Table of Contents

Public key infrastructures (PKIs) are the cornerstone for the security of the communication layer of online services relying on certificate-based authentication, such as e-commerce, e-government, online banking, cloud services, and many others. A PKI is an infrastructure based on a hierarchical model, but the use of PKIs in non-hierarchical contexts has exposed them to many types of attacks. Here, we discuss weaknesses exploited in past attacks and we propose a solution based on an original consensus algorithm developed for use on blockchain technology. In this implementation we retain the full functionality around X.509 certificates, i.e., for the triad (server name, server address, X.509 server certificate), and demonstrate a mechanism for obtaining fast consensus. The main properties of the solution are that a consensus may be reached even when not all members of the involved PKI participate in a transaction, and that no advanced trust agreement among PKIs is needed. The proposed solution is able to detect PKI attacks and can distinguish errors from attacks, allowing precise management of anomalies. Full article

Event prediction plays an important role in financial risk assessment and disaster warning, which can help government decision-making and economic investment. Previous works are mainly based on time series for event prediction such as statistical language model and recurrent neural network, while ignoring the impact of prior knowledge on event prediction. This makes the direction of event prediction often biased or wrong. In this paper, we propose a hierarchical event prediction model based on time series and prior knowledge. To ensure the accuracy of the event prediction, the model obtains the time-based event information and prior knowledge of events by Gated Recurrent Unit and Associated Link Network respectively. The semantic selective attention mechanism is used to fuse the time-based event information and prior knowledge, and finally generate predicted events. Experimental results on Chinese News datasets demonstrate that our model significantly outperforms the state-of-the-art methods, and increases the accuracy by 2.8%. Full article

This work constitutes a narrative review of the state of knowledge and advances in the intervention and treatment of addictions through the use of information and communication technologies, considering the growing demand for virtuality-mediated strategies that facilitate the approach of problems of public health such as addictions, which increase considerably year after year. To this end, the reader will be provided with a current overview of the drug use trend; subsequently, a conceptualization of the concept of addiction and its understanding from a neurobiological perspective and, finally, the progress in terms of intervention processes and therapeutic approach will be presented; which will imply an approach to the concept of e-health and rehabilitation mediated by information and communication technologies (ICT). Full article

Taking advantage of new developing technologies, power systems are being developed into smarter grids with the vision of becoming the next-generation electric grid for smart cities. Some of the emerging issues and challenges associated with the development of technologies for smarter grids and smart cities are highlighted in this special issue of the Future Internet journal. Full article

Reinforcement learning algorithms usually require a large number of empirical samples and give rise to a slow convergence in practical applications. One solution is to introduce transfer learning: Knowledge from well-learned source tasks can be reused to reduce sample request and accelerate the learning of target tasks. However, if an unmatched source task is selected, it will slow down or even disrupt the learning procedure. Therefore, it is very important for knowledge transfer to select appropriate source tasks that have a high degree of matching with target tasks. In this paper, a novel task matching algorithm is proposed to derive the latent structures of value functions of tasks, and align the structures for similarity estimation. Through the latent structure matching, the highly-matched source tasks are selected effectively, from which knowledge is then transferred to give action advice, and improve exploration strategies of the target tasks. Experiments are conducted on the simulated navigation environment and the mountain car environment. The results illustrate the significant performance gain of the improved exploration strategy, compared with traditional $ϵ$ -greedy exploration strategy. A theoretical proof is also given to verify the improvement of the exploration strategy based on latent structure matching. Full article

Information-Centric Networking (ICN) is novel paradigm considered for the future Internet, as well as for emerging architectures, such as the Internet of Things. Despite the significant research efforts that take place all around the world there are still many challenges and open issues related to the applicability of ICN. The papers of this special issue, six in total, identify such challenges, and propose solutions, design guidelines, and new research directions. Full article

Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control. Full article

Alternative healthcare solutions have been identified as a viable approach to ameliorate the increasing demand for telehealth and prompt healthcare delivery. Moreover, indoor localisation using different technologies and approaches have greatly contributed to alternative healthcare solutions. In this paper, a cost-effective, radio frequency identification (RFID)-based indoor location system that employs received signal strength (RSS) information of passive RFID tags is presented. The proposed system uses RFID tags placed at different positions on the target body. The mapping of the analysed data against a set of reference position datasets is used to accurately track the vertical and horizontal positioning of a patient within a confined space in real-time. The Euclidean distance model achieves an accuracy of 98% for all sampled activities. However, the accuracy of the activity recognition algorithm performs below the threshold performance for walking and standing, which is due to similarities in the target height, weight and body density for both activities. The obtained results from the proposed system indicate significant potentials to provide reliable health measurement tool for patients at risk. Full article

