Detecting blended Interest Flooding Attacks (IFA) and Collusive IFA (CIFA) in Named Data Networking (NDN) remains challenging due to the feature masking effect, where persistent IFA loads and transient CIFA pulses non-linearly overlap. Existing methods struggle to disentangle these superimposed signatures due to computational bottlenecks or limited temporal resolution. In this paper, we propose MST-Mamba, a unified detection framework built upon the Selective State Space Model (SSM). Our core innovations include a Multiscale Temporal Awareness (MTA) module to capture heterogeneous attack dynamics and a self-supervised pre-training strategy to eliminate topological interference. Experimental results demonstrate that MST-Mamba achieves over 99% accuracy with O(L) linear inference complexity. Even in complex blended scenarios, the model exhibits exceptional cross-topology generalization and low latency, providing a robust technical paradigm for securing NDN infrastructures.

Owing to the harsh underwater environment and limited energy replenishment, extending network lifetimes and achieving energy efficiency are critical challenges in underwater wireless sensor networks (UWSNs). In this paper, a novel multiobjective Arctic puffin optimization (MOAPO) method that is specifically tailored for clustering and routing in UWSNs is proposed. Within the proposed MOAPO framework, K-means++ clustering is first used to optimize the initial cluster head (CH) positions, thereby improving clustering performance and ensuring more effective coverage and resource utilization. A feedback-based mechanism is used to adaptively adjust the behavior conversion factor to balance global exploration and local exploitation, thereby avoiding premature convergence and improving the quality of the selected CHs. A fitness function that incorporates the node energy, communication distance, and CH selection frequency is constructed, with the weights dynamically tuned according to the current energy state of the network, which results in energy-efficient and energy-balanced CH selection. Based on the selected CHs, a multiobjective routing strategy in which the energy levels, delays, and packet loss rate are considered is applied to construct reliable data transmission paths. The simulation results confirm that compared with existing methods, the MOAPO method achieves lower energy consumption and a longer network lifetime, thus demonstrating superior robustness and adaptability.

Vehicular named data networking (VNDN), which integrates the principles of named data networks with vehicular ad hoc networks, represents a promising paradigm for future intelligent transportation systems. Nevertheless, VNDN faces significant hurdles, including broadcast storms from excessive interest packet flooding and reverse-path disruptions due to high vehicular mobility. To address these challenges, we introduce an adaptive forwarding with path optimization method. First, a dynamic caching algorithm is designed to optimize roadside unit storage efficiency and maximize cache hit rates. Second, a gated recurrent unit-based adaptive data forwarding mechanism is introduced to dynamically select optimal forwarders and preserve reverse paths via decentralized heartbeat detection and interface remapping, improving link reliability. Simulation outcomes demonstrate that the proposed approach significantly lowers data retrieval delays while curbing overall communication overhead.

Breast cancer remains a leading cause of mortality among women, with millions of new cases diagnosed annually. Early detection through screening is crucial. Using neural networks to improve the accuracy of breast cancer screening has become increasingly important. In accordance with radiologists' practices, we proposed using images from the unaffected side to create adversarial samples with critical medical implications in our adversarial learning process. By introducing beneficial perturbations, this method aims to reduce overconfidence and improve the precision and robustness of breast cancer classification. Our proposed framework is an adversarial quadruple-view classification network (NaFV-Net) incorporating images from both affected and unaffected perspectives. By comprehensively capturing local and global information and implementing adversarial learning from four mammography views, this framework allows for the fusion of features and the integration of medical principles and radiologist evaluation techniques, thus facilitating the accurate identification and characterization of breast tissues. Extensive experiments have shown the high effectiveness of our model in accurately distinguishing between benign and malignant findings, demonstrating state-of-the-art classification performance on both internal and public datasets.

Beyond the worst-case analysis of algorithms, the learning-augmented framework considers that an algorithm can leverage possibly imperfect predictions about the unknown variables to have guarantees tied to the prediction quality. We consider online non-clairvoyant scheduling to minimize the max-stretch under this framework, where the scheduler can access job size predictions. We present a family of algorithms: Relaxed-Greedy (RG) with an $O(\eta^3 \cdot \sqrt{P})$ competitive ratio, where $\eta$ denotes the prediction error for job sizes and $P$ the maximum job size ratio; Adaptive Relaxed-Greedy with an $O(\lambda^{0.5} \cdot \eta^{2.5} \cdot \sqrt{P})$ competitive ratio, where $\lambda$ denotes the error for the minimum job size; Predictive Relaxed-Greedy with an $O(\lambda^{0.5} \cdot \varphi^{0.5} \cdot \eta \cdot \max \\\{ \eta, \varphi \\\} \cdot \sqrt{P})$ competitive ratio, where $\varphi$ denotes the error for the maximum job size. We also present *${RG}^x$*, an algorithm that represents a trade-off between consistency and smoothness, with an $O(\eta^{2+2x} \cdot P^{1-x})$ competitive ratio. We introduce a general method using resource augmentation to bound robustness, resulting in *RR*-augmented *RG*, with a $(1 + \epsilon)$-speed $O(\min \\\{ \eta^3 \sqrt{P}, \frac{n}{\epsilon} \\\})$ competitive ratio. Finally, we conduct simulations on synthetic and real-world datasets to evaluate the practical performance of these algorithms.

With the widespread adoption of wearable devices, human activity recognition (HAR), which is an essential branch of multivariate time series classification (MTSC), has been broadly applied in areas such as intelligent health monitoring, smart homes, and sports training. Traditional time series processing methods focus on time-domain features and often neglect frequency-domain characteristics; however, much of the critical information related to human activities is usually reflected in the frequency components of signals. To address this issue, we propose a Fourier transform convolution fusion module for extracting frequency-domain features and integrating them with time-domain features. Based on this module, we further propose an innovative Fourier transform convolution network (FTCNet). FTC-Net efficiently integrates time-domain and frequencydomain features by combining the Fourier transform convolution fusion module with an efficient channel attention block (ECA block), which enhances the model's ability to extract key features. The experimental results show that FTC-Net achieved excellent classification performance across 11 HAR-related datasets, with an average accuracy of $87.85 \%$, significantly outperforming current mainstream models. The results of ablation studies also demonstrate the positive impact of frequency-domain features on model performance. FTC-Net offers a new solution for complex human activity recognition tasks and opens new research directions for integrating time-domain and frequency-domain features.

Vehicular Named Data Networking (VNDN) is a communication architecture that applies the concepts of Named Data Networking (NDN) to Vehicular Ad-hoc Networks (VANETs). In VNDN, reliable data transmission under vehicular mobility conditions is critical for Quality of Service (QoS) support. To address the challenges of broadcast storms and unstable communication links in VNDN, this paper proposes a Prediction-based Link Adaptive Forwarding Method (PLAFM), which effectively mitigates reverse path disruptions while reducing Interest packet flooding. Experimental results demonstrate that PLAFM significantly improves communication efficiency and data delivery reliability.

