ACM Transactions on Sensor Networks最新文献

The visual information processing technology based on deep learning (DL) can play many important yet assistant roles for unmanned aerial vehicles (UAV) navigation in complex environments. Traditional centralized architectures usually rely on a cloud server to perform model inference tasks, which can lead to long communication latency. Using transfer learning (TL) to unload deep neural networks (DNN) to the edge-fog collaborative networks has become a new paradigm for dealing with the conflicts between computing resources and communication latency. However, ensuring the security of edge-fog collaborative networks entity is still challenging. For such, we propose an anonymous authentication and group key agreement scheme for the UAV-enabled edge-fog collaborative networks, consisting of UAV authentication protocol and collaborative networks authentication protocol. Utilizing the AVISPA assessment tool and security analysis, the security requirements and functional features of the proposed scheme are demonstrated. From the performance results of the proposed scheme, we show that it is superior to existing authentication schemes and promising.

Single-modal data has a limitation on fatigue detection, while the shortage of labeled data is pervasive in multimodal sensing data. Besides, it is a time-consuming task for board-certified experts to manually annotate the physiological signals, especially hard for EEG sensor data. To solve this problem, we propose FedUSL (Federated Unified Space Learning), a federated annotation method for multimodal sensing data in the driving fatigue detection scenario, which has the innate ability to exploit more than four multimodal data simultaneously for correlations and complementary with low complexity. To validate the efficiency of the proposed method, we first collect the multimodal data (aka, camera, physiological sensor) through simulated fatigue driving. The data is then preprocessed and features are extracted to form a usable multimodal dataset. Based on the dataset, we analyze the performance of the proposed method. The experimental results demonstrate that FedUSL outperforms other approaches for driver fatigue detection with carefully selected modal combinations, especially when a modality contains only (10% ) labeled data.

Spatial crowdsourcing leverages the widespread use of mobile devices to outsource tasks to a crowd of users based on their geographical location. Despite its growing popularity, current crowdsourcing systems often suffer from a lack of transparency, centralization, and other security issues. Blockchain technology has revolutionized this sector with its potential for decentralization, security, and transparency. However, existing blockchain-based crowdsourcing systems often overlook efficient task assignment mechanisms and expose users to potential losses due to the obligatory deposit payments to smart contracts, which might be vulnerable or untrustworthy.

This paper proposes EDF-Crowd, an Efficient and Deposit-Free blockchain-based spatial crowdsoucing framework, to address these challenges. EDF-Crowd introduces an efficient, customizable task assignment mechanism based on smart contracts, operating periodically and batch-wise. We also design a fair compensation mechanism to compensate users for the extra overhead caused by invoking certain smart contracts. More importantly, we propose a series of linkage protocols. By linking users’ back-and-forth actions, EDF-Crowd can regulate user behavior without requiring users to deposit.The versatility of EDF-Crowd also allows its application to generic crowdsourcing systems with minimal modifications. We implement EDF-Crowd based on the EOS blockchain. Extensive evaluations show that EDF-Crowd achieves high task assignment efficiency and low cost.

Alzheimer’s disease (AD) is a insidious and progressive neurodegenerative disease, the annual relevant social cost for AD patients can reach about $1 trillion in the world. Therefore, early diagnosis and treatment of AD play a vital role in slowing disease progression. However, existing detection methods for cognitive impairment can not consistently screen the stage of AD. To tackle this challenge, we propose an AD detection system, Ubi-AD, which combines the features of multiple biomarkers to realize passive and accurate AD detection. Unlike existing work, Ubi-AD can passively recognize the AD digital biomarkers during daily smartwatch usage without interfering with the user. At the user end, Ubi-AD first extracts the non-speech sounds (pause words, such as em, ah), which contain no privacy-sensitive content. Then, Ubi-AD recognizes the user’s walking activity, dining activity, and sleep activity from daily activities. Ubi-AD analyzes these data from smartwatch and predicts the AD stages using a multi-modal fusion neural network at the cloud end. We evaluate our model on a collected dataset from 45 volunteers. As a result, Ubi-AD can reach a detection accuracy of (93.4% ), which means that Ubi-AD can provide multiple effective biomarkers for ubiquitous and passive detection in daily life.

In recent years, the focus has been on enhancing user comfort in commercial buildings while cutting energy costs. Efforts have mainly centered on improving HVAC systems, the central control system. However, it’s evident that HVAC alone can’t ensure occupant comfort. Lighting, blinds, and windows, often overlooked, also impact energy use and comfort. This paper introduces a holistic approach to managing the delicate balance between energy efficiency and occupant comfort in commercial buildings. We present OCTOPUS, a system employing a deep reinforcement learning (DRL) framework using data-driven techniques to optimize control sequences for all building subsystems, including HVAC, lighting, blinds, and windows. OCTOPUS’s DRL architecture features a unique reward function facilitating the exploration of tradeoffs between energy usage and user comfort, effectively addressing the high-dimensional control problem resulting from interactions among these four building subsystems. To meet data training requirements, we emphasize the importance of calibrated simulations that closely replicate target-building operational conditions. We train OCTOPUS using 10-year weather data and a calibrated building model in the EnergyPlus simulator. Extensive simulations demonstrate that OCTOPUS achieves substantial energy savings, outperforming state-of-the-art rule-based and DRL-based methods by 14.26% and 8.1%, respectively, in a LEED Gold Certified building while maintaining desired human comfort levels.

