Pervasive and Mobile Computing最新文献

There are several important factors to consider in edge computing systems including latency, reliability, power consumption, and queue load. Task replication requires additional energy costs in mobile edge offloading scenarios based on master-slave replication for fault tolerance. Excessive task offloading may lead to a sharp increase in the total energy consumption of the system including replication costs. Conversely, new tasks cannot enter the waiting queue and are lost, resulting in reliability issues. This paper proposes an adaptive task offloading strategy for balancing the edge node queue load and offloading cost (Lyapunov and Differential Evolution based Offloading schedule strategy, LDEO). The LDEO strategy innovatively customizes the Lyapunov drift-plus-penalty function by incorporating replication redundancy offloading costs to establish a balance model between the queue load and offloading cost. The LDEO strategy computes the optimal offloading decisions with dynamic adjustment characteristics by integrating a low-complexity differential evolution method, aiming to find the optimal balance point that minimizes the offloading cost while maintaining reliability performance. The experimental results show that compared with the existing strategies, LDEO strategy effectively reduces the redundancy of fault tolerance cost and the waiting time under the condition of ensuring that the task will not be discarded over time. It stabilizes the queue length in a reasonable range, controls the waiting time and loss rate of tasks, reduces the extra energy consumption paid by replication redundancy, and effectively realizes the optimal balance under multiple conditions.

A common problem in the development of Internet-of-Things (IoT) and Cyber-Physical System (CPS) applications is the complexity of these domains, due to their hybrid and distributed nature in multiple layers (hardware, network, communication, application etc.). Apart from other issues, this inherent complexity often gives room for implementation errors, which can be in many cases fatal and drive the application and/or the system to undesired states. The current work aspires to alleviate this problem by introducing a low-code approach for building IoT and CPS applications. We argue that, through the proposed approach it is possible to lower development time and risk (errors/bug-related ones) and allow a wide range of end-users to build and monitor applications for state-of-the-art domains, such as smart home and smart industry. In this context, Model-Driven Engineering (MDE) approaches are explored and SmAuto, a Domain-specific Language (DSL) is proposed for creating and executing automation tasks for smart environments. Through SmAuto it is possible to handle the heterogeneity and complexity issues of the IoT and CPS domains, this way allowing end-users are non-technical application experts to build well-designed and properly functioning smart applications. The proposed DSL implements a Sense-Think-Act-Communicate model for smart environments and enables the creation, validation, and dynamic execution of composite automation models in physical, virtual and hybrid environments, while it also enables automated code generation of virtual entities for verification purposes. By using layered abstractions to automate the development process, end-users can concentrate on the real problem instead of dwelling into technical details, thus increasing their productivity. The results of the empirical evaluation and the comparison to existing approaches show that SmAuto can make application development more rigorous, improves productivity of end-users including non-experts, i.e. citizen developers and satisfies several functional and non-functional requirements of modern DSLs, such as tool support, modular deployment, reusability, availability and extensibility.

Traditional one-time authentication mechanisms cannot authenticate smartphone users’ identities throughout the session — the concept of using behavioral-based biometrics captured by the built-in motion sensors and touch data is a candidate to solve this issue. Many studies proposed solutions for behavioral-based continuous authentication; however, they are still far from practicality and generality for real-world usage. To date, no commercially deployed implicit user authentication scheme exists because most of those solutions were designed to improve detection accuracy without addressing real-world deployment requirements. To bridge this gap, we tackle the limitations of existing schemes and reach towards developing a more practical implicit authentication scheme, dubbed MotionID, based on a one-class detector using behavioral data from motion sensors when users touch their smartphones. Compared with previous studies, our work addresses the following challenges: ① Global mobile average to dynamically adjust the sampling rate for sensors on any device and mitigate the impact of using sensors’ fixed sampling rate; ② Over-all-apps to authenticate a user across all the mobile applications, not only on-specific application; ③ Single-device-evaluation to measure the performance with multiple users’ (i.e., genuine users and imposters) data collected from the same device; ④ Rapid authentication to quickly identify users’ identities using a few samples collected within short durations of touching (1–5 s) the device; ⑤ Unconditional settings to collect sensor data from real-world smartphone usage rather than a laboratory study. To show the feasibility of MotionID for those challenges, we evaluated the performance of MotionID with ten users’ motion sensor data on five different smartphones under various settings. Our results show the impracticality of using a fixed sampling rate across devices that most previous studies have adopted. MotionID is able to authenticate users with an F1-score up to 98.5% for some devices under practical requirements and an F1-score up to roughly 90% when considering the drift concept and rapid authentication settings. Finally, we investigate time efficiency, power consumption, and memory usage considerations to examine the practicality of MotionID.