Asynchronous Byzantine Fault Tolerant (BFT) consensus protocols have garnered significant attention with the rise of blockchain technology. A typical asynchronous protocol is designed by executing sequential instances of the Asynchronous Common Sub-seQuence (ACSQ). The ACSQ protocol consists of two primary components: the Asynchronous Common Subset (ACS) protocol and a block sorting mechanism, with the ACS protocol comprising two stages: broadcast and agreement. However, current protocols encounter three critical issues: high latency arising from the execution of the agreement stage, latency instability due to the integral-sorting mechanism, and reduced throughput caused by block discarding. To address these issues, we propose Falcon, an asynchronous BFT protocol that achieves low latency and enhanced throughput. Falcon introduces a novel broadcast protocol, Graded Broadcast (GBC), which enables a block to be included in the ACS set directly, bypassing the agreement stage and thereby reducing latency. To ensure safety, Falcon incorporates a new binary agreement protocol called Asymmetrical Asynchronous Binary Agreement (AABA), designed to complement GBC. Additionally, Falcon employs a partial-sorting mechanism, allowing continuous rather than simultaneous block committing, enhancing latency stability. Finally, we incorporate an agreement trigger that, before its activation, enables nodes to wait for more blocks to be delivered and committed, thereby boosting throughput. We conduct a series of experiments to evaluate Falcon, demonstrating its superior performance.

Standing as a foundational element within blockchain systems, theByzantine Fault Tolerant(BFT) consensus has garnered significant attention over the past decade. The introduction of aDirected Acyclic Directed(DAG) structure into BFT consensus design, termed DAG-based BFT, has emerged to bolster throughput. However, prevalent DAG-based protocols grapple with substantial latency issues, suffering from a latency gap compared to non-DAG protocols. For instance, leading-edge DAG-based protocols named GradedDAG and BullShark exhibit a good-case latency of$4$and$6$communication rounds, respectively. In contrast, the non-DAG protocol, exemplified by PBFT, attains a latency of$3$rounds in favorable conditions. To bridge this latency gap, we propose Remora, a novel DAG-based BFT protocol. Remora achieves a reduced latency of$3$rounds by incorporating optimistic paths. At its core, Remora endeavors to commit blocks through the optimistic path initially, facilitating low latency in favorable situations. Conversely, in unfavorable scenarios, Remora seamlessly transitions to a pessimistic path to ensure liveness. Various experiments validate Remora's feasibility and efficiency, highlighting its potential as a robust solution in the realm of BFT consensus protocols.

Federated learning significantly enhances intelligent transportation systems by enabling collaborative model training across multiple clients, thereby improving overall performance. However, the involvement of multiple participants, each using oversized models for local data processing, leads to substantial communication volumes and reduced communication efficiency. To address this issue, we propose the Federated Client and Global (FEDCG) pruning method, employing a two-stage pruning strategy at both the client and server levels. This approach uses mutual information to assess the importance of individual neurons or filters within a neural network, allowing for global pruning on the server. Specifically, federated learning connects multiple sub-models within intelligent transportation systems, such as autonomous vehicles and vehicle detection models, by reducing redundant parameters through pruning. When handling differences between various models, we use a parameter aggregation strategy to ensure the effectiveness of the global model. Our method first performs preliminary pruning at the client side to reduce local communication overhead, followed by further pruning at the server side to aggregate effective parameters from each client into a global model. This global model is constructed based on the pruned client models to ensure efficiency and accuracy. Extensive experiments demonstrate that FEDCG effectively reduces communication overheads during both the uploading and downloading phases while maintaining high accuracy and robustness across various datasets and neural network architectures. This method provides a valuable tool for practical federated learning in intelligent transportation systems.

Data heterogeneity is a prevalent challenge in intelligent transportation systems (ITS), often arising from variations in traffic patterns across different regions or time periods. For instance, certain traffic events, such as congestion, may be more frequent in urban areas during peak hours, while other events, like accidents, might occur more often in suburban regions, leading to slightly skewed label distributions. While federated learning provides an effective solution for distributed data, its performance can degrade when client datasets exhibit such label skew. To address this, we propose a strategy that combines Gaussian mixture clustering with oversampling. Gaussian mixture clustering can handle overlapping data points in model parameters, but insufficient client samples may limit local model training. To overcome this, we introduce a Gaussian mixture-based oversampling method to generate additional samples, enhancing the robustness of federated learning under slightly skewed label scenarios. Our experiments demonstrate that this method outperforms or matches existing approaches, ensuring more reliable and accurate ITS applications.

Task scheduling in cloud computing is a challenging but crucial task for ensuring service quality and load balance. Mainstream scheduling algorithms, such as heuristic algorithms and reinforcement learning, have made progress in this area. However, online task scheduling algorithms, such as reinforcement learning, can pose computational challenges in scenarios with limited computational power and heterogeneous resources. Heuristic algorithms, which are more suitable for offline scheduling where the types and quantities of tasks are known in advance, also require substantial computational resources for online scheduling. In this work, we propose the end-edge-cloud (EEC) heterogeneous resources scheduling method (EHRSM) based on a recurrent neural network (RNN) model and particle swarm optimization (PSO). EHRSM uses an RNN model trained on a dataset generated by dynamic programming to recognize and cache online tasks, efficiently transforming online task scheduling into offline scheduling. Additionally, a PSO algorithm with Cantor expansion (CE) for coding optimization is used to complete the offline scheduling. Experimental results show that the method is effective in converting online scheduling to offline scheduling, reducing the average task completion time and waiting time. Compared with existing online scheduling methods, EHRSM reduces task completion time by up to 48.24%.

Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series applications. These factors make it difficult for users, particularly those without domain knowledge, to comprehend AI model decisions and obtain meaningful explanations. We propose CGS-Mask, a post-hoc and model-agnostic cellular genetic strip mask-based saliency approach to address these challenges. CGS-Mask uses consecutive time steps as a cohesive entity to evaluate the impact of features on the final prediction, providing binary and sustained feature importance scores over time. Our algorithm optimizes the mask population iteratively to obtain the optimal mask in a reasonable time. We evaluated CGS-Mask on synthetic and real-world datasets, and it outperformed state-of-the-art methods in elucidating the importance of features over time. According to our pilot user study via a questionnaire survey, CGS-Mask is the most effective approach in presenting easily understandable time series prediction results, enabling users to comprehend the decision-making process of AI models with ease.

Pain, a primary reason for seeking medical help, requires essential pain assessment for effective management. Studies have recognized electrodermal activity (EDA) signaling's potential for automated pain assessment, but traditional algorithms often ignore the noise and uncertainty inherent in pain data. To address this, we propose a learning framework predicated on data uncertainty, introducing two forms: a) subject-level stimulation-reaction drift; b) ambiguity in self-reporting scores. We formulate an uncertainty assessment using Heart Rate Variability (HRV) features to guide the selection of responsive pain profiles and reweight subtask importance based on the vagueness of self-reported data. These methods are integrated within an end-to-end neural network learning paradigm, focusing the detector on more accurate insights within the uncertainty domain. Extensive experimentation on both the publicly available biovid dataset and the proprietary Apon dataset demonstrates our approach's effectiveness. In the biovid dataset, we achieved a 6% enhancement over the state-of-the-art methodology, and on the Apon dataset, our method outperformed baseline approaches by over 20%.

Communication overhead remains a significant challenge in federated learning due to frequent global model updates. Essentially, the update of the global model can be viewed as knowledge transfer. We aim to transfer more knowledge through a compact model while reducing communication overhead. In our study, we introduce a federated learning framework where clients pre-train large models locally and the server initializes a compact model to communicate. This compact model should be light in size but still have enough knowledge to refine the global model effectively. We facilitate the knowledge transfer from local to global models based on pre-training outcomes. Our experiments show that our approach significantly reduce communication overhead without sacrificing accuracy.

