security features are playing an important role in modern network architecures. Dynamic selection of services and by extension of service providers are vital in today’s liberalized market of energy. On the other hand it is equally important for Service Providers to spot the one QoS Module that offers the best QoS level in a given cost. Type of service, response time, availability and cost, consist a basic set of attributes that should be taken into consideration when building a concrete Grid network. In the proposed QoS architecture Prosumers request services based on the aforementioned set of attributes. The Prosumer requests the service through the QoS Module. It is then the QoS Module that seeks the Service Provider that best fits the needs of the client. The aforementioned approach is well supplemented with an in depth analysis on existing authentication and authorization protocols. The authors believe that QoS and security can work in parallel without adding extra burden in the Smart Grid infrastructure. This is feasible by building an in advance system for placing, scheduling, and assigning of the requests for energy consumption or production, thus decongesting the traffic in the whole network

Traffic Sign Recognition based on Synthesised Training Data

To deal with the richness in visual appearance variation found in real-world data, we propose to synthesise training data capturing these differences for traffic sign recognition. The use of synthetic training data, created from road traffic sign templates, allows overcoming the problems of existing traffic sing recognition databases, which are only subject to specific sets of road signs found explicitly in countries or regions. This approach is used for generating a database of synthesised images depicting traffic signs under different view-light conditions and rotations, in order to simulate the complexity of real-world scenarios. With our synthesised data and a robust end-to-end Convolutional Neural Network (CNN), we propose a data-driven, traffic sign recognition system that can achieve not only high recognition accuracy, but also high computational efficiency in both training and recognition processes

Authentication Layer for IEC 61113-3 Applications

Mid 2010, the Stuxnet ICS attack targeted the Siemens automation products, and after this attack the ICS security was thrust into spotlight, automation products suppliers started to re-examine their business approach to cyber security. The OPC Foundation made also significant changes and improvements on its new design OPC-UA to increase security of automation applications but, what is still missing and seems to be not resolved any time soon is having security in depth for industrial automation applications. In this paper, we propose a simple but strong security control solution, what we will call a logic application level security particularly for SCADA and DCS. This proposed method is based on message integrity and should not be viewed as the main, nor the only level of protection that an industrial automation system is expected to have, but can be a low-level security procedure that avoids intelligent attacks such as Stuxnet

Multiply and conquer: A replication framework for building fault tolerant industrial applications

© 2015 IEEE. TIEC 61499 defines an execution model for distributed industrial control applications, i.e. a single application distributed among several devices. In such an environment partial failures are likely to occur. In order to avoid probable system malfunctions and breakdowns due to partial failures, the authors have previously proposed a framework where the concept of replication may be applied to the IEC 61499 execution model. This paper focuses on describing an implementation of this replication framework on the FORTE IEC 61499 execution platform, along with the results of the first tests of the implementation. A set-up for the full validation of the approach is also described

Exploiting voting strategies in partially replicated IEC 61499 applications

© 2015 IEEE. In a modern industrial environment control programs are distributed among several devices. This raises new issues and challenges especially in failure modes. Building fault tolerant applications can be the solution in order a failure of one sub-component not to jeopardize the execution of the whole application. The authors have proposed a framework to support replicated IEC 61499 applications. In this paper we augment this framework with the support for different voting strategies, propose an extension of the replication communication protocol, and analyse the resulting fault-tolerance semantics. A limited implementation of the framework is also described

Positioning as Service for 5G IoT Networks

Big Data and Artificial Intelligence are new tech- nologies to improve indoor localization. It focuses on the use of machine learning probabilistic algorithms to extract, model and analyse live and historical signal data obtained from several sources. In this respect, the data generated by 5G network and the Internet of Things is quintessential for precise indoor positioning in complex building environments. In this paper, we present a new architecture for assets and personnel location management in 5G network with an emphasis on vertical sectors in smart cities. Moreover, we explain how Big Data and Machine learning can be used to offer positioning as service. Additionally, we implement a new deep learning model for 3D positioning using the proposed architecture. The performance of the proposed model is compared against other Machine Learning algorithms