ACM Transactions on Sensor Networks最新文献

The widespread deployment of unattended LoRa networks poses a growing need to perform Firmware Updates Over-The-Air (FUOTA). However, the FUOTA specifications dedicated by LoRa Alliance fall short of several deficiencies with respect to energy efficiency, transmission reliability, multicast fairness, and security. This paper proposes FLoRa+, energy-efficient, reliable, beamforming-assisted, and secure FUOTA for LoRa networks, which is featured with several techniques, including delta scripting, channel coding, beamforming, and securing mechanisms. Specifically, we first propose a joint differencing and compression algorithm to generate the delta script for processing gain, which unlocks the potential of incremental FUOTA in LoRa networks. Then, we design a concatenated channel coding scheme with outer rateless code and inner error detection to enable reliable transmission for coding gain. Afterward, we develop a beamforming strategy to avoid biased multicast and compromised throughput for power gain. Finally, we present a securing mechanism incorporating progressive hash chain and packet arrival time pattern verification to countermeasure firmware integrity and availability attacks for security gain. Experimental results on a 20-node testbed demonstrate that FLoRa+ improves transmission reliability and energy efficiency by up to 1.51 × and 2.65 × compared with LoRaWAN. Additionally, FLoRa+ can defend against 100% and 85.4% of spoofing and Denial-of-Service (DoS) attacks.

With the rapid development and application of smart city, Cyber-Physical-Social Systems (CPSS) as its superset is becoming increasingly important, and attracts extensive attentions. For satisfying the smart requirements of CPSS design, a cloud-edge collaborative CPSS framework is first proposed in this paper. Then Coupled-Hidden-Markov-Model (CHMM) and tensor algebra are used to improve existing activity prediction methods for providing CPSS with more intelligent decision support. There are three key features (timing, periodicity and correlation) implied in CPSS data from multi-edge, which affects the accuracy of activity prediction. Thus, these features are synthetically integrated into improved Tensor-based CHMMs (T-CHMMs) to enhance the prediction accuracy. Based on the multi-edge CPSS data, three Tensor-based Viterbi Algorithms (TVA) are correspondingly proposed to solve the prediction problem for T-CHMMs. Compared with traditional matrix-based methods, the proposed TVA could more accurately compute the optimal hidden state sequences under given observation sequences. Finally, the comprehensive performances of proposed models and algorithms are validated on three open datasets by self-comparison and other-comparison. The experimental results show that the proposed methods is superior to the compared three classical methods in terms of F1 measure, average precision and average recall.

Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating reserves. In order to prevent cyber-physical attacks, issues related to the security and privacy of grid systems are receiving much attention from researchers. In this paper, privacy-aware energy grid management systems with anomaly detection networks and distributed learning mechanisms are proposed. The anomaly detection network consists of a server and a client learning network, which collaboratively learn patterns without sharing data, and periodically train and exchange knowledge. We also develop learning mechanisms with federated, distributed, and split learning to improve privacy and use Q-learning for decision-making to facilitate interpretability. To demonstrate the effectiveness and robustness of the proposed schemes, extensive simulations are conducted in different energy grid environments with different target distributions, ORRs, and attack scenarios. The experimental results show that the proposed schemes not only improve management performance but also enhance privacy and security levels. We also compare the management performance and privacy level of the different learning machines and provide usage recommendations.

The digital transformation of factories has greatly increased the number of peripherals that need to connect to a network for sensing or control, resulting in a growing demand for a new network category known as the Equipment Area Network (EAN). The EAN is characterized by its cable-free, high-capacity, low-latency, and low-power features. To meet these expectations, we present BEANet, a novel solution designed specifically for EAN that combines a two-stage synchronization mechanism with a time division protocol. We implemented the system using commercially available Bluetooth Low Energy (BLE) modules and evaluated its performance. Our results show that the network can support up to 150 peripherals with a packet reception rate of 95.4%, which is only 0.9% lower than collision-free BLE transmission. When the cycle time is set to 2 s, the average transmission latency for all peripherals is 0.1 s, while the power consumption is 18.9(mathrm{upmu } )W, which is only half that of systems using LLDN or TSCH. Simulation results also demonstrate that BEANet has the potential to accommodate over 30,000 peripherals under certain configurations.

Acoustic communication has become a research focus without requiring extra hardware and facilitates numerous near-field applications such as mobile payment. To communicate, existing researchers either use audible frequency band or inaudible one. The former gains a high throughput but endures being audible, which can be annoying to users. The latter, although inaudible, falls short in throughput due to the available (near) ultrasonic bandwidth. In this paper, we achieve both high speed and inaudibility for acoustic communication by utilizing the nonlinearity effect on microphones. We theoretically prove the maximum throughput of inaudible acoustic communication by modulating audible signal onto ultrasonic band. Then, we design and implement UltraComm, which utilizes a specially-designed OFDM scheme. The scheme takes into account the characteristics of the nonlinear speaker-to-microphone channel, aiming to mitigate the effects of signal distortion. We evaluate UltraComm on different mobile devices and achieve throughput as high as 16.24 kbps.

