Blockchain, with its immutability and decentralization, drives innovation in finance and supply chain, but the growing data volume makes storing complete ledger replicas impractical for users, especially in the resource-constrained Internet of Thing (IoT) scenarios. Existing solutions focus on nodes storing only a partial ledger to alleviate storage burdens. Nonetheless, these approaches prioritize storage optimization by minimizing the query cost and lack control over storage cost. Furthermore, these approaches overlook the relationships between network users, thus failing to fully measure the future query cost. Thus, this article proposes BSSN, a blockchain storage technology based on social networks. The combined use of storage cost and query cost is introduced for the first time to formulate the node allocation optimization (NAO) problem, and the multipopulation genetic ant colony (MGAC) algorithm will be employed to derive node allocation strategies. Specifically, we address three technical challenges: 1) to predict the transactions that nodes will participate in the future, we employ the social ties to obtain the access frequencies among users; 2) to strike a balance between the storage cost and query cost, we jointly model the two costs as a multiobjective optimization problem to formulate the NAO problem; and 3) to solve the NP-hard NAO problem, we use the MGAC algorithm, where the storage and query populations collaboratively search for solutions based on four operations. Extensive experiments indicate that compared with existing work, BSSN can reduce the average query cost to 67% with its adjustable storage cost, ensuring a balanced data storage among users.

Longitudinal MRI datasets have important implications for the study of degenerative diseases because such datasets have data from multiple points in time to track disease progression. However, longitudinal datasets are often incomplete due to unexpected quits of patients. In previous work, we proposed an augmentation method TR-GAN that can complement missing session data of MRI datasets. TR-GAN uses a simple U-Net as a generator, which limits its performance. Transformers have had great success in the research of computer vision and this paper attempts to introduce it into longitudinal dataset completion tasks. The multi-head attention mechanism in Transformer has huge memory requirements, and it is difficult to train 3D MRI data on GPUs with small memory. To build a memory-friendly Transformer-based generator, we introduce a Hilbert transform module (HTM) to convert 3D data to 2D data that preserves locality fairly well. To make up for the insufficiency of CNN-based models that are difficult to establish long-range dependencies, we propose an up/down sampling (STU/STD) module that combines the Swin Transformer module and CNN module to capture global and local information simultaneously. Extensive experiments show that our model can reduce MMSE by at least 7.16% compared to the previous state-of-the-art method.

Alzheimer's Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the doctor. In this paper, we proposed a classification method based on multi-modal data including Electroencephalogram (EEG), eye tracking and behavioral data for early diagnosis of AD and MCI. Paradigms with various task difficulties were used to identify different severity of dementia: eye movement task and resting-state EEG tasks were used to detect AD, while eye movement task and delayed match-to-sample task were used to detect MCI. Besides, the effects of different features were compared and suitable EEG channels were selected for the detection. Furthermore, we proposed a data augmentation method to enlarge the dataset, designed an extra ERPNet feature extract layer to extract multi-modal features and used domain-adversarial neural network to improve the performance of MCI diagnosis. We achieved an average accuracy of 88.81% for MCI diagnosis and 100% for AD diagnosis. The results of this paper suggest that our classification method can provide a feasible and affordable way to diagnose dementia.

Sleep posture recognition is practically important in various scenarios such as sleep healthcare, bedridden patient care, and chronic disease diagnosis. With concerns of user privacy preserving, we prefer the wireless sensing methods to computer vision methods when dealing with sleep posture recognition. However, the existing wireless sensing methods suffer from at least one of the following major limitations: (i) difficult to deploy in practice; (ii) few posture categories; (iii) insufficient accuracy; (iv) poor generalization ability. In this paper, we use commercial-off-the-shelf (COTS) mmWave radar to implement a sleep posture recognition system called PosMonitor. When designing the PosMonitor system, we need to address the following challenging issues. First, we propose an angle purification method based on multi-frame joint analysis to alleviate the sparsity and instability of the point cloud. Then, we endow the point cloud with respiratory features to enhance its representation of the sleep posture. Further, to make the system applicable to different users, we extract relative respiratory features by normalization to overcome individual differences. Extensive experimental results show that our PosMonitor system can achieve 98% accuracy on average in recognizing 6 typical sleep postures and has good reliability across different conditions.

