Edge computing facilitates the development and implementation of new mechanisms for data storage on edge servers located near users, thereby enabling low-latency edge data retrieval services. However, individual edge servers have limited storage capacity, which restricts their ability to meet edge users' diverse data demands. They often need to cooperate with neighbors. Edge data indexing systems play a crucial role in helping edge servers locate the requested data. Existing edge data indexing systems typically rely on static indexing structures, which may lead to significantly reduced query accuracy or excessive system memory overhead in dynamic edge data query (EDQ) scenarios, making it difficult to meet the high scalability requirements of edge computing systems. In this paper, we address these challenges and propose a novel adaptive edge data indexing system called Balloon, which dynamically scales memory usage to balance query accuracy and memory overhead as data volume varies at runtime. Specifically, each edge server maintains a resizable Adaptive Bloom Filter (ABF) for its local data, and builds a Balloon tree that aggregates the ABFs of neighboring servers within a latency constraint. The size of the Balloon tree dynamically scales with data volume to balance query accuracy and memory overhead. To validate Balloon, we implement and conduct comprehensive experiments using an edge storage system with 50 edge servers. The results indicate that, compared to the state-of-the-art edge indexing system, Balloon improves query accuracy by 26.74% and reduces query time by 18.16%. In the meantime, it reduces up to 35.66% memory overhead.

Mobile edge computing (MEC) can pre-cache deep neural networks (DNNs) near end-users, providing low-latency services and improving users’ quality of experience (QoE). However, caching all DNN models at capacity-limited edge servers is difficult, and the impact of model loading time on QoE remains underexplored. We explore dynamic DNNs by disassembling a complete DNN model into interrelated submodels to enable fine-grained joint optimization of submodel caching and request routing to balance inference precision and loading latency. In this paper, we study the joint dynamic model caching and request routing problem in MEC networks, aiming to maximize user request inference precision under constraints of server resources, latency, and model loading time. We propose CoCaR, an offline algorithm based on linear programming and random rounding that optimizes joint decisions with a provable performance bound. Furthermore, we develop an online extension, CoCaROL, to adapt to dynamic and unpredictable request patterns. The simulation results demonstrate that CoCaR improves the average inference precision for user requests by 40.1% over state-of-the-art baselines. In addition, CoCaR-OL achieves an improvement of at least 32.3% in users’ QoE over competitive baselines.

Abstract Single image super‐resolution (SISR) is a promising research direction in computer vision and image processing for improving the visual perception of low‐quality images. In recent years, deep learning algorithms have driven tremendous development in SR, and SR methods based on various network architectures have significantly improved the quality of reconstructed images. Although there has been a large amount of reviews focusing on SISR, few studies have focused specifically on network architectures for SISR. This paper aims to provide a systematic overview of the design ideas of SISR using multiple architectures, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Transformer, and Diffusion model. In addition, an experimental analysis and comparison of state‐of‐the‐art SR algorithms have been performed on publicly available quantitative and qualitative datasets. Finally, some future directions are discussed that may help other community researchers.

In an edge storage system, popular data can be stored on edge servers to enable low-latency data retrieval for nearby users. Suffering from constrained storage capacities, edge servers must process users' data requests collaboratively. For sourcing data, it is essential to find out which edge servers in the system have the requested data. In this paper, we make the first attempt to study this edge data query (EDQ) problem and present EDIndex, a distributed Edge Data Indexing system to enable fast data queries at the edge. First, we introduce a new index structure named Counting Bloom Filter (CBF) tree for facilitating edge data queries. Then, to improve query performance, we enhance EDIndex with a novel index structure named hierarchical Counting Bloom Filter (HCBF) tree. In EDIndex, each edge server maintains an HCBF tree that indexes the data stored on nearby edge servers to facilitate data sourcing between edge servers at the edge. The results of extensive experiments conducted on an edge storage system comprised of 90 edge servers demonstrate that EDIndex 1) takes up to 8.8x less time to answer edge data queries compared with state-of-the-art edge indexing systems; and 2) can be implemented in practice with a high query accuracy at low initialization and maintenance overheads.

Multi-access Edge Computing (MEC) has emerged as a new distributed computing paradigm for its ability to offer low-latency services to users. Suffering from constrained computational resources because of their limited physical sizes, edge servers usually cannot handle all the incoming compute tasks on time when they operate independently. Thus, they need to cooperate by peer-offloading. Incentive and trust are the two major challenges towards to cooperative computing among edge servers operating in a distrusted environment. Another specific challenge in the MEC environment is to facilitate incentive and trust in a decentralized manner. This article proposes CoopEdge+, a novel blockchain-based decentralized platform, to drive and support cooperative multi-access edge computing to tackle these challenges in a systematic manner. On CoopEdge+, an edge server can publish a compute task for other edge servers to contend for. A winner is selected from candidate edge servers as the task executor based on their reputation to perform the compute task. After that, CoopEdge+ employs a random leader election scheme to elect a task recorder without revealing its leadership until its consensus epoch. The task recorder will coordinate a consensus among edge servers to record the task executor's performance on blockchain. We implement CoopEdge+ based on Hyperledger fabric and evaluate it experimentally against a baseline implementation and three state-of-the-art implementations in a simulated MEC environment. The results validate the usefulness of CoopEdge+ and demonstrate its performance.