In the Internet of Things (IoT), the scope of wireless sensor nodes is extended to things deployed in a pervasive world. For various IoT service applications, things can gather and share their information with each other through self-decision-making. Therefore, we cannot apply the existing information aggregation methods of wireless sensor networks to the IoT environment, which aim to transmit the collected data to only a sink node or a central server. Moreover, since the existing methods involve all the sensor nodes in the process of data exchange, they can cause an increase in the network traffic, delay of data transmission, and amount of energy consumed by things. In this paper, we propose a clustering-property-based data exchange method for efficient energy consumption in IoT networks. First, the proposed method assigns properties to each thing according to the characteristics of the obtained data. Second, it constructs a cluster network considering the location of things and their energy consumption. Finally, the things in a cluster communicate with other things in a different cluster based on their properties. In the experiment, the proposed method exhibits a better performance than the existing method. Owing to the energy-saving effect, we demonstrate that the proposed method results in a more reliable network and improves the longevity of IoT networks. Full article

A troll is usually defined as somebody who provokes and offends people to make them angry, who wants to dominate any discussion or who tries to manipulate people's opinions. The problems caused by such persons have increased with the diffusion of social media. Therefore, on the one hand, press bodies and magazines have begun to address the issue and to write articles about the phenomenon and its related problems while, on the other hand, universities and research centres have begun to study the features characterizing trolls and to look for solutions for their identification. This survey aims at introducing the main researches dedicated to the description of trolls and to the study and experimentation of methods for their detection. Full article

With the improvement of technologies, people's demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort. Full article

The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information. Full article

Internet of Things (IoT) is becoming a widespread reality, as interconnected smart devices and sensors have overtaken the IT market and invaded every aspect of the human life. This kind of development, while already foreseen by IT experts, implies additional stress to already congested networks, and may require further investments in computational power when considering centralized and Cloud based solutions. That is why a common trend is to rely on local resources, provided by smart devices themselves or by aggregators, to deal with part of the required computations: this is the base concept behind Fog Computing, which is becoming increasingly adopted as a distributed calculation solution. In this paper a methodology, initially developed within the TOREADOR European project for the distribution of Big Data computations over Cloud platforms, will be described and applied to an algorithm for the prediction of energy consumption on the basis of data coming from home sensors, already employed within the CoSSMic European Project. The objective is to demonstrate that, by applying such a methodology, it is possible to improve the calculation performances and reduce communication with centralized resources. Full article

With the prosperity of the Internet of Things (IoT) industry environment, the variety and quantity of IoT devices have grown rapidly. IoT devices have been widely used in smart homes, smart wear, smart manufacturing, smart cars, smart medical care, and many other life-related fields. With it, security vulnerabilities of IoT devices are emerging endlessly. The proliferation of security vulnerabilities will bring severe risks to users’ privacy and property. This paper first describes the research background, including IoT architecture, device components, and attack surfaces. We review state-of-the-art research on IoT device vulnerability discovery, detection, mitigation, and other related works. Then, we point out the current challenges and opportunities by evaluation. Finally, we forecast and discuss the research directions on vulnerability analysis techniques of IoT devices. Full article

The smart energy system, viewed as an “Energy Internet”, consists of the intelligent integration of decentralized sustainable energy sources, efficient distribution, and optimized power consumption. That implies the fault diagnosis for a smart energy system should be of low complexity. In this paper, we propose a Strong Tracking Unscented Kalman Filter ( $STUKF$ ) and modified Bayes’ classification-based Modified Three Sigma test ( $MTS$ ), abbreviated as $SFBT$ , for smart energy networks. The theoretical analysis and simulations indicate that $SFBT$ detects faults with a high accuracy and a low complexity of $O\left(n\right)$ . Full article

The main goal of this article is to investigate techniques that can quickly lead to successful social systems by boosting network connectivity. This is especially useful when starting new online communities where the aim is to increase the system utilization as much as possible. This aspect is very important nowadays, given the existence of many online social networks available on the web, and the relatively high level of competition. In other words, attracting users’ attention is becoming a major concern, and time is an essential factor when investing money and resources into online social systems. Our study describes an effective technique that deals with this issue by introducing the notion of mermaids, special attractors that alter the normal evolutive behavior of a social system. We analyze how mermaids can boost social networks, and then provide estimations of fundamental parameters that business strategists can take into account in order to obtain successful systems within a constrained budget. Full article

