Future Internet, Volume 11, Issue 12 (December 2019) – 18 articles

After a lengthy debate within the scientific community about what constitutes the problem solving approach of computational thinking (CT), the focus shifted to enable the integration of CT within compulsory education. This publication strives to focus the discussion and enable future research in an educational setting with a strong focus on the Austrian circumstances and the goal to allow wide international adoption later on. Methodically, a literature review was conducted to gain knowledge about the current strands of research and a meta study to show the diversity of proposed and materialized studies. Three main questions were answered, establishing that CT as an idea is rooted in scientific literature dating back to the 1980s and grew in popularity after Wing introduced the concept to a broader audience. A number of authors contributed to the current state of the field, with the most cited review coming from Grover and Pea. The challenge to integrate CT in curricula around the world was met by many experiments and case studies but without a conclusive framework as of yet. Ultimately, this paper determines that expert integration is a blank spot in the literature and aims to create a strong, inclusive path to CT education by inviting practitioners. Full article

Edge computing is a distributed computing paradigm that encompasses data computing and storage and is performed close to the user, efficiently guaranteeing faster response time. This paradigm plays a pivotal role in the world of the Internet of Things (IoT). Moreover, the concept of the distributed edge cloud raises several interesting open issues, e.g., failure recovery and security. In this paper, we propose a system composed of edge nodes and multiple cloud instances, as well as a voting mechanism. The multi-cloud environment aims to perform centralized computations, and edge nodes behave as a middle layer between edge devices and the cloud. Moreover, we present a voting mechanism that leverages the edge network to validate the performed computation that occurred in the centralized environment. Full article

The journalism profession has changed dramatically in the digital age as the internet, and new technologies, in general, have created new working conditions in the media environment. Concurrently, journalists and media professionals need to be aware and possess a new set of skills connected to web technologies, as well as respond to new reading tendencies and information consumption habits. A number of studies have shown that search engines are an important source of the traffic to news websites around the world, identifying the significance of high rankings in search results. Journalists are writing to be read, and that means ensuring that their news content is found, also, by search engines. In this context, this paper represents an exploratory study on the use of search engine optimization (SEO) in news websites. A series of semi-structured, in-depth interviews with professionals at four Greek media organizations uncover trends and address issues, such as how SEO policy is operationalized and applied inside newsrooms, which are the most common optimization practices, as well as the impact on journalism and news content. Today, news publishers have embraced the use of SEO practices, something that is clear also from this study. However, the absence of a distinct SEO culture was evident in newsrooms under study. Finally, according to results, SEO strategy seems to depend on factors, such as ownership and market orientation, editorial priorities or organizational structures. Full article

Although current languages used in ontology-based data access (OBDA) systems allow for mapping source data to instances of concepts and relations in the ontology, several application domains need more flexible tools for inferring knowledge from data, which are able to dynamically acquire axioms about new concepts and relations directly from the data. In this paper we introduce the notion of mapping-based knowledge base (MKB) to formalize the situation where both the extensional and the intensional level of the ontology are determined by suitable mappings to a set of data sources. This allows for making the intensional level of the ontology as dynamic as the extensional level traditionally is. To do so, we resort to the meta-modeling capabilities of higher-order description logics, in particular the description logic $\mathit{Hi}({\mathit{DL-Lite}}_{\mathcal{R}})$ , which allows seeing concepts and relations as individuals, and vice versa. The challenge in this setting is to design efficient algorithms for answering queries posed to MKBs. Besides the definition of MKBs, our main contribution is to prove that answering instance queries posed to MKBs expressed in $\mathit{Hi}({\mathit{DL-Lite}}_{\mathcal{R}})$ can be done efficiently. Full article

The internet of things (IoT) aims to extend the internet to real-world objects, connecting smart and sensing devices into a global network infrastructure by connecting physical and virtual objects. The IoT has the potential to increase the quality of life of inhabitants and users of intelligent ambient assisted living (AAL) environments. The paper overviews and discusses the IoT technologies and their foreseen impacts and challenges for the AAL domain. The results of this review are summarized as the IoT based gerontechnology acceptance model for the assisted living domain. The model focuses on the acceptance of new technologies by older people and underscores the need for the adoption of the IoT for the AAL domain. Full article

