In long-term series forecasting (LTSF), it is imperative for models to adeptly discern and distill from historical time series data to forecast future states. Although Transformer-based models excel at capturing long-term dependencies in LTSF, their practical use is limited by issues like computational inefficiency, noise sensitivity, and overfitting on smaller datasets. Therefore, we introduce a novel time series lightweight interactive Mamba with an adaptive Fourier filter model (Affirm). Specifically, (i) we propose an adaptive Fourier filter block. This neural operator employs Fourier analysis to refine feature representation, reduces noise with learnable adaptive thresholds, and captures inter-frequency interactions using global and local semantic adaptive Fourier filters via element-wise multiplication. (ii) A dual interactive Mamba block is introduced to facilitate efficient intra-modal interactions at different granularities, capturing more detailed local features and broad global contextual information, providing a more comprehensive representation for LTSF. Extensive experiments on multiple benchmarks demonstrate that Affirm consistently outperforms existing SOTA methods, offering a superior balance of accuracy and efficiency, making it ideal for various challenging scenarios with noise levels and data sizes.

Single-domain generalization (SDG) in time series classification (TSC) poses significant challenges for current time-series domain generalization methods due to the extremely limited data available from only one source domain. In this study, we propose Segment-dErived Expansion of Domains (SEED), a diffusion-based method that effectively expands domain diversity for SDG. We reveal that individual instances exhibit intrinsic temporal shifts over time, which provides a principled foundation for creating multiple pseudo domains by segmenting each instance into distinct parts. To do so, SEED extracts two complementary representations from each time-series segment: 1) a segment-specific representation that captures diverse distributional variations, and 2) a segment-invariant representation that preserves class semantics. SEED formulates these representations as pseudo-domain prompts to guide a diffusion model in generating diverse yet semantically consistent time-series data. Additionally, SEED introduces a novel prompt-fused sampling for diffusion, enabling flexible recombination of segment-specific features to continuously expand the pseudo-domain space. We provide both theoretical analysis and extensive empirical evaluations on four widely used TSC benchmarks to validate its ability in reducing generalization error and improving model's performances in SDG. In our experiments, SEED significantly improves classification accuracy by 7.68% on average compared to the strong baselines.

Learning time series representations with sparse labels presents notable challenges. The surge in unsupervised contrastive learning has garnered increasing interest due to its immense advancements in deriving meaningful representations in semi-supervised settings, typically involving a two-stage process: pretraining on large unlabeled data followed by fine-tuning with few labeled samples. However, this approach has inherent drawbacks: poor knowledge transfer, reduced generalizability, and failure to directly utilize unsupervised contrastive loss from pretraining and valuable supervised loss guided by ground truth to impact the downstream tasks. In response, we introduce a novel end-to-end semi-supervised framework, Semi4TSF, for time series forecasting (TSF). It optimizes unsupervised loss on massive unlabeled data and integrates supervised contrastive and forecasting losses on limited labeled data, enabling the model to see other unlabeled embeddings meanwhile learning useful labeled embeddings, improving generalization. The three losses are jointly to refine the encoder and forecaster. Specifically, the unsupervised learning module applies two instance-wise augmentation banks over the entire series to capture long-term dependencies, suggests a learnable Fourier layer, and fuses temporal and frequency information to uncover intricate temporal-frequency correlations through cross-domain interactions to capture nuanced representations. Extensive experiments on five benchmarks demonstrate that Semi4TSF is an effective and superior end-to-end framework that fills the gap in semi-supervised TSF.

