Vision-Language Models (VLMs) have achieved impressive performance across various tasks, but often struggle to apply newly introduced visual concepts during inference. A common failure pattern is what we call Mixing Things Up: VLMs frequently confuse concept names, resulting in vague descriptions and failure to ground the concept correctly. Existing approaches mainly address person-related concepts through text prompts or tokenizer modifications. However, VLMs still miss or misinterpret untrained visual concepts, underscoring the need to learn new concepts directly from visual input, without relying on prior textual injection. To overcome these limitations, we propose BISCUIT (Basis-aligned Inference through Structured Concept Unification and Identification-aware Tuning), a two-step training method. Step I proposes a dual-stream structure-aware vision encoder that fuses RGB and edge-based embeddings within a shared basis space to enhance concept recognition. Step II enhances generation quality through identification-aware tuning, which encourages alignment between the generated text and the newly introduced visual concepts. Existing methods mainly focus on person concepts and lack comprehensive evaluation across diverse visual categories. We further propose a benchmark BiscuitVQA to evaluate VLMs performance on recognizing and applying novel image-introduced concepts across diverse concept types and task types, including real people, cartoons, animals, and symbolic content. We apply BISCUIT to LLaVA-1.5 and Qwen2.5-VL, achieving competitive results among open-source models and narrowing the gap to Gemini-2.5 and GPT-4o. Interestingly, our BISCUIT maintains strong generalization, showing minimal degradation on other downstream tasks.

In recent years, the significance of pre-trained transformer audio models has been increasingly recognized. However, existing pre-trained transformer audio models are based on single-channel audio. They cannot be directly applied to multi-channel audio for Sound Event Localization and Detection (SELD) tasks. To address this issue, in this paper, we propose SELD-SSAST, a novel model based on the single-channel Self-Supervised Audio Spectrogram Transformer (SSAST). Specifically, we first introduce a fusion feature that enables SSAST to learn the unique features in SELD problems effectively. Secondly, we input the multi-channel audio features into a single SSAST module to learn the temporal information across channels through channel-mixing. Finally, to enable SSAST to learn the relationships between multi-channel audio features, we propose a Convolutional Cross Attention (CCA) module to replace the Transformer’s Self-Attention and an intensity vector (IV) enhanced module to learn the differences between channel features. Our experiments show that using SELD-SSAST improved performance by 23.5% and 20.2% over the baseline on two datasets, respectively. Additionally, with the same data scale, SELD-SSAST outperforms the models in state-of-the-art (SOTA) methods on two datasets.

Vision-Language Models (VLMs) effectively align images and text, but they often struggle with fine-grained reasoning tasks. Fine-grained training also typically demand substantial GPU memory and a large number of trainable parameters. While existing Parameter-Efficient Fine-Tuning (PEFT) methods address these challenges, they also introduce new limitations, such as compromising generalization performance with additional parameters and causing overfitting with partial fine-tuning. To overcome these limitations, we propose History Tracker, a novel PEFT method designed to enhance fine-grained reasoning performance in VLMs. History Tracker leverages the stacked architecture of the image encoder, dividing its layers into shallow, middle, deep, and global groups. It then retrieves and compresses embeddings from these groups into compact tokens, reinforcing the distribution of image embeddings and allowing the text decoder to capture fine-grained details. Furthermore, we introduce a fine-grained dataset to fine-tune and evaluate models. This dataset focuses on tasks where highly similar text needs to be ranked based on its alignment with the given image. Experimental results demonstrate that History Tracker outperforms existing fine-tuning methods on both general benchmarks and fine-grained reasoning tasks. By fine-tuning only 7.03% of parameters, our method strikes a balance between fine-grained reasoning, generalization, and efficiency.

Vision-Language Models (VLMs) have achieved significant advances across various downstream tasks. However, as their performance improves, the increasing number of parameters results in slower prefilling speeds and longer inference times. To overcome these limitations, we observe that most VLMs do not require a large number of image tokens for inference, we propose BOLT (Basis-Oriented Lightweight Token-Trimming), a training-free and cross-attention-free token compression method. Unlike existing approaches, BOLT addresses the challenge of insufficient visual cues in textual prompts by leveraging token internal data distributions. We categorize tokens into three types: key tokens, proxy tokens, and remaining tokens. Then, by applying basis space similarity, we merge and filter the remaining tokens with the proxy tokens to retain the most informative ones. To account for the differences in VLM architectures and model sizes, we evaluate BOLT on LLaVA-Next-Llama3 and LLaVA-1.5 (7B and 13B). Our results show that BOLT achieves state-of-the-art performance, with a 90% token compression ratio leading to a 3.3× increase in pre-filling speed and a 1.5× improvement in inference speed, outperforming other methods.

Federated Learning (FL) enables model sharing between clients and a server instead of data transfer, enhancing user privacy. In this study, we employ an asynchronous training approach, allowing the server to proceed without waiting for crashed clients, thus improving round efficiency. Besides, because there is highly heterogeneous data distribution between clients, we propose a dynamic personalized federated learning approach, which helps the global model better fit each client’s unique data distribution. In addition, we also consider that the outdated models can lower the global model’s accuracy because they fail to capture the latest user preferences. we have designed a switchable aggregation algorithm. If there is a large data distribution difference between the latest updated clients and other outdated clients, which means the users’ preferences have changed a lot. To solve this challenge, when aggregating, their model version will be considered. If there is a little difference, which means the users’ preferences haven’t changed a lot, we allow all models in the cache to participate in this round of aggregation regardless of their model version, which greatly improves the efficiency of the aggregation. In this way, the global model will be tilted towards clients’ model with the latest data. Ultimately, PFL-SA demonstrates higher accuracy and reduced communication overhead compared to the other federated learning methods, as evidenced by the experimental results.

