In cloud platforms, the occurrence of failures in cloud services is usually accompanied by a substantial number of alarms, posing challenges to the operation, management, and maintenance. To address this issue, this paper proposes an alarm prediction framework based on propagation dependency and reinforcement learning for cloud services. First, a three-layer cloud service alarm knowledge graph is constructed, encompassing the device layer, service layer, and alarm layer, with the objective of integrating alarm data. Then, to filter out irrelevant alarms, an alarm compression schema is designed, taking into account both structural and semantic information. It achieves the preservation of propagation information by mining frequent alarm subtrees. After that, a reinforcement learning-based alarm prediction model is devised, including an alarm-driven reinforcement learning network and a global alarm discriminator. The former formulates the alarm propagation problem as a reinforcement learning inferencing task, and designs an immediate reward function to simulate the local propagation patterns of alarms. The latter guides the agent to learn alarm evolution patterns from a global perspective through an adversarial approach. Experimental results on two datasets demonstrate the effectiveness of our alarm compression and prediction models.

As a cloud-hosted virtual desktop service, cloud desktop supports various fields such as telecommuting, collaborative development, while enabling real-time user interaction through video stream. The stability of this process is determined by bandwidth, which significantly influences the user experience. Therefore, precise bandwidth prediction of video streams is essential in cloud desktops. This work proposes DeskPred for video stream transmission in cloud desktops, focusing on dynamic bandwidth prediction. In the startup stage, the limited data amount poses a challenge for achieving precise bandwidth predictions. We propose an Affinity-based Federated Learning algorithm, which leverages the historical records of high-affinity users for assisted training, all while protecting user privacy. During the long-term adjustment stage, we propose a Fluctuation-based Adaptive Incremental Prediction algorithm for independent training to address the issue of pattern forgetting. The algorithm considers both periodic features and instantaneous features, incorporating new patterns while revisiting previous knowledge through the memory module and Adversarial Elastic Weight Consolidation. We have verified DeskPred through an actual cloud desktop project supported by Lenovo Research. Through experiments conducted on a total of over 18 million data items (approximately 10 GB), DeskPred achieves the highest total score of 71.11%, making it highly suitable for cloud desktop environments.

In autonomous driving and robotic navigation, multi-sensor fusion technology has become increasingly mainstream, with precise sensor calibration as its foundation. Traditional calibration methods rely on manual effort or specific targets, limiting adaptability to complex environments. Learning-based calibration methods still face challenges, such as insufficient overlap between the fields of view (FoV) of multiple sensors and suboptimal cross-modal feature association, which hinder accurate parameter regression. Unlike traditional CNN-based networks, we propose a KansFormer-based self-Calibration Network for camera and LiDAR (KFCalibNet) that replaces fixed activation functions and linear transformations with learnable nonlinear activation functions. This enables the extraction of more fine-grained features from both image and point cloud, significantly enhancing the network's robustness in scenarios with limited FoV overlap. We also employ a multihead attention (MHA) module to compute correlations between image and point cloud features, significantly enhancing cross-modal feature association. To reduce learning complexity, we designed KansFormer with FastKAN as the feedforward network, enabling deep fusion and regression of fine-grained cross-modal features for accurate extrinsic calibration. KFCalibNet achieves an absolute average calibration error of 0.0965 cm in translation and 0.0234° in rotation on the KITTI Odometry dataset, outperforming existing state-of-the-art calibration methods. Moreover, its accuracy and generalization capability have been validated across multiple real-world railway lines.

