Local Meteorological and Synoptic Characteristics of Fogs Formed over Incheon International Airport in the West Coast of Korea

Fogs observed over Incheon international airport （IIA） in the west coast of Korea from January 2002 to August 2006 are classified into categories of coastal fog, cold sea fog, and warm sea fog based on the areal extent of the fogs and the difference between the air temperature （T ） and the SST, i.e., cold sea fog if TSST = T -SST 〉 0~0C and warm sea fog if TSST 〈 0~0C. The numbers of coastal, cold, and warm sea fog cases are 64, 26, and 9. Coastal fogs form most frequently in winter, while cold sea fogs occur mostly in summer and warm sea fogs are observed from January to May but not in November and December. On average the air gets colder by 1.6~0C during the three hours leading up to the coastal fog formation, and an additional cooling of 1.1~0C occurs during the fog. The change in the dew point temperature （T_d） is minimal except during the fog （0.6~0C）. Decreases in T for the cold and warm sea fogs are relatively smaller. The average Td is higher than SST during the cold sea fog periods but this T_d is more than 4~0C higher than that for the corresponding non-fog days, suggesting that cold sea fogs be formed by the cooling of already humid air （i.e., T_d〉SST）. Increases of T_d are significant during the warm sea fog periods （1.4~0C）, implying that effcient moisture supply is essential to warm sea fog formation. Four major synoptic patterns are identified in association with the observed fogs. The most frequent is a north Pacific high that accounts for 38% of cases. Surface or upper inversions are present in 77%, 69%, and 81% of the fog periods for coastal, cold, and warm sea fogs, respectively.

Grey Markov chain and its application in drift prediction model of FOGs

A novel grey Markov chain predictive model is discussed to reduce drift influence on the output of fiber optical gyroscopes (FOGs) and to improve FOGs' measurement precision. The proposed method possesses advantages of grey model and Markov chain. It makes good use of dynamic modeling idea of the grey model to predict general trend of original data. Then according to the trend, states are divided so that it can overcome the disadvantage of high computational cost of state transition probability matrix in Markov chain. Moreover, the presented approach expands the applied scope of the grey model and makes it be fit for prediction of random data with bigger fluctuation. The numerical results of real drift data from a certain type FOG verify the effectiveness of the proposed grey Markov chain model powerfully. The Markov chain is also investigated to provide a comparison with the grey Markov chain model. It is shown that the hybrid grey Markov chain prediction model has higher modeling precision than Markov chain itself, which prove this proposed method is very applicable and effective.

Comparative Assessment on the Performance of Open-Loop and Closed-Loop IFOGs

In this paper, we evaluated comprehensively the structure and operation of open-loop interferometric optical fiber gyroscopes (IFOG). To complete the previous works, a digital approach to derive the rotation angle in optical fiber gyroscopes is investigated theoretically. Results are simulated by the MATLAB software;therefore we could compare the results in simulated area with the values derived from theory. Also, feedback Erbium-doped fiber amplifier (EFDA) FOGs, called FE-FOG, is categorized in closed-loop IFOGs. The procedure of finding the Sagnac shift for open-loop and closed-loop IFOG have been studied and compared to one another. The signal processing in the open-loop IFOG was simulated using Matlab software and for the closed-loop IFOG by PSCAD. In the open-loop IFOG the analogue formulation of the IFOG in order to extract the phase shift is analyzed. A novel and promising method for derivation of Sagnac phase shift based on digital finite impulse response filtering is proposed. Based on our simulation results, the reliability and accuracy of the method is determined. In the closed-loop IFOG, the shift was derived through frequent use of Sagnac loop. The output signal is injected in the input again as feedback. The shift phase between clockwise and counterclockwise waves in each complete route, including primary and feedback route, is identified as Sagnac shift phase.

