Timely and efficient dissemination of service information is critical in compute-first networking systems, where user requests arrive dynamically and computing resources are constrained. In such systems, the access point (AP) plays a key role in forwarding user requests to a server based on its latest received service information. This paper considers a single-source, single-destination system and introduces a PPO–based reinforcement learning framework for efficient information updating, guided by a newly designed reward metric called Age-and-Value-Aware (AVA). Unlike traditional freshness-based metrics, AVA explicitly incorporates variations in server-side service capacity and AP’s forwarding decisions, allowing more context-aware update evaluation. Under this reward structure, the PPO agent autonomously learns when to trigger service information updates, achieving a dynamic balance between communication cost and decision accuracy. Extensive simulations under diverse user request patterns and varying service capacities demonstrate that AVA reduces the update frequency by over 90% on average compared to baselines, with reductions reaching 98% in certain configurations. This reduction is achieved without compromising the quality of decision making.

This paper focuses on severe variable missingness and variable sparsity in device-edge collaboration. Inference-based methods yield unacceptable reconstruction errors under high missing rates, thus necessitating retransmission. However, this approach can lead to network congestion and even break-down in emergency response scenarios. To overcome these limitations, we propose a Similarity-based Multivariate Data Completion Framework (SMDCF). Unlike traditional retransmission approaches, SMDCF adopts an innovative similarity-based learning paradigm. For handling variable missingness, the framework retrieves similar samples from offline datasets by analyzing sparse observations and then effectively transfers relevant variables from these reference samples to complement the incomplete data; for handling variable sparsity, SMDCF employs graph neural networks to systematically model three key relationships: inter-sample correlations, inter-variable dependencies, and intra-variable patterns, thereby achieving robust and reliable missing-value inference. Comprehensive experimental results demonstrate that SMDCF consistently delivers superior performance under various data missing scenarios. Notably, even under an extreme 90% missing data rate, our framework reduces the reconstruction error by 8.37%.

Recent studies highlight the significant potential of edge computing and federated learning (FL) in advancing artificial intelligence. However, challenges, such as unstable device performance and the heterogeneously distributed feature of local data, pose threats to the efficiency of global model training. To address these issues, we propose an efficient federated edge learning protocol with key innovations: 1) propose a timing query mechanism, which controls the impact of slow clients on interaction time and changes the parameter server from a passive receiver to an active querier to guarantee the aggregated subset size; 2) propose a screening supplementary strategy from the perspective of optimizing the quality of the data combination; and 3) integrate the timing query mechanism and screening supplementary strategy in a flexible manner. This combination improves the interaction efficiency of global model training, focusing on both interaction time and aggregation update quality. Experiments show that while ensuring the convergence efficiency of each round and the global model performance, the interaction efficiency has been improved by 25% to 75%, thus ensuring the efficiency of FL.

In edge computing scenarios, there is a need for modeling dedicated features and heterogeneous devices functions, as well as integrating multiple complex scenarios with diverse objectives and frequent interactions. However, existing platforms modeling for the whole device ignores the independence between functional components resulting in limited scenario support. We propose an open-source simulator named EasiEI. EasiEI addresses the need for higher level feature replaceability and independence in modeling complex edge scenarios through independent functional component-level modeling and microkernel architecture. This approach enables users to assemble independent functional components in a plug-and-play manner for heterogeneous devices or different application requirements. EasiEI is fully compatible with all the existing built-in modules in NS3 (a powerful network discrete event simulator). To verify the flexibility and extensibility of EasiEI, we implement several centralized and decentralized computing paradigms cases in a step-by-step way. These cases restore and simulate the performance state of various real devices in real time, meeting the requirements for verifying the edge computing ideas such as task scheduling in a distributed manner. Results show that the simulations have well reflected the characteristics of the real world and can construct complex environment flexibly.

Despite placing services and computing resources at the edge of the network for ultra-low latency, we still face the challenge of centralized scheduling costs, including delays from additional request forwarding and resource selection. To address this challenge, we propose SmartBuoy, a new computing paradigm. Our approach starts with a service coverage concept that assumes users within the coverage have high access availability. To enable users to perceive service status, we design a distributed metric table that synchronizes service status periodically and distributively. We propose coverage indicator updating principles to make the updating process more effective. We then implement two distributed methods, SmartBuoy-Time and SmartBuoy-Reliability, that enable users to perceive service capability directly and immediately. To determine the metric table update window size, we provide an analysis method based on user access patterns and offer a theoretical upper bound in a dynamic environment, making SmartBuoy easy to use. Finally, we implement the proposed methods distributively on an open-source edge computing simulator. Experiments on a real-world network topology dataset demonstrate the efficiency of SmartBuoy in reducing delays and improving the success rate.