Satellite-terrestrial integrated networks based on edge computing can provide computation offloading service to terminal devices in remote areas. However, it faces various limitations, including satellite energy consumption, computation delay, and environmental dynamics, etc. In this paper, we propose a satellite-terrestrial integrated cloud and edge computing network (STCECN) architecture, including satellite layer, terrestrial layer and cloud center, where computing resources exist in multi-layer heterogeneous edge computing clusters. Optimization of system delay and energy consumption is defined as a mixed-integer programming problem. Moreover, we present a deep reinforcement learning-based computation offloading decision algorithm that can adapt to the dynamics and variability of satellite networks. A dual network computation offloading decision method is proposed for delay and energy consumption based on deep reinforcement learning offloading (DRLO), including deep convolutional network update method, quantization strategy, and bandwidth resource allocation. Meanwhile, the proposed method is based on previous experience and integrates deviation adjustment strategies for decision making to solve the problem of pseudo-patch loss caused by satellite network switching. The simulation results indicate that the proposed method performs almost consistently with traditional heuristic algorithms, with only 20% of the time consumption of the latter, and the number of pseudo packet loss also decreases to the original 10–20%.

Fine-grained human motion detection has become increasingly important with the growing popularity of human computer interaction (HCI). However, traditional gesture-based HCI systems often require the design of new operation modes rather than conforming to user habits, thus increasing system learning costs. In this paper, we present TapWristband, a novel wearable sensor-based vibration sensing system that detects finger tapping by measuring wrist vibrations. We first perform real-world experiments to collect measurements for modeling the effects of the tapping motion on wearable wristband sensors including piezoelectric transducer (PZT) and inertial measurement unit (IMU). We find that a damped vibration model can be used to represent the relaxing phase of a vibration response due to tapping motion. Thus, we propose a mutual cross-correlation-based event segmentation algorithm to extract the vibration signal during the relaxing phase. After that, we develop feature extraction and classification algorithms to recognize the tapping patterns of five fingers across twelve key locations of a keypad system. Finally, we performed extensive experiments with thirteen participants to evaluate our system. Experimental results show that our low-cost vibration sensing system can achieve an average accuracy of over 93% with a tapping speed of over 100 taps per minute in real-world tapping scenarios.

The development of the Internet of Things promotes the increasing demand for edge computing, resulting in a large amount of temporal data. Protecting data from tampering has become key to industrial intelligent management. Blockchain technology has become an ideal choice for ensuring data trustworthiness due to its immutability and other characteristics. However, existing technologies do not provide sufficient support for temporal data. There are still issues such as chaotic data organization, low query efficiency, and insufficient lightweight validation. To address these challenges, we combine the Secure Hash Algorithm and Merkle tree to serialize temporal data. We design a temporal Merkle prefix forest on the blockchain cloud main chain and construct an index for intra-block localization. Verification can be completed by monitoring the latest tree, significantly improving query efficiency. For edge-side devices with limited computing resources, we design a temporal Bloom Merkle tree, where lightweight nodes only need to pass the Merkle proof of the root node to verify data integrity. Experimental results demonstrate that our method significantly improves query efficiency and reduces storage requirements, meeting the reliability and lightweight requirements of temporal data management in the Internet of Things.

Edge servers and mobile devices are often assigned a large number of computing tasks. However, the data involved in computing tasks is often sensitive in terms of privacy. Our initial proposal is a federated edge learning strategy based on real-world scenarios, which combines blurred data or pseudo shared data. Federated learning is used to train device models with the aim of protecting privacy while enabling mobile devices to more effectively utilize data for decision-making. In the case of limited energy on mobile devices, we propose a federated edge learning algorithm with blurred data sharing. This algorithm can generate more accurate models by uploading partially blurred data. In order to further improve model accuracy and protect privacy of mobile devices, we propose a federated edge learning algorithm with pseudo data sharing based on dataset distillation and generative adversarial networks (GANs) in scenarios with relatively sufficient energy. The experimental results on several traditional datasets show that our proposed algorithms outperform traditional algorithms in terms of accuracy and energy consumption.

Nowadays, Mobile Edge Computing (MEC) appears as a new computing paradigm with its ability to utilize the computing power of both local devices and edge servers. In MEC, edge pricing and resource allocation are two important problems. Edge servers make a profit by selling computing services to users. To maximize their revenue, they need to determine an appropriate price for each user, and decide the amount of resources allocated to each user. However, none of the existing works consider the effect of users’ task assignment strategy on the revenue of the edge. In fact, edge pricing and resource allocation will affect the users’ task offloading decision, as they expect to minimize their total cost. In turn, the users’ decision will also influence the revenue of the edge. Therefore, the interaction between mobile users and edge servers should be considered carefully and the interests of both sides need to be maximized simultaneously. In this paper, we model the interaction between the two sides as a Stackelberg game. First, given a specified edge pricing and resource allocation strategy, we derive a near-optimal task assignment strategy for each user to minimize the total cost based on a greedy algorithm UTA-G. Then, by applying the backward induction method, two pricing and resource allocation schemes with different granularity, i.e., EPRA-U and EPRA-T are proposed to bring higher revenue to the edge. Experimental results demonstrate that all the proposed algorithms can have good performance in task-intensive, resource-deficient and workload-heavy scenarios.