Macro-and Micro-Structures of Heavy Fogs and Visibility in the Dayaoshan Expressway

Belonging to the southern subtropical moist type of monsoon climate, the Nanling mountainous area experiences heavy fogs whenever quasi-stationary fronts appear there from September to May. There can be as many as 15-18 days of heavy fogs per month. Fogs have more serious consequences in the Lechang-Ruyuan section of the Beijing-Zhuhai Expressway （the longest expressway in China） that passes through the main part of the Nanling Mts., where the road rises from 200 m to more than 800 m above sea level （ASL）. For a major motorway in the mountainous areas of Nanling Mts., two multidisciplinary integrated field observations were carried out, which measured visibility by the naked eyes, visibility by instrument, spectrum of fogdrops, liquid water content （LWC） of fog, tethered sounding, dual-parameter low-level sounding, turbulence diffusion within fog layers, aerosol spectra of size and composition, sampled fog water compositions, and sampled rainwater compositions. Typical cases were probed for their analyses of synoptics, micro- and macro-structures and microphysics. It is understood that heavy fogs take place with high frequency in the area and bring about serious consequences. Being typical advection and upslope fogs, they are in essence low-lying clouds appearing at high altitudes, which are closely related with the activity of South China frontal processes, especially the South China quasi-stationary fronts, and reflect on the role of local terrain as well. The heavy fogs are characteristic of long duration, extremely low visibility, well-organized lumpshaped structure, large-size fog-drops, moderate concentration, high LWC, and stronger turbulent diffusion within the fog layers than in fine sky. They differ much from radiation fogs, which are better documented in previous study in China. It is found that fog LWC is in significant anti-correlation with visibility so that large LWC is associated with small visual range. It is also noted that one of the reasons for the fluctuation of characteristic quantities of micro-structure such as the LWC of fog in the area is, in addition to the inhomogeneous structure of the fog itself, the effect of advection and inhomogeneous underlying surface; during the translation of fog with the ambient wind, irregular upslope and cross-over movement is another reason for the inhomogeneous structure and fluctuation of fog. The spectrum of the aerosol size displays itself as the power function of monotonous descent. The concentration of submicrometer particles is even higher. The high-concentration sulfate particles found in the aerosols of Nanling Mts. are actually good nuclei for condensation, which are favorable for the formation of fog. The presence of fog can help cleanse the trace compositions in the atmosphere so that fog droplets contain high levels of polluting elements. In the meantime, compared to cloud droplets, fog droplets are easier to be captured by the vertical surfaces of objects on the land surface, such as vegetation and buildings to constitute another kind of cleansing process. In vast stretches of forest like the Nanling Mts., this kind of cleansing may be quite important. Studying the characteristic variation of fogs in the area realistically assists in setting up a forecast and warning system for local fogs and provides basic information for fog dispersal experiments.

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

Task Offloading in Edge Computing Using GNNs and DQN

In a network environment composed of different types of computing centers that can be divided into different layers(clod,edge layer,and others),the interconnection between them offers the possibility of peer-to-peer task offloading.For many resource-constrained devices,the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity.In this scenario,it is worth considering transferring these tasks to resource-rich platforms,such as Edge Data Centers or remote cloud servers.For different reasons,it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions.At the same time,establishing an optimal offloading policy,which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy.This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms,Graph Neural Networks(GNN)and Deep Q-Network(DQN).It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSimand compares them with the two defaultmethods,Trade-Off and Round Robin.Experiments showed that variants offer a slight improvement in task success rate and workload distribution.In terms of energy efficiency,they provided similar results.Finally,the success rates of different computing centers are tested,and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated.These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively,considering the quality of its connections and constant updates.The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed.Simultaneously,the suitability of Reinforcement Learning(RL)techniques is demonstrated due to the dynamism of the network environment,considering all the key factors that affect the decision to offload a given task,including the actual state of all devices.

Target Detection Algorithm in Foggy Scenes Based on Dual Subnets

Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the image by the universal detection network.Thus,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this paper.Firstly,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes.In the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the model.Secondly,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous fog.Finally,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is redesigned.In this way,the feature extraction ability of the network for deep-level information can be enhanced.The experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection model.The DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.

