Cross-domain interoperability using federated interoperable semantic IoT/Cloud testbeds and applications: The FIESTA-IoT approach

This work is funded by the European Commission under the EU-H2020 Project Grant ”Federated Interoperable Semantic IoT/cloudTestbeds andApplications (FIESTA)” with the Grant Agreement No. CNECT-ICT-643943

Big Data Mining and Semantic Technologies: Challenges and Opportunities

Big data a term coined due to the explosion in the quantity and diversity of high frequency digital data which is having a potential for valuable insights has drawn the most attention in the area of research and development. Converting big data to actionable insights requires depth understanding of big data, its characteristics, challenges and current technological trends. A rise of big data is changing the existing data storage, management, processing and analytical mechanisms and leads to the new architecture/ecosystems to handle big data applications. This paper covers finding of our research study about big data characteristic, various types of analysis associated with it and basic big data types. First, we are presenting the big data study from data mining and analysis perspective and discuss the challenges and next, we present the result of research study on meaningful use of big data in the context of semantic technologies. Moreover, we discuss various case studies related to social media analysis and recent development trends to identify potential research directions for big data with semantic technologies. DOI: 10.17762/ijritcc2321-8169.150711

'Inside the box': a cooperative game for co-creating energy efficient retail spaces

Although sustainability is one of the drivers of change in the retail sector, employees still treat energy management as a lower priority compared with other operational tasks. As digital technologies are flourishing, gamification is an emerging method of raising energy awareness, with most examples however targeting individuals, and therefore not supporting teamworking approaches to handling end user building energy demand. As such, combining behavioural incentivisation and technological development is a critical socio-technical challenge within the retail environments. The development of a new cooperative role-playing game that harnesses the participatory character of game theory to boost collegiality and encourage the energy-conscious behaviour of staff in a supermarket located in the UK, is described. By feeding the game with energy simulation results, this can be regarded as a novel synergy between behavioural science and game theory within the field of building energy. Future research will focus on testing the real-world potential of the game to engage retail staff in co-creating energy efficient stores

A Combined CNN and LSTM Model for Arabic Sentiment Analysis

Deep neural networks have shown good data modelling capabilities when dealing with challenging and large datasets from a wide range of application areas. Convolutional Neural Networks (CNNs) offer advantages in selecting good features and Long Short-Term Memory (LSTM) networks have proven good abilities of learning sequential data. Both approaches have been reported to provide improved results in areas such image processing, voice recognition, language translation and other Natural Language Processing (NLP) tasks. Sentiment classification for short text messages from Twitter is a challenging task, and the complexity increases for Arabic language sentiment classification tasks because Arabic is a rich language in morphology. In addition, the availability of accurate pre-processing tools for Arabic is another current limitation, along with limited research available in this area. In this paper, we investigate the benefits of integrating CNNs and LSTMs and report obtained improved accuracy for Arabic sentiment analysis on different datasets. Additionally, we seek to consider the morphological diversity of particular Arabic words by using different sentiment classification levels.Comment: Authors accepted version of submission for CD-MAKE 201

Exploiting Multimodal Biometrics in E-Privacy Scheme for Electronic Health Records

Existing approaches to protect the privacy of Electronic Health Records (EHR) are either insufficient for existing medical laws or they are too restrictive in their usage. For example, smartcard-based encryption systems require the patient to be always present to authorize access to medical records. A major issue in EHR is how patient’s privacy and confidentiality can be maintained because there are known scenarios where patients’ health data have been abused and misused by those seeking to gain selfish interest from it. Another issue in EHR is how to provide adequate treatment and have access to the necessary information especially in pre-hospital care settings. Questionnaires were administered by 50 medical practitioners to identify and categorize different EHR attributes. The system was implemented using multimodal biometrics (fingerprint and iris) of patients to access patient record in pre-hospital care. The software development tools employed were JAVA and MySQL database. The system provides applicable security when patients’ records are shared either with other practitioners, employers, organizations or research institutes. The result of the system evaluation shows that the average response time of 6seconds and 11.1 seconds for fingerprint and iris respectively after ten different simulations. The system protects privacy and confidentiality by limiting the amount of data exposed to users. The system also enables emergency medical technicians to gain easy and reliable access to necessary attributes of patients’ EHR while still maintaining the privacy and confidentiality of the data using the patient’s fingerprint and iris. Keywords: Electronic Health Record, Privacy, Biometric

Named data networking for efficient IoT-based disaster management in a smart campus

Disasters are uncertain occasions that can impose a drastic impact on human life and building infrastructures. Information and Communication Technology (ICT) plays a vital role in coping with such situations by enabling and integrating multiple technological resources to develop Disaster Management Systems (DMSs). In this context, a majority of the existing DMSs use networking architectures based upon the Internet Protocol (IP) focusing on location-dependent communications. However, IP-based communications face the limitations of inefficient bandwidth utilization, high processing, data security, and excessive memory intake. To address these issues, Named Data Networking (NDN) has emerged as a promising communication paradigm, which is based on the Information-Centric Networking (ICN) architecture. An NDN is among the self-organizing communication networks that reduces the complexity of networking systems in addition to provide content security. Given this, many NDN-based DMSs have been proposed. The problem with the existing NDN-based DMS is that they use a PULL-based mechanism that ultimately results in higher delay and more energy consumption. In order to cater for time-critical scenarios, emergence-driven network engineering communication and computation models are required. In this paper, a novel DMS is proposed, i.e., Named Data Networking Disaster Management (NDN-DM), where a producer forwards a fire alert message to neighbouring consumers. This makes the nodes converge according to the disaster situation in a more efficient and secure way. Furthermore, we consider a fire scenario in a university campus and mobile nodes in the campus collaborate with each other to manage the fire situation. The proposed framework has been mathematically modeled and formally proved using timed automata-based transition systems and a real-time model checker, respectively. Additionally, the evaluation of the proposed NDM-DM has been performed using NS2. The results prove that the proposed scheme has reduced the end-to-end delay up from 2% to 10% and minimized up to 20% energy consumption, as energy improved from 3% to 20% compared with a state-of-the-art NDN-based DMS

A Jamming Attacks Detection Approach Based on CNN based Quantum Leap Method for Wireless Sensor Network

The wireless sensor network is the most significant largest communication device. WSN has been interfacing with various wireless applications. Because the wireless application needs faster communication and less interruption, the main problem of jamming attacks on wireless networks is that jamming attack detection using various machine learning methods has been used. The reasons for jamming detection may be user behaviour-based and network traffic and energy consumption. The previous machine learning system could not present the jamming attack detection accuracy because the feature selection model of Chi-Squared didn’t perform well for jamming attack detections which determined takes a large dataset to be classified to find the high accuracy for jamming attack detection. To resolve this problem, propose a CNN-based quantum leap method that detects high accuracy for jamming attack detections the WSN-DS dataset collected by the Kaggle repository. Pre-processing using the Z-score Normalization technique will be applied, performing data deviations and assessments from the dataset, and collecting data and checking or evaluating data. Fisher’s Score is used to select the optimal feature of a jamming attack. Finally, the proposed CNN-based quantum leap is used to classify the jamming attacks. The CNN-based quantum leap simulation shows the output for jamming attacks with high precision, high detection, and low false alarm detection

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

Smartphones and NFC technology applied to interdisciplinary action towards a multidisciplinary vision

This work aims to offer a daily basis picture about student´s performance and evolution, besides a wide view based on formative assessments in different subjects that will offer the possibility to one teacher interacts with another using an asynchronous interdisciplinary methodology, a resource which can help in collaborative social actions of educators and managers, as proposed by Habermas43243