Blockchain is a new technology, often referred to as the Internet of Value. As with all new technologies, there is no consensus on its potential value, with some people claiming that it will bring more disruptive changes than the Internet and others contesting the extent of its importance. Despite predictions that the future is perilous, there is evidence that blockchain is a remarkable, new technology that will change the way transactions are made, based on its ability to guarantee trust among unknown actors, assure the immutability of records, while also making intermediaries obsolete. The importance of blockchain can be confirmed by the interest in digital currencies, the great number of published blockchain papers, as well as MDPI’s journal Future Internet which exclusively publishes blockchain articles, including this special issue covering present and future blockchain challenges. This paper is a survey of the fast growing field of blockchain, discussing its advantages and possible drawbacks and their implications for the future of the Internet and our personal lives and societies in general. The paper consists of the following parts; the first provides a general introduction and discusses the disruptive changes initiated by blockchain, the second discusses the unique value of blockchain and its general characteristics, the third presents an overview of industries with the greatest potential for disruptive changes, the forth describes the four major blockchain applications with the highest prospective advantages, and the fifth part of the paper ends with a discussion on the most notable subset of innovative blockchain applications—Smart Contracts, DAOs (Decentralized Autonomous Organizations) and super safe networks—and their future implications. There is also a concluding section, which summarizes the paper, describes the future of blockchain, and mentions the challenges to be overcome. Full article

The rapid growth of not just mobile devices but also Internet of Things (IoT) devices has introduced a new paradigm in mobile networks. This evolution and the continuous need to provide spectrum efficient, high data rates, low latency, and low energy consumption radio access networks have led to the emergence of fifth generation (5G) networks. Due to technical and economical limitations, the satellite air interface is expected to complement the 5G terrestrial air interface in the provision of 5G services including IoT communications. More importantly, it is on this premise that the 5G satellite air interface is expected to provide network access to IoT devices in rural and remote areas termed Internet of Remote Things (IoRT). While this remains an interesting solution, several radio resource management issues exist. One of them, spectrum management, in the 5G satellite as it affects IoRT communications, remains unclear. Hence, the aim of this paper is to investigate and recommend the spectrum management scheme that will be most suitable not only for Human-to-Human communications but also Machine-to-Machine communications in 5G satellite networks. In order to conduct this investigation, a new dynamic scheduling scheme that will be suitable for such a scenario is proposed in this paper. The investigation is conducted through simulations, using throughput, delay, spectral efficiency, and fairness index as the performance metrics. Full article

Pre-emphasis filters are used to pre-compensate for the transmitter frequency response of coherent systems to mitigate receiver noise enhancement. This is particularly essential for low-cost, low-power coherent transceivers due to having an extremely bandlimited transmitter. However, the pre-emphasis filter also increases the signal peak-to-average power ratio (PAPR), thus posing a higher effective number of bits (ENoB) requirement for the arbitrary waveform generator (AWG). In this paper, we first numerically study the PAPR impact of partial pre-emphasis filters. We show that with partial pre-emphasis, an ENoB reduction from 5 to 4.5 bits is attainable at the same signal-to-noise ratio (SNR) out of the AWG. Next, we experimentally investigate the overall performance penalty of partial pre-emphasis in a 50 Gbaud 16QAM coherent system. A manageable Q factor penalty of around 0.5 dB is found for both single-polarization and dual-polarization systems with a 0.8 dB PAPR reduction. Full article

