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Understanding Uncertainty of Edge Computing:New Principle and Design ApproachSejin Seo, Sang Won Choi, Sujin Kook, Seong-Lyun Kim, and Seung-Woo Ko

Abstract—Due to the edge’s position between the cloud andthe users, and the recent surge of deep neural network (DNN)applications, edge computing brings about uncertainties thatmust be understood separately. Particularly, the edge users’locally specific requirements that change depending on time andlocation cause a phenomenon called dataset shift, defined as thedifference between the training and test datasets’ representations.It renders many of the state-of-the-art approaches for resolvinguncertainty insufficient. Instead of finding ways around it, weexploit such phenomenon by utilizing a new principle: AImodel diversity, which is achieved when the user is allowed toopportunistically choose from multiple AI models. To utilize AImodel diversity, we propose Model Diversity Network (MoDNet),and provide design guidelines and future directions for efficientlearning driven communication schemes.

I. INTRODUCTION

The strive for making lighter consumer devices equippedwith the access to unlimited applications pushed the resourcesto servers in the cloud. Now, the lack of bandwidth and thedemand for real-time and user specific services are pulling theresources back to the network edge. The edge, which primarilyserved to act as access points for the radio, tries to expandits role as storage and computing servers for user specificneeds [1]. With this evolution, more diverse computationtasks are demanded from the edge, ranging from traditionaltasks with deterministic results to tasks that utilize the recentadvances in machine learning (ML) and related developmentsof artificial intelligence (AI) solutions [2]. The reason behindthis is that AI solutions, especially deep neural networks(DNN) models, strongly embody the specific features requiredby a task. Complying with the demand for more complextasks notwithstanding the edge’s rather limited computationcapacity, DNN based AI solutions that used to belong tothe realm of heavy computing servers are now transformedto more compact versions and implemented in mobile edgecomputers and even in light consumer devices [3].

Despite these efforts, the required performance may not beguaranteed, because all models are imperfect embodiment ofthe reality. This is intensified for the compact models that areused at the edge, because they lack the ability to representthe complex features of the reality. Moreover, the data at theedge are noisy, sparse, and biased, causing the degradation of

Sejin Seo, Sujin Kook, and Seong-Lyun Kim are with the School ofElectrical and Electronic Engineering, Yonsei University, Seoul, Korea (email:sjseo, sjkook, slkim@ramo.yonsei.ac.kr); Sang Won Choi is with KoreaRailroad Research Institute, Uiwang, Korea (email: swchoi@krri.re.kr); andSeung-Woo Ko is with the Division of Electronics and Electrical InformationEngineering at Korea Maritime and Ocean University, Busan, Korea (email:swko@kmou.ac.kr).

performance while also increasing the variance of the result.The variance of performance is understood as the uncertaintyof the performance, which becomes fatal for tasks that arelife-and-death related. For example, this is a serious issue forautonomous driving: Tesla and Uber autopilot accidents allresulted due to poor reflection of the visual factors that shouldhave been considered, e.g. concrete barriers and data exposedto sunlight.

In this article, we define computation uncertainty from theperspective of edge computing. To the best of our knowledge,it is the first work to address this issue comprehensively.Specifically, we summarize the common factors and conditionsthat cause computation uncertainty, including uncertainties incloud servers, DNN models, and the edge. In the computerscience literature, most works focus on designing ways torepresent all relevant features into a single AI model byincreasing its complexity, which is infeasible at the edge.Instead, we aim at choosing the best option available amongmultiple lighter AI models, defined as AI model diversity. Tothis end, we propose a novel edge computing architecture,which realizes AI model diversity in an edge environmentrelying on wireless access. Lastly, we end by providing severalinteresting directions for optimizing the architecture from thewireless communication perspective.

II. COMPUTATION UNCERTAINTY OF EDGE COMPUTING

A. Computation Uncertainty

Computation uncertainty is the variance of a computationresult. The uncertainty is caused by various reasons, and theyare classified differently according to the application of con-cern. In cloud computing, they are classified into two groups:parametric uncertainties and system uncertainties [4]. Para-metric uncertainties are caused by environmental parametersthat cannot be reduced directly by adjusting the system. Onthe other hand, system uncertainties are related to factors thatcan be controlled by the system, e.g. by acquiring additionalinformation or designing a more adequate system. In [4],different causes of uncertainties are listed and some methodsfor resource provisioning and scheduling under uncertainty areprovided.