Rapid advances in wireless communication services has made limited spectrum resources increasingly scarce. One promising solution for enhancing spectrum utilization is cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs). However CSS is vulnerable to Byzantine attack. Current researches show that Byzantine attack is easily defended for their fixed attack probability. In this context, we propose an improved attack model called the dominated cooperative probabilistic attack (DCPA) model in the actual situation, building upon the generalized collaborative probabilistic Byzantine attack model. This DCPA model contains auxiliary cooperative attackers (ACAs) who launch attacks and a dominant attacker (DA) who determines ACAs’ attack probability intervals based on their respective credibility. The DCPA model allows ACAs to flexibly launch attacks, without being identified by the traditional reputation defense algorithm, significantly compromising the sensing performance of CSS. To successfully resist the threat posed by the DCPA model to CSS, we propose a JS-divergence-based improved reputation algorithm that can distinguish honest users (HUs) from attackers. This algorithm analyzes two consecutive sensing reports to identify differences in sensing behavior between HUs and attackers. Through Python simulation analysis, we demonstrate that, compared to the generalized cooperative probabilistic attack (CPA) model and the attack-free CSS (AFC) model, the proposed DCPA model is more concealed and significantly more disruptive to the performance of traditional reputation defense algorithms. Furthermore, our approach greatly enhances the performance of CSS by promoting the participation of HUs and suppressing attackers during the final data fusion. And also compared with the PAM2 algorithm, the conventional voting rule (CVR) algorithm and the traditional reputation defense algorithm, our proposed algorithm improves the detection performance by at least 7%, 15% and 50%.

Advanced computing capabilities embedded in modern vehicles enable them to accommodate a variety of intelligent transportation systems and real-world applications that help improve driving safety and compliance with road regulations. However, some of these applications are computationally demanding, and the local processing capabilities of vehicles may not always be enough to support them. To address this issue, existing research has proposed offloading the excessive workload to other computing facilities, such as nearby base stations, roadside units, or remote cloud servers. Still, these facilities have several limitations, including frequent unavailability, congestion, and high fees. In this paper, we explore a more pervasive and cost-effective solution: offloading excessive workloads to nearby peer vehicles via peer-to-peer connections. This approach, referred to as peercloud-vehicle, is an extension of the peercloud approach, which has been proposed for mobile social networks in the literature. The objective of this work is to have a comprehensive study on the feasibility and reliability of vehicle-to-vehicle offloading. First, we analyze two real-world vehicular network datasets to study the robustness of the vehicle contacts and estimate contact durations with deep learning-based regression methods. Second, we design reliable vehicle-to-vehicle offloading approaches based on two optimization objectives: min-delay task offloading to minimize the overall execution delay, and cost-aware task offloading to minimize the cost of task offloading. Experimental results based on real-world datasets demonstrate that peercloud-vehicle significantly outperforms existing approaches.

In the context of 5th-generation (5G) mobile communication technology, deploying indoor small-cell base stations (SBS) to serve visitors has become common. However, indoor SBS is constrained by factors such as service capacity, signal interference, and structural layout. Merchants within large buildings frequently host diverse activities to attract visitors, significantly increasing indoor traffic and crowd-gathering phenomenon. Consequently, SBS faces challenges of excessive energy consumption, compromised communication quality, and an inability to provide service to all visitors. Merchants aim to deploy SBS that can effectively curtail energy consumption costs while fulfilling visitor needs. However, due to the intermittent nature of high footfall situations, employing additional fixed SBS is not economically viable. Therefore, we address the challenge of maintaining service quality and mitigating energy consumption of SBS during footfall fluctuations by proposing an SBS model with a dynamic sleep mechanism. We simulate the internal structure of a three-dimensional (3D) building and the footfall over time. Within this model, we leverage the flexibility of mobile small-cell base stations (MSBS) to seamlessly traverse service regions. We compute the transmission power and location of SBS and MSBS based on energy efficiency (EE), combining their strengths to tackle the challenge. This approach maintains SBS communication quality while curbing energy consumption. We attain the optimal hybrid deployment strategy by enhancing the adaptive differential evolution with optional external archive (JADE) algorithm and incorporating the final fitness formula, the adaptive ranking mutation operator strategy, and the disorder replacement strategy (DRS) in it to form the proposed joint adaptive fusion with ranking (JAFR) algorithm. Our comparative simulation experiments demonstrate the effectiveness of JAFR in addressing the challenges against conventional methods, recent differential evolution algorithms, and mobile base station (MBS) deployment approaches posed by this model. The results indicate that the JAFR algorithm yields superior SBS deployment strategies in most cases.