Medical text categorization is a specific area of text categorization. Classification for medical texts is considered a special case of text classification. Medical text includes medical records and medical literature, both of which are important clinical information resources. However, medical text contains complex medical vocabularies, medical measures, which has problems with high-dimensionality and data sparsity, so text classification in the medical domain is more challenging than those in other general domains. In order to solve these problems, this paper proposes a unified neural network method. In the sentence representation, the convolutional layer extracts features from the sentence and a bidirectional gated recurrent unit (BIGRU) is used to access both the preceding and succeeding sentence features. An attention mechanism is employed to obtain the sentence representation with the important word weights. In the document representation, the method uses the BIGRU to encode the sentences, which is obtained in sentence representation and then decode it through the attention mechanism to get the document representation with important sentence weights. Finally, a category of medical text is obtained through a classifier. Experimental verifications are conducted on four medical text datasets, including two medical record datasets and two medical literature datasets. The results clearly show that our method is effective. Full article

The text data of the social network platforms take the form of short texts, and the massive text data have high-dimensional and sparse characteristics, which does not make the traditional clustering algorithm perform well. In this paper, a new community detection method based on the sparse subspace clustering (SSC) algorithm is proposed to deal with the problem of sparsity and the high-dimensional characteristic of short texts in online social networks. The main ideal is as follows. First, the structured data including users’ attributions and user behavior and unstructured data such as user reviews are used to construct the vector space for the network. And the similarity of the feature words is calculated by the location relation of the feature words in the synonym word forest. Then, the dimensions of data are deduced based on the principal component analysis in order to improve the clustering accuracy. Further, a new community detection method of social network members based on the SSC is proposed. Finally, experiments on several data sets are performed and compared with the K-means clustering algorithm. Experimental results show that proper dimension reduction for high dimensional data can improve the clustering accuracy and efficiency of the SSC approach. The proposed method can achieve suitable community partition effect on online social network data sets. Full article

Peer-to-peer (P2P) systems have offered users an efficient way to share various resources and access diverse services over the Internet. In unstructured P2P systems, resource storage and indexation are fully distributed among participating peers. Therefore, locating peers sharing pertinent resources for a specific user query is a challenging issue. In fact, effective query routing requires smart decisions to select a certain number of peers with respect to their relevance for the query instead of choosing them at random. In this respect, we introduce here a new query-oriented approach, called the reinforcement learning-based query routing approach (RLQR). The main goal of RLQR is to reach high retrieval effectiveness as well as a lower search cost by reducing the number of exchanged messages and contacted peers. To achieve this, the RLQR relies on information gathered from previously sent queries to identify relevant peers for forthcoming queries. Indeed, we formulate the query routing issue as the reinforcement learning problem and introduce a fully distributed approach for addressing it. In addition, RLQR addresses the well-known cold-start issue during the training stage, which allows it to improve its retrieval effectiveness and search cost continuously, and, therefore, goes quickly through the cold-start phase. Performed simulations demonstrate that RLQR outperforms pioneering query routing approaches in terms of retrieval effectiveness and communications cost. Full article

Static multi-controller deployment architecture cannot adapt to the drastic changes of network traffic, which will lead to a load imbalance between controllers, resulting in a high packet loss rate, high latency, and other network performance degradation problems. In this paper, an efficient dynamic load balancing scheme based on Nash bargaining is proposed for a distributed software-defined network. Firstly, considering the connectivity of network nodes, the switch migration problem is transformed into a network mapping relationship reconstruction problem. Then, we establish the Nash bargaining game model to fairly optimize the two contradictory goals of migration cost and load balance. Finally, the model is solved by an improved firefly algorithm, and the optimal network mapping state is obtained. The experimental results show that this scheme can optimize the migration cost and load balance at the same time. Compared with the existing research schemes, the migration process of the switch is optimized, and, while effectively balancing the load of the control plane, the migration cost is reduced by 14.5%. Full article

With the rapid growth of Internet of Things (IoT) devices around the world, thousands of mobile users share many data with each other daily. IoT communication has been developed in the past few years to ensure direct connection among mobile users. However, wireless vulnerabilities exist that cause security concerns for IoT device-to-device (D2D) communication. This has become a serious debate, especially in smart environments where highly sensitive information is exchanged. In this paper, we study the security requirements in IoT D2D communication. In addition, we propose a novel authentication approach called Secure Key Exchange with QR Code (SeKeQ) to verify user identity by ensuring an automatic key comparison and providing a shared secret key using Diffie-Hellman key agreement with an SHA-256 hash. To evaluate the performance of SeKeQ, we ran a testbed using devices with a WiFi-Direct communication interface. The obtained results depict that our proposal can offer the required security functions including key exchange, data confidentiality, and integrity. In addition, our proposal can reach the same security performances as MANA (Manual Authentication) and UMAC (Universal-Hashing Message Authentication Code) but with 10 times fewer key computations and reduced memory occupancy. Full article