Multi-source sound localization can find applications in many domains including auditory scene analysis, fault detection and diagnosis in manufacturing, augmented reality, etc. In far fields, 3D sound source localization is equivalent to finding the direction of arrival (DOA), namely, the azimuth and elevation angles of sound sources. Recent DOA estimation pipelines take multichannel audio inputs, extract spectral features from each channel and then feed them into a deep neural network. Unfortunately, the spectral features contain only the time-frequency information of the audio signals, while spatial information is only implicitly captured in the signals across different channels, which is highly dependent on the acoustic array geometry. To embed the spatial information of the sound source into the spectral feature representation, we propose a DSB-based spatial mapping method encode sound source location information. It can be combined with different feature extraction methods and machine learning models for DOA estimation. Furthermore, a redundancy removal procedure is proposed to accelerate DSB computation so that the pipeline can run in real-time on embedded GPUs, such as NVidia Jeston Nano. We conduct extensive experiments using two neural network models along with the DSB method on two datasets. The experiments demonstrate that the DOA errors can be effectively reduced using the DSB method. When combining DSB for feature extraction, the DOA errors are reduced by up to 19.24%. In addition, the feature extraction process is accelerated by up to 30.42% after the application of redundancy removal.

Federated learning (FL) is an emerging paradigm to train model with distributed data from numerous Internet of Things (IoT) devices. It inherently assumes a uniform capacity among participants. However, due to different conditions such as differing energy budgets or executing parallel unrelated tasks, participants have diverse computational resources in practice. Participants with insufficient computation budgets must plan for the use of restricted computational resources appropriately; otherwise, they would be unable to complete the entire training procedure, resulting in model performance decline. To address this issue, we propose a strategy for estimating local models without computationally intensive iterations. Based on it, we propose computationally customized federated averaging (CC-FedAvg), which allows participants to determine whether to perform traditional local training or model estimation in each round based on their current computational budgets. Both theoretical analysis and exhaustive experiments indicate that CC-FedAvg has the same convergence rate and comparable performance as FedAvg without resource constraints. Furthermore, CC-FedAvg can be viewed as a computation-efficient version of FedAvg that retains model performance while considerably lowering computation overhead.

The burgeoning field of smart agriculture is increasingly leveraging unmanned aerial vehicles (UAVs) for data collection. However, inadequate visual features and plant occlusion can hamper visual-based simultaneous localization and mapping (SLAM) of UAVs. As a potential solution, RFID can work as an efficient reference infrastructure, enabling a connection between aerial imagery and real-world contexts. Despite this promise, hurdles remain in attaining high-accuracy, high-throughput, and long-range RFID localization, as well as practical deployment of RFID tags and reader implementation on UAVs. Overcoming these challenges holds significant potential, particularly considering their impact on numerous applications, such as large-scale agricultural management and plant stand reduction detection.

In response to the growing demand for clients' privacy protection, federated learning framework often needs appropriate privacy protection to protect clients' privacy better, such as the client uploading a blinded local model instead of the original real local model to the server, which makes the real value of the local model unobservable to the server. Although the privacy is protected, it will to be a huge challenge to the server to distinguish clients which are Byzantine clients. We propose a federated learning framework based on broad learning that can simultaneously achieve protection of clients' privacy and robustness against Byzantine attacks, i.e. FBL-BP. We apply differential privacy techniques to perturb the local models of the clients, which can protect clients' privacy. The server receives the perturbed local models and then guarantees the Byzantine-resilience of global model through an outlier removal mechanism based on cosine similarity. Finally, experimental results show that FBL-BP has significant Byzantine robustness and satisfactory accuracy, and possesses less time consumption than traditional methods.

With the rapid growth of the number of devices generating and collecting data, dispersion becomes an important feature of data in Internet of Things. Federated learning (FL) provides a feasible way to mine information in such distributed data. It involves training machine learning models over multiple distributed participants without raw data transmission. However, due to the data heterogeneity among participants, the performance of the FL model degrades dramatically. Currently, improved methods mainly reduce data heterogeneity from the perspective of modifying the process of model training, which usually have problems, such as high-resource consumption or the need for auxiliary data. In this article, we enhance FL model from another perspective, focusing on data rather than model training. We reduce data heterogeneity by enhancing the trained local data to improve FL performance. Specifically, we propose an FL method based on data augmentation (abbreviated as FedM-UNE), implementing the classic data augmentation method MixUp in federated scenarios without transferring raw data. Furthermore, in order to adapt this method to regression tasks, we first modify MixUp by bilateral neighborhood expansion (MixUp-BNE), and then propose a federated data augmentation method named FedM-BNE based on it. Compared with the conventional FL method, both FedM-UNE and FedM-BNE increase negligible overhead. To demonstrate the effectiveness, we conduct exhaustive experiments on six data sets employing a variety of loss functions. The results indicate that FedM-UNE and FedM-BNE consistently improve the performance of the FL model. Moreover, our methods are compatible with existing FL enhancements, which yield further improvements in performance.