The use of drones for collecting information and detecting bugs in orchards covered by nets is a challenging problem. The nets help in reducing pest damage, but they also constrain the drone’s flight path, making it longer and more complex. To address this issue, we model the orchard as an aisle-graph, a regular data structure that represents consecutive aisles where trees are arranged in straight lines. The drone flies close to the trees and takes pictures at specific positions for monitoring the presence of bugs, but its energy is limited, so it can only visit a subset of positions. To tackle this challenge, we introduce the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the orienteering problem, where likely infested locations are prioritized by assigning them a larger profit. Additionally, the drone’s movements have a cost in terms of energy, and the objective is to plan a drone’s route in the most profitable locations under a given drone’s battery. We show that SOAP can be optimally solved in polynomial time, but for larger orchards/instances, we propose faster approximation and heuristic algorithms. Finally, we evaluate the algorithms on synthetic and real data sets to demonstrate their effectiveness and efficiency.

Recent years have witnessed a remarkable proliferation of applications in smart cities. Crowd analysis is a crucial subject, and it incorporates two subtasks in smart city systems, i.e., crowd counting and crowd localization. Nevertheless, the presence of adverse intrinsic factors, i.e., scale variation and background noise severely degrades the performance of counting and localization. Although great efforts have been made on separate research on counting and localization, few works are capable of performing both tasks at the same time. To this aim, the scale attentive aggregation network (SA²Net) is proposed to solve the problems of scale variation and background noise in crowd counting and localization tasks synchronously. Specifically, the SA²Net has two vital modules, namely multiscale feature aggregator (MFA) module and background noise suppressor (BNS) module. The MFA module is designed in a four-pathway structure, and it aggregates the multiscale feature so as to facilitate the correlation between different scales. The BNS module utilizes the contextual information between the input keys matrix and self-attention matrix to suppress the background noise. Furthermore, a global consistency loss combined with the Euclidean loss is utilized to optimize the network in counting and localization tasks. Extensive experimental results prove that the SA²Net outperforms the state-of-the-art competitors both subjectively and objectively.

LoRa is widely deploying in aquatic environments to support various Internet of Things applications. However, floating LoRa networks suffer from serious performance degradation due to the polarization loss caused by the swaying antenna. Existing methods that only control the transmission starting from the aligned attitude have limited improvement due to the ignorance of aligned period length. In this paper, we propose PolarScheduler, a dynamic transmission control method for floating LoRa networks. PolarScheduler actively controls transmission configurations to match polarization aligned periods. We propose a V-zone model to capture diverse aligned periods under different configurations. We also design a low-cost model establishment method and an efficient optimal configuration searching algorithm to make full use of aligned periods. To deal with packet collisions in a multiple-node environment, we further propose an Attitude-aware Slot-allocation MAC protocol, which avoids both packet collisions and polarization loss. We implement PolarScheduler on commercial LoRa platforms and evaluate its performance in a deployed network. Extensive experiments show that PolarScheduler can improve the packet delivery rate and throughput by up to 20.0% and 15.7%, compared to the state-of-the-art method.

With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available computing resources of end devices and edge servers cannot continuously guarantee the performance of intelligent inference. In order to guarantee the sustainability of intelligent services in smart city, we propose the Adaptive Model Selection and Partition Mechanism (AMSPM) in 5G smart city where EI provides services, which mainly consists of Adaptive Model Selection (AMS) and Adaptive Model Partition (AMP). In AMSPM, the model selection and partition of deep neural network (DNN) are formulated as an optimization problem. Firstly, we propose a recursive-based algorithm named AMS based on the computing resources of edge devices to derive an appropriate DNN model that satisfies the latency demand of intelligent services. Then, we adaptively partition the selected DNN model according to the computing resources of edge devices. The experimental results demonstrate that, when compared with state-of-the-art model selection and partition mechanisms, AMSPM not only reduces latency but also enhances computing resource utilization.

Due to an explosive growth in mobile devices and the rapid evolution of wireless communication technologies, local-edge-cloud computing is becoming an attractive solution for providing a higher-quality service by exploiting the multi-computation power of mobile devices, edge servers and cloud. However, as the tasks are latency and privacy sensitive, highly credible task offloading becomes a crucial problem in a local-edge-cloud orchestrated computing system. In this paper, we study the computation offloading problem for latency and privacy sensitive tasks in a hierarchical local-edge-cloud network by using federated learning method. Our goal is to minimize the operational time of latency-sensitive tasks requested by mobile devices that have data privacy concerns, while each task can be executed under local, edge or cloud computing mode with no need to rely on privacy data. We first build system models to analyze the latency incurred under different computing modes, and then develop a constrained optimization problem to minimize the latency consumed by the federated offloading collaboration. A Hierarchical Federated Averaging method based on Differential Evolution algorithm (HierFAVG-DE) is proposed for solving the problem in-hand, and extensive simulations are conducted to verify the superiority of our approach.