Showing papers in "Pervasive and Mobile Computing in 2016"

TL;DR: This application implements cloud server-enabled user revocation, offering an alternative yet more efficient solution to the user revocation problem in the context of fine-grained encryption of cloud data.

TL;DR: Textile-based surface pressure mapping is presented as a novel, unobtrusive information source for activity recognition by analysing subtle features of such interaction, and various complex activities, often ones that are difficult to distinguish using other unobtruses, can be well recognised.

TL;DR: This paper presents an approach to speed-up the p ( o | l ) computation without any approximation, and the consequent positioning scheme is called Smart P-FP.

TL;DR: A system able to classify the state of patients suffering from bipolar disorder using sensed information from smartphones is introduced and it is shown that it is possible to classify with high confidence the course of mood episodes or relapse in bipolar patients.

TL;DR: A novel model to describe the energy consumption of a multisite application execution and a discrete time Markov chain (DTMC) to model fading wireless mobile channels are presented and the proposed EMOP algorithm, built on a value iteration algorithm (VIA), finds the efficient solution to the multisite partitioning problem.

TL;DR: The recent trend for lifelogging, continuously documenting ones life through wearable sensors and cameras, presents a clear opportunity to augment human memory beyond simple reminders and actually improve its capacity to remember.

TL;DR: This paper defined and implemented a novel methodology to mine popular travel routes from geo-tagged posts and infers interesting locations and frequent travel sequences among these locations in a given geo-spatial region.

TL;DR: It is demonstrated that employees perceived response efficacy and justice positively affect intention to comply with organizational BYOD security policies (ISSP).

TL;DR: The local mobile clouds formed by nearby mobile devices are introduced and the mathematical models of the mobile devices and their applications are given and the adaptive, probabilistic scheduling algorithm is formulated.

TL;DR: This paper reviews this type of attack and survey the leading works in the literature to highlight the underpinning motivations and threat model, and provides insights into the practicality, prospects, and limitations of the different approaches.

TL;DR: A novel cloud supported model for efficient community health awareness in the presence of a large scale WBANs data generation that aim to process the collected data from Monitored Subjects (MSs) in a large Scale to generate useful facts, observations or to find abnormal phenomena within the monitored data.

TL;DR: MetaQ is described, an ontology-based hybrid framework for activity recognition in Ambient Assisted Living (AAL) environments that combines SPARQL queries and OWL 2 activity patterns that allows the formal representation of activity meta-knowledge by means of DOLCE+DnS Ultralite (DUL) ontology patterns.

TL;DR: Compared with the state-of-the-art lower limb information used to detect FoG, the wrist increases the number of false detected events, while preserving the FoG hit-rate and detection latency, suggesting that wrist sensors can be a feasible alternative to the cumbersome placement on the legs.

TL;DR: Large-scale datasets recording individuals spatio-temporal locations from eight major world cities show significant similarities in people's mobility habits regardless of the city and nature of the dataset and unveil three persistent traits present in an individual's urban mobility: repetition, preference for shortest-paths, and confinement.

TL;DR: The notion of an activity curve, which represents an abstraction of an individual's normal daily routine based on automatically-recognized activities, is introduced and methods to detect changes in behavioral routines by comparing activity curves are proposed to analyze the possibility of changes in cognitive or physical health.

TL;DR: SPISM is presented, a novel information-sharing system that decides (semi-)automatically, based on personal and contextual features, whether to share information with others and at what granularity, whenever it is requested, and provides both ease of use and privacy features.

TL;DR: A generic attribute-based data sharing system based on a hybrid mechanism of CP-ABE and a symmetric encryption scheme which features constant computation cost and constant-size ciphertexts and is proven selective-secure in the random oracle model.

TL;DR: This paper describes the development of a Human Activity Recognition and Segmentation (HARS) system based on Hidden Markov Models (HMMs) that uses inertial signals from a smartphone to recognize and segment six different physical activities.

TL;DR: In-the-wild user study showed that, specifically for the users with greater sensitivity for interruptive notification timings, notification scheduling in Attelia's breakpoint timing reduced users frustration by 28% in users' real smart phone environments.

TL;DR: This research investigates probable soft computing techniques that are comparatively applied to localizations in a variety of components and proposes an alternative scheme that utilizes an extreme learning machine to increase the estimation accuracy and demonstrates effectiveness compared to other state-of-the-art soft-computing-based range-free localization schemes.

TL;DR: Of the three optimization methods tested, Gauss-Newton proves to be the most adequate, and among the three medium access methods evaluated, code division multiple access acoustic transmission provided the best results.

TL;DR: This publication presents techniques for classifying strategic information, namely financial figures which make it possible to determine the standing of an enterprise or an organisation, and the use of cryptographic information sharing protocols in cognitive systems for data analysis.

TL;DR: It is shown that significant improvements are achieved in the ability to explain small-footprint anomalies by accounting for information credibility and further discriminating among high-information-gain items according to the size of their spatial footprint.

TL;DR: A novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection is proposed and evaluated against four real-world datasets across different environments including the datasets with multiple residents.

TL;DR: L lane-level positioning is required for several location-based services such as advanced driver assistance systems, driverless cars, predicting driver's intent, among many other emerging applications, and current outdoor localization techniques fail to provide the required accuracy for estimating the car's lane.

TL;DR: The main merits of the architecture are its ability to maximize smartphone battery lifetime, that can reach an entire working shift, very satisfactory accuracy of BLE tag-smartphone distance estimation, high probability of detecting all the tags present in the construction site, as well as a suitably short Aging time.

TL;DR: An integrated static detection framework, which consists of four layers of filtering mechanisms, that is, the message digest (MD5) values, the combination of malicious permissions, the dangerous permissions, and the dangerous intention, respectively, is proposed.

TL;DR: Experimental results exhibit that the proposed framework achieves high accuracy as compared to the state-of-the-art approaches in terms of disease risk assessment and expert user recommendation.

TL;DR: This survey aims to fill the void in the challenges of power-aware smartphone-based sensing with a particular focus on mobility sensing systems (e.g., human activity recognition, location-based services), presenting a comprehensive review of relevant strategies aimed at solving this issue.

TL;DR: Insight is provided on how to enable suitable resource calibration and perform network troubleshooting for high user experience for both the therapist and the senior, and realize a Big Data architecture for PTaaS and other similar personalized healthcare services to be remotely delivered at a large-scale in a reliable, secure and cost-effective manner.