Industrial environments pose distinctive challenges for anomaly detection, primarily stemming from the complexities associated with high dimensionality and the dynamic nature of data patterns over time. These properties determine that the model’s proper convergence on unlabeled data is unpromising, consequently leading to less efficient discrimination of anomalies in previous anomaly detection (AD) works. To address this problem, we present AnoDual, a novel, meta-learning AD framework. From the perspective of data reconstruction, we introduce the multi-memory enhanced VAE reconstructor M2ER, which learns to extract the most salient patterns in unlabeled noisy data through a self-supervised manner. This design eases impacts from potential anomalous components during data reconstruction, and enhances the discernibility of anomalies. To address performance degradation caused by the numerical deviation based AD scheme in most existing works, we design a dual-source self-supervised discriminator DSD, which examines characteristics in the domain of representations. This model actively assesses discrepancies between data pairs and representation pairs in parallel, and conducts AD on a fine-grained scale. In this way, anomalies that used to be unnoticed due to a less prominent numerical deviation can be spotted. Besides, we propose a meta-learning powered training pipeline to enable model training even when no real label is available, which is common in the industry. Extensive experiments on five large-scale real-world industrial datasets suggest that AnoDual achieves an average F1-Score with a substantial increment of 3.39 %, outperforming the latest state-of-the-art baseline. Note to Practitioners—A generative model plus a numerical threshold based detection approach currently takes a significant share in both academia and the industry. However, the performance of this workflow is not promising in actual applications, with multiple factors contributing to this situation. The proper convergence of such generative models is difficult when the training material contains noisy samples - an over-expressed generative model would result in less significant reconstruction discrepancies for anomalies that are hard to notice. In addition, selecting a numerical threshold, which is used to spot anomalies, requires multiple laborious attempts, and can hardly adapt to an ever-changing pattern in industrial environments. These circumstances make it challenging to apply prior works in practical production, which, in turn, urges the need to develop an effective methodology to address the need for industrial anomaly detection. This manuscript includes a novel, meta-learning powered framework AnoDual, which is tailored for industrial scenarios. This framework discards the conventional design of comparing the reconstruction error numerically, but introduces a solution based on the differentiation of the representations. Besides, the multi-head attention enhanced variational autoencoder also leads to a much more pronounced discrepancy for anomalous samples, which benefits their successful detection. Providing a flexible and robust way to detect anomalies on deployed IoT assets, this work can be further transformed to serve applications in many other domains.

With the widespread application of big data and intelligent information systems, the tenant has become the main form of most scenarios. As a data mining technique, the portrait has been widely used to provide targeted services. Therefore, we transfer the traditional user-driven portrait into tenant driven for churn prediction. To achieve it, this paper first proposes a three-layer architecture and defines the fine-grained features for creating portraits from the perspective of tenants. In a large-scale telecommunication industry dataset of 100,000 tenants, we construct the tenant portrait through the proposed framework, and analyze the influences of the defined features on churn possibility. Then, considering the information missing caused by privacy concerns, we come up with theCrossMatch, a portrait completion model based on semi-supervised and graph convolution, which combines the relation characteristics among tenants for recovering missing information. On this basis, we design the tenant churn prediction method based on a directed attention network. Moreover, we recover missing information on three public node datasets withCrossMatch, achieving around 1-2$\%$improvement. We then apply the directed attention network for churn prediction and achieve an Accuracy of 75.06$\%$, Precision of 77.78$\%$, and F1-score of 71.43$\%$, which outperforms all the baselines.

Most existing works in Industrial Internet of Things (IIoT) anomaly detection either depend on computationally intensive models that exceed the capabilities of multiaccess edge computing (MEC) servers, or lightweight models that lack robustness, making them unadaptable in IIoT infrastructures. To address these challenges, we proposeTHREADS, a hierarchical anomaly detection framework tailored for IIoT applications. TheInstance threadutilizes an efficient variational auto encoder to produce instant feedback and offloads most of the workload to MECs. On the other hand, theShadow threademploys an attention-enhanced transformer discriminator to examine low-confidence results in the cloud. Experimental results on five large-scale datasets showTHREADSachieves an averageF1-Scoreof 0.8537 in the hierarchical mode where most of the workloads are handled by MECs, and the random access memory and CPU usage is reduced by up to 29% and 88%, respectively. Meanwhile,THREADSachieves anF1-Scoreof 0.8563 in a cloud-based mode, consistently outperforming state-of-the-art approaches.

Accurately perceiving service states of the Internet of Things (IoT) is crucial for maintaining system stability and long-term sustainability. Recently, Large Language Models (LLMs) have emerged as a novel technological advancement, offering unprecedented possibilities in various domains due to their advanced comprehension capabilities. However, the limited data availability problem poses a challenge to the integration of domain knowledge into LLMs and the enhancement of their ability to perceive service states. In this article, a novel adversarial augmentation-based domain LLM framework for IoT services is proposed to solve this problem. We first construct an LLM fine-tuning dataset through an elaborate hierarchical prompt template, which integrates task-specific instructions, heterogeneous service data, and statistical features to the service contextual description. Then, inspired by using specialized compact small-models to enhance the capabilities of LLMs, we design two dedicated domain-specific small-models, including a service topology prediction model and a running status prediction model, to capture evolution patterns of services from different perspectives. On this basis, we design an LLM fine-tuning framework based on the domain adversarial distillation, which comprises an LLM parameter tuning module and a small-model adversarial distillation module. The former leverages the Low-Rank Adaptation (LoRA) algorithm to fine-tune the LLM. The latter distills the domain knowledge of small-models into the LLM through aligning the latent vector distributions of the LLM with those of small-models. Following the adversarial training, the LLM will be equipped with domain capabilities of small-models. Experimental results on two datasets demonstrate the effectiveness of our LLM framework.

