Pervasive and Mobile Computing最新文献

Detecting risky driving has been a significant area of focus in recent years. Nonetheless, devising a practical, effective, and unobtrusive solution remains a complex challenge. Presently available technologies predominantly rely on visual cues or physical proximity, complicating the sensing. With this incentive, we explore the possibility of utilizing mmWave radars exclusively to identify dangerous driving behaviors. Initially, we scrutinize the attributes of unsafe driving and pinpoint distinct patterns in range-doppler readings brought about by nine common risky driving manoeuvres. Subsequently, we create an innovative Fused-CNN model that identifies instances of hazardous driving amidst regular driving and categorizes nine distinct types of dangerous driving actions. After conducting thorough experiments involving seven volunteers driving in real-world settings, we note that our system accurately distinguishes risky driving actions with an average precision of approximately 97% with a deviation of $\pm 2\text{\%}$. To underscore the significance of our approach, we also compare it against established state-of-the-art methods.

With the development of passive sensing technology, WiFi-based identification research has attracted much attention in areas such as human–computer interaction and home security. Although WiFi sensing-based human identification has achieved initial success, it is currently mainly applicable to scenarios where the user’s identity category is fixed and not applicable to scenarios where the user’s identity category changes frequently. In this paper, we propose an identification system (CIU-L) in a scenario where user’s identity categories frequently change, allowing for incremental registration and unregistration of identity categories. To the best of our knowledge, this is the first attempt to register and unregister user identity information under the previous identity category constraints. CIU-L proposes a training and updating strategy in the registration phase of new user to avoid catastrophic forgetting of old user’s identity information, and trains a targeted noise for the user to be unregistered in the unregistration phase of old user, achieving precise removal of the user to be unregistered without affecting the retained users. In addition, this paper presents adequate comparative experiments of CIU-L with other systems in the user identity category fixing scenario. The experimental results show that the average difference between CIU-L and other systems in terms of Accuracy, Precision, Recall and F1-Score is within 5% of each other, while running time and storage space are saved by more than 6 times, which is more capable of meeting the needs of identity recognition in real scenarios.

Bluetooth Low Energy (BLE) has emerged as one of the reference technologies for the development of indoor localization systems, due to its increasing ubiquity, low-cost hardware, and to the introduction of direction-finding enhancements improving its ranging performance. However, the intrinsic narrowband nature of BLE makes this technology susceptible to multipath and channel interference. As a result, it is still challenging to achieve decimetre-level localization accuracy, which is necessary when developing location-based services for smart factories and workspaces. To address this challenge, we present BmmW, an indoor localization system that augments the ranging estimates obtained with BLE  5.1’s constant tone extension feature with mmWave radar measurements to provide 3D localization of a mobile tag with decimetre-level accuracy. Specifically, BmmW embeds a deep neural network (DNN) that is jointly trained with both BLE and mmWave measurements, practically leveraging the strengths of both technologies. In fact, mmWave radars can locate objects and people with decimetre-level accuracy, but their effectiveness in monitoring stationary targets and multiple objects is limited, and they also suffer from a fast signal attenuation limiting the usable range to a few meters. We evaluate BmmW’s performance experimentally, and show that its joint DNN training scheme allows to track mobile tags with a mean 3D localization accuracy of 10 cm when combining angle-of-arrival BLE measurements with mmWave radar data. We further assess two variations of BmmW: BmmW-Lite and BmmW-Lite+, both tailored for single-antenna BLE devices, which eliminates the necessity for bulky and expensive multi-antenna arrays and represents a cost-effective solution that is easy to integrate into compact IoT devices. In contrast to classic BmmW (which utilizes angle-of-arrival info), BmmW-Lite uses raw in-phase/quadrature (I/Q) measurements, and achieves a mean localization accuracy of 36 cm, thus facilitating precise object tracking in indoor environments even when using budget-friendly single-antenna BLE devices. BmmW-Lite+ extends BmmW-Lite by allowing the localization task to be transferred from the edge to the cloud due to device memory and power constraints. To this end, BmmW-Lite+ employs a goal-oriented communication paradigm that compresses initial BLE features into a more compact semantic format at the edge device, which allows to minimize the amount of data that needs to be sent to the cloud. Our experimental results show that BmmW-Lite+ can compress raw BLE features by up to 12% of their initial size (hence allowing to save network bandwidth and minimize energy consumption), with negligible impact on the localization accuracy.