As the theory of complex networks is further studied, the scale of nodes in the network is increasing, which makes it difficult to find useful patterns from only the analysis of nodes. Therefore, this paper proposes a complex network node layout method based on community compression, which can effectively display the mesoscale structure characteristics of the network, making it more convenient for users to analyze the status and function of a single node or a class of nodes in the whole complex network. To begin with, the whole network is divided into communities with different granularity by the Louvain algorithm. Secondly, the method of nodes importance analysis based on topological potential theory is extended from the network to the community structure, and the internal nodes of the community are classified into three types, namely important nodes, relatively important nodes, and fringe nodes. Furthermore, a compression algorithm for the community structure is designed to realize the compression of the network by retaining important nodes and merging fringe nodes. Finally, the compression network is laid out by the traditional force-directed layout method. Experimental results show that, compared with the compression layout methods of a complex network based on degree or PageRank, the method in this paper can retain the integrated community composition and its internal structure, which is convenient for users to effectively analyze the topology structure of a complex network. Full article

The avalanche of (both user- and device-generated) multimedia data published in online social networks poses serious challenges to researchers seeking to analyze such data for many different tasks, like recommendation, event recognition, and so on. For some such tasks, the classical “batch” approach of big data analysis is not suitable, due to constraints of real-time or near-real-time processing. This led to the rise of stream processing big data platforms, like Storm and Flink, that are able to process data with a very low latency. However, this complicates the task of data analysis since any implementation has to deal with the technicalities of such platforms, like distributed processing, synchronization, node faults, etc. In this paper, we show how the RAM ${}^3$ S framework could be profitably used to easily implement a variety of applications (such as clothing recommendations, job suggestions, and alert generation for dangerous events), being independent of the particular stream processing big data platforms used. Indeed, by using RAM ${}^3$ S, researchers can concentrate on the development of their data analysis application, completely ignoring the details of the underlying platform. Full article

On-demand broadcast is a scalable approach to disseminating information to a large population of clients while satisfying dynamic needs of clients, such as in vehicular networks. However, in conventional broadcast approaches, only one data item can be retrieved by clients in one broadcast tick. To further improve the efficiency of wireless bandwidth, in this work, we conduct a comprehensive study on incorporating network coding with representative on-demand scheduling algorithms while preserving their original scheduling criteria. In particular, a graph model is derived to maximize the coding benefit based on the clients’ requested and cached data items. Furthermore, we propose a heuristic coding-based approach, which is applicable for all the on-demand scheduling algorithms with low computational complexity. In addition, based on various application requirements, we classify the existing on-demand scheduling algorithms into three groups—real-time, non-real-time and stretch optimal. In view of different application-specific objectives, we implement the coding versions of representative algorithms in each group. Extensive simulation results conclusively demonstrate the superiority of coding versions of algorithms against their non-coding versions on achieving their respective scheduling objectives. Full article

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation. Full article

Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it is important for a learning model to consider the nature of the dataset, its unbalancing properties and the correlation between different attributes. In this paper, we evaluated the importance of these properties for a specific dataset and proposed a novel methodology for learning the effort aware just-in-time prediction of defect inducing changes. Moreover, we devised an ensemble classifier, which fuses the output of three individual classifiers (Random forest, XGBoost, Multi-layer perceptron) to build an efficient state-of-the-art prediction model. The experimental analysis of the proposed methodology showed significant performance with 77% accuracy on the sample dataset and 81% accuracy on different datasets. Furthermore, we proposed a highly competent reinforcement learning technique to avoid false alarms in real time predictions. Full article