Named data networking (NDN) has been regarded as a promising scheme for next-generation network architecture, with network security remaining a key issue. In NDN, a new Distributed Denial of Service (DDoS) attack model called the interest flooding attack (IFA) has emerged and poses a serious threat to NDN. In addition, to increase the complexity of detection and defense against attacks, a variant of the IFA called the collusive interest flooding attack (CIFA) has emerged recently. Although many IFA and CIFA detection methods have been proposed, most existing countermeasures focus solely on detecting either IFA or CIFA. More importantly, to the best of our knowledge, there is no research on effective detection scheme for scenarios where IFA and CIFA coexist in a blending attack yet. In this paper, motivated by the concept that network traffic exhibits time series characteristics, we propose an attack identification and detection scheme based on WEASEL (Word ExtrAction for time SEries cLassification) aimed at accurately identifying and detecting the blending IFAs in NDN.

A wireless body area network (WBAN) is a crucial technology for implementing intelligent health monitoring. Traditional WBANs focus on the monitoring and classification of single physiological signals, which cannot meet the comprehensive requirements for monitoring human health and behavior. This paper proposes a local feature channel fusion convolutional neural network (CNN) model that can monitor and analyze multiple physiological signals collected by WBANs and can be used for disease identification and human activity recognition. The model employs convolution kernels to perform convolution operations on each channel, extracting local features of each channel and then performing channel fusion convolution operations to effectively integrate information between different channels. Additionally, the model incorporates an attention mechanism to dynamically adjust feature weights, highlight important features, and suppress redundant information. Experiments conducted on 11 WBAN-related datasets from the UEA database demonstrate that the proposed model achieves optimal classification performance.

The deep neural networks are envisaged for the early disease diagnosis from medical images. However, in the early stage of the disease, the medical images of patients and healthy people have only subtle visual differences. Distinguishing the medical images for early diagnosis belongs to the Fine-Grained Visual Classification (FGVC) task. Many recent works are based on a standard FGVC learning paradigm: locate the discriminative regions first and then classify by fusing the information of these regions. However, it is still not enough for medical images. Because the shape and size of the lesions are variable, and the relationship between lesions and the background is complex. In order to solve these problems, we propose a fine-grained lesion classification framework for early auxiliary diagnosis. We first locate and extract multiple lesions with different sizes and shapes from the original image and then fuse the feature of lesion and background based on attention mechanism. As shown by experiment results in two real-world clinical data sets, our model can locate accurately and perform better.

Shipboard integrated power systems (SIPSs) are usually equipped with multiple pulsed power loads (PPLs). In complex scenarios with strict operational requirements, the performance of the SIPS is greatly affected by the output energy of PPLs, especially in achieving high suppressive capability in a short period. To maximize the short-time output efficiency of PPLs in emergency situations, the aperiodic scheduling method is studied, aiming at optimizing the coordination of multiple PPLs and energy storage. The proposed aperiodic scheduling model aims to maximize the total utility of PPLs in the given period. The model fully considers constraints related to energy storage, system power balance, aperiodic charging and discharging process, time sequence, and charging power. To solve this complex non-linear model, a bi-layer dynamic programming algorithm is proposed. Simulation tests of SIPS performance are carried out to validate the advantage of the proposed method. The results show that the performance of single PPL and multi PPLs with aperiodic scheduling is improved by 42.3% and 25.7% respectively compared with periodic scheduling.

In recent years, the Internet of Vehicles (IoV) has experienced significant growth, but the lack of effective secret key establishment remains a security concern due to the dynamic and ad-hoc nature of IoV communications. Physical layer key generation has emerged as a promising solution for establishing a pair of cryptographic keys in a lightweight and information-theoretic secure manner. However, previous works have primarily focused on legacy communication technologies, such as Wi-Fi, ZigBee, and 5 G, which are limited to short-range IoV communications. With the emergence of Long-range (LoRa) communication technology, which features long-range, low power, and extremely low data rates, new challenges arise for key generation in long-range IoV scenarios. This paper presentsVehicle-Key, a secret key generation system designed to secure LoRa-enabled IoV communications.Vehicle-Keypresents an innovative scenario adaptive deep learning model that performs channel prediction and quantization concurrently while reducing the training cost through a data augmentation pipeline and enhancing the model's generalization using a domain-adaption method. Additionally, we propose a bloom filter-assisted autoencoder-based reconciliation method to significantly improve the key agreement rate. Comprehensive real-world experiments show thatVehicle-Keysurpasses the State-of-the-Art, achieving a 15.26%–50.35% improvement in key agreement rate and a 9–15× increase in key generation rate. Moreover, the proposed method attains a 4.37--9.33% improvement when adapted to new scenarios with limited data sizes. A security analysis demonstrates thatVehicle-Keyis resilient against several common attacks. Furthermore, we implementVehicle-Keyon a Raspberry Pi and demonstrate its ability to execute within 3.5 ms.

Intraoperative hypotension (IOH) events warning plays a crucial role in preventing postoperative complications, such as postoperative delirium and mortality. Despite significant efforts, two fundamental problems limit its wide clinical use. The well-established IOH event warning systems are often built on proprietary medical devices that may not be available in all hospitals. The warnings are also triggered mainly through a predefined IOH event that might not be suitable for all patients. This work proposes a composite multi-attention (CMA) framework to tackle these problems by conducting short-term predictions on user-definable IOH events using vital signals in a low sampling rate with demographic characteristics. Our framework leverages a multi-modal fusion network to make four vital signals and three demographic characteristics as input modalities. For each modality, a multi-attention mechanism is used for feature extraction for better model training. Experiments on two large-scale real-world data sets show that our method can achieve up to 94.1% accuracy on IOH events early warning while the signals sampling rate is reduced by 3000 times. Our proposal CMA can achieve a mean absolute error of 4.50 mm Hg in the most challenging 15-minute mean arterial pressure prediction task and the error reduction by 42.9% compared to existing solutions.

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly.

Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods.

Non-intrusive load monitoring (NILM) is a cost-effective energy disaggregation means to estimate the energy consumption of individual appliances from a central load reading. Learning-based methods are the new trends in NILM implementations but require large labeled data to work properly at end-user premises. We first formulate an unsupervised multi-source domain adaptation problem to address this challenge by leveraging rich public datasets for building the NILM model. Then, we prove a new generalization bound for the target domain under multi-source settings. A hybrid loss-driven multi-source domain adversarial network (HLD-MDAN) is developed by approximating and optimizing the bound to tackle the domain shift between source and target domains. We conduct extensive experiments on three real-world residential energy datasets to evaluate the effectiveness of HLD-MDAN, showing that it is superior to other methods in single-source and multi-source learning scenarios.

With the expansion scale of interconnected power systems and refined state perception, scientific power calculations become more complex and diverse. They need faster computation speed and better scalability to support power flow calculation, reactive power optimization, and static/transient stability analysis for unit scheduling. Therefore, this paper proposes a novel cloud data center task mapping algorithm of the Stoer-Wagner binary tree (SWBT) to support accelerated executions of these calculations. Firstly, based on the block bordered-diagonal form of the admittance matrix, high-time complexity scientific power calculations are transformed into a unified multi-task decomposition-coordination directed acyclic graph (DC-DAG). And then, the critical tasks in this DC-DAG are found and the virtual machines encapsulating them are matched with physical machines in the data center preferentially. Finally, on CloudSim, a cloud computing platform, the multi-job mixed experiments of 118-13659 bus power systems are carried out. In addition, real-time workload performance is enhanced in two very large real-world power systems. Studies illustrate that SWBT can improve the underlying physical machine resource utilization and reduce data interaction transmission hops to achieve better computing acceleration performance.