The popularity of cloud services has provided a new medium for video streams transmission. Cloud desktops, as a representative multimedia application, facilitate interaction between users and cloud via video streams, garnering widespread adoption in various fields. The network condition directly affects the transmission. Therefore, accurate throughput prediction helps guide the allocation of network resources, avoiding a decline in user experience due to insufficient resources and waste caused by excessive resources. Recent works focus more on the temporal characteristics of throughput. However, we believe that throughput of video streaming is significantly influenced by usage scenario. In this paper, we propose a transfer-based autoencoder framework DeskTransfer for throughput prediction in frequent switching cloud desktop scenarios. Specifically, we construct the Scenario Autoencoder and Throughput Autoencoder to respectively learn the scenario and throughput features from historical usage records. By adopting an adversarial mechanism, we design transfer algorithm using latent vectors, enabling the model suitable for multiple scenarios. We collect real-world data from a project cooperated with Lenovo Research for experiment and compare our solution with leading methods on public datasets to validate its effectiveness.

To identify pattern deviations in large-scale industrial infrastructures, anomaly detection is crucial yet challenging. Previous research has not adequately addressed the characteristics and deployment considerations in these complex scenarios. In this paper, we presentInoU, a scalable anomaly detection framework to process unlabeled multivariate time-series data. We incorporate a VAE filter to ease impacts from noisy components in training materials. We propose a scalable trio-attention U-Transformer to construct the typical representation of high-dimensional streams and produce pseudo labels that enable the later training process. The ultra perception and intra-/ inter-flow attention mechanisms are delicately designed to aggregate information from different flows with variable granularities while keeping a global view of the data. Its nested structure helps to maintain high efficiency even when the model is scaled down. We introduce a Siamese discriminator that projects target data into manifolds, and collates discrepancies at the embedding level. This paradigm elevates detection performance far beyond segment-wise error comparison in prior works. We apply contrastive and adversarial learning techniques to optimize manifold projection and detection performance when processing unseen samples. Extensive experiments on five large-scale datasets demonstrate the effectiveness ofInoUwith an averageF1-Scoreimprovement of 5.58%, significantly outperforming the state-of-the-art.

Due to the unrestricted location of cloud resources, an increasing number of users are opting to apply for them. However, determining the appropriate resource quota has always been a challenge for applicants. Excessive quotas can result in resource wastage, while insufficient quotas can pose stability risks. Therefore, it's necessary to propose an adaptive quota scheme for cloud resource. Most existing researches have designed fixed quota schemes for all users, without considering the differences among users. To solve this, we propose an adaptive cloud quota scheme through dynamic portraits and task-resource optimal matching. Specifically, we first aggregate information from text, statistical, and fractal three dimensions to establish dynamic portraits. On this basis, the bidirectional mixture of experts (Bi-MoE) model is designed to match the most suitable resource combinations for tasks. Moreover, we define the time-varying rewards and utilize portrait-based reinforcement learning (PRL) to obtain the optimal quotas, which ensures stability and reduces waste. Extensive simulation results demonstrate that the proposed scheme achieves a memory utilization rate of around 70%. Additionally, it shows improvements in task execution stability, throughput, and the percentage of effective execution time.