The ubiquity of acoustic devices and the fine-grained sensing of acoustic signals have made acoustic device tracking a popular option. We propose to expand the use of commercial devices with microphones as an extension of the audio system to support intelligent applications, such as VR/AR. This paper introduces a novel 6D acoustic pose estimation system. To realize device-based pose estimation, most existing systems deploy multiple speakers. However, due to limited inaudible bandwidth, concurrent transmissions with multiple speakers pose challenges in balancing resolution and frame rate. To address this problem, we design 2×Track, a band multiplexing signal model that doubles the availability of limited bandwidth by utilizing a unique encoding strategy for concurrent transmissions. We also propose solutions to enhance signal feature estimation and implement a 6DoF pose tracking scheme tailored for distributed systems. The prototype is deployed on a typical circular microphone array, and experimental results show that 2×Track achieves a median position and orientation error of 7.6mm and 4.1°, respectively, in a 4-speaker setup. Our extended applications on commercial devices also showcase the versatility of our system, particularly in face orientation detection, air mouse and drone tracking.

A wide range of scenarios, such as warehousing, and smart manufacturing, have used RFID mobile robots for the localization of tagged objects. The state-of-the-art RFID-robot based localization works are based on the premise of stable speed. However, in reality this assumption can hardly be guaranteed because Commercial-Off-The-Shelf (COTS) robots typically have inconsistent moving speeds, and a small speed inconsistency will cause a large localization error. To this end, we propose a Speed Inconsistency-Immune approach to mobile RFID robot Localization (SILoc) system, which accurately locates targets when the robot moving speed varies or is even unknown. We propose an optimized unwrapping method to maximize the use of data, and a lightweight algorithm to calculate the locations in both 2D and 3D spaces. By utilizing the characteristics of tag-antenna distance and combining the phase data from multiple antennas, SILoc can effectively eliminate the side effects of speed inconsistency. To increase the flexibility, we further optimize the system and propose SILoc $+$ , which enables the system to achieve localization with part of the data, keeping speed inconsistency-immune. Extensive experiments demonstrate that SILoc and SILoc $+$ can achieve a centimeter-level localization accuracy in the scenario with an inconsistent or unknown robot moving speed.

In large-scale retrieval, hash learning is favored by people owing to its fast speed. Nowadays, many hashing methods based on deep learning are proposed, because they have better performance than traditional feature representation methods. Both in supervised hash learning and unsupervised hash learning, similarity matrix is used in the objective function. In the similarity matrix, if two images share at least one label, the similarity is "1", otherwise it is "0". However, this kind of similarity can not reflect the similarity ranking of multi-label images well, which is vulnerable to pixel interference. Therefore, in order to improve the retrieval accuracy of multi-label data, we improve the traditional deep learning hashing method by dividing the multi-label images into "strong similarity" and "weak similarity". In addition, although the deep neural network can judge the label of the images directly through the pixels, it does not understand the high-level semantics of the images. Hence, we take the feature invariance of the images into consideration, which means that the transformed image should have the same feature representation with the original image. In this way, we propose a novel Deep Hash learning method based on Feature-Invariant representation (FIDH), which focuses on deep understanding rather than deep learning. Experiments on common single-label and multi-label datasets show that our method obtains better performance than state-of-the-art methods in large-scale image retrieval.

Fine-grained recognition of human manipulation activities on objects is crucial in the era of human-computer-object integration. However, there is a lack of solutions for simultaneous recognition of human identity, manipulation activities (including drawing and rotation), and manipulated objects. Therefore, we propose an RF-Camera system that combines RFID and computer vision techniques to address this challenge in multi-person and multi-object scenarios. In RF-Camera, we employ a skeleton-assisted method to extract facial images of target individuals, enabling precise recognition of their identities. To identify manipulation activities, we analyze the 3D hand trajectory and fingertip vector angle, differentiating drawing and rotation manipulation activities. Additionally, we model target person?s hand movements to predict phase data of the target tag, enabling the determination of person-object relationships. Implementing RF-Camera using COTS RFID and Kinect devices involves overcoming challenges such as extracting effective data from noisy streams, predicting virtual phase data considering hand-tag offset, and ensuring high tag reading rates in tag-dense scenarios. We conducted experiments involving six participants performing object manipulation activities, including drawing letters/symbols and rotating movements. Extensive experimental results show that RF-Camera achieves over 90% accuracy in recognizing person identity, manipulation activities, and person-object matching in most conditions.

