Showing papers in "Pervasive and Mobile Computing in 2020"

TL;DR: This paper proposes an overview of the main Blockchain-based Online Social Media platforms, and describes in detail these platforms by highlighting the main features and services they offer, but also the common drawbacks of these platforms.

TL;DR: This survey considers the smart contracts and the smart contacts formal verification methods, and investigates the security assurance for smart contracts using vulnerabilities detection methods and presents the used methods and tools.

TL;DR: This work aims to increase MQTT security by mitigating data tampering, eavesdropping and Replay attacks by using the Elliptic Curve Cryptography (ECC), timestamps and wake up patterns, with the purpose of preserving node energy.

TL;DR: A novel approach for textual input which is based on air-writing recognition using smart-bands enables the user to hand-write in the air in an intuitive and natural way, where text is recognized by analyzing the motion signals captured by an off-the-shelf smart-band worn by the user.

TL;DR: The problem of resources management is addressed by proposing two distributed load balancing algorithms, tailored to deal with heterogeneity, which demonstrate the effectiveness of the algorithms under a wide range of heterogeneity, overall providing a remarkable improvement compared to the case of not cooperating nodes.

TL;DR: A new framework of cloud manufacturing by integrating the blockchain technology is proposed, and consensus-oriented mechanisms are employed to generate the operating standards for the blockchain cloud manufacturing model.

TL;DR: The results show that ECCbAP provides a greater security compared with other protocols evaluated in this paper, but it is also more resource-efficient, which renders it more appropriate for the constrained environment like Radio Frequency Identification (RFID) tags or Bluetooth Low Energy (BLE) sensors.

TL;DR: This work proposes a lightweight probabilistic proof of elapsed work and luck (PoEWAL) consensus method for non-cooperative blockchain in IoT environments, and analyzed suitability of PoEWAL for resource constrained devices, using Contiki Cooja simulator.

TL;DR: An in-depth analysis is done through calibration, experiments, and deployment which confirms the power efficiency, flexibility, reliability and accuracy of the proposed internet of things-enabled low power environment monitoring system for real-time monitoring of ten significant air quality parameters.

TL;DR: This paper defines the concept of scope in a Multi-IoT scenario and proposes two formalizations of this concept allowing the computation of its values, and presents two possible applications of scope.

TL;DR: This paper presents the tool chain that comprises the formalization of the semantics of smart contracts, via a functional specification of blockchain operations towards a formal representation of the smart contract at question that can be formally analyzed for correct implementation via model checking.

TL;DR: In this article, the authors proposed mechanisms to transform sensor data before sharing them with applications running on users' devices, aiming at eliminating patterns that can be used for user re-identification or for inferring potentially sensitive activities, while introducing a minor utility loss for the target application or task.

TL;DR: Experimental results indicate the high accuracy of the localization technique, the success of the two-stage offloading decision and the scaling mechanism, while meeting the performance requirements of the LBS.

TL;DR: This contribution reports on the methodological aspects related to the estimation of present population density, for which a general and modular methodological structure is presented that generalises previous proposals found in the academic literature.

TL;DR: In this article, the authors describe how to couple reputation systems with distributed consensus protocols to provide a scalable permissionless consensus protocol with a low barrier of entry, while still providing strong resistance against Sybil attacks for large peer-to-peer networks of untrusted validators.

TL;DR: Experimental analysis showed that the proposed solution is secure and efficient enough to be integrated within a real world application.

TL;DR: The aim of this work is to develop a system that detects three road events — speed-breakers, potholes and broken road patches, with improved accuracy even under varying conditions and rough roads and can be used as an effective crowdsourcing system for road quality monitoring.

TL;DR: Experimental results show that SmartWheels is a viable solution to detect urban features, in particular by applying specific strategies based on the confidence assigned to predictions by the classifier.

TL;DR: This work presents the SocializeME framework, a framework designed to collect proximity information and to detect social interactions through heterogeneous personal mobile devices, and the results obtained by SME-D algorithm, designed to automatically detects social interactions based on the collected wireless signals.

TL;DR: It is shown that even category-level aggregated application usage can predict Big Five traits at up to 86%–96% prediction fit in the authors' sample, and that when studying personality, application categories can provide sufficient predictions in general traits.

TL;DR: It is found that the network characteristics of the Lightning Network mainnet, which was launched on March 15, 2018, shows strong scale-free network characteristics and implies that Lightning Network can be vulnerable to DDoS attacks targeting some specific nodes in the network.

TL;DR: A context and emotion-aware system for the recommendation and playing of Spotify songs is presented, consisting in a location-based mobile application that interacts with a novel emotional wearable and a recommendation service that predicts the next song to be recommended.

TL;DR: A mobile sensing platform able to pervasively collect environmental data and an application for pedestrian navigation that works both on smartphones (also using Augmented Reality and on smartwatches, thus ensuring an appropriate exposition of the mobile device (and its sensors) when collecting data.

TL;DR: A Deep Learning methodology applied to data coming from a non-invasive Bluetooth IoT monitoring system deployed inside a cultural space is presented and discussed, the main goal is to predict the occupancy of the available rooms.

TL;DR: DF-WiSLR reported better recognition accuracies with SVM for static and dynamic gestures in both home and office environments, and adopted machine learning classifiers such as SVM, KNN, RF, NB, and a deep learning classifier CNN, for measuring the gesture recognition accuracy.

TL;DR: This work introduces an epidemic diffusion model for evaluating the impact of the expiration time in a mobile crowdsensing scenario, showing a significant reduction on buffer usage and nodes message transmissions, and therefore, decreasing battery consumption.

TL;DR: This article introduces HIBEY, a fixed 1-line solution that further decreases the screen real-estate usage by hiding the layout, and surprisingly performs much better than FITE, and achieves a mean text entry rate that is comparable to other state-of-the-art approaches.

TL;DR: A blockchain-based privacy-preserving SVM classification (BPPSVC) between mutually distrustful data owners is introduced, and the security analysis indicates that the proposed system is secure and it provides fairness and protection against Denial of Service (DoS) attacks.

TL;DR: This paper proposes an Unsupervised Domain adaptation technique for Activity Recognition, called UDAR, which supports sharing and transferring activity models from one dataset to another heterogeneous dataset without the need of activity labels on the latter.