Federated learning (FL) training global machine learning models over distributed edge devices has attracted sustained attentions. However, the heterogeneity of client data severely degrades the performance of FL compared with that in centralized training. On the one hand, it slows down or even stalls global updates, leading to inefficient communication. On the other hand, it enlarges the distances between local models, resulting in an aggregated global model with poor performance. Fortunately, these shortcomings can be mitigated by reducing the angle between the directions in which a local model move. Based on this observation, we propose FedCos, which reduces the directional inconsistency of local models by introducing a cosine-similarity penalty. It promotes local model iterations toward an auxiliary global direction. Moreover, our approach is auto-adapted to various non-identically and independently distributed (IID) settings without an elaborate selection of hyperparameters. Experimental results on both vision and language tasks with a variety of models (including CNN, ResNet, LSTM, etc.) show that FedCos outperforms the well-known baselines and can enhance them under a variety of FL scenes, including varying degrees of data heterogeneity, different number of participants, and cross-silo and cross-device settings. Besides, FedCos improves the communication efficiency by 2–5 times. With the help of FedCos, multiple FL methods require significantly fewer communication rounds than before to obtain a comparable model.

The appearance of Mobile Edge Computing (MEC) successfully solves the bottlenecks of traditional Cloud based networks. Since mobile edges, e.g., base stations, and mobile devices have certain data processing capabilities, it is not necessary to offload all the tasks to the cloud for handling. Therefore, it is quite important to decide the optimal task assignment in MEC systems, and a series of algorithms have been proposed. However, the existing algorithms ignored the data distribution during task assignment, so that their applied ranges are quite limit. Considering the data sharing is quite important in a MEC system, this paper studies task assignment algorithms in Data Shared Mobile Edge Computing Systems in detail. Specifically, three algorithms are proposed to deal with the unordered and ordered holistic tasks respectively. Meanwhile, the situation that the tasks are divisible is also considered, and two algorithms for rearranging the divisible tasks are proposed for different optimization goals. The hardness of the problem, the correctness, complexities, and ratio bounds of the proposed algorithms are analyzed theoretically. Finally, extensive experimental results are carried out. Both theoretical analysis and experiment results show that all the proposed algorithms have high performance in terms of latency, satisfied rate, and energy consumption.

Variations of stochastic gradient decedent (SGD) methods are at the core of training deep neural network models. However, in distributed deep learning, where multiple computing devices and data segments are employed in the training process, the performance of SGD can be significantly limited by the overhead of gradient communication. Local SGD methods are designed to overcome this bottleneck by averaging individual gradients trained over parallel workers after multiple local iterations. Currently, both for theoretical analyses and for practical applications, most studies employ periodic synchronization scheme by default, while few of them focus on the aperiodic schemes to obtain better performance models with limited computation and communication overhead. In this paper, we investigate local SGD with an arbitrary synchronization scheme to answer two questions: (1) Is the periodic synchronization scheme best? (2) If not, what is the optimal one? First, for any synchronization scheme, we derive the performance boundary with fixed overhead, and formulate the performance optimization under given computation and communication constraints. Then we find a succinct property of the optimal scheme that the local iteration number decreases as training continues, which indicates the periodic one is suboptimal. Furthermore, with some reasonable approximations, we obtain an explicit form of the optimal scheme and propose Aperiodic Local SGD (ALSGD) as an improved substitute for local SGD without any overhead increment. Our experiments also confirm that with the same computation and communication overhead, ALSGD outperforms local SGD in performance, especially for heterogeneous data.

With the development of embedded systems and Cross-Technology Communication (CTC) techniques, high throughput communication among heterogeneous IoT devices in the same frequency band (ISM band) can be achieved, which provides opportunities to en-hance the coexistence and cooperation for heterogeneous IoT de-vices. However, such improvement on the throughput is limited, since parallel communication has not been considered by most of the existing CTC techniques. There still exists unavoidable distortion in the reliability of the existing CTC techniques because of the heterogeneous properties of the protocols, hardware, and operating systems. Therefore, to enhance the communication throughput among heterogeneous IoT devices as much as possible, we study the parallel physical-layer CTC (PCTC) in this paper. We propose two advanced physical-layer CTCs. The first one improves the communication reliability between two heterogeneous IoT devices by retrieving the candidate emulation frames with high quality, and the other orthogonalizes the above candidate frames to achieve concurrent transmission. PCTC is designed for WiFi to ZigBee communication, and it is also implemented in USRP B210, which can improve the reliability and concurrency of the physical-layer CTC. Both theoretical analysis and experiment results verify its trans-mission reliability and improvement of throughput by comparing our technique with the existing ones.

With the widespread of Human-Cyber-Physical Systems (HCPS), the fine-grained human movement detection becomes more and more important. Especially for the slightly motions of human’s hands, they are not only bring abundant information, but also provide a new way for the interaction between users and systems. In this paper, we focus on the problem of how to detect the human’s typing motion, and designed a new system, named as Typing Wristband, to obtain the vibration of wrist using piezoelectric transducer (PZT). Then, a robust denoising, event detection and classification algorithms are proposed to deal with the signal collected by Typing Wristband and detect the typing motions. Typing Wristband can recognized the movements of 3 fingers and 9 keys with high accuracy. Furthermore, it is very cheap and can be embedded into existing smart devices, e.g. a smart watch, so that it supports the wireless sensing very well in practice. Both of the analysis and experimental results verify that our Typing Wristband has the better performance in terms of accuracy and convenience.