Particulate matter with a diameter of less than 2.5 microns (PM2.5) in the air is one of the most critical pollutants related to air quality. Exposure to high levels of PM2.5, which can be inhaled and carry harmful chemicals deep into the lungs and bloodstream, can have acute or chronic adverse health effects. A reliable, convenient and low-cost method to obtain PM2.5 concentration can help people improve their awareness of it. At the same time, it can also provide a certain reference for the prevention and control of haze, for air purification and for some other work to reduce the harm of air pollution to human health. In this paper, from the perspective of deep learning, with the help of photos that can be taken anywhere, we combined convolution neural network and support vector regression machine to estimate PM2.5. The method used in this paper is compared with other methods using the same dataset to prove its effectiveness.

Layover is a kind of geometric distortion in radar systems with side-look imaging, especially in mountainous and dense urban areas. It causes phase distortion and alters target characteristics in the acquired images, which directly hinders the application of radar images. In this letter, the multilayer feature fusion attention mechanism (MF2AM) is proposed to extract layover from interferometric synthetic aperture radar (InSAR) imagery automatically. First, the SAR image, the corresponding coherence map, and interferometric phases are channel-fused to enhance semantic information of layover areas. Then, the fused image is fed into MF2AM to extract the essential features of layover. Finally, the detection results are produced via MF2AM. MF2AM consists of the encoder and the decoder. The encoder contains three parts: the resnet101, attention-based atrous spatial pyramid (AASP), and the semantic embedding branch (SEB). In the decoder, step decoding is used to better fuse high- and low-level features and improve the effect of edge segmentation. To verify the proposed method, the images of millimeter wave InSAR system are used for the experiment, and the performance is compared with DeepLabV3+ and Geospatial Contextual Attention Mechanism (GCAM). The results show that the MF2AM has achieved obvious performance advantages. The average pixel accuracy and average intersection over union (IOU) are 0.9601 and 0.9310, respectively, and the average test time is only 7.97 s.

Edge computing (EC) has recently emerged as a novel computing paradigm that offers users low-latency services. Suffering from constrained computing resources due to their limited physical sizes, edge servers cannot always handle all the incoming computation tasks timely when they operate independently. They often need to cooperate through peer-offloading. Deployed and managed by different stakeholders, edge servers operate in a distrusted environment. Trust and incentive are the two main issues that challenge cooperative computing between them. Another unique challenge in the EC environment is to facilitate trust and incentive in a decentralized manner. To tackle these challenges systematically, this paper proposes CoopEdge, a novel blockchain-based decentralized platform, to drive and support cooperative edge computing. On CoopEdge, an edge server can publish a computation task for other edge servers to contend for. A winner is selected from candidate edge servers based on their reputations. After that, a consensus is reached among edge servers to record the performance in task execution on blockchain. We implement CoopEdge based on Hyperledger Sawtooth and evaluate it experimentally against a baseline and two state-of-the-art implementations in a simulated EC environment. The results validate the usefulness of CoopEdge and demonstrate its performance.

This paper presents a 10-bit SAR ADC with a sampling rate of 200 MS/s. To reduce area and power consumption, the ADC adopts a split-capacitor DAC, where the gain error is estimated and subsequently removed by means of a PRBS signal injected into the DAC and detected at the ADC output. The ADC has been designed and fabricated in a 22nm FD-SOI CMOS process, and achieves an SNDR of 48.7 dB and an SFDR of 66.7 dB for input frequencies up to the sampling frequency, with a power consumption of 4.3mW and an FoM of 152 dB.

The prediction of flight delays plays a significantly important role for airlines and travellers because flight delays cause not only tremendous economic loss but also potential security risks. In this work, we aim to integrate multiple data sources to predict the departure delay of a scheduled flight. Different from previous work, we are the first group, to our best knowledge, to take advantage of airport situational awareness map, which is defined as airport traffic complexity (ATC), and combine the proposed ATC factors with weather conditions and light information. Features engineering methods and most state-of-the-art machine learning algorithms are applied to a large real-world data sources. We reveal a couple of factors at the airport which has a significant impact on flight departure delay time. The prediction results show that the proposed factors are the main reasons behind the flight delays. Using our proposed framework, an improvement in accuracy for flight departure delay prediction is obtained.

Correct estimation of symbol timing, Carrier Frequency Offset (CFO), and Signal-to-Noise Ratio (SNR) is crucial in Orthogonal Frequency Division Multiplexing (OFDM) communication. Typically, high estimation accuracy is desired, but often comes with increased complexity. Which has a direct repercussion in energy consumption. In this article, an architecture based on Sign-Bit estimation with low complexity, and hence low power dissipation, is presented. The architecture, is capable of estimating the afore-mentioned parameters in virtually any OFDM standard. The proof of concept has been fabricated in 65nm CMOS technology with low-power high-VT cells. Measurements performed with supply voltage of 1.2V. resulted in a power dissipation of 350 μW, 6 times smaller to that of an equivalent 8-bit architecture, and the lowest power density reported in literature.