Future Internet, Volume 11, Issue 4 (April 2019) – 19 articles

Currently, the deployment of services for real-time delivery of an electrocardiogram to a remote site has a cost that prevents its widespread diffusion, which would contribute to saving lives with prevention, assistance and rescue efficiency. To fill this gap, we introduce the design of a remote electrocardiogram service that privileges open, low-cost options. The architecture is based on the HyperText Transfer Protocol (HTTP) and uses commercial off-the-shelf devices to implement the sensor on the patient’s side. The doctor uses a laptop browser to display the tracing, and a cloud computing instance connects the two using WebSockets. A prototype is built to evaluate overall performance, the power consumption of the patient’s side device, and the quality of rendering on doctor’s browser. The patient’s sensor prototype device is portable, and its power consumption is below 1 Watt, thus allowing a daylong autonomy when operated on batteries. Its cost is below 50$, and the required hardware is commercially available. The whole design is ready for on-field evaluation, and it is available in a public repository. Full article

In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features. Full article

The Internet of Things (IoT), being a “network of networks”, promises to allow billions of humans and machines to interact with each other. Owing to this rapid growth, the deployment of IoT-oriented networks based on mesh topologies is very attractive, thanks to their scalability and reliability (in the presence of failures). In this paper, we provide a comprehensive survey of the following relevant wireless technologies: IEEE 802.11, Bluetooth, IEEE 802.15.4-oriented, and Sub-GHz-based LoRa. Our goal is to highlight how various communication technologies may be suitable for mesh networking, either providing a native support or being adapted subsequently. Hence, we discuss how these wireless technologies, being either standard or proprietary, can adapt to IoT scenarios (e.g., smart cities and smart agriculture) in which the heterogeneity of the involved devices is a key feature. Finally, we provide reference use cases involving all the analyzed mesh-oriented technologies. Full article

The performance of mobile opportunistic networks mainly relies on collaboration among nodes. Thus far, researchers have ignored the influence of node sociality on the incentive process, leading to poor network performance. Considering the fact that followers always imitate the behavior of superstars, this paper proposes FollowMe, which integrates the social importance of nodes with evolutionary game theory to improve the collaborative behavior of nodes. First, we use the prisoner’s dilemma model to establish the matrix of game gains between nodes. Second, we introduce the signal reference as a game rule between nodes. The number of nodes choosing different strategies in a game round is used to calculate the cumulative income of the node in combination with the probability formula. Finally, the Fermi function is used to determine whether the node updates the strategy. The simulation results show that, compared with the random update rule, the proposed strategy is more capable of promoting cooperative behavior between nodes to improve the delivery rate of data packets. Full article

Aiming at the problems of poor time performance and accuracy in bus stops network optimization, this paper proposes an algorithm based on complex network and graph theory and Beidou Vehicle Location to measure the importance of bus stops. This method narrows the scope of points and edges to be optimized and is applied to the Jinan bus stop network. In this method, the bus driving efficiency, which can objectively reflect actual road conditions, is taken as the weight of the connecting edges in the network, and the network is optimized through the network efficiency. The experimental results show that, compared with the original network, the optimized network time performance is good and the optimized network bus driving efficiency is improved. Full article

Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit sentiment linguistic knowledge, which hinders the development of text sentiment analysis. In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which bridges the gap between traditional sentiment linguistic knowledge and current popular deep learning methods. Second, we develop an improved deep neural network to extract sequential correlation information and text local features by combining bidirectional gated recurrent units with a convolutional neural network, which further enhances the ability of comprehensive text representation learning. With this design, the SDNN model can generate a powerful semantic representation of text to improve the performance of text sentiment classification tasks. Extensive experiments were conducted to evaluate the effectiveness of the proposed SDNN model on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results demonstrated that the SDNN achieved substantially better performance than the strong competitors for text sentiment classification tasks. Full article

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions. Full article

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers. Full article

An increasing number of implementations of IoT for development use the LoRaWAN protocol as many of them leverage the free network and application servers provided by The Things Networks (TTN) to fulfill their needs. Unfortunately, in some countries in Sub-Saharan Africa and South Asia, Internet access cannot be taken for granted, therefore, TTN might not be available. Moreover, low-cost and low-power consumption options devices are the most sustainable ones. In this paper, we propose a LoRaWAN network with autonomous base stations that can work without Internet connectivity for essential services, while being able to provide additional features whenever Internet access becomes available, even in an intermittent fashion. Security and privacy are preserved, with support for mobile nodes. Full article

Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic. Full article

In recent years, gesture recognition has been used in many fields, such as games, robotics and sign language recognition. Human computer interaction (HCI) has been significantly improved by the development of gesture recognition, and now gesture recognition in video is an important research direction. Because each kind of neural network structure has its limitation, we proposed a neural network with alternate fusion of 3D CNN and ConvLSTM, which we called the Multiple extraction and Multiple prediction (MEMP) network. The main feature of the MEMP network is to extract and predict the temporal and spatial feature information of gesture video multiple times, which enables us to obtain a high accuracy rate. In the experimental part, three data sets (LSA64, SKIG and Chalearn 2016) are used to verify the performance of network. Our approach achieved high accuracy on those data sets. In the LSA64, the network achieved an identification rate of 99.063%. In SKIG, this network obtained the recognition rates of 97.01% and 99.02% in the RGB part and the rgb-depth part. In Chalearn 2016, the network achieved 74.57% and 78.85% recognition rates in RGB part and rgb-depth part respectively. Full article