In the past decade, significant advancements have been made in utilizing deep learning for breast lesion segmentation. Recently, researchers have increasingly focused on harnessing the power of multiple modalities, recognizing its potential for enhancing segmentation performance. We observe that in clinical practice, many radiologists often rely on two types of ultrasound images, namely ultrasound (US) and contrast-enhanced ultrasound (CEUS) data for diagnosis. This motivates us to propose a multi-modal segmentation network, called as IMAN (Iterative Mutual-Aid Network), based on these two modalities. The architecture of IMAN adopts a novel hourglass shape, featuring two branches connected by an ‘X’ pathway. One branch is dedicated to processing CEUS data, while the other branch handles US data. Each branch generates segmentation results specific to its respective modality. The ’X’ pathway, realized by a margin mask generator module, serves as a bridge between these branches by forcing the segmentation results from one branch as additional input to the other. This head-to-tail pathway effectively facilitates mutual aid between the two modalities. In addition, we propose an iterative training policy during the training process to fully exploit the information from both US and CEUS data. Experimental results on a Breast-US-CEUS dataset comprising 169 samples demonstrate the effectiveness of IMAN, achieving Dice Similarity Coefficient of 83.96% and 81.16% for US images and CEUS videos, respectively. These scores surpass those obtained by many state-of-the-art segmentation methods. Furthermore, IMAN exhibits robust generalization capabilities across different segmentation structures.

Computation and/or communication-intensive collaborative services accompanied by several distributed tasks/components, such as the services in Internet of Things, can be anywhere nowadays. These services are usually used by users at the Internet edge, making cloud computing struggles with the high end-to-end latency. Thanks to edge computing which pushes resources to the edge, the goals with lower latency can be well satisfied. However, in actual scenarios especially under dynamic edge computing networks, changes exist in resources, including computing, bandwidth, and nodes. Meanwhile, data packets (or flow) among collaborative tasks/components of a service can also not be conserved. These characteristics lead the service reliability hard to be guaranteed and make existing reliability evaluation methods no longer accurate. To study the effect of distributed and collaborative service deployment strategies under such background, we propose a reliability evaluation method (REMR). We first look for the solution set which can meet the time constraints. Then, we calculate the reliability of service supported by the solution set based on the principle of inclusion–exclusion with distributions of available transmission bandwidth and computing resources. Finally, we provide an illustrative example with several real-world data sets to make REMR easy to follow. To make REMR more reliable, we also propose and implement a Monte Carlo simulation method. Experiments prove that the reliability calculated by REMR is nearly the same as the simulation results and both the latencies and the jitters are also at a lower level.

Named data networking (NDN) constructs a network by names, providing a flexible and decentralized way to manage resources within the edge computing continuum. This paper aims to solve the question, “Given a function with its parameters and metadata, how to select the executor in a distributed manner and obtain the result in NDN?” To answer it, we design R2 that involves the following stages. First, we design a name structure including data, function names, and other function parameters. Second, we develop a 2-phase mechanism, where in the first phase, the function request from a client-first reaches the data source and retrieves the metadata. Then the best node is selected while the metadata responds to the client. In the second phase, the chosen node directly retrieves the data, executes the function, and provides the result to the client. Furthermore, we propose a stop condition to intelligently reduce the processing time of the first phase and provide a simple proof and range analysis. Simulations confirm that R2 outperforms the current solutions in terms of resource allocation, especially when the data volume and the function complexity are high. In the experiments, when the data size is 100 KiB and the function complexity is$\mathcal {O}(n^{2})$, the speedup ratio is 4.61. To further evaluate R2, we also implement a general intermediate data processing logic named “Bolt” implemented on an app-level in ndnSIM. We believe that R2 shall help the researchers and developers to verify their ideas smoothly.