With the explosive growth of internet information, cross-modal retrieval has become an important and valuable frontier hotspot. Due to its low storage consumption and high search speed, deep hashing has achieved significant success in cross-modal retrieval. Current research on unsupervised cross-modal hashing algorithms mainly focuses on two aspects: extracting high-level semantic information from given instances' raw data and designing network structures suitable for unsupervised learning. However, despite the abundance of unsupervised method research found in the literature, many current studies overlook the fact that the data distributions of different modalities are highly distinct. In fact, asymmetric network structures are more in line with cross-modal data learning. Therefore, this paper proposes an asymmetric deep cross-modal unsupervised hashing method based on generative adversarial networks (referred to as UDCMH-GAN algorithm). This method utilizes the image network channel as the reconstruction network to learn more valuable high-level semantic information, while the text network is set to a conventional network structure. The introduction of generative and adversarial mechanisms aims to achieve better modality fusion and bridge the semantic gap. The proposed method is validated on widely used datasets, and the results demonstrate that asymmetric learning methods are indeed more reasonable and accurate for different modalities.

Wireless signal analytics in IoT systems can enable various promising wireless sensing applications such as localization, anomaly detection, and human activity recognition. As a matter of fact, there are significant correlations in terms of dimension, spatial and temporal aspects among wireless signals from multiple sensors. However, none of the wireless sensing research currently in use directly incorporates or exploits the signal correlations. Therefore, there is still substantial scope for improvement in regards to accuracy and robustness. We are introducing a novel framework called Signal Correlation Learning (SCL). This framework utilizes a directed graph to explicitly represent the signal correlation across various wireless sensors. We use signal embedding to depict the correlation features of a multi-dimensional sensor that arise from a multi-sensor system. Then, we perform Kullback-Leibler (KL) divergence on embedding vectors of any pair of sensors in the system to construct a subgraph at a given time point, which can measure the spatial signal correlation of sensors. Subsequently, several subgraphs spanning a specific time frame are fused into a coherent universal graph based on the small-world theory. This universal graph represents the three types of signal correlation simultaneously. A signal correlation aggregation structure is utilized to extract the features from the universal graph. These features can be used to address target sensing problems. We implement SCL in real RFID, Bluetooth, WIFI, and Zigbee systems, and evaluate its performance in three common wireless sensing problems including localization, anomaly detection, and human activity recognition. Extensive experiments demonstrate that our SCL framework significantly outperforms state-of-the-art wireless sensing algorithms by increasing 80% similar to 190% in terms of accuracy, and by increasing 160% similar to 220% in terms of robustness.

Serverless computing typically relies on the third-party forwarding method to transmit data between functions. This method couples control flow and data flow together, resulting in significantly slow data transmission speeds. This challenge makes it difficult for the serverless computing paradigm to meet the low-latency requirements of web services. To solve this problem, we propose decoupling the control flow from the data flow, enabling direct data transfer between functions. We introduce Fuyao, the first intermediate data transfer solution capable of reducing data transfer latency to the sub-millisecond level. Fuyao provides four different data transfer methods to cater to diverse data transfer requirements within or between nodes. For function pairs that communicate frequently, Fuyao builds a stateful direct connection between them, enabling rapid inter-function data exchange. We evaluate Fuyao using real-world representative benchmarks. Experimental results show that Fuyao outperforms state-of-the-art systems by up to 57x on latency.

Voice command systems have been widely deployed on many smart devices for remote control. To further enrich the intelligence of these smart devices, the location of sound plays an important role in context-aware acoustic services. Despite initial steps made toward reliable voice localization, the state-of-the-arts rely on prior knowledge of device location, device orientation and an indoor electronic map. To mitigate this additional cost, this article presents VoiceMap, an autonomous mapping system of acoustic devices for voice localization. The insight behind VoiceMap is to explore the cooperation of sweeping robots and voice devices. Specifically, the sweeping robot is responsible for exploring the electronic map of the environment, while the microphone array is responsible for localizing the sweeping robot, so that we can establish the positional relationship between them. The core challenges are how to accurately locate the continuously moving robot, and how to synchronize the coordinate systems of the sweeping robot and the voice devices. To this end, we first design an inertial-based super-resolution method to estimate the angle of arrival (AoA) with respect to the robot. Then, we develop an effective coordinate synchronization mechanism, so that VoiceMap can automatically locate the voice devices on the electronic map generated by the robot. Finally, we implement a prototype system using commercial devices, and conduct comprehensive experiments to verify the proposed system. The experimental results show that we can realize a median error of 0.12 m in terms of device localization.