Map matching aims to align GPS trajectories to their actual travel routes on a road network, which is an essential pre-processing task for most of trajectory-based applications. Many map matching approaches utilize Hidden Markov Model (HMM) as their backbones. Typically, HMM treats GPS samples of a trajectory as observations and nearby road segments as hidden states. During map matching, HMM determines candidate states for each observation with a fixed searching range, and computes the most likely travel route using theViterbialgorithm. Although HMM-based approaches can derive high matching accuracy, they still suffer from high computation overheads. By inspecting the HMM process, we find that the computation bottleneck mainly comes from improper candidate sets, which contain many irrelevant candidates and incur unnecessary computations. In this paper, we present$\mathtt {LiMM}$– a learned road network index structure for efficient map matching.$\mathtt {LiMM}$improves existing HMM-based approaches from two aspects. First, we propose a novel learned index for road networks, which considers the characteristics of road data. Second, we devise an adaptive searching range mechanism to dynamically adjust the searching range for GPS samples based on their locations. As a result,$\mathtt {LiMM}$can provide refined candidate sets for GPS samples and thus accelerate the map matching process. Extensive experiments are conducted with three large real-world GPS trajectory datasets. The results demonstrate that$\mathtt {LiMM}$significantly reduces computation overheads by achieving an average speedup of$11.7\times$than baseline methods, merely with a subtle accuracy loss of 1.8%.

Existing domain generalization (DG) methods for cross-person sensor-based activity recognition tasks often struggle to capture both intra- and inter-domain style diversity, leading to significant domain gaps with the target domain. In this study, we explore a novel perspective to tackle this problem, a process conceptualized as domain padding. This proposal aims to enrich the domain diversity by synthesizing intra- and inter-domain style data while maintaining robustness to class labels. We instantiate this concept using a conditional diffusion model and introduce a style-fused sampling strategy to enhance data generation diversity, termed Diversified Intra- and Inter-domain distributions via activity Style-fused Diffusion modeling (DI2SDiff). In contrast to traditional condition-guided sampling, our style-fused sampling strategy allows for the flexible use of one or more random style representations from the same class to guide data synthesis. This feature presents a notable advancement: it allows for the maximum utilization of possible combinations among existing styles to generate a broad spectrum of new style instances. We further extend DI2SDiff into DI2SDiff++ by enhancing the diversity of style guidance. Specifically, DI2SDiff++ integrates a multi-head style conditioner to provide multiple distinct, decomposed substyles and introduces a substyle-fused sampling strategy that allows cross-class substyle fusion for broader guidance. Empirical evaluations on a wide range of datasets demonstrate that our generated data achieves remarkable diversity within the domain space. Both intra- and inter-domain generated data have been proven significant and valuable, enabling DI2SDiff and DI2SDiff++ to surpass state-of-the-art DG methods in various cross-person activity recognition tasks.

Existing domain generalization (DG) methods for cross-person generalization tasks often face challenges in capturing intra- and inter-domain style diversity, resulting in domain gaps with the target domain. In this study, we explore a novel perspective to tackle this problem, a process conceptualized as domain padding. This proposal aims to enrich the domain diversity by synthesizing intra- and inter-domain style data while maintaining robustness to class labels. We instantiate this concept using a conditional diffusion model and introduce a style-fused sampling strategy to enhance data generation diversity. In contrast to traditional condition-guided sampling, our style-fused sampling strategy allows for the flexible use of one or more random styles to guide data synthesis. This feature presents a notable advancement: it allows for the maximum utilization of possible permutations and combinations among existing styles to generate a broad spectrum of new style instances. Empirical evaluations on a broad range of datasets demonstrate that our generated data achieves remarkable diversity within the domain space. Both intra- and inter-domain generated data have proven to be significant and valuable, contributing to varying degrees of performance enhancements. Notably, our approach outperforms state-of-the-art DG methods in all human activity recognition tasks.

Learning semantic-rich representations from unlabeled time series data with intricate dynamics is a notable challenge. Traditional contrastive learning techniques predominantly focus on segment-level augmentations through time slicing, a practice that, while valuable, often results in sampling bias and suboptimal performance due to the loss of global context. Furthermore, they typically disregard the vital frequency information to enrich data representations. To this end, we propose a novel self-supervised general-purpose framework called Temporal-Frequency and Contextual Consistency (TFCC). Specifically, this framework first performs two instance-level augmentation families over the entire series to capture nuanced representations alongside critical long-term dependencies. Then, TFCC advances by initiating dual cross-view forecasting tasks between the original series and its augmented counterpart in both time and frequency domains to learn robust representations. Finally, three specially designed consistency modules 'temporal, frequency, and temporal-frequency' aid in further developing discriminative representations on top of the learned robust representations. Extensive experiments on multiple benchmarks demonstrate TFCC's superiority over the state-of-the-art classification and forecasting methods and exhibit exceptional efficiency in semi-supervised and transfer learning scenarios.