Convolutional neural networks (CNNs) have been applied in numerous Internet of Things (IoT) devices for multifarious downstream tasks. However, with the increasing amount of data on edge devices, CNNs can hardly complete some tasks in time with limited computing and storage resources. Recently, filter pruning has been regarded as an effective technique to compress and accelerate CNNs, but existing methods rarely prune CNNs from the perspective of compressing high-dimensional tensors. In this article, we propose a novel theory to find redundant information in 3-D tensors, namely, quantified similarity between feature maps (QSFM), and utilize this theory to guide the filter pruning procedure. We perform QSFM on data sets (CIFAR-10, CIFAR-100, and ILSVRC-12) and edge devices and demonstrate that the proposed method can find the redundant information in the neural networks effectively with comparable compression and tolerable drop of accuracy. Without any fine-tuning operation, QSFM can compress ResNet-56 on CIFAR-10 significantly (48.7% FLOPs and 57.9% parameters are reduced) with only a loss of 0.54% in the top-1 accuracy. For the practical application of edge devices, QSFM can accelerate MobileNet-V2 inference speed by 1.53 times with only a loss of 1.23% in the ILSVRC-12 top-1 accuracy.

With the alarming increase in breast cancer cases, researchers have considered it a challenging research problem to propose dependable solutions. It is quite essential for early detection, prevention, and control against breast cancer. Existing schemes still does not utilize recent information technology support, and hence preventive measures and factors are also not appropriate. This paper adopts cloud computing to present association rule-based breast cancer prevention and control system. We have categorized our work into two phases. In phase 1 titled prevention and control, we propose item association rule (IAR) algorithm and N-IAR algorithm for n-item associations. It can be used to discover risk factors for breast cancer. Our algorithm discovers more risk factors than the traditional logistics method. Some factors which can be modified are used for breast cancer prevention and control. In addition, existing risk assessment models are not applicable to Chinese women as well. In phase 2, we manage this by introducing a new model based on machine learning. It utilizes real data from Chinese women and more risk factors for breast cancer. Moreover, we have identified and evaluated a number of new common risk factors. Results prove that our system achieves higher assessment values as compared to preliminaries.

The pathogenesis of breast cancer is not the same in all countries and regions; therefore, some existing breast cancer risk assessment models are not well adapted to all countries and regions, including China. This paper puts forward a new model named BCRAM (a social-network-inspired breast cancer risk assessment model) that depends on epidemiological factors, which is more adaptive to the populous country like China than those models based on gene. The model utilizes the similarities among epidemiological factors to construct a breast cancer high-risk group, the members of which have high similarity with breast cancer patients. Then, three tests based on real data are used to determine the assessment value of BCRAM. The AUC of BCRAM is 0.785, which is larger than that of the classic Gail model, a modified Gail model, the Tyrer-Cuzick model, and the Liu-Yu model for Chinese women. F-Measure value is 0.696, which is the largest among those of all models. Moreover, follow-up data are used to demonstrate that the model can give early warning to a high proportion of patients discovered to have breast cancer in the future. Therefore, the model is meaningful for the prevention and control of breast cancer. And the unique design of the method for selecting risk factors related to breast cancer results in our model having good generality, and it can be generalized to other countries and regions.

Breast cancer risk assessment model can assess whether a people is at a high risk of developing breast cancer disease or not and confirm a breast cancer high-risk group. Because the etiology of breast cancer disease is different in different country and region, the existing risk assessment model is only adaptive to certain countries and regions. And the parameters of these models are fixed, so these models have poor generality. Aiming at these problems, the paper puts forward a new breast cancer risk assessment model named as Shrink. Using the idea of social network, Shrink constructs a medical social network to show the similarity among people, and uses group division algorithm to divide the network into breast cancer high-risk group and low-risk group. The parameters of this model can be set according to the needs of the breast census, and these parameters can be directly acquired through questionnaire, therefore Shrink has good generality. Moreover, under the uncertain classification standard, Shrink adopts a new classification method to discover breast cancer high-risk group. Based on the real data from questionnaires, we make experiments in Matlab, and obtain the evaluation index of the model. The experiment proves that the model itself has good evaluation result and is better than classic Gail model.