As DNNs are gaining unprecedented amount of attention,computation uncertainty is being defined specifically for them.In the ML literature, the uncertainties are classified as eitheraleatoric or epistemic. First, aleatoric uncertainty is the un-certainty in the data caused by inherent randomness in thegeneration of the data. Second, epistemic uncertainty is theuncertainty in the model caused by insufficient training data

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Figure 1. One-dimensional illustration of computation uncertainties anddataset shift at the edge.

for representing all necessary features of the input domain.The uncertainties are often linked with the level of trust wecan put into a model. This notion is drawing more attention asDNN is expanding its applications to those that require highlevel of reliability, thus requiring not only the point accuracyof a model but the confidence in a model [5].

It is worth noting the relationship between the training dataand model uncertainty. When the data are sparse, the modeluncertainty is significantly increased, because ML models areheavily dependent on the training data provided to them. ForDNN, this tendency worsens, because DNNs are excellentinterpolators but poor extrapolators of knowledge.

B. Challenges at the Edge

Uncertainty has to be understood separately for the edge,because it inherits uncertainties from other areas and facesnew challenging uncertainties as well. First of all, the edgeinherits the uncertainties present in cloud computing, becauseedge computing is similar in structure to cloud computing,except it has more stringent requirements and limitations. Inaddition, the edge inherits the uncertainties present in DNNas well, when the edge utilizes DNN models. We followthe conventions of the ML literature, which encapsulates theuncertainties at the edge well.

Aside from these shared difficulties, the edge is posed witha more challenging situation due to the locally specific re-quirements like user, location, and time specific requirements.The bias incurred by the locally specific requirements makesthe target distribution deviate from the universal distribution,i.e. distribution representing the entire feature space. Thisdeviation is precisely described with the phenomenon termeddataset shift, which happens when the joint distribution of theinput and output data are different for the training and teststages [6]. As shown in Fig. 1, the prevalence of dataset shiftin edge computing greatly increases the level of uncertainty.

Let us revisit the autopilot problem with this perspective.Consider that a universal DNN model is used for the autopilot.

This model is trained for diverse situations, giving it very highaccuracy on average. However, if this model is not trainedcomprehensively for situations with high level of sunlight,the model’s performance becomes extremely uncertain forsituations where the camera sensor only takes in imagesexposed to intense sunlight. Now, if the designers becomeaware of this gap, they can solve this local problem by trainingthe model further with these new images exposed to intensesunlight. Nonetheless, if these adjustments are not made inreal-time, then accidents become inevitable. To make thingsworse, although heavy DNN models are required to representall these diverse features simultaneously, they cannot be run atthe edge due to computation capacity limitation. As a result,compact models have to be used, but they further increasethe uncertainty due to their inherent lack of capabilities torepresent complex features.

C. The State-of-the-Art

In computer science, numerous efforts have been madeto overcome performance degradation due to computationuncertainty. Firstly, most works that address data uncertaintytry to understand the noise factors as extra features for theirmodels. These works either try to filter the noise to recoverthe original data or include it into their models to avoidperformance degradation. For example, deep ensembles usedistributional parameter estimation to understand the varianceof the output [7], and denoising autoencoder (DAE) canbe trained to recover the uncorrupted data by intentionallytraining them with noise [8].

Secondly, to address the model uncertainty, most approachesutilize randomness in their models and exploit the ensembleaverage. These randomization methods either work to addcomplexity into their models to address underfitting issuesor regularization to avoid overfitting. An approach calledBayesian neural networks (BNN) use prior distributions togenerate the weight parameters randomly instead of usingdeterministic weights to represent uncertainty [5], but theyrequire heavy computation and do not scale well. Anotherapproach that tries to overcome such shortcomings is dropout,which randomly omits weight parameters to perform varia-tional Bayesian approximation [9]. Though useful, they are notfeasible at the edge because they either fall short of decreasingthe complexity of the models or suffer performance degrada-tion when faced with significant dataset shift, all because theystill rely on the expectation of the dataset.

As shown in Table I, the approaches that deal with datauncertainty, i.e. deep ensembles and DAE, do not deal withmodel uncertainty; the approaches that deal with model uncer-tainty, i.e. BNN and dropout, do not deal with data uncertainty;also, all approaches except dropout are not scalable for time,location, and user dynamics, because their model complexityincreases along with those factors. To this end, new ways toovercome uncertainty at the edge must be devised.