Transformer is a popular machine learning model used by many intelligent applications in smart cities. However, it has high computational complexity and it would be hard to deploy it in weak-edge devices. This paper presents a novel two-round offloading scheme, called A-MOT, for efficient transformer inference. A-MOT only samples a small part of image data and sends it to edge servers, with negligible computational overhead at edge devices. The image is recovered by the server with the masked autoencoder (MAE) before the inference. In addition, an SLO-adaptive module is intended to achieve personalized transmission and effective bandwidth utilization. To avoid the large overhead on the repeat inference in the second round, A-MOT further contains a lightweight inference module to save inference time in the second round. Extensive experiments have been conducted to verify the effectiveness of the A-MOT.

The energy harvesting sensor network is a new network architecture to further prolong the lifetime of sensor networks and enhance the quality of IoT services. Due to the inherent problems of energy harvesting sensor networks, it is really hard to collect fresh and useful sensory data. In order to solve the above problems, we investigate the data collection scheme in edge-assisted energy harvesting sensor networks and try to collect fresh and useful sensory data from such networks. Enlightened by the concept of the age of information, we define a new metric, the age of useful information (AoUI) to measure the usefulness and freshness of the sensory data. Furthermore, we define the Minimizing the Maximum Age of Useful Information problem (Min-AoUI) to construct a sensory data collection method to minimize the AoUI of the sensory data. We prove that the Min-AoUI problem is NP-Hard and approximation algorithms are proposed to solve this problem. The time complexity and the approximation ratio of this algorithm are analyzed. The performance of the algorithm is also verified by extensive experimental results.

No abstract available.

It is an important aspect to fully leverage complementary sensors of images and point clouds for objects classification and 6D pose estimation tasks. Prior works extract objects category from a single sensor such as RGB camera or LiDAR, limiting their robustness in the event that a key sensor is severely blocked or fails. In this work, we present a robust objects classification and 6D object pose estimation strategy by dual fusion of image and point cloud data. Instead of solely relying on 3D proposals or mature 2D object detectors, our model deeply integrates 2D and 3D information of heterogeneous data sources by a robustness dual fusion network and an attention-based nonlinear fusion function Attn-fun(.), achieving efficiency as well as high accuracy classification for even missed some data sources. Then, our method is also able to precisely estimate the transformation matrix between two input objects by minimizing the feature difference to achieve 6D object pose estimation, even under strong noise or with outliers. We deploy our proposed method not only to ModelNet40 datasets, but also to a real fusion vision rotating platform for tracking objects in outer space based on the estimated pose.

As an emerging mobility-on-demand service, bike-sharing system (BSS) has spread all over the world by providing a flexible, cost-efficient, and environment-friendly transportation mode for citizens. Demand-supply unbalance is one of the main challenges in BSS because of the inefficiency of the existing bike repositioning strategy, which reallocates bikes according to a pre-defined periodic schedule without considering the highly dynamic user demands. While reinforcement learning has been used in some repositioning problems for mitigating demand-supply unbalance, there are significant barriers when extending it to BSS due to the dimension curse of action space resulting from the dynamic number of workers and bikes in the city. In this paper, we study these barriers and address them by proposing a novel bike repositioning system, namely BikeBrain, which consists of a demand prediction model and a spatio-temporal bike repositioning algorithm. Specifically, to obtain accurate and real-time usage demand for efficient bike repositioning, we first present a prediction model ST-NetPre, which directly predicts user demand considering the highly dynamic spatio-temporal characteristics. Furthermore, we propose a spatio-temporal cooperative multi-agent reinforcement learning method (ST-CBR) for learning the worker-based bike repositioning strategy in which each worker in BSS is considered an agent. Especially, ST-CBR adopts the centralized learning and decentralized execution way to achieve effective cooperation among large-scale dynamic agents based on Mean Field Reinforcement Learning (MFRL), while avoiding the huge dimension of action space. For dynamic action space, ST-CBR utilizes a SoftMax selector to select the specific action. Meanwhile, for the benefits and costs of agents’ operation, an efficient reward function is designed to seek an optimal control policy considering both immediate and future rewards. Extensive experiments are conducted based on large-scale real-world datasets, and the results have shown significant improvements of our proposed method over several state-of-the-art baselines on the demand-supply gap and operation cost measures.