Indoor localization availability is still sporadic in industry, especially at the black-hole, i.e., there only exist cellular signals, no GPS or WiFi signals. Based on our 2-year observations at the DiDi ride-hailing platform in China, there are$ 68\,\text{k}$orders everyday created at black-hole. In this paper, we presentTransparentLoc, a large-scale cellular localization system for pickup position recommendation of the DiDi platform. Specifically, we design a CNN model for real-time localization based on a crowdsourcing fingerprint set constructed by outdoor trajectories and abnormal cell tower detection. Then we leverage a DeepFM model to recommend an optimal pickup position for passengers. We share our 2-year experience with 50 million orders across 13 million devices in 4541 cities to address practical challenges including sparse cell towers, unbalanced user fingerprints, temporal variations, and abnormal cell towers in terms of four major service metrics, i.e., pickup position error, over-30-meters ratio, cancel ratio, and call ratio. The large-scale evaluations show that our system achieves a$ 0.54\,\text{m}$lower median pickup position error compared to the iOS built-in cellular localization system, regardless of environmental changes, smartphone brands/models, time, and cellular providers. Additionally, the over-30-meters ratio, cancel ratio, and call ratio have significant reductions of 0.88%, 0.88%, and 5.13%, respectively.

Data imputation is prevalent in crowdsensing, especially for Internet of Things (IoT) devices. On the one hand, data collected from sensors will inevitably be affected or damaged by unpredictability. On the other hand, extending the active time of sensor networks has urgently aspired environmental monitoring. Using neural networks to design a data imputation algorithm can take advantage of the prior information stored in the models. This paper proposes a preprocessing algorithm to extract a subset for training a neural network on an IoT dataset, including time window determination, sensor aggregation, sensor exclusion and data frame shape selection. Moreover, we propose a data imputation algorithm using deep compressed sensing with generative models. It explores novel representation matrices and can impute data in the case of a high missing ratio situation. Finally, we test our subset extraction algorithm and data imputation algorithm on the EPFL SensorScope dataset, respectively, and they effectively improve the accuracy and robustness even with extreme data loss.

Transmission scheduling is a fundamental energy-saving problem in many wireless sensor networks (WSN), especially for mHealth systems with multiple distributed sensing modalities. Recent work has made significant progress to balance the transmission efficiency and timeliness, e.g., periodically entering sleep modes to reduce the power, but such corresponded missing samples impede data integrity and timeliness for realtime diagnosis. In this paper, we intuitively combine transmission scheduling with data imputation, and propose a novel software-hardware cooperated framework for energy-efficient mHealth systems. Specially, we devise a Wasserstein Generative Adversarial Imputation Network (WGAIN) model to impute missing samples, which exploits heterogeneous correlation, temporal dependency, and missing patterns by a divide-and-conquer strategy. We also propose a dropout-based uncertainty approximation method inside the imputation model, prove its equivalence to Gaussian process with variational inference, and explore a heuristic transmission scheduling algorithm for lifecycle management among heterogeneous sensory modules. Extensive experiments on the MIT-BIH dataset and our mHealth prototype have demonstrated the effectiveness compared with state-of-the-art.

Location awareness is the basis for enabling pickup service at ride-hailing platforms. In contrast to the almost pervasive coverage outdoors, indoor localization availability is still sporadic in industry since it largely relies on RF signatures from certain IT infrastructure, e.g., WiFi access points. Based on our 2-year observations at DiDi ride-hailing platform in China, there are 68k orders everyday created at black-hole, i.e., where only cellular signals exist. In this paper, we present the design, development, and deployment of TransparentLoc, a large-scale cellular localization system for pickup position recommendation, and share our 2-year experience with 50 million orders across 13 million devices in 4541 cities to address practical challenges including sparse cell towers, unbalanced user fingerprints, and temporal variations. Our system outperforms the iOS built-in cellular localization system in terms of four major service metrics, regardless of environmental changes, smartphone brands/models, time, and cellular providers.

Graphic-pattern-based implicit authentication has been successfully exploited to elevate the security of smartphones. On-screen pressure is one of the key features in such an approach since it can reveal users’ touch pattern. However, state-of-the-art approaches rely on a system API to obtain on-screen pressure, which is not adequately accurate and cannot meet the demands of robust implicit authentication. To bridge this gap, we propose PresSafe, a novel implicit authentication system that utilizes the smartphone’s built-in barometer sensor to measure pressure during the unlocking process, and to utilize the pressure data in authentication. A key technical challenge in utilizing barometer sensing, however, is to understand the user activity through measured pressure. To overcome this challenge, PresSafe leverages barometer data along with data from other conventional but heterogeneous ambient sensors to produce accurate and robust user activity descriptions. PresSafe utilizes a transfer-learning-based hybrid workflow to integrate user activity representation learning with a lightweight classical authentication algorithm to obtain a unified model. This approach offloads the computational cost from the terminal and addresses privacy concerns. To ensure applicability of our approach despite data heterogeneity and insufficient training data, we utilize a channel-adaptive data processing mechanism. Extensive experiments utilizing more than 70000 records from 23 volunteers in six different locations show that PresSafe achieves an FAR of 0.45%, an FRR of 0.49%, and an EER of 0.47%, which clearly demonstrate its superiority over several existing solutions.