A 6-hour early detection of sepsis leads to a significant increase in the chance of surviving it. Previous sepsis early detection studies have focused on improving the performance of supervised learning algorithms while ignoring the potential correlation in data mining, and there was no reliable method to deal with the problem of incomplete data. In this paper, we proposed the Denoising Transformer AutoEncoder (DTAE) for the first time combining transformer and unsupervised learning. DTAE can learn the correlation of the features required for early detection of sepsis without the label. This method can effectively solve the problems of data sparsity and noise and discover the potential correlation of features by adding DTAE enhancement module without modifying the existing algorithms. Finally, the experimental results show that the proposed method improves the existing algorithms and achieves the best results of early detection.

Recent years have witnessed the remarkable growth of the Internet of Vehicles (IoV). Due to the high dynamics and ad-hoc nature of IoV communication, the lack of effective secret key establishment in IoV remains a security bottleneck. Physical layer key generation has emerged as a promising technology to establish a pair of cryptographic keys in a lightweight and information-theoretic secure way. However, prior works mainly focus on legacy communication technologies such as Wi-Fi, ZigBee, and 5G which can only achieve short range IoV communications. The emergence of Long-range (LoRa) communication technology that features long-range, low power, and extremely low data rate, brings new challenges for key generation in long range IoV scenarios. In this paper, we present Vehicle-Key, which is a secret key generation system to secure LoRa-enabled IoV communications. In Vehicle-Key, we design a novel deep learning model that can achieve channel prediction and quantization simultaneously. Additionally, we propose an autoencoder-based reconciliation method that improves the key agreement rate significantly. Extensive real-world experiments show that Vehicle-Key improves the key agreement rate by 15.10%–49.81% and key generation rate by 9–14× compared with the state-of-the-art. Security analysis demonstrates that Vehicle-Key is secure against several common attacks. Moreover, we implement Vehicle-Key on a Raspberry Pi and show that it can be executed in 3.4 ms.

The recent revival in learning theory gives us improved capabilities for accurate predictions and increased opportunities for performance enhancement. This work extends the research agenda of augmenting algorithms with predictions to one of the central scheduling problems – soft real-time scheduling on single and parallel machines to minimize the mean response time. We design an algorithm, PEDRMLF (Predictions Enhanced Dynamic Randomized MultiLevel Feedback), that incorporates job size predictions, achieving an optimal competitive ratio under perfect predictions and the best-known competitive ratio under any predictions. PEDRMLF is the first algorithm that simultaneously achieves optimal consistency and bounded robustness. Simulations show that the proposed algorithm performs close to the theoretically optimal bound while consistently outperforming state-of-the-art benchmarks.

We consider the problem of non-clairvoyant scheduling on single machine to minimize the total flow time with job size predictions. The existing algorithm achieves 2-consistency to predictions, but no algorithm can simultaneously attain bounded robustness. This work finds a sufficient condition for any algorithm to achieve optimal O(P)-robustness, where P is the maximum ratio of any two job sizes. We give the first algorithm that achieves optimal robustness up to a constant multiplicative factor and optimal consistency using this condition. Finally, for addressing small prediction errors, we present an algorithm that we conjecture to achieve the optimal O(η^2) competitive ratio, where η is the prediction error. Proving the claimed bound is our ongoing work.

Smart space has emerged as a new paradigm that combines sensing, communication, and artificial intelligence technologies to offer various customized services. A fundamental requirement of these services is person identification. Although a variety of person-identification approaches has been proposed, they suffer from several limitations in practical applications, such as low energy efficiency, accuracy degradation, and privacy issue. This article proposes an energy-harvesting-based privacy-preserving gait recognition scheme for smart space, which is named PrivGait. In PrivGait, we extract discriminative features from 1-D gait signal and design an attention-based long short-term memory (LSTM) network to classify different people. Moreover, we leverage a novel Bloom filter-based privacy-preserving technique to address the privacy leakage problem. To demonstrate the feasibility of PrivGait, we design a proof-of-concept prototype using off-the-shelf energy-harvesting hardware. Extensive evaluation results show that the proposed scheme outperforms state of the art by 6%–10% and incurs low system cost while preserving user’s privacy.

As a critical infrastructure of urban water systems, sewer networks suffer from serious corrosion and odor problems, which can be controlled by chemical dosing. It is a challenging task to optimize chemical distribution in such a hybrid system with continuous hydraulic flow, discrete pump operations, and dynamic constraints. In this article, we study real-time control of multiple pumps to achieve the desired chemical distribution in a sewer network. A novel hybrid optimization approach is developed, which involves an event-triggered scheme triggered by predicting proper pumping events at uncontrolled pumping stations, and an improved nature-inspired elephant herding optimization (iEHO) algorithm for scheduling pumping at controllable pumping stations. The proposed method is validated through simulation studies of a real-life sewer network using real measured data. Our strategy significantly improves chemical distribution with reduced costs, despite an astronomic searching space. The iEHO algorithm outperforms the genetic algorithm in terms of the quality of solutions and convergence efficiency.

Wireless capsule endoscopy is a modern non-invasive Internet of Medical Imaging Things that has been increasingly used in gastrointestinal tract examination. With about one gigabyte image data generated for a patient in each examination, automatic lesion detection is highly desirable to improve the efficiency of the diagnosis process and mitigate human errors. Despite many approaches for lesion detection have been proposed, they mainly focus on large lesions and are not directly applicable to tiny lesions due to the limitations of feature representation. As bleeding lesions are a common symptom in most serious gastrointestinal diseases, detecting tiny bleeding lesions is extremely important for early diagnosis of those diseases, which is highly relevant to the survival, treatment, and expenses of patients. In this article, a method is proposed to extract and fuse multi-scale deep features for detecting and locating both large and tiny lesions. A feature extracting network is first used as our backbone network to extract the basic features from wireless capsule endoscopy images, and then at each layer multiple regions could be identified as potential lesions. As a result, the features maps of those potential lesions are obtained at each level and fused in a top-down manner to the fully connected layer for producing final detection results. Our proposed method has been evaluated on a clinical dataset that contains 20,000 wireless capsule endoscopy images with clinical annotation. Experimental results demonstrate that our method can achieve 98.9% prediction accuracy and 93.5% score, which has a significant performance improvement of up to 31.69% and 22.12% in terms of recall rate and score, respectively, when compared to the state-of-the-art approaches for both large and tiny bleeding lesions. Moreover, our model also has the highest AP and the best medical diagnosis performance compared to state-of-the-art multi-scale models.

Knee osteoarthritis (KOA) as a disabling joint disease has doubled in prevalence since the mid-20th century. Early diagnosis for the longitudinal KOA grades has been increasingly important for effective monitoring and intervention. Although recent studies have achieved promising performance for baseline KOA grading, longitudinal KOA grading has been seldom studied and the KOA domain knowledge has not been well explored yet. In this paper, a novel deep learning architecture, namely adversarial evolving neural network (A-ENN), is proposed for longitudinal grading of KOA severity. As the disease progresses from mild to severe level, ENN involves the progression patterns for accurately characterizing the disease by comparing an input image it to the template images of different KL grades using convolution and deconvolution computations. In addition, an adversarial training scheme with a discriminator is developed to obtain the evolution traces. Thus, the evolution traces as fine-grained domain knowledge are further fused with the general convolutional image representations for longitudinal grading. Note that ENN can be applied to other learning tasks together with existing deep architectures, in which the responses characterize progressive representations. Comprehensive experiments on the Osteoarthritis Initiative (OAI) dataset were conducted to evaluate the proposed method. An overall accuracy was achieved as 62.7%, with the baseline, 12-month, 24-month, 36-month, and 48-month accuracy as 64.6%, 63.9%, 63.2%, 61.8% and 60.2%, respectively.