Standard k -means clustering necessitates computing pairwise Euclidean distances between each instance x in a data set D and all cluster centers, resulting in inadequate efficiency when dealing with high-dimensional data sets. Given its widespread usage, it is imperative that k -means clustering should be performed quickly to ensure efficient solutions. This paper is dedicated to exploring ways to improve the efficiency of the k -means algorithm in high-dimensional space. Unlike approximated approaches, our proposed method LBKC can achieve acceleration while yielding clustering results that are the same as what standard k -means clustering generates. LBKC utilizes the lower bound of Euclidean distance to safely avoid a large number of unnecessary distance calculations, thus achieving the goal of accelerating k -means process. Three carefully designed lower bounds based on the block vector, segment mean, and nonlinear embedding are presented in this paper, and they are employed in the proposed method. Furthermore, our approach LBKC is orthogonal to state-of-the-art methods, and we show how LBKC can be naturally combined with them to further improve their performance. Comprehensive experiments are conducted on a variety of data sets to evaluate the performance of the proposed approaches and related competitors, and the experimental results verify the effectiveness of our proposals.

While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further investigate and understand their underlying mechanisms. In this work, we propose a Temporal Convolutional Explorer (TCE) to empirically explore the learning behavior of 1D-CNNs from the perspective of the frequency domain. Our TCE analysis highlights that deeper 1D-CNNs tend to distract the focus from the low-frequency components leading to the accuracy degradation phenomenon, and the disturbing convolution is the driving factor. Then, we leverage our findings to the practical application and propose a regulatory framework, which can easily be integrated into existing 1D-CNNs. It aims to rectify the suboptimal learning behavior by enabling the network to selectively bypass the specified disturbing convolutions. Finally, through comprehensive experiments on widely-used UCR, UEA, and UCI benchmarks, we demonstrate that 1) TCE's insight into 1D-CNN's learning behavior; 2) our regulatory framework enables state-of-the-art 1D-CNNs to get improved performances with less consumption of memory and computational overhead.

The nearest neighbor search is an essential operation for many computer vision, data mining, and machine learning problems. Since it is so widely used, the nearest neighbor search should be as fast as possible. This paper explores lower bound-based approaches to speed up the exact nearest neighbor search in high dimensional Euclidean space. We compute the lower bound of Euclidean Distance by using the block vectors and Cauchy–Schwartz inequality. The proposed lower bound is calculated efficiently and is close to the real Euclidean Distance. Besides, the preprocessing step of the proposal has linear time complexity. Given a query, during the procedure of identifying the nearest neighbor, our method can eliminate many expensive actual distance computations using the lower bound to approximate Euclidean Distance. In addition, we develop a multilevel lower bound strategy, which calculates the lower bound step by step and utilizes the multistep filtering mechanism to improve the searching process further. Theoretical analysis is provided to show that the proposals can guarantee to obtain the same result as the brute-force search. Comprehensive experiments on 16 public data sets collected from various domains demonstrate that our approach performs well in finding the exact nearest neighbor compared to related competitors. The experimental results also illustrate that the multilevel lower bound strategy is effective.