Although location awareness is prevalent outdoors due to GNSS systems and devices, pedestrians are back into darkness in indoor buildings such as underground parking lots. Frequently we forget where we park the car and get confused by such maze-like structure. In order to track pedestrians without any additional equipment and map support, we propose PeTrack which is a smartphone-only approach that collects the inertial measurement unit (IMU) data for long-term tracking. Our intuition is to train the tracking model with crowdsourced outdoor trajectories, and infer customized user's trace with only inertial readings at indoors. Specially, we propose an inertial sequence learning framework with outdoor geo-tags. We also exploit opportunistic landmark detection and structure cues to refine the trajectory. We have developed a prototype and conducted experiments in an underground parking lot, and results have shown our effectiveness.

Industrialization has resulted in a relatively high airborne PM2.5 concentration in most developing areas, causing severe consequences due to its physico-chemical properties. In this paper, we propose a crowdsourced image driven PM2.5concentration estimation approach based on meteorological en-hanced 3-channel feature map. To fulfill this framework, we first use dark channel prior and Rayleigh's law of atmospheric scattering to correct the proportion of the sky area in the crowdsourced images and then construct hybrid 3-channel feature maps. We then use deep learning models to perform feature extraction on hand-engineered feature maps and provide fine-grained PM2.5 concentration estimation. We also conduct a series of experiments to evaluate the performance of our model. Evaluation on dataset collected at 8 sites over nearly 2 years demonstrates that, our system achieves an MAE of 23.27$\mu\mathrm{g}/\mathrm{m}^{3}$, outperforming baseline solutions.

Estimating the origin-destination travel time is a fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has made significant progress to accuracy, but they largely rely on GPS trajectories which are too coarse to model many personalized driving behaviors, e.g., differentiating novice and veteran drivers. In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS trajectories, smartphone inertial data, and road network within a deep recurrent neural network. It constructs a road link traffic database with topology representation, speed statistics, and query distribution. It also calibrates inertial readings, estimates the arbitrary phone’s pose in car, and detects multiple aggressive driving events (e.g., bump judders, sharp turns, sharp slopes, frequent lane shifts, overspeeds, and sudden brakes). Finally, we demonstrate our solution on two typical transportation problems, i.e., predicting traffic speed at holistic level and estimating customized travel time at personal level, within a multi-task learning structure. Experiments on two large-scale real-world traffic datasets from DiDi platform show our effectiveness compared with the state-of-the-art.

Next point-of-interest (POI) recommendation has been an important task for location-based intelligent services. However, the application of such promising technique is still limited due to the following three challenges: 1) the difficulty of capturing complicated spatiotemporal patterns of user movements; 2) the hardness of modeling fine-grained long-term preferences of users; and 3) the effective learning of interaction between long- and short-term preferences. Motivated by this, we propose a memory augmented hierarchical attention network (MAHAN), which considers both short-term check-in sequences and long-term memories. To capture the complicated interest tendencies of users within a short-term period, we design a spatiotemporal self-attention network (ST-SAN). For long-term preferences modeling, we employ a memory network to maintain fine-grained preferences of users and dynamically operate them based on users' constantly updated check-ins. Moreover, we first employ a coattention network/mechanism to integrate the proposed ST-SAN and memory network, which can fully learn the dynamic interaction between long- and short-term preferences. Our extensive experiments on two publicly available data sets demonstrate the effectiveness of MAHAN.

In the context of mobile crowd sensing, incentive mechanism is crucial to recruit mobile users to participate in the sensing task and ensure participants to provide high-quality sensing data. In this paper, we investigate a staged incentive mechanism for mobile crowd sensing. We firstly divide the incentive process into two stages: recruiting stage and sensing stage. In the recruiting stage, we introduce the payment incentive coefficient and design a Stackelberg based game method. The participants can be recruited via game interaction. In the sensing stage, we propose a time-space correlation algorithm in the interaction and the winners can be screened after the sensing task. Finally, Extensive experiments show that compared to the existing positive auction incentive mechanism (PAIM) and reverse auction incentive mechanism (RAIM), our staged incentive mechanism (SIM) can effectively improve participants' motivation and achieve high-quality sensing data from both space dimension and time dimension by extending the motivation from the recruitment stage to the sensing process.