Sustainable energy management systems have been increasingly studied in recent years. Non-intrusive load monitoring (NILM), as a key component, estimates the power consumption of individual appliances from the main readings only. However, most NILM approaches are computationally expensive, and their generality is negatively affected by the data drift occurred when the models are used across domains. Besides, the threats of privacy violation will rise in the model transfer due to the possible leakage of the personal information of the users from the source domain. To address all these challenges, we designed a cost-efficient learning method using LightGBM for energy disaggregation. We also proposed a model-based transfer learning algorithm using feature importance analysis, which enhances the generalisation capability of tree-based ensemble models applied in different domains while protecting privacy. We conducted experiments with real-world data sets. The performance of our approach is superior to the state-of-the-art solutions.

The increasing use of renewable energy sources and electrical energy storage systems creates a new energy paradigm for residential houses and buildings. Such design reduces carbon footprint, but it also introduces a new challenge for minimizing the electricity bill while still meeting users’ needs. This challenge is often accompanied by deep uncertainty in user load demands, electricity tariffs, and renewable energy generations. To address this challenge, we propose an online algorithm, Virtual Algorithm-based Lightweight Online Scheduling (VALOS), to manage electricity purchasing and battery operations. Our solution does not use any prediction components in dealing with uncertainties. Instead, our algorithm employs a lightweight routine, Virtual Algorithm (VA), for making critical decisions to manage uncertainty. We prove that VA achieves an expected probability of 1/e for choosing the optimal purchasing timing online and incurs only a logarithmic computational cost. With VA, VALOS decides how the energy storage reacts to the load demands, which incurs only a linear-logarithmic online computational cost while achieving optimal performance under the specified conditions. Finally, we conduct extensive trace-driven simulations on real-world datasets to confirm the theoretical results and demonstrate the potential of VALOS.

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present InaudibleKey, an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, InaudibleKey exploits the acoustic channel frequency response of two legitimate devices as a common secret to generating keys. InaudibleKey employs several novel technologies to significantly improve its performance. We conduct extensive experiments to evaluate the proposed system in different real environments. Compared to state-of-the-art works, InaudibleKey improves key generation rate by 3 times, extends pairing distance by 3.2 times, and reduces information reconciliation counts by 2.5 times. Security analysis demonstrates that InaudibleKey is resilient to a number of malicious attacks. We also implement InaudibleKey on modern smartphones and resource-limited IoT devices. Results show that it is energy-efficient and can run on both powerful and resource-limited IoT devices without incurring excessive resource consumption.

Summary Mobile data offloading has already appeared to offer the means of addressing the challenges of limited computing capability and battery life of mobile devices. Most existing code offloading frameworks only consider migrating an application within a single network at a time and failed to fully utilize the energy efficiency mechanism of the latest CPU chips. To tackle these issues, we propose an offloading approach under both a multitasking environment and a heterogeneous network to increase energy and execution efficiency. The offloading problem was formulated as a biobjective optimization problem that aims to save energy and keep a good performance by combining mobile cloud computing with big.LITTLE technology under a heterogeneous network equipped with cellular and WiFi connectivity. By varying the applications and network scenarios, the experimental results show that with offloading a single application using our proposed framework, the data‐intensive application can obviously benefit when the access point density of WiFi reaches 0.0002 unit/m2. Under multiple application scenarios, our proposed framework can increase processing speed by an average of 0.3× over the single application and can save an average of 25% power over the single application.

The paradigm of the Internet of Things (IoT) and cloud-edge computing plays a significant role in future smart grids. The data-driven solution integrating the artificial intelligence functionalities brings novel methods to address the nontrivial task of economic dispatch in microgrids in the presence of uncertainties of renewable generations and loads. This article proposes a learning-based decision-making framework for the economic energy dispatch of an islanding microgrid based on the cloud-edge computing architecture. Cloud resources are utilized to solve the optimal dispatch decision sequences over historical operating patterns. It can be considered as a sample labeling process for the supervised training that can implement the complex mapping of input-output space through an advanced machine learning model. Then, the well-trained model can be adopted locally at edge computing devices keeping the long-term parameters unchanged for implement the real-time microgrid energy dispatch. The key benefit of the proposed solution is that it effectively avoids the prediction of multiple stochastic variables and the design of sophisticated regulation strategies or reward policy functions for real-time dispatch. The solution is extensively assessed through simulation experiments by the use of real data measurements for a set of operational scenarios and the numerical results validate the effectiveness and benefit of the proposed algorithmic solution.

In recent years, the rapid development of the Internet of Things (IoT) has produced a large amount of data that needs to be processed in a timely manner. Traditional cloud computing systems can provide us with plentiful resources to process such data. However, the increasing requirements of IoT applications on data privacy, energy consumption savings and location-aware data processing pushes the emergence and the interplay of fog computing and cloud computing. This paper examines the resource scheduling issue under such a system to minimize makespan and energy consumption. A multi-objective estimation of distribution algorithm (EDA) as well as a partition operator is adopted to divide the graph and determine the task processing permutation and processor assignment. Single and multiple application simulation were both conducted. The comparative results show that the Pareto set produced by our proposed algorithm is able to dominate a large proportion of those solutions by the heuristic method and the simple EDA under single application simulation. When it comes to multi-application simulation, IoT devices can have a much longer lifetime with our proposed scheduling algorithm as well having similar performance to the other algorithms on fog node energy consumption and much better on makespan.

Only when we understand how hackers think, can we defend against their attacks. Towards this end, this paper studies the cyber-attacks that aim to remove nodes or links from network topologies. We particularly focus on one type of such attacks called Maximum Vertex Coverage Attacks under Knapsack constraint (MVCAK), in which a hacker has a fixed budget to remove nodes from a network with the nodes involving different costs for removal, and the hacker's goal is to maximize the number of links incident to the nodes removed. Since the MVCAK problem is NP-hard, we firstly propose an optimal solution by Integer Linear Program formulation. Secondly, we give an approximate solution by Linear Programming relaxation that achieves an approximation ratio of 3/4, outperforming the existing 1 - 1/sqrt(e) (about 0.39). Thirdly, since the straightforward implementation of our approximate solution has a high time complexity, we propose two heuristics to significantly reduce its complexity while preserving the approximation ratio. We formally prove the correctness and the effectiveness of these two heuristics. Finally, we conduct extensive experiments on both artificial and real-world networks, showing that our approximate solution produces almost the same results as the optimal solution in practice and has an acceptable running time.

Recently, Deep Learning (DL) is widely applied to intelligent systems equipped with resource-constraint hardware accelerators. With multiple DL applications sharing the resource, the execution model can be divided into two stages: (i) batching independent inference tasks initiated by each application, and (ii) scheduling batches to run in a time-sharing manner. The state-of-the-art DL serving systems employ the execution model by organizing sequential tasks into batches and then scheduling batches concerning their targeting deep neural network (DNN) models in a round-robin manner. However, we demonstrated that these practices fail to alleviate the slowdown of tasks, and there is a need to re-visit batching and scheduling in terms of efficiency and fairness. To this end, we formulated batching as a resource allocation problem and investigated scheduling in terms of each application's utilization on the device. Then, we proposed the fine-grained batching scheme and fairness-driven scheduling scheme for DL serving and implemented a prototype system called DyBatch. To be exact, DyBatch accomplishes efficient batching by taking into account Pareto efficiency of and envy between batches. Besides, DyBatch's fair scheduler monitors the resource utilization of all applications and assigns a batch from the application with the lowest utilization for execution first. Evaluation under various benchmarks with comparison to the baseline system Tensorflow Serving (TFS) shows the superiority of DyBatch, which achieves up to 55% reduction of slowdown, and up to 12% improvement of throughput.