In the contemporary landscape of energy infrastructure, the “smart-grid metering infrastructure (SGMI)” emerges as a pivotal entity for efficiently monitoring and regulating electricity generation in response to client behavior. Within this context, SGMI addresses a spectrum of pertinent security and privacy concerns. This study systematically addresses the inherent research problems associated with SGMI and introduces a lattice-based blockchain-assisted certificateless data authentication and key exchange scheme. The primary aim of this scheme is to establish quantum resistance, conditional anonymity, dynamic participation, and the capacity for key updates and revocations, all of which are imperative facets for the robust implementation of mutual authentication within SGMI. Our scheme harnesses blockchain technology to mitigate the vulnerabilities associated with centralized administrative control, thus eliminating the risk of a single-point failure and distributed denial-of-service attacks. Furthermore, our proposed scheme is meticulously designed to accommodate the resource constraints of smart meters, characterized by lightweight operations. Rigorous formal security analysis is conducted within the framework of the quantum-accessible random oracle model, fortified by ’history-free reduction,’ substantiating its security credentials. Complementing this, simulation orchestration serves to underscore its superiority over existing methodologies, particularly in the realms of energy efficiency, data computation, communication, and the costs associated with private key storage.

Nowadays, the Global Positioning System (GPS) plays an critical role in providing navigational services for transportation and a variety of other location-dependent applications. However, the emergent threat of GPS spoofing attacks compromises the safety and reliability of these systems. In response, this study introduces a cutting-edge computer vision-based methodology, the SEquential dashcam-based vEhicle localization frameworK Plus (SEEK+), designed to counteract GPS spoofing. By analyzing dashcam footage to ascertain a vehicle’s actual location, SEEK+ scrutinizes the authenticity of reported GPS data, effectively identifying spoofing incidents. The application of dashcam imagery for localization, however, presents inherent obstacles, such as adverse lighting and weather conditions, seasonal and temporal image variations, obstructions within the camera’s field of view, and fluctuating vehicle velocities. To overcome these issues, SEEK+ integrates innovative strategies within its framework, demonstrating superior efficacy over existing approaches with a notable detection accuracy rate of up to 94%.

In this paper, we present all-embracing Transformers (AaTs) that are capable of deftly manipulating attention mechanism for Received Signal Strength (RSS) fingerprints in order to invigorate localizing performance. Since most machine learning models applied to the RSS modality do not possess any attention mechanism, they can merely capture superficial representations. Moreover, compared to textual and visual modalities, the RSS modality is inherently notorious for its sensitivity to environmental dynamics. Such adversities inhibit their access to subtle but distinct representations that characterize the corresponding location, ultimately resulting in significant degradation in the testing phase. In contrast, a major appeal of AaTs is the ability to focus exclusively on relevant anchors in RSS sequences, allowing full rein to the exploitation of subtle and distinct representations for specific locations. This also facilitates disregarding redundant clues formed by noisy ambient conditions, thus enhancing accuracy in localization. Apart from that, explicitly resolving the representation collapse (i.e., none-informative or homogeneous features, and gradient vanishing) can further invigorate the self-attention process in transformer blocks, by which subtle but distinct representations to specific locations are radically captured with ease. For that purpose, we first enhance our proposed model with two sub-constraints, namely covariance and variance losses at the Anchor2Vec. The proposed constraints are automatically mediated with the primary task towards a novel multi-task learning manner. In an advanced manner, we present further the ultimate in design with a few simple tweaks carefully crafted for transformer encoder blocks. This effort aims to promote representation augmentation via stabilizing the inflow of gradients to these blocks. Thus, the problems of representation collapse in regular Transformers can be tackled. To evaluate our AaTs, we compare the models with the state-of-the-art (SoTA) methods on three benchmark indoor localization datasets. The experimental results confirm our hypothesis and show that our proposed models could deliver much higher and more stable accuracy.

Tracking pedestrian flow in large public areas is vital, yet ensuring privacy is paramount. Traditional visual-based tracking systems are raising concerns for potentially obtaining persistent and permanent identifiers that can compromise individual identities. Moreover, in areas such as the vicinity of restrooms, any form of data acquisition capturing human behavior should be refrained from, making it also crucial to appropriately address and complement these blind spots for a comprehensive analysis of pedestrian movement in the entire area. In this paper, we present our pedestrian tracking algorithm using distributed 3D LiDARs (Light Detection and Ranging), which capture pedestrians as 3D point clouds, omitting identifiable features. Our system bridges blind spots by leveraging historical movement data and 3D point cloud features, complemented by a generative diffusion model to predict trajectories in unseen areas. In a large-scale testbed with 70 LiDARs, the system achieved a 0.98 F-measure, highlighting its potential as a leading privacy-preserving tracking solution.