Edge computing, as a computing paradigm that enables the decentralization of cloud computing services to the edge of the network, effectively addresses the issue of service unavailability caused by power constraints on end devices when handling user application requests. End users offload computational tasks and associated data to the infrastructures at the network edge. Even if executing tasks at the edge can reduce energy consumption and computational latency compared to local execution on end devices, offloading a large number of tasks consumes wireless channel resources and computational resources of the edge infrastructures, resulting in additional transmission costs and energy consumption. Moreover, competition among multiple users for limited resources at the edge nodes leads to a situation where it is challenging to balance the utilities of all the users and the Edge Service Provider (ESP). In this paper, we address the scenarios where both the battery capacity of end devices and the resource capacity at the edge are limited. We propose a computation offloading scheme based on a master-slave Stackelberg game. We provide theoretical proof of the existence of a unique Nash equilibrium in the proposed game and optimize the energy consumption and user benefits during the offloading process. Furthermore, ESP improves its revenue by servicing more user requests. The simulation results show that the proposed algorithm performs well in terms of energy consumption and user utility.

This chapter of research explores the strategic deployment of computing services at the edge of the network to optimize social welfare and system performance. It involves building models for where and how to place services within edge computing systems, considering factors such as demand, cost, and usability. Additionally, this chapter investigates dynamic pricing frameworks that allow for real-time pricing adjustments to operate resource allocation efficiently. These strategies aim to balance the economic objectives of service providers, such as profit maximization and cost minimization, with the quality of service delivered to users. The goal is to develop pricing and service placement mechanisms that are both economically beneficial and capable of meeting the latency, computational, and energy consumption demands of edge applications.

With the advent of Network Function Virtualization (NFV) and Mobile Edge Computing (MEC), outsourcing network functions (NFs) to the MEC is becoming popular among network service providers (NSPs), since it brings the scalability and flexibility for NF deployment and maintenance. Each user’s request will go through a service function chain (SFC), which consists of several virtual network functions (VNFs, software substitutions for traditional hardware-based middleboxes) in a specific order, and then get a response. Unlike conventional hardware-based middleboxes, VNFs are not very reliable due to potential software faults and host malfunctions. Thus, a sensible way is to add redundancy for the primary VNFs of an SFC to enhance its availability. Nevertheless, which MEC node to place each VNF, and how many backup instances are enough to ensure the availability requirement of each SFC? These issues have not yet been resolved. In this article, we present the availability-aware provision of SFC (APoS) in the MEC environment with the primary goal of maximizing the number of served requests while meeting the requirements and reliability expectations of SFCs. For the APoS, we have primarily addressed the following two fundamental challenges: (i) First, how to efficiently map these primary and backup VNFs to meet the availability requirements of SFCs? At this point, we formulate it as an integer nonlinear programming (INLP) under the limitation of each MEC node’s resources. This issue is NP-hard, and a novel binary N-back search method is proposed to derive the optimal solution for the primary and backup VNFs mapping; (ii) Second, how can we reduce the latency for users to access their desired SFCs? Then, we investigate how to minimize the average delay for all requests in each time slot. To solve this problem, we design an online service switching (OSS) method, which jointly considers the queuing delay, communication delay, and switching delay. It achieves the optimal solution with a theoretical guarantee. Finally, we evaluate the proposed methods with real-world datasets. The results demonstrate that, compared with the benchmarks, our practices can achieve approximately 20% request acceptance gain and up to 30% delay reduction, on average.

Mild cognitive impairment (MCI) is the preliminary stage of dementia, and has a high risk of progression to Alzheimer's disease (AD) in the elderly. Early detection of MCI plays a vital role in preventing progression of AD. Clinical diagnosis of MCI requires many examinations, which are highly demanding on hospital equipment and expensive for patients. Electroencephalography (EEG) offers a non-invasive and less expensive way to diagnose MCI early. In this paper, we propose a multi-modal fusion classification framework for MCI detection. We collect EEG data using a delayed match-to-sample task and analyze the differences between the two groups. Based on analysis results, we extract Power spectral density (PSD), PSD enhanced, Event-related potential (ERP) features in EEG signal along with physiological features and behavioral features of the subjects to classify MCI and healthy elderly. By comparing the effect of different features on classification performance, we find that the time-domain based ERP features are better than the frequency-domain based PSD or PSD enhanced features to overcome inter-individual differences to distinguish MCI, and these two features have good complementarity, fusing ERP and PSD enhanced features can greatly improve the classification accuracy to 84.74%. The final result shows that MCI and healthy elderly can be well classified by using this framework.