Quantum Key Distribution (QKD) holds the promise of a secure exchange of cryptographic material between applications that have access to the same network of QKD nodes, interconnected through fiber optic or satellite links. Worldwide several such networks are being deployed at a metropolitan level, where edge computing is already offered by the telco operators to customers as a viable alternative to both cloud and on-premise hosting of computational resources. In this paper, we investigate the implications of enabling QKD for edge-native applications from a practical perspective of resource allocation in the QKD network and the edge infrastructure. Specifically, we consider the dichotomy between aggregating all the applications on the same source–destination path vs. adopting a more flexible micro-flow approach, inspired from Software Defined Networking (SDN) concepts. Our simulation results show that there is a fundamental trade-off between the efficient use of resources and the signaling overhead, which we managed to diminish with the use of suitable hybrid solutions.

Range-based localization has received considerable attention in wireless sensor networks due to its ability to efficiently locate the unknown source of a signal. However, the localization accuracy with a single set of measurements may be inadequate, especially in dynamic and noisy environments. To mitigate this problem, received signal strength difference (RSSD) and time difference of arrival (TDOA) measurements are used to develop an efficient estimator to reduce the bias and improve localization accuracy. First, the RSSD/TDOA-based maximum likelihood (ML) localization problem is transformed into a hybrid information nonnegative constrained least squares (HI-NCLS) framework. Then, this framework is used to develop an effective bias-reduction localization approach (BRLA) with a two-step linearization process. The first step employs a linear solving method (LSM) which exploits an active set method to obtain a sub-optimal estimator. The second step uses a bias reduction method (BRM) to mitigate the correlation from linearization and a weighted instrumental variables matrix (IVM) which is weakly correlated with the noise but strongly correlated with the data matrix (DM) is used in place of the DM. Performance results are presented which demonstrate that the proposed BRLA provides better localization performance than state-of-the-art methods in the literature.

Traffic count (or link count) data represents the cumulative traffic in the lanes between two consecutive signalised intersections. Typically, dedicated infrastructure-based sensors are required for link count data collection. The lack of adequate data collection infrastructure leads to lack of link count data for numerous cities, particularly those in low- and middle-income countries. Here, we address the research problem of link count estimation using crowd-sourced trajectory data to reduce the reliance on any dedicated infrastructure. A stochastic queue discharge model is developed to estimate link counts at signalised intersections taking into account the sparsity and low penetration rate (i.e., the percentage of vehicles with known trajectory) brought on by crowdsourcing. The issue of poor penetration rate is tackled by constructing synthetic trajectories entirely from known trajectories. The proposed model further provides a methodology for estimating the delay resulting from the start-up loss time of the vehicles in the queue under unknown traffic conditions. The proposed model is implemented and validated with real-world data at a signalised intersection in Kolkata, India. Validation results demonstrate that the model can estimate link count with an average accuracy score of 82% with a very low penetration rate (not in the city, but at the intersection) of 5.09% in unknown traffic conditions, which is yet to be accomplished in the current state-of-the-art.

The popularization of Fog Computing has provided the foundation for a computational environment better suited to applications demanding low communication latency. However, Fog environments has limited resources and restricted coverage areas, besides the user mobility that needs continuous migrations to maintain accessible and nearby content. To enable applications to harness the low latency offered by Fog, it is crucial to develop migration strategies capable of addressing the complexities of the Fog environment while ensuring content availability regardless of user location. This work proposes CMFog${}_V$, a proactive content migration strategy that leverages mobility prediction in a multi-level fog. Our results show that CMFog${}_V$ is able to provide enhanced flexibility in the migration decision process across a wide diversity of scenario.

Recently, several movement control algorithms have been proposed to move robots to the desired locations and complete various collaborative tasks. These algorithms usually require information exchange among robots, which in turn relies on efficient neighbor discovery. However, neighbor discovery of robots is a challenging problem due to limited communication range and beacon collision. Here, how to set the time slot length and contention window to alleviate the beacon collisions is a major issue: too large or too small slot length and contention window values cannot achieve good performance in the robotic network. Therefore, we propose a neighbor discovery protocol with an adaptive collision alleviation mechanism, i.e. ND-ACA. ND-ACA adopts a simple mechanism to detect and avoid potential collisions before sending beacon, and adaptively adjusts both slot length and backoff window according to the number of historical beacon collections and beacon collisions. Extensive simulation results show the efficiency of our proposed approach. To the best of our knowledge, this is the first work in the literature that explore the joint implementation effects that simultaneously consider the robot mobility model and neighbor discovery.

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.