In recent years, the ubiquitous nature of Internet-of-Things (IoT) applications as well as the pervasive character of next-generation communication protocols, such as the 5G technology, have become widely evident. In this work, we identify the need for low-cost security in current and next-generation IoT networks and address this demand through the implementation, testing, and validation of an intrinsic

The improvement of coverage quality in the construction of multiple-barrier coverage is a critical problem in a wireless sensor network. In this article, we investigate the K-barrier coverage construction problem in passive bistatic radar sensor networks. In contrast to traditional bistatic radar networks, the transmitters in a passive bistatic radar network are predeployed and noncooperative. To construct

Internet of Things (IoT) has emerged as a huge paradigm shift by connecting a versatile and massive collection of smart objects to the Internet, coming to play an important role in our daily lives. Data produced by IoT devices can generate a number of computational tasks that cannot be executed locally on the IoT devices. The most common solution is offloading these tasks to external devices with higher

Introduced as an extension of the Cloud at the network edge for computing and storage purposes, the Fog is increasingly considered a key enabler for Internet-of-Things applications whose latency requirements are not compatible with a Cloud-only approach. Unlike Cloud platforms, which can elastically accommodate large numbers of requests, Fog deployments are usually dimensioned for an average traffic

The Mobile IoT domain has been significantly expanded with the proliferation of drones and unmanned robotic devices. In this new landscape, the communication between the resource-constrained device and the fixed infrastructure is similarly expanded to include new messages of varying importance, control, and monitoring. To efficiently and effectively control the exchange of such messages subject to

Industrial networks typically connect hundreds or thousands of sensors and actuators in industrial facilities, such as manufacturing plants, steel mills, and oil refineries. Although the typical industrial Internet of Things (IoT) applications operate at low data rates, they pose unique challenges because of their critical demands for reliable and real-time communication in harsh industrial environments

In recent years, a new trend of advanced applications with huge demands in terms of Quality of Service (QoS) is gaining ground. Even though Cloud computing provides mature management facilities with ubiquitous capabilities, novel requirements and workloads, foisted by new services, start to expose its weaknesses. In this context, a new Information and Communication Technologies (ICT) trend aims at

In the industrial Internet of Things domain, applications are moving from the Cloud into the Edge, closer to the devices producing and consuming data. This means that applications move from the scalable and homogeneous Cloud environment into a potentially constrained heterogeneous Edge network. Making Edge applications reliable enough to fulfill Industry 4.0 use cases remains an open research challenge

Wireless sensor-actuator network (WSAN) technology is gaining rapid adoption by industrial Internet of Things applications in recent years. A WSAN typically connects sensors, actuators, and controllers in industrial facilities, such as steel mills, oil refineries, chemical plants, and infrastructures implementing complex monitoring and control processes. IEEE 802.15.4–based WSANs operate at low power

In this work, we describe an urban Internet of Things (IoT) architecture, grounded in big data patterns and focused on the needs of cities and their key stakeholders. First, the architecture of the dedicated platform USE4IoT (Urban Service Environment for the Internet of Things), which gathers and processes urban big data and extends the Lambda architecture, is proposed. We describe how the platform

The revolutionary development of the Internet of Things has triggered a huge demand for Internet of Things devices. They are extensively applied to various fields of social activities, and concerning manufacturing, they are a key enabling concept for the Industry 4.0 ecosystem. Industrial Internet of Things (IIoT) devices share common vulnerabilities with standard IoT devices, which are increasingly

Multi-task learning has been widely applied to Alzheimer's Disease study due to its capability to simultaneously rating the disease severity (classification) and predicting corresponding clinical scores (regression). In this paper, we propose a novel technique of Adaptive Multi-task Dual-Structured Learning, named AMDSL, by mutually exploring the dual manifold structure for label and regression score

In the last decade, twin support vector machines (TWSVMs) classifiers have been achieved considerable emphasis on pattern classification tasks. However, the TWSVM formulation still suffers from the following two shortcomings: (i) TWSVM deals with the inverse matrix calculation in the Wolfe-dual problems which is intractable for large scale datasets with numerous features and samples. (ii) TWSVM minimizes

Blockchain has attracted an increasing amount of research, with lots of refreshing implementations in different fields. Cryptocurrency, as its most famous implementation, suffers economic loss due to phishing scams. In our work, accounts and transactions are treated as nodes and edges, thus detection of phishing accounts can be modelled as a node classification problem. To tackle the problem, we proprose

Large networks of IoT devices, each consisting of one or more sensors, are being increasingly deployed for comprehensive real-time monitoring of cyber-physical systems. Such networks form an essential component of the emerging edge computing paradigm and are expected to increase in complexity and size. The physical phenomenon sensed by different sensors (within the same or different IoT devices in