The post-discharge support is increasingly suggested for stroke patients to be discharged earlier and start rehabilitation at home. Considering that stroke patients usually have a high chance of recurrence, a good prognostic program is essential to improve diagnostic capabilities while reducing readmission rate to further save medical sources. In this context, various machine learning methods have been leveraged to obtain diagnostic findings and guide further treatments. However, those approaches mainly focus on performing analysis using a single data source obtained from the hospital, which could ignore the information complementarity between different groups of features and several subtle and discrete differences of physical interpretation among them. In this paper, we propose an Edge-based system design for post-stroke surveillance and warning prediction, called PSMART (Post-Stroke Mobile Auxiliary Rudiment Treatment), for processing enriched pathogenic factors of ischemic stroke from multi-sensors (views) to make readmission warning predictions. Our approach can considerably enrich the distinctive features from raw data, as well as exploit the consistency and complementary proprieties of different views, leading to better learning results. We evaluate the performance of the proposed approach on a real-world dataset, and the accuracy can reach up to 98.98%. Moreover, experiment results also show that our proposed approach can provide better accuracy when compared to the single-view ones.

With the Internet of Things continuously penetrating into all spheres of our daily lives, the increasing use of smart devices enabled the emergence of the edge computing paradigm. To meet the needs of saving energy and reducing electricity bills for each household, solar energy is exploited by using photovoltaic (PV) panels that can be integrated into an edge computing platform based on a cost-effective scheduling scheme. However, it is still a major challenge to determine the optimal energy allocation of renewable energy due to the intermittent nature of renewable energy generation. In this paper, we propose a unified clustering-based prediction framework with two tree-based algorithms to provide short-term prediction of PV power output. We also provide the in-terpretability analysis for our approach to reveal the features that are important for the prediction. The experimental results show our proposed framework is superior to other benchmark machine learning algorithms.

With the continuous growth in population and energy demands more attention has been paid to energy consumption issues in residential environments. At the user-end, the home energy management system (HEMS) has been proposed as a cost-effective solution to reduce the electricity cost in households, while maintaining users' comfort and reducing the pressure on energy providers. However, it is a challenge to design a cost-effective scheduling strategies for HEMS which takes many objectives into consideration while potentially benefiting both users and providers. In our work, we propose a new approach named adaptive multi-objective salp swarm algorithm (AMSSA) based on traditional multi-objective salp swarm algorithm (MSSA) to realise a multi-objective optimisation approach for the power scheduling problem. AMSSA not only fulfils the trade-off among users' comfort, electricity cost and peak to average ratio (PAR), but also enhances the convergence speed for the overall optimisation process. Moreover, we also set up a testbed by using smart appliances and implemented our design on an edge-based energy management system. The experiment results demonstrated a reduction in both electricity cost (47.55%) and PAR (45.73%), compared with the case without a scheduling scheme.

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, our system exploits channel frequency response of two legitimate devices as a common secret to generate keys. Extensive experiments are conducted to evaluate the proposed system in different real environments. Evaluation results show that the proposed system can generate the same secret key for two mobile devices with high probability.

The power efficiency of photovoltaic (PV) modules is highly correlated with their health status. Under dynamically changing environments, PV defects could spontaneously form and develop into fatal faults during the daily operation of PV power plants. To facilitate defect detection with less human intervention, a nondestructive, contactless, and automatical visual inspection system with the help of unmanned aerial vehicles and edge computing is proposed in this article. During the processing of the incoming data stream, the system may collect some new, unknown, and unlabeled defects that have not been identified yet in the existing database. To distinguish them from the existing ones, a deep embedded restricted cluster algorithm is designed to identify the unknown and unlabeled PV module defects in an unsupervised manner. Limited by the resources of edge devices and the availability of images of PV defects for training, we developed an online solution combined with deep learning, data argumentation, and transfer learning to properly address the issues of running resource-hungry applications on edge devices and lack of training samples faced by the deep learning approaches used in the field. In addition, pointwise convolution layers are introduced into the network to reduce the parameters and the size of the model. With the reduction of the network depth of the deep convolutional neural network model and the features transferred from the learned defects, the resource consumption of our proposed approach is significantly reduced, and thus can be used on a wide range of edge devices to complete defect detection in a timely manner with high accuracy. The experimental results clearly demonstrate the practicality and effectiveness.

Hosting virtualized network functions (VNF) has been regarded as an effective way to realize network function virtualization (NFV). Considering the cost diversity in cloud computing, from the perspective of service providers, it is significant to orchestrate the VNFs and schedule the traffic flows for network utility maximization (NUM) as it implies maximal revenue. However, traditional heuristic solutions based on optimization models usually follow some assumptions, limiting their applicability. Recent studies have shown that deep reinforcement learning (DRL) is a promising way to tackle such limitations. However, DRL agent training also suffers from slow convergence problem, especially with complex control problems. We notice that optimization models actually can be applied to accelerate the DRL training. Therefore, we are motivated to design a model-assisted DRL framework for VNF orchestration in this paper. Other than letting the agent blindly explore actions, the heuristic solutions are used to guide the training process. Based on such principle, the DRL framework is also redesigned accordingly. Experiment results validate the high efficiency of our model-assisted DRL framework as it not only converges 23× faster than traditional DRL algorithm, but also with higher performance at the same time.

An isolated power system (IPS) usually operates in an islanded mode. Because of the lack of support from an external power grid, stability is a prominent issue for IPSs. This article proposes a novel stability analysis approach for IPSs based on the input-to-state stability (ISS) theory. Compared with existing stability analyses that use simulations and direct methods, the proposed method decomposes the stability analysis process by considering the ISS properties of subsystems and a network equation that reflects their connections. Thus, it has good adaptability for the stability analysis of systems with flexible operating conditions. Algorithms are presented for estimating the ISS properties of subsystems, and asymptotic gains in a piecewise linear form are adopted. The small gain theorem is used for the decomposed stability analysis, and a practical algorithm to numerically check the small gain condition is presented. Time-domain simulations were performed with a test system to verify the effectiveness of the proposed decomposition-based stability analysis approach.Note to Practitioners—Power systems used in shipboards, airplanes, remote areas, and so on are usually classified as isolated power systems (IPSs). The continuity of power supply in IPSs is the prerequisite of fulfilling certain tasks. Due to the lack of support from the bulk power grid, the normal operation of IPSs can be threatened by various external disturbances, such as disasters, battle damages, device failures, and so on. To maintain the survivability and reliability of IPSs under extreme conditions, fast reconfiguration and emergency control approaches are often performed, which lead to system topology changes and frequent connection/disconnection operation of devices in IPSs. Because of the limited generation capacity of an IPS, a stability analysis after an emergency is important for ensuring that the IPS can perform tasks normally, and can provide guidance for designing fast reconfiguration and emergency control strategies. However, current stability analysis approaches have limited applicability or are overly conservative for analyzing the stability of IPSs. To address the challenge of changeable topologies for the stability analysis of an IPS, this article proposes a decomposition-based analysis approach using input-to-state stability (ISS) theory. By decomposing the entire system into several subsystems, the system’s stability can be checked through the ISS properties of subsystems and their connections. The ISS properties of subsystems can be estimated offline, which saves time for online calculation. To reduce the conservativeness of stability analysis, the asymptotic gains in piecewise linear form are adopted in this article. Practical algorithms are designed for efficiently checking the proposed decomposition-based stability conditions. The research outcome provides a fast and flexible stability analysis approach that can adapt to the frequent changes in the operating conditions of IPSs.