In the field of traffic-information acquisition, one pervasive solution is to use wireless sensor networks (WSNs) to realize vehicle classification and counting. By adopting heterogeneous sensors in a WSN, we can explore the potential of using complementary physical information to perform more complicated sensing computation. However, the collaboration among heterogeneous sensors, such as the collaborative sensing mechanism (CSM), is not well studied in current state-of-the-art research. In this paper, we design and implement EasiSee, a real-time vehicle classification and counting system based on WSNs. Our contributions are as follows. First, we propose a CSM, which coordinates the power-hungry camera sensor and the power-efficient magnetic sensors, reducing the overall system energy consumption and maximizing system lifetime. Second, we propose a robust vehicle image-processing algorithm, i.e., a low-cost image processing algorithm (LIPA). LIPA reduces environment noise and interference with low computation complexity. In the verification section, the vehicle detection accuracy turned out to be 95.31%, which pave the way for CSM. The time of image processing is around 200 ms, which indicates that our LIPA is computationally economical. With the overall energy consumption reduced, EasiSee achieves classification accuracy of 93%. Based on these experiments and analysis, we conclude that EasiSee is a practical and low-cost affordable solution for traffic-information acquisition.

Collaboration between the low-quality sensor and high-quality sensor can achieve the tradeoff between accuracy and energy efficiency in heterogeneous sensor networks (HSNs). Generally, HSNs are deeply integrated with dynamic physical environments. Dynamics of the monitored target are the most important and common factors of the dynamic environments and have great influence on the system performance. If the state of the monitored target changes, some important parameters (e.g., active opportunity and sampling frequency) fails to adapt to the changes, which undermines the collaboration's performance. Even the performance of the system is not up to the requirements or a large amount of energy is consumed. To address this problem, we propose an adaptive collaboration method (EasiAC) by the collaboration between magnetic and camera sensors. First, for the dynamics of the monitored target, EasiAC utilizes the magnetic sensors to predict the target's state via Bayesian filtering. Second, to achieve good performance of collaboration between the above two kinds of sensors, EasiAC adjusts the camera sensors' sampling frequency and active opportunity dynamically according to the estimated results from the magnetic sensors. Finally, we evaluate EasiAC through simulations and real road environment experiments. The results demonstrate that EasiAC needs less energy consumption than traditional solutions, while maintaining the performance at acceptable level in the presence of dynamics of the monitored target.

To enable the prospect of Wireless Multimedia Sensor Networks(WMSNs), in this paper the lightweight image processing algorithms embeddable on the camera sensor node are proposed. (1) the target segmentation algorithm effectively solves the influence of changing light and low gray contrast, and reserves complete target shape. (2) the target shape feature extraction algorithm well removes the influence of segmentation holes and scatter noise, and accurately extracts key shape features. In the evaluation, the renderings processed by the algorithms illustrate fully the effectiveness of target segmentation and the accuracy of target shape feature extraction.

Pulse Diagnosis Theory (PDT) has the advantages of non-invasive treatment and disease prevention. Combining these merits with Wireless Sensor Network(WSN), we propose a novel networked low-cost and wearable healthcare monitoring system, namely PDhms, for pulse data collection, pulse analysis and pulse diagnosis. Some practical challenges still exist in PDhms such as exerting appropriate pressure on a human radial artery, overcoming seriously limited resources, improving low Signal to Noise Ratio(SNR) and conducting resistance of interference. To address these challenges, we present a robust external pressure control algorithm for pulse data collection, and propose FEA, a novel light-weight and adaptive feature extraction algorithm for sensed pulse data. We conduct the large-scale pulse data collection experiments of 1356 pulse samples, the comparison experiment between the FEA and the typical derivative-based algorithm, as well as pulse diagnosis experiments based on SVM. Experimental results show that PDhms is a valuable solution for low-cost wearable healthcare monitoring system. It will benefit the public, especially low-income groups because small pulse-sensor node size, low system cost, as well as wearable pulse data collection and analysis.

Traditional Chinese Pulse Diagnosis is a convenient and noninvasive method for disease diagnosis and healthcare. We have designed and implemented a Chinese wrist-pulse retrieval system based on the principle of Traditional Chinese Pulse Diagnosis (TCPD), called EasiCPRS. It is designed to be small in size, low in cost, with flexibility in deployment, and simplicity in operation. The contributions of this work are: 1. The wrist-pulse at "cun, guan and chi"points over the radial artery are obtained by applying a moderate and adjustable taking pressure during wrist-pulse retrieval. 2. A wrist-pulse signal conditioning circuit and a robust external taking pressure control algorithm are designed to overcome low signal-to-noise ratio (SNR). 3. A lightweight algorithm for wrist-pulse feature extraction is achieved on a resource-constrained platform to economize energy and bandwidth.