Internet-enabled devices operating in the physical world are increasingly integrated in modern distributed systems. We focus on systems where the dynamics of spatial distribution is crucial; in such cases, devices may need to carry out complex computations (e.g., analyses) to check satisfaction of spatial requirements. The requirements are partly global—as the overall system should achieve certain

Nature-inspired optimization is one of the most prevalent research domains with a confounding history that fascinates the research communities. Particle Swarm Optimization (PSO) is one of the well-known optimizer that belongs to the family of nature inspired algorithms. It often suffers from premature convergence leading to a local optimum. To address this, several methods were presented using different

As one of the essential applications of Health Information Technology (HIT), the eHealth system plays a significant role in enabling various internet medicine service scenes, most of which primarily rely on service recommendation or evaluation mechanism. To avoid privacy leakage, some privacy-preserving mechanisms must be adopted to protect raters' privacy and make evaluation trust reliable. To tackle

This paper presents a social influence based collaborative filtering (CF) framework over heterogeneous information networks with three unique features. First, we integrate different types of entities, links, attributes, and activities from rating, social, and activity networks into a unified CF model through intra-network and inter-network social influence. Second, we propose three social influence

We propose a BBDS system model with its security requirements before we propose a concrete construction by combining $\Sigma$-protocol, Paillier and AES encryption schemes, and the ECDSA signature scheme. To demonstrate the utility of our proposed BBDS system, we present a security analysis and compare our system with other solutions. We implement the prototype in Remix to analyze the gas cost and

Automatically summarizing a group of short texts that mainly share one topic is a fundamental task in many applications, e.g., summarizing the main symptoms for a disease based on a group of medical texts that are usually short. Conventional unsupervised short text summarization techniques tend to find the most representative short text document. However they may cause privacy issues, e.g., personal

Parkinson's disease (PD) is a neurodegenerative disease that affects millions of people around the world and cannot be cured fundamentally. Automatic identification of early Parkinson's disease on feature data sets is one of the most challenging medical tasks today. Many features in these datasets are useless or suffering from problems like noise, which affect the learning process and increase the

Blockchain is regarded as one of the most promising technologies to upgrade e-commerce. This paper analyzes the challenges that current e-commerce is facing and introduces a new scenario of e-commerce enabled by blockchain. A framework for mining tasks in this scenario offloaded onto edge servers based on mobile edge computing is proposed. Then the offloading issue is modelled as a multi-constrained

Fake news is risky, since it has been created to manipulate readers’ opinions and beliefs. In this work, we compared the language of false news to the real one of real news from an emotional perspective, considering a set of false information types (propaganda, hoax, clickbait, and satire) from social media and online news article sources. Our experiments showed that false information has different

Currently, the integration of the supply chain and blockchain is promising, as blockchain successfully eliminates the bullwhip effect in the supply chain. Generally, concurrent PBFT (Practical Byzantine Fault Tolerance) consensus method, named C-PBFT, is powerful to deal with the consensus inefficiencies, caused by the fast node expansion in the supply chain. However, due to the tremendous complicated

Sentiment analysis or opinion mining for subject information extraction from the text has become more and more dependent on natural language processing, especially for business and healthcare, since the online products and service reviews affect the consuming behaviors. Word embeddings that can map the words to low-dimensional vector representations have been widely used in natural language processing

Containers, as a lightweight application virtualization technology, have recently gained immense popularity in mainstream cluster management systems like Google Borg and Kubernetes. Prevalently adopted by these systems for task deployments of diverse workloads such as big data, web services, and IoT, they support agile application deployment, environmental consistency, OS distribution portability,

Ethereum, a blockchain, supports its own cryptocurrency named Ether and smart contracts. Although more than 8M smart contracts have been deployed on Ethereum, little is known about the characteristics of its users, smart contracts, and the relationships among them. We conduct the first systematic study on Ethereum by leveraging graph analysis to characterize three major activities on Ethereum, namely

The performance evaluation results showed that the optimal EEG electrode locations were F7, F8, FC5, and FC6, whereas the accuracy of learning and test data of user-intention recognition was found to be 94.2% and 92.3%, respectively, which suggests that the proposed system can be used to recognize user intention for specific behavior. The system proposed in the present study can allow continued rehabilitation

As online sources are becoming more prevalent in journalism and scientific literature, the ephemeral nature of the World Wide Web is becoming an increasingly serious issue for their verifiability, replicability, and reproducibility. The architecture of Webchain, a new system enabling source and reference verifiability on the Web, is combining distributed ledger technologies with secure timestamping

Trust in SSL-based communications is provided by Certificate Authorities in the form of signed certificates. Despite the existence of several revocation checking mechanisms, sophisticated cyber-attackers can still trick web browsers to trust a revoked certificate, believing that it is still valid. In this paper, we present CCSP: a system to provide timely information regarding certificates status.