We investigate a workflow scheduling problem with stochastic task arrival times and fuzzy task processing times and due dates. The problem is common in many real-time and workflow-based applications, where tasks with fixed stage number and linearly dependency are executed on scalable cloud resources with multiple price options. The challenges lie in proposing effective, stable, and robust algorithms under stochastic and fuzzy tasks. A triangle fuzzy number-based model is formulated. Two metrics are explored: the cost and the degree of satisfaction. An iterated heuristic framework is proposed to periodically schedule tasks, which consists of a task collection and a fuzzy task scheduling phases. Two task collection strategies are presented and two task prioritization strategies are employed. In order to achieve a high satisfaction degree, deadline constraints are defined at both job and task levels. By designing delicate experiments and applying sophisticated statistical techniques, experimental results show that the proposed algorithm is more effective and robust than the two existing methods.

Edge computing is an effective approach for resource provisioning at the network edge to host virtualized network functions (VNF). Considering the cost diversity in edge computing, from the perspective of service providers, it is significant to orchestrate the VNFs and schedule the traffic flows for network utility maximization (NUM) as it implies maximal revenue. However, traditional model-based optimization methods usually follow some assumptions and impose certain limitations. In this paper, inspired by the success of deep reinforcement learning in solving complicated control problems, we propose a deep deterministic policy gradients (DDPG) based algorithm. We first formulate the NUM problem with the consideration of end-to-end delays and various operation costs into a non-convex optimization problem and prove it to be NP-hard. We then redesign the exploration method and invent a dual replay buffer structure to customize the DDPG. Meanwhile, we also apply our formulation to guide our replay buffer update. Through extensive trace-driven experiments, we show the high efficiency of our customized DDPG based algorithm as it significantly outperforms both model-based methods and traditional non-customized DDPG based algorithm.

Mobile edge computing has significantly reduced the response time of mobile applications by executing services in close proximity to mobile consumers. However, the capabilities and resources of edge servers are typically limited; additionally, service requirements in mobile environments are becoming increasingly complex and diverse. In this context, properly dispatching service requests to edge and cloud servers to improve the quality of services has become a crucial problem. In this paper, we focus on this problem and aim to minimize the response time of service invocations in mobile edge computing systems. The problem is formulated as an optimization problem, and a heuristic algorithm that combines the Genetic algorithm and the simulated Annealing algorithm for service selection in Mobile Edge Computing systems (GAMEC) is proposed to solve the problem. A series of experiments has demonstrated that the method performs well in reducing the response time of service invocations in mobile edge computing systems. Moreover, the execution time of GAMEC is of a low order of magnitude, and the algorithm scales well as the experimental scale increases.

Oral health has significant impact on people’s over-all well-being. While many activity recognition systems exist in the literature, accurately sensing toothbrushing activities remains an unsolved challenging problem due to the diversity of tooth-brushing habits among different users and subtle distinctions between different brushing actions. In this work, we propose Hygiea, an energy-efficient and highly-accurate toothbrushing monitoring system which exploits IMU-based wrist-worn gesture sensing using unmodified toothbrushes. To address toothbrushing variety, Hygiea incorporates a number of novel signal preprocessing techniques to automatically transform the sensory input during arbitrary toothbrushing activities to the consistent user coordinate system. To distinguish different brushing actions, Hygiea leverages an emerging deep learning model (e.g., AT-LSTM) to achieve fine-grained activity recognitions. Moreover, a POMDP model is incorporated for sampling control to balance activity detection and energy efficiency. Extensive real-world experiments show that the Hygiea system achieves a 11.7% accuracy gain compared to the state-of-the-art while maintaining energy-efficiency and zero modification on the toothbrushes.

There is a growing trend for employing cyber-physical systems to help smart homes improve the comfort of residents. However, a residential cyber-physical system is different from a common cyber-physical system since it directly involves human interaction, which is full of uncertainty. The existing solutions could be effective for performance enhancement in some cases when no inherent and dominant human factors are involved. Besides, the rapidly rising interest in the deployments of cyber-physical systems at home does not normally integrate with energy management schemes, which is a central issue that smart homes have to face. In this article, we propose a cyber-physical-system-based energy management framework to enable a sustainable-edge computing paradigm while meeting the needs of home energy management and residents. This framework aims to enable the full use of renewable energy while reducing electricity bills for households. A prototype system was implemented using real-world hardware. The experiment results demonstrated that renewable energy is fully capable of supporting the reliable running of home appliances most of the time and electricity bills could be cut by up to 60% when our proposed framework was employed.

The emerging of mobile edge computing has significantly reduced the response time and Internet risk of service invocations. However, due to the distributed architecture and limited resources, balancing the load between edge servers to minimize the overall response time has become a critical objective for mobile edge computing. This problem is generally related to two aspects, request dispatching and service scheduling. To address this issue, we proposed a novel heuristic method called GASD (combined Genetic algorithm and simulated Annealing algorithm for Service request Dispatching). It tackles the problem by jointly conducting request dispatching and service scheduling. In addition, a solution combination algorithm is applied to reduce the computation complexity of the method. The experimental results show that the GASD method can achieve much lower overall response time than the compared methods. Moreover, the execution time of GASD is in a low order of magnitude and the algorithm performs excellent scalability as the experimental scale increases.

There is a growing trend for engaging edge computing to help smart homes to improve the living comfort of residents. However, the rapidly rising interest in such deployments does not normally integrate with energy management schemes, which is a central issue that smart homes have to face. In this paper, we propose a unified energy management framework for enabling a sustainable edge computing paradigm while meeting the needs of home energy management and smart home applications. This framework aims to enable the full use of renewable energy while reducing electricity bills for households. A prototype system was implemented by using low-cost and easy-to-get hardware. The experiment results demonstrated that renewable energy is fully capable of supporting the reliable running of edge computing devices and electricity bills could be cut by up to 86% when our proposed framework was employed.

Due to the limited capabilities and resources, edge servers cannot meet the increasingly complex and diverse service requirements in mobile edge computing environments. In this circumstance, how to dispatch the component tasks of service requests to edge and cloud servers to reduce the time delay has become a crucial problem. Therefore, we focus on this problem and propose a heuristic algorithm called GAMEC (combined Genetic algorithm and simulated Annealing algorithm for service selection in Mobile Edge Computing systems). The simulated experiments have demonstrated the high effectiveness of the method.

In recent years, Edge computing has emerged as a new paradigm that can reduce communication delays over the Internet by moving computation power from far-end cloud servers to be closer to data sources. It is natural to shift the design of cloud-based IoT applications to Edge-based ones. Activity recognition in smart homes is one of the IoT applications that can benefit significantly from such a shift. In this work, we propose an Edge-based solution for addressing the activity recognition problem in smart homes from multiple perspectives, including architecture, algorithm design and system implementation. First, the Edge computing architecture is introduced and several critical management tasks are also investigated. Second, a realization of the Edge computing system is presented by using open source software and low-cost hardware. The consistency and scalability of running jobs on Edge devices are also addressed in our approach. Last, we propose a convolutional neural network model to perform activity recognition tasks on Edge devices. Preliminary experiments are conducted to compare our model with existing machine learning methods, and the results demonstrate that the performance of our model is promising.