III. AI MODEL DIVERSITY: EXPLOITING DATASET SHIFT

From the data science perspective, the edge is character-ized by the frequent occurrence of dynamic dataset shift,

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Table ITHE STATE-OF-THE-ART APPROACHES: ADDRESSING PROBLEMS AT THE EDGE

Approach Data Uncertainty Model Uncertainty Scalability Dynamic Dataset Shift

Deep ensembles [7] O X X PartiallyDAE [8] O X X PartiallyBNN [5] X O X XDropout [9] X O O Partially

MoDNet (proposed) O O O Exploited

where the test data domain is often shifted depending onthe environment. This occurs because the test data requiredby an edge user are biased or even bound to certain edgespecific features, which may even be considered peripheral to auniversal representation. Such deviance renders other state-of-the-art approaches insufficient for the edge, because they areall focused on building a more comprehensive representationof the entire test data of interest, regardless of the dataset shift.To resolve this issue, we introduce a new principle called AImodel diversity.

A. AI Model Diversity

Instead of passively resolving dataset shift, it is exploitedby using AI model diversity granted by allowing the userto choose from multiple compact AI models that representdifferent edge specific features. Despite their limitations, eventhe compact models are capable when they only need to focuson the specific features that are magnified by the dataset shift.This simple but powerful scheme helps resolve data and modeluncertainties, while ensuring scalability.

• Data uncertainty: AI model diversity resolves datauncertainty by making each model train for the noisefactors that are correlated with the edge. For instance,recurring background of an edge should be considered asa significant feature to be trained along with the objectitself.

• Model uncertainty: Using multiple AI models grantswider coverage of the feature space that should be rep-resented for the edge. Also, it exploits the dataset shiftto gain efficiency and simplicity. Whereas most neuralnetworks are designed to represent a universal need, thecompact models in an edge server efficiently covers thefeatures dominant at the edge. This difference must benoted due to the edge’s constraints.

• Scalability: The proposed approach is very scalable fortime, location, and user dynamics. Whereas adaptationis very costly for complex and heavy AI models, it canreadily adapt to a changing environment by choosing ortraining a compact model that better represents the currentsituation.

Recently, a few works explore diversity for ML regardingthe data, model, and inference, e.g. [10]. However, similarto the state-of-the-art approaches, they do not address thecomplications and implementation issues related to the edge,which are this article’s major interest.

B. Experimental Verification: Object Detection

The following object detection experiment is conductedto illustrate and verify how the dynamic dataset shift atthe edge can be exploited by using AI model diversity.The experiment uses state-of-the-art real-time object detectionalgorithms, i.e. You-Only-Look-Once (YOLO) and YOLO-tinymodels, to represent a universal and local model, respectively[11]. To explain in more detail, three tiny models that can berun on realistic edge computing devices (e.g. NVidia JetsonXavier) are trained with scarce unmanned aerial vehicles(UAV) images, while a full model that has to be trained in aheavier computing server (RTX 2080 Ti), i.e. YOLO, is trainedwith abundant UAV images.

In the experiment, 737 annotated original images containingone or more UAVs are used as training data. Additionally,edge specific models use manually augmented images alongwith the original images for training, as illustrated in Fig. 2.Lastly, the test dataset is exclusive from the training dataset.

The four AI models are juxtaposed for comparison: a fulluniversal model, a tiny universal model, a tiny nighttimespecific model, and a tiny daytime specific model. The fulluniversal model, which is likely to be run at the cloud, uses all737 training data. With RTX 2080 Ti, it takes approximately3 hours 45 minutes to train. Considering the constraints at theedge, a tiny universal model is trained by randomly selecting50 images from the entire training dataset. The tiny modelneeds approximately 55 minutes to train.

The edge specific models are trained with 40 images thatare randomly selected from the original train dataset and 10images that are augmented to represent certain edge specificfeatures. The tiny nighttime specific model is tuned by using10 UAV images (without background) augmented with anighttime image. Similarly, the tiny daytime specific model istuned by training 10 augmented UAV images with sun flaresto represent UAVs exposed to sunlight. These models are alltrained for 6000 iterations or less depending on the level ofoverfitting. Similar to the tiny universal model, both of theedge specific models need approximately 55 minutes to train.

To evaluate the performance of each model, three testdatasets with 20 images each are used. The test datasetsrepresent a general environment, nighttime environment, anddaytime environment. From the graph in Fig. 3, it is evidentthat the universal model is nearly perfect for the generalenvironment by using more depth and nodes, whereas all tinymodels suffer from minor performance degradation due to thelack thereof.

However, the full universal model’s performance plummets

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Figure 2. Graphical explanation of four AI model training used to verify AI model diversity. Further explanation and data are available at:https://github.com/kgbssj.

