Pervasive and Mobile Computing最新文献

Context:

Engineering Ambient Assisted Living applications for the elderly is challenging due to the diversity and rapid changes of both end users’ needs and technological environment equipment.

Objective:

Assistive applications can be provided as combinations of functionalities provided by IoT devices. With the pervasive availability of functionally equivalent IoT devices, they should be selected according to the specific deployment context in terms of user needs and conditions, device availability, and regulations when the operative context dynamic conditions can be set. Such selection is the objective of this work.

Methods:

We rely on a conceptual framework for self-adaptation as the enabler for a run-time decision-making process. It allows for representing relations among IoT devices, the functionalities they deliver, and the different modalities these functionalities are provided with in terms of goals, devices, and norms. The framework is based on three fundamental principles: (1) high-level abstractions separating the expected functionality, how it can be delivered, and who is responsible for its delivery; (2) AAL applications as the run-time composition of atomic functionalities; (3) centrality of the user in the system.

Result:

The Device-Goal-Norm framework is proposed to specify diagrams for different AAL applications, together with the semantics to transform these diagrams into run-time models. We also provide a running implementation of a run-time model based on the belief–desire–intention paradigm.

With the rise of Internet of Things (IoT), the smart home is another emerging concept and application of IoT, where security and private data of devices are important. In this paper, fog computing is applied to the smart home environment, where fog can provide many smart features and services to the smart home. Fog computing has many advantages, such as low latency and real-time interaction. However, when fog computing is combined with smart home, it also faces some security threats: first, some fog nodes and smart home devices are deployed in public places, vulnerable to damage or theft by attackers, not considered fully trusted, and vulnerable to device loss/theft attacks, impersonation attacks, and message tampering attacks, etc. These threats can lead to adversaries controlling devices in the smart home or modifying messages to make smart home devices execute wrong commands, causing irreparable damage; Second, the smart home system should have good real-time interaction, and the authentication process using the low latency feature of fog computing should not be involved by the cloud. Considering these, it is necessary to design a secure and effective fog-enabled smart home authentication system that is secure against various known attacks, especially when the fog node is not fully trusted or the smart home device is captured as well. Finally, the authentication scheme should also be lightweight due to the limited resources of many smart home devices. To address these issues, this paper proposes a lightweight authentication scheme for the fog-enabled smart home system. It also employs a physical unclonable function to achieve mutual authentication among three parties: smart home devices, fog nodes and users. Formal security analysis under the Real-Or-Random model shows that this scheme is provably secure. And informal security analysis shows that our scheme is robust against various known attacks. At the same time, the proposed scheme requires less computation cost (8.239 ms) and is approximately 40% to 390% faster than existing related schemes. Although the communication cost is slightly higher (4512 bits), it is reasonable because the proposed scheme implements fog/gateway node compromised attack that has not been achieved by any other existing related schemes.

The COVID-19 pandemic has considerably shifted the focus of scientific research, speeding up the process of digitizing medical monitoring. Wearable technology is already widely used in medical research, as it has the potential to monitor the user’s physical activity in daily life. This study aims to explore in-home collected wearable-derived signals for frailty status assessment. A sample of 35 subjects aged 70+, autonomous in basic activities of daily living and cognitively intact, was collected. After being clinically assessed for frailty according to Fried’s phenotype, participants wore a wrist device equipped with inertial motion sensors for 24 h, during which they led their usual life in their homes. Signal-derived traces were split into 10-s segments and labeled classified as gaits, other motor activities, or rests. Gait and other motor activity segments were used to calculate the Subject Activity Level (SAL), an index to quantify how users were active throughout the day. The SAL index was then combined with gait-derived features to design a novel frailty status assessment algorithm. In particular, subjects were classified as robust or non-robust, a category that includes both Fried’s frail and pre-frail phenotypes. For some users, activity levels alone enabled accurate frailty assessment, whereas, for others, a Gaussian Naive Bayes classifier based on the gait-derived features was required to assess frailty status. Overall, the proposed method showed extremely promising results, allowing discrimination of robust and non-robust subjects with an overall 91% accuracy, stemming from 95% sensitivity and 88% specificity. This study demonstrates the potential of unobtrusive, wearable devices in objectively assessing frailty through unsupervised monitoring in real-world settings.