LCD面板C/FOG工艺制造虚拟计量方法研究

针对液晶显示器(LCD)面板的“Chip/FPC on Glass”(C/FOG)工艺生产制造过程中存在的计量延迟大、生产异常无法提前预测的问题,本文提出一种基于神经网络的C/FOG工艺生产制造虚拟计量方法。该方法利用生产机台上的传感器采集生产过程中的过程状态数据,构建基于多尺度一维卷积及通道注意力模型(MS1DC-CA)的虚拟计量模型。通过多个尺度的卷积核提取不同尺度范围内的状态数据特征。在对含有缺失值的原始数据预处理中,提出了基于粒子群算法改进的K近邻填补方法(PSO-KNN Imputation)进行缺失值填充,保留特征的同时,减少因填充值引入的干扰。最后在实际生产采集的数据上进行实验对比分析,实际不良率主要集中在0.1%~0.5%,该虚拟计量模型的拟合均方误差为0.397 7‱,低于其他现有拟合模型,在平均绝对误差、对称平均绝对百分比误差和拟合优度3种评价指标下也均优于其他现有的拟合模型,具有良好的预测性能。

LPCS Ni-Zn-Al_(2)O_(3) Intermediate Layer Enhanced SPS NiCr-Cr_(3)C_(2) Coating with Higher Corrosion and Wear Resistances

The double-layer NiCr-Cr_(3)C_(2)/Ni-Zn-Al_(2)O_(3) coatings with sufficient corrosion and wear resistance were prepared on low carbon steel substrates.The intermediate layers Ni-Zn-Al_(2)O_(3) were fabricated by using low-pressure cold spray (LPCS) method to improve the salt fog corrosion resistance properties of the supersonic plasma spray (SPS) NiCr-Cr_(3)C_(2) coatings.The friction and wear performance for the double-layer and single-layer NiCr-Cr_(3)C_(2) coatings were carried out by line-contact reciprocating sliding,respectively.Combined with the coating surface analysis techniques,the effect of the salt fog corrosion on the tribological properties of the double-layer coatings was studied.The results showed that the double-layer coatings exhibited better wear resistance than that of the single-layer coatings,due to the better corrosion resistance of the intermediate layer;the wear mass losses of the double-layer coatings was reduced by 70%than that of the single layer coatings and the wear mechanism of coatings after salt fog corrosion conditions is mainly corrosion wear.

Subdural effusion associated with COVID-19 encephalopathy: A case report

BACKGROUND The precise mechanism by which severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)impacts the central nervous system remains unclear,with manifestations spanning from mild symptoms(e.g.,olfactory and gustatory deficits,hallucinations,and headache)to severe complications(e.g.,stroke,seizures,encephalitis,and neurally demyelinating lesions).The occurrence of single-pass subdural effusion,as described below,is extremely rare.CASE SUMMARY A 56-year-old male patient presented with left-sided limb weakness and slurred speech as predominant clinical symptoms.Through comprehensive imaging and diagnostic assessments,he was diagnosed with cerebral infarction complicated by hemorrhagic transformation affecting the right frontal,temporal,and parietal regions.In addition,an intracranial infection with SARS-CoV-2 was identified during the rehabilitation process;consequently,an idiopathic subdural effusion developed.Remarkably,the subdural effusion underwent absorption within 6 d,with no recurrence observed during the 3-month follow-up.CONCLUSION Subdural effusion is a potentially rare intracranial complication associated with SARS-CoV-2 infection.

Production of Artificial Fog in the PAVIN Fog and Rain Platform: In Search of Big Droplets Fog

In fog, visibility is reduced. This reduction in visibility is measured by the meteorological optical range (MOR), which is important for studying human perception and various sensors in foggy conditions. The Cerema PAVIN Fog & Rain platform is capable of producing calibrated fog in order to better analyses it and understand its consequences. The problem is that the droplets produced by the platform are not large enough to resemble real fog. This can have a major impact on measurements since the interaction between electromagnetic waves and fog depends on the wavelength and diameter of the droplets. To remedy this, Cerema is building a new platform with new equipment capable of generating fog. This study analyses different nozzles and associated usage parameters such as the type of water used and the pressure used. The aim is to select the best nozzle with the associated parameters for producing large-diameter droplets and therefore more realistic fog.