This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state. Full article

The advancements in digital communication technology have made communication between humans more accessible and instant. However, personal and sensitive information may be available online through social networks and online services that lack the security measures to protect this information. Communication systems are vulnerable and can easily be penetrated by malicious users through social engineering attacks. These attacks aim at tricking individuals or enterprises into accomplishing actions that benefit attackers or providing them with sensitive data such as social security number, health records, and passwords. Social engineering is one of the biggest challenges facing network security because it exploits the natural human tendency to trust. This paper provides an in-depth survey about the social engineering attacks, their classifications, detection strategies, and prevention procedures. Full article

With the development of techniques, such as the Internet of Things (IoT) and edge computing, home energy management systems (HEMS) have been widely implemented to improve the electric energy efficiency of customers. In order to automatically optimize electric appliances’ operation schedules, this paper considers how to quantitatively evaluate a customer’s comfort satisfaction in energy-saving programs, and how to formulate the optimal energy-saving model based on this satisfaction evaluation. First, the paper categorizes the utility functions of current electric appliances into two types; time-sensitive utilities and temperature-sensitive utilities, which cover nearly all kinds of electric appliances in HEMS. Furthermore, considering the bounded rationality of customers, a novel concept called the energy-saving cost is defined by incorporating prospect theory in behavioral economics into general utility functions. The proposed energy-saving cost depicts the comfort loss risk for customers when their HEMS schedules the operation status of appliances, which is able to be set by residents as a coefficient in the automatic energy-saving program. An optimization model is formulated based on minimizing energy consumption. Because the energy-saving cost has already been evaluated in the context of the satisfaction of customers, the formulation of the optimization program is very simple and has high computational efficiency. The case study included in this paper is first performed on a general simulation system. Then, a case study is set up based on real field tests from a pilot project in Guangdong province, China, in which air-conditioners, lighting, and some other popular electric appliances were included. The total energy-saving rate reached 65.5% after the proposed energy-saving program was deployed in our project. The benchmark test shows our optimal strategy is able to considerably save electrical energy for residents while ensuring customers’ comfort satisfaction is maintained. Full article

The customer’s participation is important to the survival of a brand community. By drawing on flow theory, this research identified the most important factors that motivate the customers’ participation intention than others in a social network-based brand community. Data were collected from the Sina micro-blog. This study adopted two different but complementary methods to analyse the conceptual model: Structure equation model (SEM) and fuzzy set qualitative analysis (fsQCA). Results support most of the research hypothesis. Specifically, the findings obtained from the fsQCA indicate that information quality and platform-interactivity are necessary conditions that encourage the customers’ participation in a brand community. Full article

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud. Full article

5G is the latest generation of cellular mobile communications. Due to its significant advantage in high data rate, reduced latency and massive device connectivity, the 5G network plays a vital role in today’s commercial telecommunications networks. However, the 5G network also faces some challenges when used in practice. This is because it consists of various diverse ingredients, termed heterogeneity. The heterogeneity of the 5G network has two consequences: first, it prevents us to use this technology in a uniform way, preventing the wide use of 5G technology; second, it complicates the structure of the 5G network, making it hard to monitor what is going on in a 5G network. To break through this limitation, researchers have worked in this field and design their own protocol, in which software-defined networking (SDN) is one key design concept. By separating control and data plane, SDN can make the 5G network functional and programmable, such that we can handle the heterogeneity in traditional 5G networks. In light of this, we say that SDN-5G network is attractive, but its advantages are not free. The intelligence centralization used in SDN has its own drawbacks when it comes to security. To break through this limitation, we propose a robust security architecture for SDN-based 5G Networks. To find the illegal request from malicious attackers, we add extra cryptographic authentication, termed synchronize secret. The basic idea of our scheme is leveraging preload secrets to differ attacks from regular network communications. The simulation results indicate that our work can completely handle the security problem from SDN with a low disconnect rate of 0.01%, which is much better than that from state of the art. Full article

Gender role norms have been widely studied in the offline partner violence context. Different studies have indicated that internalizing these norms was associated with dating violence. However, very few research works have analyzed this relation in forms of aggression against partners and former partners using information and communication technologies (ICT). The objective of the present study was to examine the co-occurrence of cyber dating abuse by analyzing the extent to which victimization and perpetration overlap, and by analyzing the differences according to conformity to the masculine gender norms between men who are perpetrators or victims of cyber dating abuse. The participants were 614 male university students, and 26.5% of the sample reported having been a victim and perpetrator of cyber dating abuse. Nonetheless, the regression analyses did not reveal any statistically significant association between conformity to masculine gender norms and practicing either perpetration or victimization by cyber dating abuse. Full article

Presently, we are observing an explosion of data that need to be stored and processed over the Internet, and characterized by large volume, velocity and variety. For this reason, software developers have begun to look at NoSQL solutions for data storage. However, operations that are trivial in traditional Relational DataBase Management Systems (DBMSs) can become very complex in NoSQL DBMSs. This is the case of the join operation to establish a connection between two or more DB structures, whose construct is not explicitly available in many NoSQL databases. As a consequence, the data model has to be changed or a set of operations have to be performed to address particular queries on data. Thus, open questions are: how do NoSQL solutions work when they have to perform join operations on data that are not natively supported? What is the quality of NoSQL solutions in such cases? In this paper, we deal with such issues specifically considering one of the major NoSQL document oriented DB available on the market: MongoDB. In particular, we discuss an approach to perform join operations at application layer in MongoDB that allows us to preserve data models. We analyse performance of the proposes approach discussing the introduced overhead in comparison with SQL-like DBs. Full article