The appearance of the integrated chips with multiple communication modules and cross-technology communication (CTC) methods make it possible to improve the quality of wireless communication by utilizing the cooperation of different protocols. In this paper, we took the cooperation of WiFi and Bluetooth Low Energy (BLE) as an example, and proposed a method to optimize the channel selection of BLE on condition that the CSI of related WiFi is fully used. The real experiments based on USRP were carried out. Both theoretical analysis and experimental results verify that our method has better performance in terms of RSSI and SNR.

The appearance of the Mobile Edge Computing (MEC) successfully solves the bottlenecks of traditional Cloud based networks as computation ability of each mobile edge is sufficiently utilized. Since the mobile edges, including mobile devices and base stations, have certain data processing abilities, it is not necessary to offload all the computation tasks to the remote could for handling. Therefore, it is quite important to decide the optimal task assignment in a MEC system, and a series of algorithms have been proposed to solve it. However, the existing algorithms ignored the data distribution during task assignment, so that the applied ranges of these algorithms are quite limit. Considering the data sharing is very important and common in a MEC system, this paper studies the task assignment algorithm in Data Shared Mobile Edge Computing Systems, and three algorithms are proposed to deal with holistic tasks and divisible tasks, respectively. The theoretical analysis on the hardness of the problem, the correctness, complexities and ratio bounds of the algorithms are also provided. Finally, the extensive experiment results were carried out. Both of the theoretical analysis and experiment results show that our algorithms have high performance in terms of latency and energy consumption.

A new network architecture, named as RF-based battery-free sensor network, was proposed in recent years to overcome the lifetime limitation of traditional wireless sensor networks. In an RF-based battery-free sensor network, the battery-free nodes equip no battery and can be recharged by RF-signals. The Dominating Set (DS) is a key method to maintain the coverage of traditional WSNs, and it can be also adopted in the RF-based battery-free sensor networks. However, considering the specific features of RF-based battery-free sensor networks, the DS construction is totally different from that in traditional WSNs. Thus, the problem of constructing DS in a battery-free sensor network is deeply investigated in this article. The NP-Hardness of such problem is proved. Four approximation algorithms are proposed to deal with the snapshot and continuous DS construction requirements, respectively. The approximation ratios of these four algorithms have been analyzed, and the theoretical results show that all these four algorithms are effective. Furthermore, the electromagnetic interference problem in the RF-based battery-free sensor network is considered and defined. An approximated algorithm is proposed to solve such problem. Finally, extensive simulations are carried out. The experimental results verify that the proposed algorithms have high performance in terms of accuracy and efficiency.

As an important application of smart home, the smart keys, which can record the locking information of users, are quite useful in our daily lives and guarantee the security of our houses and properties. The existing techniques for supporting smart keys either require to change the locks or need a large amount of sensory data to build a complex model, so that they will cost too much money or energy, and are not very practical in the real applications. Therefore, we propose a sensing-free smart key handle, named as sKey, in this demo. Compared with the existing techniques, our sKey does not depend on any detection model, and thus no resources will be consumed for sensory data acquisition and transmission during using sKey. Besides, our sKey has high recognition precision, and is quite cheap and easy to deploy according to our analysis and experimental results.

Data aggregation is an essential operation for the sink to obtain summary information in a Wireless Sensor Network (WSN). The problem of Minimum Latency Aggregation Schedule (MLAS) which seeks a fastest and collision-free aggregation schedule has been well studied when nodes are always awake. However, in duty-cycle WSNs, nodes can only receive data in active state. In such networks, it is of great importance to exploit the limited active time slots to reduce aggregation latency. Unfortunately, few studies have addressed this issue and most previous aggregation methods rely on fixed structures which greatly limit the exploitation of the active time slots from other neighbors. In this paper, we investigate the MLAS problem in duty-cycle WSNs without considering structures. We propose the first distributed aggregation algorithm for duty-cycle WSNs, in which the aggregation tree and a conflict-free schedule are generated simultaneously. Compared with the previous centralized and distributed methods, the aggregation latency and the utilization ratio of available time slots are greatly improved. The theoretical analysis and simulation results verify that the proposed algorithm has high performance in terms of latency and communication cost.

Beaconing is a fundamental networking service where each node broadcasts a packet to all its neighbors locally. Unfortunately, the problem Minimum Latency Beaconing Schedule (MLBS) in duty-cycled scenarios is not well studied. Existing works always have rigid assumption that each node is only active once per working cycle. Aiming at making the work more practical and general, MLBS problem in duty-cycled network where each node is allowed to active multiple times in each working cycle (MLBSDCA for short) is investigated in this paper. Firstly, a modified first-fit coloring based algorithm is proposed for MLBSDCA under protocol interference model. After that, a (ρ + 1)2*|W|-approximation algorithm is proposed to further reduce the beaconing latency, where ρ denotes the interference radius, and |W| is the maximum number of active time slots per working cycle. When ρ and |W| is equal to 1, the approximation ratio is only 4, which is better than the one (i.e., 10) in existing works. Furthermore, two approximation algorithms for MLBSDCA under physical interference model are also investigated. The theoretical analysis and experimental results demonstrate the efficiency of the proposed algorithms in term of latency.