Examination of Microphysical Relationships and Corresponding Microphysical Processes in Warm Fogs

In this paper, the microphysical relationships of 8 dense fog events collected from a comprehensive fog observation campaign carried out at Pancheng（32.2 N, 118.7 E） in the Nanjing area, China in the winter of 2007 are investigated. Positive correlations are found among key microphysical properties（cloud droplet number concentration, droplet size, spectral standard deviation, and liquid water content） in each case, suggesting that the dominant processes in these fog events are likely droplet nucleation with subsequent condensational growth and/or droplet deactivation via complete evaporation of some droplets. The abrupt broadening of the fog droplet spectra indicates the occurrence of the collision-coalescence processes as well, although not dominating. The combined efects of the dominant processes and collision-coalescence on microphysical relationships are further analyzed by dividing the dataset according to visibility or autoconversion threshold in each case. The result shows that the specific relationships of number concentration to volume-mean radius and spectral standard deviation depend on the competition between the compensation of small droplets due to nucleation-condensation and the loss of small droplets due to collision-coalescence. Generally, positive correlations are found for diferent visibility or autoconversion threshold ranges in most cases, although negative correlations sometimes appear with lower visibility or larger autoconversion threshold. Therefore, the compensation of small droplets is generally stronger than the loss, which is likely related to the sufcient fog condensation nuclei in this polluted area.

Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of data.The major concerns in this domain are security and privacy.Therefore,attaining a reliable level of confidentiality in the fog computing environment is a pivotal task.Among different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing technologies.Hence,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog data.Chaotic Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,etc.To boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat map.The sequence generated by this map is used to transform the coordinates of the fog data.The improved range of the proposed map is depicted using bifurcation analysis.The quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate entropy.We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack.The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

Blockchain-Enabled Secure and Privacy-Preserving Data Aggregation for Fog-Based ITS

As an essential component of intelligent transportation systems(ITS),electric vehicles(EVs)can store massive amounts of electric power in their batteries and send power back to a charging station(CS)at peak hours to balance the power supply and generate profits.However,when the system collects the corresponding power data,several severe security and privacy issues are encountered.The identity and private injection data may be maliciously intercepted by network attackers and be tampered with to damage the services of ITS and smart grids.Existing approaches requiring high computational overhead render them unsuitable for the resource-constrained Internet of Things(IoT)environment.To address above problems,this paper proposes a blockchain-enabled secure and privacy-preserving data aggregation scheme for fog-based ITS.First,a fog computing and blockchain co-aware aggregation framework of power injection data is designed,which provides strong support for ITS to achieve secure and efficient power injection.Second,Paillier homomorphic encryption,the batch aggregation signature mechanism and a Bloom filter are effectively integrated with efficient aggregation of power injection data with security and privacy guarantees.In addition,the fine-grained homomorphic aggregation is designed for power injection data generated by all EVs,which provides solid data support for accurate power dispatching and supply management in ITS.Experiments show that the total computational cost is significantly reduced in the proposed scheme while providing security and privacy guarantees.The proposed scheme is more suitable for ITS with latency-sensitive applications and is also adapted to deploying devices with limited resources.

Cache in fog computing design,concepts,contributions,and security issues in machine learning prospective

The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures.To deal with this problem,communication networks consider fog computing as one of promising technologies that can improve overall communication performance.It brings on-demand services proximate to the end devices and delivers the requested data in a short time.Fog computing faces several issues such as latency,bandwidth,and link utilization due to limited resources and the high processing demands of end devices.To this end,fog caching plays an imperative role in addressing data dissemination issues.This study provides a comprehensive discussion of fog computing,Internet of Things(IoTs)and the critical issues related to data security and dissemination in fog computing.Moreover,we determine the fog-based caching schemes and contribute to deal with the existing issues of fog computing.Besides,this paper presents a number of caching schemes with their contributions,benefits,and challenges to overcome the problems and limitations of fog computing.We also identify machine learning-based approaches for cache security and management in fog computing,as well as several prospective future research directions in caching,fog computing,and machine learning.