The similarity search on sensor data generated by a myriad of sensing devices is a frequently encountered problem in the era of the Internet of Things (IoT). This sensor data generally appear in the form of time series, a temporally ordered sequence of real numbers obtained regularly in time. It has been widely accepted that the dynamic time warping (DTW) currently is the most prevalent similarity measure in the time-series mining community, mainly due to its flexibility and broad applicability. However, calculating DTW between two time series has quadratic time complexity, leading to unsatisfactory efficiency when performing the similarity search over the large time-series data set. The main contribution of this article is to propose a method called product quantization (PQ)-based DTW (PQDTW) for fast time-series approximate similarity search under DTW. The PQ, a well-known approximate nearest neighbor search approach, is used in PQDTW. Nevertheless, the conventional PQ is developed with the Euclidean distance and is not designed for DTW. To solve this problem, the DTW barycenter averaging (DBA) technique is utilized to adapt the PQ for DTW before using it. We employ PQDTW along with thefilter-and-refineframework to efficiently and accurately perform the time-series similarity search. Our method can reasonably reduce many DTW computations in the filtering phase; thus, the query process is accelerated. We compare PQDTW with related popular algorithms using public time-series data sets. Experimental results verify that the proposal achieves the best tradeoff between query efficiency and retrieval accuracy compared to the competitors.

Time series similarity search is an essential operation in time series data mining and has received much higher interest along with the growing popularity of time series data. Although many algorithms to solve this problem have been investigated, there is a challenging demand for supporting similarity search in a fast and accurate way. In this paper, we present a novel approach, TS2BC, to perform time series similarity search efficiently and effectively. TS2BC uses binary code to represent time series and measures the similarity under the Hamming Distance. Our method is able to represent original data compactly and can handle shifted time series and work with time series of different lengths. Moreover, it can be performed with reasonably low complexity due to the efficiency of calculating the Hamming Distance. We extensively compare TS2BC with state-of-the-art algorithms in classification framework using 61 online datasets. Experimental results show that TS2BC achieves better or comparative performance than other the state-of-the-art in accuracy and is much faster than most existing algorithms. Furthermore, we propose an approximate version of TS2BC to speed up the query procedure and test its efficiency by experiment.

Battery lifetime is one of the main challenges that impedes the deployment of energy-constrained wireless networks, such as unattended Internet-of-Things (IoT) systems. To prolong battery lifetime, the duty-cycle mode is utilized in many IoT systems, especially in environment monitoring Wireless Sensor Networks (WSN) and Low-Power Wide-Area Networks (LPWAN). In duty-cycle mode, devices transmit packets during the active phase, which lasts for a short time, and sleeps the rest of the time. Prior research mainly focuses on energy efficiency in the active phase; energy consumption during the sleep phase, however, is always ignored, as it is assumed to have little margin to be optimized. In this work, we reveal that sleep phase can become a significant battery consumer due to the misconfiguration of General-Purpose Input/Output (GPIO) pins of micro-controllers. We propose OPCIO, which incorporates a genetic algorithm to obtain energy-efficient GPIO configurations automatically to squeeze the energy waste during the sleep phase. We prototype OPCIO on off-the-shelf devices and evaluate it on two ARM devices. Experiment results show that OPCIO can effectively find multiple low-power configurations that prolong the lifespans up to 10×.

Neighbor discovery is important for docking applications, where mobile nodes communicate with static nodes situated at various rendezvous points. Among the existing neighbor discovery protocols, the probabilistic methods perform well in average cases but they have aperiodic, unpredictable, and unbounded discovery latency. Yet, deterministic protocols can provide bounded worst-case discovery latency by sacrificing the average-case performance. In this study, we propose a mobility-assisted slot index synchronization (MASS), which is a new synchronization technique that can improve the average-case performance of deterministic neighbor discovery protocols via slot index synchronization without incurring additional energy consumption. Furthermore, we propose an optimized beacon strategy in MASS to mitigate beaconing collisions, which can lead to discovery failures in situations where multiple neighbors are in the vicinity. We evaluate MASS with theoretical analysis and simulations using real traces from a tourist tracking system deployed at the Mogao Grottoes, which is a famous cultural heritage site in China. We show that MASS can reduce the average discovery latency of state-of-the-art deterministic neighbor discovery protocols by up to two orders of magnitude.