Does social media addiction impair the well-being of non-clinical individuals? Despite the Internet being able to be considered as a promoting factor for individual empowerment, previous literature suggests that the current massive availability of Information and Communication Technologies (ICT) may be dangerous for users' well-being. This article discusses the relationship between the most used social media addiction measures (i.e., the Bergen Facebook Addiction Scale—BFAS, the Bergen Social Media Addiction Scale—BSMAS) and well-being. A systematic review considering all the publications indexed by PsycInfo, PsycArticles, PubMed, Science Direct, Sociological Abstracts, Academic Search Complete, and Google Scholar databases was performed to collect the data. Ten of 635 studies were included in the qualitative synthesis. Overall, most of the included works captured a negative but small relationship between BFAS/BSMAS and well-being, across multiple definitions and measurement. Full article

The emergence of BPML (Business Process Modeling Language) has favored the development of languages for the composition of services. Process-oriented approaches produce imperative languages, which are rigid to change at run-time because they focus on how the processes should be built. Despite the fact that semantics is introduced in languages to increase their flexibility, dynamism is limited to find services that have disappeared or become defective. They do not offer the possibility to adapt the composite service to execution. Although rules-based languages were introduced, they remain very much dependent on the BPML which is the underlying technology. This article proposes the specification of a rule-based declarative language for the composition of services. It consists of the syntactic categories which make up the concepts of the language and a formal description of the operational semantics that highlights the dynamism, the flexibility and the adaptability of the language thus defined. This paper also presents a verification framework made of a formal aspect and a toolset. The verification framework translates service specifications into Promela for model checking. Then, a validation framework is proposed that translates the verified specifications to the operational system. Finally, a case study is presented. Full article

Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilities provided by the current generation of autonomous devices. In this sense, this paper presents and discusses state-of-the-art transportation technologies and systems, highlighting the advances that the concepts of Internet of Things and Value are providing. Decentralized technologies, such as blockchain, are been extensively investigated by the industry, however, its widespread adoption in cities is still desirable. Aligned with operations research opportunities, this paper identifies different points in which cities’ services could move to. This also study comments about different combinatorial optimization problems that might be useful and important for an efficient evolution of our cities. By considering different perspectives, didactic examples are presented with a main focus on motivating decision makers to balance citizens, investors and industry goals and wishes. Full article

We propose an adaptive joint pre- and post- compensation to correct the filtering effects caused by cascading reconfigurable optical add drop multiplexers (ROADMs). The improvement is achieved without using additional hardware (HW) on the link or within the signal processor in the transponders. Using Monte Carlo simulations, the gradient-descent based method shows an improvement of 0.6 dB and 1.1 dB in the required optical signal-to-noise ratio (R-OSNR) at the threshold pre-decoder bit error rate (BER) of 0.02 versus pre-compensation only in the linear and nonlinear operating region of fiber respectively. We experimentally verified the method with lab measurements in the presence of heavy filtering and optical impairments. We observed a gain up to ~0.4 dB compared to typically used pre-compensation only. Additionally, other tangible system benefits of our method are listed and discussed. Full article

Network tomography has emerged as one of the lean approaches for efficient network monitoring, especially aiming at addressing the ever-increasing requirements for scaling and efficiency in modern network architectures and infrastructures. In this paper, we explore network coding and compressed sensing as enabling technologies in the context of network tomography. Both approaches capitalize on algebraic tools for achieving accuracy while allowing scaling of operation as the size of the monitored network increases. Initially, a brief overview of the tomographic problems and the related classification of methods is provided to better comprehend the problems encountered and solutions provided to date. Subsequently, we present representative approaches that employ either one of the aforementioned technologies and we comparatively describe their fundamental operation. Eventually, we provide a qualitative comparison of features and approaches that can be used for further research and technology development for network monitoring in future Internet infrastructures. Full article

The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and processing to communicate with ubiquitous objects and services. Often this paradigm (IoT) using Wireless Sensor Networks must cover large area of interest (AoI) with huge number of devices. As these devices might be battery powered and randomly deployed, their long-term availability and connectivity for area coverage is very important, in particular in harsh environments. Moreover, a poor distribution of devices may lead to coverage holes and degradation to the quality of service. In this paper, we propose an approach for self-organization and coverage maximization. We present a distributed algorithm for “Maintaining Connectivity and Coverage Maximization” called $MCCM$ . The algorithm operates on different movable devices in homogeneous and heterogeneous distribution. It does not require high computational complexity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices to increase the coverage while maintaining connectivity. Simulation results show that the proposed algorithm can achieve higher coverage and lower nodes’ movement over existing algorithms in the state of the art. Full article