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Task offloading is a key strategy in Fog Computing (FC). Thedefinition of resource-constrained devices no longer applies to sensors andInternet of Things (IoT) embedded system devices alone. Smart and mobileunits can also be viewed as resource-constrained devices if the power, cloudapplications, and data cloud are included in the set of required resources. Ina cloud-fog-based architecture, a task instance running on an end device mayneed to be offloaded to a fog node to complete its execution. However, ina busy network, a second offloading decision is required when the fog nodebecomes overloaded. The possibility of offloading a task, for the second time,to a fog or a cloud node depends to a great extent on task importance, latencyconstraints, and required resources. This paper presents a dynamic service thatdetermines which tasks can endure a second offloading. The task type, latencyconstraints, and amount of required resources are used to select the offloadingdestination node. This study proposes three heuristic offloading algorithms.Each algorithm targets a specific task type. An overloaded fog node can onlyissue one offloading request to execute one of these algorithms accordingto the task offloading priority. Offloading requests are sent to a SoftwareDefined Networking (SDN) controller. The fog node and controller determinethe number of offloaded tasks. Simulation results show that the average timerequired to select offloading nodes was improved by 33% when compared tothe dynamic fog-to-fog offloading algorithm. The distribution of workloadconverges to a uniform distribution when offloading latency-sensitive nonurgenttasks. The lowest offloading priority is assigned to latency-sensitivetasks with hard deadlines. At least 70% of these tasks are offloaded to fognodes that are one to three hops away from the overloaded node.

Priority Detector and Classifier Techniques Based on ML for the IoMT

Emerging telemedicine trends,such as the Internet of Medical Things(IoMT),facilitate regular and efficient interactions between medical devices and computing devices.The importance of IoMT comes from the need to continuously monitor patients’health conditions in real-time during normal daily activities,which is realized with the help of various wearable devices and sensors.One major health problem is workplace stress,which can lead to cardiovascular disease or psychiatric disorders.Therefore,real-time monitoring of employees’stress in the workplace is essential.Stress levels and the source of stress could be detected early in the fog layer so that the negative consequences can be mitigated sooner.However,overwhelming the fog layer with extensive data will increase the load on fog nodes,leading to computational challenges.This study aims to reduce fog computation by proposing machine learning(ML)models with two phases.The first phase of theMLmodel assesses the priority of the situation based on the stress level.In the second phase,a classifier determines the cause of stress,which was either interruptions or time pressure while completing a task.This approach reduced the computation cost for the fog node,as only high-priority records were transferred to the fog.Low-priority records were forwarded to the cloud.Four MLapproaches were compared in terms of accuracy and prediction speed:Knearest neighbors(KNN),a support vector machine(SVM),a bagged tree(BT),and an artificial neural network(ANN).In our experiments,ANN performed best in both phases because it scored an F1 score of 99.97% and had the highest prediction speed compared with KNN,SVM,and BT.

An Optimized Offloaded Task Execution for Smart Cities Applications

Wireless nodes are one of the main components in different applications that are offered in a smart city.These wireless nodes are responsible to execute multiple tasks with different priority levels.As the wireless nodes have limited processing capacity,they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity.Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications.This execution delay is reduced by placing fog computing nodes near these application nodes.A fog node has limited processing capacity and is sometimes unable to execute all the requested tasks.In this work,an optimal task offloading scheme that comprises two algorithms is proposed for the fog nodes to optimally execute the time-sensitive offloaded tasks.The first algorithm describes the task processing criteria for local computation of tasks at the fog nodes and remote computation at the cloud server.The second algorithm allows fog nodes to optimally scrutinize the most sensitive tasks within their task capacity.The results show that the proposed task execution scheme significantly reduces the execution time and most of the time-sensitive tasks are executed.

Time to forge ahead:The Internet of Things for healthcare

Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.