Duty-cycling is generally adopted in existing sensor networks to reduce power consumption and these networks depend on neighbor discovery protocols to ensure that nodes wake up and discover each other. For different neighbor discovery protocols, the discovery latency is determined by two factors: the wake-sleep pattern and slot size. To the best of our knowledge, previous works on neighbor discovery have thus far been focused on improving the wake-sleep pattern. In this paper, we investigate the extent to which we can improve discovery latency by reducing the slot size. We found that by reducing the slot size, i.e., reducing the listening time in active slots, the collisions between beacons and synchronization between nodes become more severe, which can lead to discovery failures that are not predicted by existing theoretical models. We show that we can mitigate these effects by reducing the number of beacons and introducing randomization. We propose a new continuous-listening-based neighbor discovery algorithm called Spotlight. Our evaluations with a practical sensor testbed suggest that Spotlight can achieve a 50% reduction in discovery latency over existing state-of-the-art neighbor discovery protocols without increasing power consumption in existing sensor networks.

Neighbor discovery is essential for docking applications, where mobile nodes communicate with static nodes situated at various rendezvous points. In existing neighbor discovery protocols, the probabilistic protocols perform well in the average-case but have a periodic, unpredictable and unbounded discovery latency. While the deterministic protocols can provide a bounded worst-case discovery latency, they achieve this by sacrificing the average-case performance. In this paper, we propose a new synchronization technique, called Mobility-Assisted Slot index Synchronization (MASS). MASS improves the average-case performance of deterministic neighbor discovery protocols via slot index synchronization, without incurring additional energy consumption. We evaluate MASS through both theoretical analysis and simulations of the real traces from a tourist tracking system deployed at Mogao Grottoes, a famous cultural heritage site in China. We show that MASS can reduce the average discovery latency of state-of-the-art deterministic neighbor discovery protocols by up to 2 orders of magnitude.

Real-world, long-running wireless sensor networks (WSNs) require intense user intervention in the development, hardware testing, deployment, and maintenance stages. A majority of network design is network centric and focuses primarily on network performance, for example, efficient sensing and reliable data delivery. Although several tools have been developed to assist debugging and fault diagnosis, it is yet to systematically examine the underlying heavy burden that users face throughout the lifetime of WSNs. In this article, we propose a general Multimode user-CentriC (MC 2 ) framework that can, with simple user inputs, adjust itself to assist user operation and thus reduce the users' burden at various stages. In particular, we have identified utilities that are essential at each stage and grouped them into modes . In each mode, only the corresponding utilities will be loaded, and modes can be easily switched using the customized MC 2 sensor platform. As such, we reduce the runtime interference between various utilities and simplify their development as well as their debugging. We validated our MC 2 software and the sensor platform in a long-lived microclimate monitoring system deployed at a wildland heritage site, Mogao Grottoes. In our current system, 241 sensor nodes have been deployed in 57 caves, and the network has been running for over five years. Our experimental validation shows that the MC 2 framework shortens the time for network deployment and maintenance, and makes network maintenance doable by field experts (in our case, historians).

Microclimates in many wildland cultural heritage sites are not under surveillance up to now, due to the lack of power supply and network access. However, accurate microclimate data in cultural heritage sites are very important for research and conservation. In this paper, we present a wireless sensor system for long-term microclimate monitoring in wildland cultural heritage sites, and its deployment at the Mogao Grottoes. The system integrates wireless sensor network (WSN), long-distance wireless polling network (LWPN) and Internet to fit the complex geography of the Mogao Grottoes. Both robust hardware and fault tolerance strategies have been developed to ensure long-term stable monitoring. The currently deployed system consists of 241 data sensors covering 57 typical caves. The reliability and long life-time of the system are verified through network and battery performance evaluations at the end of the paper.

Role-based access control (RBAC) models have generated a great interest in the security community as a powerful and generalized approach to security management. One of important aspects in RBAC is constraints that constrain what components in RBAC are allowed to do. There are lots of research have been achieved to specify constraints for secure system developers. However more work is need urgently to met requirements for interoperability of machine and people understandable constraints specification in open and distributed environment. In this paper we propose another approach to specify constraints using Semantic Web technologies. The Web Ontology Language (OWL) specification of basic RBAC components and constraints are described in detail.