In this paper we present MPG (Mobile Personal Guide), which provides: i) a set of diverse yet surprisingly interesting venue recommendations that are aligned with user preferences; and ii) a set of route recommendations that are constructed from the recommended venues. We also introduce EPUI (an Experimental Platform for Urban Informatics), a testbed we developed to evaluate venue and route recommendation

The study aims at exploring the Internet of things (IoT) system from the perspective of data, and further improving the performance of the IoT system. The IoT data energy collection and information transmission system model is constructed by combining IoT and wireless relay cooperative transmission technology. Moreover, the energy efficiency, outage probability, and accuracy of the model are evaluated

Current implementations of iterative double auction rely on a trusted third-party to handle the auction process. This imposes the risk of single point of failures and monopoly. We tackle this problem by proposing a novel decentralized and trustless blockchain-based framework for iterative double auction. Our design extends the state channel technologies to enable a double auction process among trading

Interpersonal conflict between couples is a significant source of stress with long-lasting effects on partners’ physical and psychological health. Motivated by findings in psychological science, we study how couples with distinct relationship functioning characteristics experience conflict in real life. We propose sub-population specific machine learning models using hierarchical and adaptive learning

The increasing popularity of social media platforms such as Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In this article

Today, misogyny and xenophobia are some of the most important social problems. With the increase in the use of social media, this feeling of hatred toward women and immigrants can be more easily expressed, and therefore it can have harmful effects on social media users. For this reason, it is important to develop systems capable of detecting hateful comments automatically. In this article, we analyze

In recent years, vaccine incidents occurred around the world. This paper presents a blockchain based solution to protect the whole process of vaccine circulation. We first propose a model to supervise the vaccine circulation process by incorporating existing regulatory practices. Then, we propose a blockchain based tracing system to implement this model. The proposed system takes the blockchain as

Mobile Edge Computing (MEC) is a promising architecture that pushes mobile computing to the network edge. Recent studies deploy MEC applications in Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chaining (SFC). In this paper, we solve the SFC placement problem in the MEC-NFV environment. We formulate the placement problem as a

This study proposes FinPrivacy, a privacy-preserving mechanism for fingerprint identification. This mechanism utilizes the low-rank matrix approximation to reduce the dimensionality of fingerprint and the exponential mechanism to carefully determine the value of the optimal rank. Thereafter, FinPrivacy injects Laplace noise to the singular values of the approximated singular matrix, thereby trading

Universum based support vector machine (USVM) incorporates prior information about the distribution of data in training of the classifier using universum data points. This leads to better generalization performance, but with increased computation cost. Various twin hyperplane based classifiers are proposed to reduce the computation cost of universum based algorithms. In this work, we present an efficient

In recent years, the increasing propagation of hate speech in online social networks and the need for effective counter-measures have drawn significant investment from social network companies and researchers. This has resulted in the development of many web platforms and mobile applications for reporting and monitoring online hate speech incidents. In this article, we present MANDOLA, a big-data processing

A crucial component of emerging cryptographic schemes is a third-party authority (TPA) that must be trusted. However, several types of attacks and certificate mis-issuance problems in the CA infrastructure indicate that the successful deployment of newer emerging cryptographic schemes will also significantly depend on the trustworthiness of the TPAs. To address this issue, we propose an authority transparency

Time series medical images are an important type of medical data that contain rich spatio-temporal information. Recently, computer-aided diagnosis algorithms are often used on honest-but-curious servers to analyze medical images, which introduces severe privacy concerns. This paper proposes a convolutional-LSTM network (HE-CLSTM) for analyzing encrypted medical image sequences. Specifically, several

DNA motif finding is an important study to understand the regulation of gene expression, however, the existing methods generally ignore the potential sensitive information that may be exposed in the process. In this work, we utilize the differential privacy model to provide provable privacy guarantees which is independent of attackers? background knowledge. Our method makes use of sample databases

There have been significant advances in the field of Internet of Things (IoT) recently. In most IoT based systems or applications, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well

In the analysis of information loss, it is found that with the number of identifiers increasing in the Magic and EIA datasets, the algorithm in this paper is significantly lower than that in k-anonymity and l-diversity models, and achieves the expected effect. In the availability scoring analysis, for both Magic data set and EIA data set, the usability improvement value of k-anonymity-differential

This paper proposed a new and effective integrity auditing mechanism of sensor data based on a bilinear map accumulator. Using the proposed approach will examine all the data blocks in the dataset, not just some of the data blocks, thus eliminating the possibility of any cloud manipulation. Compared with other schemes, our proposed solution has been proved to be highly secure for all necessary security