For Software Defined Network(SDN), security is an important factor which affects its large-scale deployment. The existing security solutions for SDN mainly focus on the controller itself, which has to handle all the security protection task by using the programmability of network. It will undoubtedly bring the heavy burden to the controller. More devastating, once the controller itself is attacked, the entire network will be paralyzed. Motivated by this, this paper proposes a novel security protection architecture for SDN. We design a security service orchestration center in the control plane of SDN. This center physically decouples from SDN controller, and thus constructs SDN security service to defense security threats. In particular, we set up a security meta-function base which can be used for security service composition in a rule engine. The rule engine can be realized by using the optimized rule composition algorithms. Finally, a series of experimental results show that the proposed security protection architecture for SDN can achieve effective security protection with small burden of controller.

In this paper, we explore the area coverage optimization problem by directional sensors with tunable sensing orientations. We firstly introduce the concept of "sensing centroid", which is the geometric center of a sensing sector to simplify the pending problem. Particularly, we regard "sensing centroid" as artificial fish (AF), and search an optimal solution in the solution space by simulating fish swarm behaviors (such as prey, swarm and follow) with a tendency toward high food consistence. Fully considering that AFs have to satisfy both kinematic constraint and dynamic constraint in the process of motion, we propose a Constrained Artificial Fish-Swarm Algorithm (CAFSA), and discuss the control laws to guide the behaviors of AFs with high convergence speed. Finally, we evaluate the effect of some primary parameters on the performance of our solution through extensive simulations.

Barrier coverage is an important problem for sensor networks to fulfill some given sensing tasks. Barrier coverage guarantees the detection of events happened crossing a barrier of sensors. In majority study of barrier coverage using sensor networks, sensors are assumed to have an isotropic sensing model. However, in many applications such as monitoring an area using video camera, the sensors have directional sensing model. In this paper, we investigate strong barrier coverage using directional sensors, where sensors have arbitrarily tunable orientations to provide good coverage. We investigate the problem of finding appropriate orientations of directional sensors such that they can provide strong barrier coverage. By exploiting geographical relations among directional sensors and deployment region boundaries, we first introduce the concept of virtual node to reduce the solution space from continuous domain to discrete domain. We then construct a directional barrier graph (DBG) to model this barrier coverage question such that we can quickly answer whether there are directional sensors' orientations that can provide strong barrier coverage over a given belt region. If the belt region is strong barrier covered, we then develop energy-efficient solutions to find strong barrier path(s) that will approximately minimize the total or the maximum rotation angles of all directional sensors. Extensive simulations are conducted to verify the effectiveness of our solution.

Complicated multimedia sensor networks pose several theoretic and technical challenges. These challenges center on cooperative work supported by sensor networks. This paper presents, with the aid of previous works on CSCW, a novel conception - sensor networks supported cooperative work (SNSCW). First, we classify the cooperative work supported by sensor networks into: cooperation between human and sensor nodes, and cooperation among sensor nodes. Then we depict the hierarchical collaboration by speech-action model and layered abstract model (activity-task-cooperation). Moreover, this paper proposes a multi-level C/S architecture for SNSCW system. Finally, we utilize a case $the fire monitoring of intelligent building - to show that our works are effective to cooperative work supported by sensor networks

Application reconfiguration is essential in order to complement the flexibility and adaptability for sensor networks in the environment monitoring domain. In this paper, we first describe an effective environment adaptive application reconfiguration (EAAR) mechanism. Then we present a push-based paradigm to carry out EAAR, where instead of a pull-based paradigm proposed in our prior work. This paradigm suits for clustered sensor networks, which provides a software entity, reconfiguration agent, to perform the process of reconfiguration on cluster head nodes. Reconfiguration agent is a novel concept, and has great potential in providing energy-efficient reconfiguration processing with low latency. A prototype is constructed to show the workflow of reconfiguration agent. Particularly, we design two metrics: execution time and energy consumption, and use the simulation platform to quantitatively measure the performance of different paradigms in the reconfiguration procedure. Experimental results show that push-based paradigm performs better than pull-based one