Understanding users’ social relationships plays an important role in many disciplines, including marketing, management science, and so on and is the fundamental context information required in many context-aware applications. However, despite significant research progress in social learning, sensing and reconstructing the off-line social structures in an accurate and nonobtrusive way is still a challenging open problem. In this paper, we propose SODAR, an off-line SOcial colocation Detection And network Reconstruction system, a social learning system that exploits wireless probes emitted by the smartphones carried by users to learn and infer their social relationships and reconstruct their off-line social structures. The probe capturing and filtering mechanisms collects high-quality wireless probe information, and the passive localization techniques are used to process the data and detect colocation events, which are used for the novel social representation learning. The learned social representation vector for each user contains rich social information and can be used to determine the social distances for each pair of users. With projection and clustering techniques, the off-line social structures can be visualized and reconstructed. We implemented the system and deployed the system to different indoor spaces covering more than 1000 m2. The evaluation results show that the SODAR system is able to reliably learn social representations for each user and effectively reconstruct the off-line social structures.

The emerging Internet of Things (IoT) paradigm creates a growing need to analyze a significant amount of data produced by the interconnected IoT devices. Since IoT devices have limited computation capabilities, Fog Computing is a natural complement, to provide distributed, location-aware, and easy-to-access computation resources. In this work, we address the problem of application processing and data offloading in a Fog-integrated IoT ecosystem. By leveraging the Lyapunov optimization technique, we design an online and distributed system control policy called the Distributed Weighted Backpressure (DWB) policy that asymptotically minimizes the cost of IoT devices. A three-way tradeoff among queue backlogs, communication cost, and computation cost is then investigated. Finally, simulation study has been conducted to validate the correctness and usefulness of the proposed DWB policy.

The fusion of remote sensing images with different spatial and temporal resolutions is needed for diverse Earth observation applications. A small number of spatiotemporal fusion methods that use sparse representation appear to be more promising than weighted- and unmixing-based methods in reflecting abruptly changing terrestrial content. However, none of the existing dictionary-based fusion methods consider the downsampling process explicitly, which is the degradation and sparse observation from high-resolution images to the corresponding low-resolution images. In this paper, the downsampling process is described explicitly under the framework of compressed sensing for reconstruction. With the coupled dictionary to constrain the similarity of sparse coefficients, a new dictionary-based spatiotemporal fusion method is built and named compressed sensing for spatiotemporal fusion, for the spatiotemporal fusion of remote sensing images. To deal with images with a high-resolution difference, typically Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS), the proposed model is performed twice to shorten the gap between the small block size and the large resolution rate. In the experimental procedure, the near-infrared, red, and green bands of Landsat-7 and MODIS are fused with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than five state-of-the-art methods, which prove the feasibility of incorporating the downsampling process in the spatiotemporal model under the framework of compressed sensing.

Distributed storage systems, e.g., Hadoop Distributed File System (HDFS), have been widely used in datacenters for handling large amounts of data due to their excellent performance in terms of fault tolerance, reliability and scalability. However, these storage systems usually adopt the same replication and storage strategy to guarantee data availability, i.e., creating the same number of replicas for all data sets and randomly storing them across data nodes. Such strategies do not fully consider the difference requirements of data availability on different data sets. More servers than necessary should thus be used to store replicas of rarely-used data, which will lead to increased energy consumption. To address this issue, we propose an energy-efficient storage strategy for cloud datacenters based on a novel hypergraph coverage model. According to users' requirements of data availability in different applications, our proposed algorithm can selectively determine the corresponding minimum hyperedge coverage, which represents the minimum set of data nodes required in the datacenter. Hence, some other data nodes can be turned off for the purpose of energy saving. We have also implemented our proposed algorithm as a dynamic runtime strategy in a HDFS based prototype datacenter for performance evaluation. Experimental results show that the variable hypergraph coverage based strategy can not only reduce energy consumption, but can also improve the network performance in the datacenter.

The steep rise of Internet of Things (IoT) applications along with the limitations of Cloud Computing to address all IoT requirements promotes a new distributed computing paradigm called Fog Computing, which aims to process data at the edge of the network. With the help of Fog Computing, the transmission latency and monetary spending caused by Cloud Computing can be effectively reduced. However, executing all applications in fog nodes will increase the average response time since the processing capabilities of fog is not as powerful as cloud. A tradeoff issue needs to be addressed within such systems in terms of average response time and average cost. In this paper, we develop an online algorithm, unit-slot optimization, based on the technique of Lyapunov optimization. It is a quantified near optimal solution and can online adjust the tradeoff between average response time and average cost. We evaluate the performance of our proposed algorithm by a number of experiments. The experimental results not only match up the theoretical analyses properly, but also demonstrate that our proposed algorithm can provide cost-effective processing while guaranteeing average response time.

SUMMARY Multi‐functional wireless sensor network (WSN) system is a new design trend of WSNs, which are evolving from dedicated application‐specific systems to an integrated infrastructure that supports the execution of multiple concurrent applications. Such system offers inherent advantages in terms of cost and flexibility because it allows the effective utilization of available sensors and resource sharing among multiple applications. However, sensor nodes are very constrained in resources, mainly regarding their energy. Therefore, the usage of such resources needs to be carefully managed, and the sharing with several applications imposes new challenges in achieving energy efficiency in these networks. In order to exploit the full potential of multi‐functional WSN systems, it is crucial to design mechanisms that effectively allocate tasks onto sensors so that the entire system lifetime is maximized while meeting various application requirements. However, it is likely that the requirements of different applications cannot be simultaneously met. In this paper, we present the Multi‐Application Requirements Aware and Energy Efficiency algorithm as a new resource allocation heuristic for multi‐functional WSN system to maximize system lifetime subject to various application requirements. The heuristic effectively deals with different quality of service parameters (possibly conflicting) trading those parameters and exploiting heterogeneity of multiple WSNs. Copyright © 2013 John Wiley & Sons, Ltd.

Most Wireless Sensor Network (WSN) applications require distributed signal and collaborative data processing. One of the critical issues for enabling collaborative processing in WSNs is how to schedule tasks in a systematic way, including assigning tasks to sensor nodes, and determining their execution and communication sequence. Since WSN nodes are very resource constrained, mainly regarding their energy supply, one major concern when scheduling tasks in such environments is to minimize and balance the energy consumption, so that the system operational lifetime is maximized. We propose a heuristic-based three-phase algorithm (TPTS) for allocating tasks to multiple clusters in hierarchical WSNs that aims at finding a scheduling scheme that minimizes the overall energy consumption and balances the workload of the system while meeting the application's deadline. The performance of the proposed algorithm and the effect of several parameters on its behavior were evaluated by simulations, with promising results. The experimental results show that the time and energy performance of TPTS are close to the time and energy of benchmarks in most cases, while load balance is always provided.

Cyber physical systems (CPS) have recently emerged as a promising approach to improve the synergy between physical and virtual worlds. CPS applications can be built on top of Wireless Sensor Networks (WSNs) exploiting the physical information collected by them to bridge real and cyber realms. In this paper, we investigated the emerging problem of how to schedule multiple applications onto cyber-physical systems encompassed of multiple WSNs, while meeting demands of these applications. Our goal is to find out an optimal scheduling scheme for each application so that: (i) it minimizes the overall energy consumption, (ii) it meets the application's deadline, (iii) it provides the required data accuracy to applications, and (iv) it balances the workload of the system. To achieve such goals, we developed a polynomial-time three phase task scheduling heuristic, named HTPTS. Experimental results show that the time and energy performance of HTPTS are close to the time and energy of the benchmark in most of the cases, while load balance is satisfied.