Currently, the limitation of battery has become a serious obstacle for the development of Internet of things (IoTs). Therefore, a new network architecture, named as battery-free network, were proposed. In a typical battery-free network, the battery-free nodes are equipped with any battery and can only gain energy from the environment. Such network extremely expands the scope of the IoT applications, however, it also brings many troubles for some network operations, e.g. data collection, since the energy of each node is quite small. Considering that the Connected Dominating Sets (CDSs) are commonly used to support data collection and network communication in wireless networks, and thus we will also investigate the CDS construction problem in battery-free networks. In this paper, the problem of constructing CDS in a battery-free network is formally defined, and we prove that it is NP-Complete. Thus, four approximation algorithms were proposed to deal with the snapshot, continuous and time-window based CDS construction requirements, respectively. Finally, the extensive experiments were carried out and the results verify that the proposed algorithms have high performance in term of accuracy and efficiency.

The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks (WSNs). The scale of sensory data in many applications has already exceeded several petabytes annually, which is beyond the computation and transmission capabilities of conventional WSNs. On the other hand, the information carried by big sensory data has high redundancy because of strong correlation among sensory data. In this paper, we introduce the novel concept of ϵ-Kernel Dataset, which is only a small data subset and can represent the vast information carried by big sensory data with the information loss rate being less than ϵ, where ϵ can be arbitrarily small. We prove that drawing the minimum ϵ-Kernel Dataset is polynomial time solvable and provide a centralized algorithm with O(n3) time complexity. Furthermore, a distributed algorithm with constant complexity O(1) is designed. It is shown that the result returned by the distributed algorithm can satisfy the ϵ requirement with a near optimal size. Furthermore, two distributed algorithms of maintaining the correlation coefficients among sensor nodes are developed. Finally, the extensive real experiment results and simulation results are presented. The results indicate that all the proposed algorithms have high performance in terms of accuracy and energy efficiency.

Multicast is an important way to diffuse data in duty-cycled wireless sensor networks (WSNs), where nodes can receive data only in active state. The communication delay can be extremely large if inappropriate schedules are adopted. Unfortunately, most previous methods do not consider controlling multicast delay energy-efficiently. This paper studies the minimum active time slot augmentation for delay-bounded multicast (MAADM) problem in duty-cycled WSNs. The MAADM problem is proved to be NP-hard even under the node-exclusive interference model. An optimal algorithm is proposed for the MAADM problem when K = 2 and a heuristic latency bounding algorithm is proposed for source-to-all communications, where K denotes the number of the destination nodes. When K > 2, two (K-1)-approximation algorithms are designed for the MAADM problem. In addition, a low computation-complexity distributed algorithm is proposed. To the best of our knowledge, this is the first work that develops a series of efficient centralized and distributed algorithms for the MAADM problem in dutycycled WSNs. The theoretical analysis and experimental results verify that all the proposed algorithms have high performance in terms of delivery delay and energy consumption.

Duty-cycle scheduling is an effective way to balance energy consumptions and prolong network lifetime of wireless sensor networks (WSNs), which usually requires a connected dominating set (CDS) to guarantee network connectivity and coverage. Therefore, the problem of finding the largest number of CDSs is important for WSNs. The previous works always assume all the nodes are non-rechargeable. However, WSNs are now taking advantages of rechargeable nodes to become energy harvest networks (EHNs). To find the largest number of CDSs then becomes completely different. This is the first work to investigate, how to identify the largest number of CDSs in EHNs to prolong network lifetime. The investigated novel problems are proved to be NP-Complete and we propose four approximate algorithms, accordingly. Both the solid theoretical analysis and the extensive simulations are performed to evaluate our algorithms.

Holistic aggregations are popular queries for users to obtain detailed summary information from Wireless Sensor Networks. An aggregation operation is holistic if there is no constant bound on the size of the storage needed to describe a sub-aggregation. Since holistic aggregation cannot be distributable, it requires that all the sensory data should be sent to the sink in order to obtain the exact holistic aggregation results, which costs lots of energy. However, in most applications, exact holistic aggregation results are not necessary; instead, approximate results are acceptable. To save energy as much as possible, we study the approximated holistic aggregation algorithms based on uniform sampling. In this article, four holistic aggregation operations, frequency, distinct-count, rank, and quantile, are investigated. The mathematical methods to construct their estimators and determine optional sample size are proposed, and the correctness of these methods are proved. Four corresponding distributed holistic algorithms to derive (ϵ, δ)-approximate aggregation results are given. The solid theoretical analysis and extensive simulation results show that all the proposed algorithms have high performance on the aspects of accuracy and energy consumption.

A Wireless Sensor Network consists of a number of sensors. The energy of each sensor is limited which limits network lifetime. There are many existing energy efficiency algorithms to prolong network lifetime. Basically, there are two kinds of methods. One is energy-efficiency management, such as duty-cycling using virtual-backbones. The other one is energy provision, such as energy harvest from the environment. In this paper, we introduce a new problem, CDSEH, to combine these two methods together. We also propose a new standard to define the network lifetime of a WSN. We prove that the CDSEH problem is NP-Complete and propose two approximate algorithms accordingly. Extensive simulation results are shown to validate the performance of our algorithms.