Links inevitably fail in expanding networks, leading to user-perceived service interruptions. To localize link failures quickly and accurately is thus essential, and route-aware active probing makes it possible. Given the limited routing capacity and high traffic overhead, cross verification enables a light-weight probing scheme using reachability verification for distinct subsets of crossed paths to pinpoint the exact faulty links. Aiming to quickly optimize the crossed path design, we propose pruning genetic algorithm (PGA), which builds a pruning module on top of genetic algorithm to consistently produce high-quality solutions across various networks and avoid slow convergence in an exponentially large solution space by eliminating redundant paths. PGA also introduces extra repair operations to guarantee solution feasibility after crossover and mutation. Our experimental results on real-world network topologies demonstrate that PGA achieves a significant reduction of 23.0% to 58.3% in probing cost and 23.0% to 45.3% in forwarding cost in seconds or even milliseconds compared to its counterparts.

Edge computing is migrating services from remote clouds to the network edge, where a vast amount of data is also flowing into edge nodes. In this context, the Edge Data-Sharing System (EDSS) enhances service quality by enabling edge nodes to cooperate. However, the EDSS is suitable for precise search and faces the existing high overhead when many users retrieve similar data. To solve the obstacle, this paper proposes a similarity-based edge storage system, SESS, which leverages the software-defined edge network to realize efficient storage and retrieval of similarity data. We first design RealminHash, a core module of SESS, for efficient signature and and index for each data. Then, SESS calculates the storage strategy based on the similarity between data. Importantly, SESS adjusts this strategy using periodic network information to ensure load balancing. Experimental results demonstrate that SESS realizes the nearest-neighbor storage while maintaining load balancing. SESS outperforms the well-known k-means and spectral clustering methods in terms of accuracy and latency and supports millisecond similar queries.

In-memory key-value stores are widely used in modern web services to support large-scale user requests by caching popular data. Their performance is critical, and BMC, the state-of-the-art work, builds an in-kernel cache and processes requests before the stack using eBPF to reduce the overhead of the kernel network stack. However, BMC fails to support stateful protocol TCP because pre-stack processing creates TCP state bias between the client and server.TCP is widely used by in-memory key-value stores, is even the only choice for some applications (e.g., Redis), and also suffers from performance issues. In this work, we present MiddleCache, a TCP-enabled in-memory key-value store acceleration design. Our key observation is that the TCP state bias of the client and server can be inferred and eliminated with packet length. The design of MiddleCache has two key parts: (i) A compact TCP state maintenance mechanism that accumulates packet lengths and applies corrections to the packet header, which realize TCP support within the constrains of eBPF. (ii) Lock-free accumulation counters that support high-performance concurrent access by utilizing Receive Side Scaling (RSS). Our experiments show that, compared with Memcached, MiddleCache reduces 56% processing latency on cache hit and achieves a 3.8× throughput improvement on Facebook-like small-size requests workload.

Mobile edge computing (MEC) is becoming increasingly popular due to its remarkable computing capacities in close proximity to end users or devices. With the widespread use of Industrial Internet of Things, more and more cloud service providers move their services to the edge of the network for a better quality of service and become MEC providers. These MEC providers require to rent wide area network (WAN) connections to transfer industrial data, which is a considerable expense. In this article, we propose a framework called Sublessor to reduce the WAN transmission cost for a group of cooperative MEC providers. The key idea of Sublessor is allowing some specific MEC providers to act as Internet transit brokers, transmitting not only their own network traffic but also the traffic of their partners under a reasonable reselling price. This article formulates the problem as a mixed-integer programming and finds the most suitable broker number and corresponding reselling price without damaging the profit of both brokers and partners by a deep-reinforcement-learning-based algorithm. Experimental results show that our algorithm can significantly reduce the traffic transmission cost by up to 35%.