Recently, real-time parking availability prediction has attracted much attention since the rapid development of sensor technologies and urbanisation. Most existing works have applied various models to predict long and short-term parking occupancy using historical records. However, historical records are not available for many real-world scenarios, such as new urban areas, where parking lots are fast adjusted and extended. In this paper, we aim to predict parking occupancy using historical data in other areas and contextual information within the targeted area that lacks historical data. We propose a two-step framework to first learn the important contextual features from areas where parking records already existed. Then we transfer these features to the other new areas without historical data records. Through conducting a real-world dataset with various clustering methods combined with different regression models, we observe that multiple contextual features are likely to influence parking availability prediction. We find the best combination (i.e., $k$-shape clustering algorithm and LSTM regression model) to build parking occupancy prediction model based on the subsequent quantitative correlation analysis between contextual features and parking occupancy. The experimental results show that (1) the conventional internal clustering evaluation does not work well for spatio-temporal data clustering for the prediction purpose; (2) our proposed approach achieves approximately 3% error rate in 30 minutes of prediction, which is significantly better than the estimation of the occupancy rate using the rate in the adjacent regions (13.3%).

Blockchain has proven in sensor networks as a distributed solution for transparent and secure storage, which allows its application in mobile wireless sensor networks (MWSNs). The consensus mechanism, an essential aspect of blockchain technology, must concern the high mobility, resource-constrained nature, and weak physical defenses of sensor nodes in MWSNs. To secure MWSN data storage in clustered communication, we design a proof-of-information (PoI) variant for fair miner campaigning via the amount of valid data generated from environmental information, including a dynamic adjustment of the data volume threshold to detect malicious nodes and prevent them from misreporting information. Additionally, we introduce a filtering mechanism through the dynamic integrated trust (DIt) of nodes, which integrates the trust evaluation of peer nodes across the network combining objective performance to prevent malicious nodes from infiltrating the final consensus group. The multi-level filtering technique improves the campaign fairness while isolating malicious nodes, ensuring complexity-sensitive PBFT algorithm efficiency in large-scale networks. Simulation results show that the scheme isolates 90% of the malicious nodes and screens 20% of members to produce a smaller final consensus group. Further analysis of impacts on the performance considering network topology and mobility patterns and comparisons of the relevant solutions are presented.

Intelligent Transportation Systems (ITS) are one of the pillars of smart cities that enable smart traffic and smart mobility. Vehicular Ad-hoc Networks (VANETs) are utilized as platforms for ITS applications. In VANETs, vehicles collect relevant information from sensors and exchange information about road conditions and traffic status with each other and with roadside units (RSUs). Roadside units facilitate reliable communication among the vehicles and perform real-time processing for the sensed data before sending to the cloud. Although cloud computing offers high performance computational and storage resources, it does not conform to the real-time nature of the ITS applications and the massive amount of data exchange and generation rate due to its centralized nature and high communication latency. In this paper we propose a cost-effective strategy to solve the configuration and localization problem of fog-based RSU deployment in VANETs. The proposed strategy is able to assign the computational capacity of each fog node based on the amount of computational demand inside its coverage region. The problem is formulated as a Satisfiability Modulo Theories (SMT) problem. Our proposed strategy is more efficient than other strategies in terms of the total deployment cost and the overall satisfied computational demand.

The growth of Location-Based Services (LBSs) has been made possible by the widespread use of GPS-enabled devices. Some important LBSs require the ability to quickly process moving spatial-keyword queries over moving objects, such as when a moving customer is looking for a nearby mobile fuel delivery service. While there have been solutions proposed for scenarios where either the queries or the objects being queried are moving, there is still a need for solutions that can handle scenarios where both are in motion. This research focuses on the application of fog computing to provide real-time processing of moving spatial-keyword queries for LBSs. Specifically, the research proposes a new model, FogLBS, designed to efficiently process moving continuous top-k spatial-keyword queries over moving objects in a directed streets network, with a particular emphasis on the use case of a mobile service provider. FogLBS computes queries’ answer sets for time intervals and incrementally updates them using novel optimization techniques and indexing structures. By implementing FogLBS in a fog computing architecture, the model is able to meet the real-time requirements of the service provider application and other similar LBSs. The results of extensive experiments demonstrate the effectiveness of the proposed model in terms of efficiency, scalability, and accuracy, making it a valuable contribution to the field of fog computing in LBSs.

In today’s world, Internet of Things (IoT) has penetrated every aspect of us. The security of IoT is challenging because of the open nature of wireless communication and limited resources of sensor nodes (SNs). In this paper, we propose an efficient heterogeneous signcryption scheme from IoT to an Internet server, in which the server is in a public key infrastructure (PKI) environment, while the IoT is in a certificateless cryptosystem (CLC) environment. The proposed scheme is briefly referred to as CP-EHSC. CLC can settle the drawbacks of key escrow in identity-based cryptography (IBC) and public key certificate management in PKI. At the same time, PKI is suitable for the server since it has been widely adopted on the Internet. Our proposed scheme can provide security proof in the random oracle model (ROM). Performance analysis shows that the proposed scheme not only achieves great optimization in calculation cost and communication consumption but also makes great progress in total energy consumption. In addition, we illustrate the practical applicability of our proposed scheme by providing a realistic use case scenario.