Holistic aggregation results are important for users to obtain summary information from Wireless Sensor Networks (WSNs). Holistic aggregation requires all the sensory data to be sent to the sink, which costs a huge amount of energy. Fortunately, in most applications, approximate results are acceptable. We study the approximated holistic aggregation algorithms based on uniform sampling. In this paper, four holistic aggregation operations are investigated. The mathematical methods to construct their estimators and determine the optional sample size are proposed, and the correctness of these methods is proved. Four corresponding distributed holistic algorithms are presented. The theoretical analysis and simulation results show that the algorithms have high performance.

The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks. The scale of the sensory data in many applications has already exceeds several petabytes annually, which is beyond the computation and transmission capabilities of the conventional WSNs. On the other hand, the information carried by big sensory data has high redundancy because of strong correlation among sensory data. In this paper, we define the concept of e-dominant dataset, which is only a small data set and can represent the vast information carried by big sensory data with the information loss rate being less than e, where e can be arbitrarily small. We prove that drawing the minimum e-dominant dataset is polynomial time solvable and provide a centralized algorithm with 0(n3) time complexity. Furthermore, a distributed algorithm with constant complexity (O(l)) is also designed. It is shown that the result returned by the distributed algorithm can satisfy the e requirement with a near optimal size. Finally, the extensive real experiment results and simulation results are carried out. The results indicate that all the proposed algorithms have high performance in terms of accuracy and energy efficiency.

Myopia becomes a more and more serious worldwide problem as the number of myopic people (especially young people) grows rapidly. Efficient methods are required to monitoring the deterioration of nearsightness so as to take further treatment. This demo realizes a noval nearsightness monitoring system, called iSee, which utilizes the widely used smartphones to detect the deterioration of nearsightness by monitoring and analysing the the distance between the eyes and the smartphone screen. A prototype of iSee has been developed to evaluated the effectiveness under different environmental conditions.

To observe the complicated physical world, the sensors in a network sense and sample the data from the physical world. Currently, most existing works use the Equi-Frequency Sampling (EFS) methods or EFS based methods for data acquisition. However, the accuracy of EFS and EFS based methods cannot be guaranteed in practice since the physical world keeps changing continuously, and these methods do not effectively support reconstruction of the monitored physical world. To overcome the shortages of EFS and EFS based methods, this paper focuses on designing physical-world-aware data acquisition algorithms to support O(ε)-approximation to the physical world for any ε ≥ 0. Two physical-world-aware data acquisition algorithms are proposed. Both algorithms can adjust the sensing frequency automatically based on the changing trend of the physical world and the given ε. The thorough analysis on the performances of the algorithms are also provided. It is proven that the error bounds of the algorithms are O(ε) and the complexities of the algorithms are O(1/(ε1/4)). Based on the new data acquisition algorithms, an algorithm for reconstructing the physical world is proposed and analyzed. The theoretical analysis and experimental results show that the proposed algorithms have high performances on the aspects of accuracy and energy consumption.

Continuous aggregation is usually required in many sensor applications to obtain the temporal variation information of aggregates. However, in a hostile environment, the adversary could fabricate false temporal variation patterns of the aggregates by manipulating a series of aggregation results through compromised nodes. Existing secure aggregation schemes conduct one individual verification for each aggregation result, which could incur great accumulative communication cost and negative impact on transmission scheduling for continuous aggregation. In this paper, we identify distinct design issues for protecting continuous in-network aggregation and propose a novel scheme to detect false temporal variation patterns. Compared with the existing schemes, our scheme greatly reduces the verification cost by checking only a small part of aggregation results to verify the correctness of the temporal variation patterns in a time window. A sampling-based approach is used to check the aggregation results, which enables our scheme independent of any particular in-network aggregation protocols as opposed to existing schemes. We also propose a series of security mechanisms to protect the sampling process. Both theoretical analysis and simulations show the effectiveness and efficiency of our scheme.

To observe the complicate physical world by a WSN, the sensors in the WSN senses and samples the data from the physical world. Currently, most of the existing work use equi-frequency sampling methods (EFS) or EFS based sampling methods for data acquisition in sensor networks. However, the accuracies of EFS and EFS based sampling methods cannot be guaranteed in practice since the physical world usually varies continuously, and these methods does not support reconstructing of the monitored physical world. To overcome the shortages of EFS and EFS based sampling methods, this paper focuses on designing physical-world-aware data acquisition algorithms to support O(ϵ)-approximation to the physical world for any ϵ ≥ 0. Two physical-world-aware data acquisition algorithms based on Hermit and Spline interpolation are proposed in the paper. Both algorithms can adjust the sensing frequency automatically based on the changing trend of the physical world and given c. The thorough analysis on the performance of the algorithms are also provided, including the accuracies, the smooth of the outputted curves, the error bounds for computing first and second derivatives, the number of the sampling times and complexities of the algorithms. It is proven that the error bounds of the algorithms are O(ϵ) and the complexities of the algorithms are O(1/ϵ1/4). Based on the new data acquisition algorithms, an algorithm for reconstructing physical world is also proposed and analyzed. The theoretical analysis and experimental results show that all the proposed algorithms have high performance in items of accuracy and energy consumption.