Traffic information acquisition is often implemented by video cameras or inductive loops, which is expensive or inconvenient from installation and maintenance perspectives. We designed and implemented a pervasive traffic information acquisition system based on wireless sensor networks called EasiTia. Unlike existing solutions, the implementation of the system does not require extra devices in the road infrastructure or vehicle, nor the excavation of the road surfaces. EasiTia can easily be deployed at roadsides. It is of low cost and resource efficient. Our contributions are given as follows: 1) To deal with low signal-to-noise ratios (SNRs) and stochastic disturbances in traffic information acquisition, we proposed and implemented a cross-correlation-based vehicle-detection algorithm. 2) To resolve the problems of data association, vehicle velocity calculation, and vehicle identification, we proposed a collaborative traffic information processing mechanism in the EasiTia system. Based on real road environment experimental analysis, we demonstrate that EasiTia is an applicable and cost-effective candidate for a pervasive traffic information acquisition system.

To meet the increasing needs of home-based healthcare, we propose a novel networked low-cost and wearable healthcare monitoring system, namely TCM-PCA, for pulse analysis in Traditional Chinese Medicine(TCM). The unique innovations of the system are as follows: (1) We propose a novel adaptive features extraction algorithm for pulse waveform to improve physiological feature extraction accuracy of sensed pulse waveform data. (2) We propose a light-weight information fusion framework for human symptom identification according to our previous work. (3) We present a robust external pressure control algorithm for pulse measure to obtain stable pulse waveform data on the pulse-sensor nodes. In this demo, we will demonstrate the working procedure of the system and prove the innovations summarized above.

This paper presents an artificial ant colony approach to distributed sensor wakeup control (SWC) in wireless sensor networks (WSN) to accomplish the joint task of surveillance and target tracking. Each sensor node is modeled as an ant, and the problem of target detection is modeled as the food locating by ants. Once the food is found, the ant will release pheromone. The communication, invalidation, and fusion of target information are modeled as the processes of pheromone diffusion, loss, and accumulation. Since the accumulated pheromone can measure the existence of a target, it is used to determine the probability of ant-searching activity in the next round. To the best of our knowledge, this is the first biologically inspired SWC method in the WSN. Such a biologically inspired method has multiple desirable advantages. First, it is distributive and does not require a centralized control or cluster leaders. Therefore, it is free of the problems caused by leader failures and can save the communication cost for leader selection. Second, it is robust to false alarms because the pheromone is accumulated temporally and spatially and thus is more reliable for wakeup control. Third, the proposed method does not need the knowledge of node position. Two theorems are presented to analytically determine the key parameters in the method: the minimum and maximum pheromone. Simulations are carried out to evaluate the performance of the proposed method in comparison with representative methods.

There is a growing need for medical care resources when people pay more and more attention to their health conditions. The hospitals, however, can not satisfy all those ever-increasing requirements. There is hence a contradiction between the needs of the medical care and resource availability. A remote telemedical care system is a possible optimistic way towards solving this problem with the support of emerging new technologies. In this paper, we present the development of a wireless networked Chinese telemedicine system for remote pulse information monitoring, namely WNCTs, based on the principle of Chinese pulse diagnosis in Chinese medicine (CPD). The proposed wearing system may collect the pulse condition, analyze the information by data fusion and provide diagnostic results. The distinctive features of the proposed wireless system include: small in size, light in weight, multiple of type pulse detection and networked users' remote management. In this paper we provide the design and implementation of the wearing wireless networked Chinese telemedicine system. We also demonstrate the use of the system in pulse information retrieval. Our results indicate significant practical feasibility of the networked telemedicine system.

In wireless sensor networks (WSN), it is a fundamental issue to balance two conflicted performance indexes: sensing ability and energy cost, via the self-organization (SO). Here each sensor node in the WSN is mapped to an ant in ant colony system and node communication information is modeled by the current pheromone. The SO problem of the WSN is transformed to the swarm intelligence optimization problem of ant colony. If an ant detects an interested target, it will lay pheromone, which can diffuse in its neighbor zone. The accumulated pheromone is calculated to adaptively and distributively determine the waking probability of the ant so that the self organization of the WSN can be implemented automatically. Hence a new swarm intelligence method for the SO of WSN is proposed. The simulations show the effectiveness of our method.