In the applications of wireless sensor networks, the peak values, such as largest sensed values and their locations, are very useful for detecting abnormal events happened in the monitored region. Although the results returned by the traditional top-k queries provide k largest sensed values, they ignore the spatial-correlation of the sensed data so that the locations of the returned values are very close to each other and only tell a small area being abnormal or few number of abnormal events happening. Due to this reason, the Location Aware Peak Value Query, denoted by LAP-(D,k) query, is proposed in this paper. For any given D and k, the LAP-(D,k) query returns k largest sensed values and their locations, and the distance between the any two locations is larger than D. The problem of processing LAP-(D,k) query is proved to be NP-hard, and two distributed approximation algorithms are proposed to solve this problem. One is a distributed greedy algorithm with ratio bound 5.8. The other one is a region partition based algorithm with ratio bound 3. The theoretical analysis and experimental results show that the proposed algorithms have high performance in terms of accuracy and energy consumption.

Aggregation operations are important in WSN applications. Since large numbers of applications only require approximate aggregation results rather than the exact ones, some approximate aggregation algorithms have been proposed to save energy. However, the error bounds of these algorithms are fixed and it is impossible to adjust the error bounds automatically, so they cannot meet the requirement of arbitrary precision required by various users. Thus, a uniform sampling-based algorithm was proposed by the authors of this paper to satisfy arbitrary precision requirement. Unfortunately, this uniform sampling-based algorithm is only suitable for static sensor networks. To overcome the shortcoming of the uniform sampling-based algorithm, this paper proposes four Bernoulli sampling-based and distributed approximate aggregation algorithms to process the snapshot and continuous aggregation queries in dynamic sensor networks. Theoretical analysis and experimental results show that the proposed algorithms have high performance in terms of accuracy and energy consumption.

In-network aggregation provides an energy-efficient way to extract summarization information from sensor networks. Continuous aggregation is usually required in many sensor applications to obtain the temporal variation information of some interesting aggregates. However, for the continuous in-network aggregation in a hostile environment, the adversary could manipulate a series of aggregation results through compromised nodes to fabricate false temporal variation patterns of the aggregates. Existing secure aggregation schemes conduct one individual verification for each aggregation result. Due to the high rate and the long period of a continuous aggregation, directly applying these schemes to detect false temporal variation pattern would incur a great communication cost. In this paper, we identify distinct design issues for protecting continuous in-network aggregation and propose a novel scheme to detect false temporal variation patterns. Compared with the existing schemes, our scheme greatly reduces the communication cost by selecting and checking only a small part of aggregation results to verify the correctness of the temporal variation patterns in a time window. The checking of the aggregation results uses a sampling-based approach, which enables our scheme independent of any particular in-network aggregation protocol. We also propose a series of security mechanisms to protect the sampling process. Both theoretical analysis and simulations show the effectiveness and efficiency of our scheme.

Target tracking is a well studied topic in wireless sensor networks. However, uncertainty existed in sensor networks presents new challenges for it. Besides the energy conservation of networks, target tracking has to deal with different kinds of uncertainty, such as the impreciseness of positioning systems, environment noise and limited sensitivity of sensors. In this paper, we study the problem of target tracking under uncertainty in wireless sensor networks. We first investigate the uncertainty existed in sensor networks and propose a series of general models. Then, we introduce the problem of probabilistic k-nearest neighbors (PkNN) and provide an efficient tracking algorithm based on PkNN retrieval under the proposed models. Finally, a comprehensive set of simulations are presented. We conclude that the proposed tracking algorithm yields excellent tracking performance in wireless sensor networks.

Aggregations of sensed data are very important for users to get summary information about monitored area in applications of wireless sensor networks (WSNs). As the approximate aggregation results are enough for users to perform analysis and make decisions, many approximate aggregation algorithms are proposed for WSNs. However, most of the algorithms have fixed error bounds and cannot meet arbitrary precision requirement, the uniform sampling based algorithm which can reach arbitrary precision is just suitable for the static networks. Considering the dynamic property of WSNs, in this paper, we propose an approximate aggregation algorithm based on Bernoulli sampling to satisfy arbitrary precision requirement. Besides, two adaptive algorithms are also proposed, one is for adapting the sample with varying of precision requirement, the other is for adapting the sample with varying of sensed data. The theoretical analysis and experiment results show that the proposed algorithms have high performance in terms of accuracy and energy consumption.

Aggregation operations are important for users to get summarization information in WSN applications. As large numbers of applications only require approximate aggregation results rather than the exact ones, some approximate aggregation algorithms are proposed to save energy. However, the error bounds of these algorithms are fixed and it is impossible to adjust their error bounds automatically. Therefore, these algorithms cannot reach arbitrary precision requirement given by user. This paper proposes a sampling based approximate aggregation algorithm to satisfy the requirement of arbitrary precision. Besides, two sample data adaptive algorithms are also provided. One is to adapt the sample with the varying of precision requirement. The other is to adapt the sample with the varying of the sensed data in networks. The theoretical analysis and experiment results show that the proposed algorithms have high performance in terms of accuracy and energy cost.