(70%) for images exposed to sunlight during daytime. Thisis even worse for the tiny universal model (25%), but itis very efficiently recovered for the tiny daytime specificmodel (80%). This tendency continues for the tests during thenighttime; the tiny nighttime specific model is shown to out-perform (95%) the full universal model (90%). These resultsdemonstrate how the dynamic dataset shift directly impacts theperformance. To explain, the dataset shift makes some featureshighly relevant at the edge, rather exclusively. These featuresshould be exploited and less relevant features should be prunedto achieve higher accuracy without the expense of using largerweights or designing a specific architecture for each edge.

C. Realizing AI Model Diversity at the Edge: Data Augmen-tation and Fit Probability

Due to data scarcity and dynamic dataset shift at theedge, two major steps are necessary for exploiting AI modeldiversity. The first step is to use appropriate data augmentationto train for the edge specific features to avoid overfitting tothe training data. Sometimes, simple augmentation tools likeflipping or darkening will suffice as in the object detectionexperiment. And yet, more sophisticated augmentation likerandom feature insertion or self-adversarial training (SAT)may be necessary when a better performance is required.

Now that the models are trained to satisfy some of theedge specific features, the second step is to take advantageof them. To exploit AI model diversity, it is crucial to obtainan estimation of the models’ accuracy. For this purpose, the fitprobability between the data and an AI model is defined as theprobability that the program returns a correct answer. Moreprecisely, the fit probability measures the distance betweenthe feature distributions of the user’s test dataset and themodel’s representation. If the latent distributions of the user’s

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Figure 3. The average object detection accuracy of four AI models describedin Fig. 2 and Sec. III-B.

needs and the model are known in advance, this could beunderstood as the Kullback-Leibler (KL) divergence of thesedistributions. However, in a real system, these distributionsare unknown, so the distance between them is estimated bysampling the model’s response to the train dataset, which actsas the surrogate for the actual feature distribution. Thus, theusers augment annotated data based on the data augmentationprocess above and use them as pilot data to test and esti-mate the fit probability. It is analogous to transmitting pilotsequences for channel estimation in wireless communications,which aims at ensuring a desired data rate by evaluating thechannel’s state.

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Figure 4. Task offloading and model training procedure of MoDNet.

D. Edge Computing Network Architecture: MoDNet

To realize AI model diversity at the edge, we proposea new edge computing architecture called Model DiversityNetwork (MoDNet), comprising edge users that act as theclients of computation tasks and edge servers that are linkedwith the edge users through wireless links; the edge servershave diverse AI models in them. Here, the tasks are parallelin meaning to the test data from above, but distinguishedaccording to the edge computing perspective for more clarity.Due to their lack of computation capability, the edge usersneed to offload the tasks to the edge servers. There are differentdesign issues in both sides of edge users and servers, becauseusers do not know how well an AI model would respond totheir data and the servers do not know what kind of results theuser would demand. To address the issues in both directions,the architecture is designed and described as follows:

1) Task Offloading Process: To start, there are pre-trainedAI models in the servers. To decide whether it is beneficial tooffload the task, the edge user has to learn the fit probabilitybetween its task and the server’s AI models. To do so, theseprocesses illustrated in Fig. 4 are necessary; and they allhappen within a second.

a) Pilot data generation: When the user requests for a classof object, the server returns the user with generic objectmaterial that does not contain any edge specific features.The user augments the generic material with edge specificfeatures, e.g. background and lighting. These pilot data donot need manual annotation, because the material intrinsi-cally contains its annotation.

b) Fit probability learning: The user transmits the pilot data

to determine whether the AI model in the edge server isfit for its tasks. Using the pilot data, the server samplesthe model’s response and compares the result with itsannotation. It is called “success” if the result is correct.The comparison result is returned back to the user. Theuser then estimates the fit probability by computing theratio of the number of success events to the total numberof transmitted pilot data.

c) Task offloading: Based on the estimated fit probabilities,the user chooses the best AI model with the highest fitprobability and offload its tasks to the server possessingthe AI model.

2) Model Training Process: On the server, model trainingprocess continues along with the task offloading processexplained above. However, the training occurs in an offlinemanner, because the compact AI models are very sensitive totraining. This means that the newly trained models are not useduntil their performance is verified. Eventually, the edge servertrains and maintains its AI models to achieve higher overallfit probability with the users. These processes take more time;they take at most few hours to complete.

a) Local model training: The server uses the pilot data totrain its local AI models to better suit the user’s needs. Noadditional training data is required, because they receive thepilot data, which is annotated and designed to representan edge user’s tasks. Besides, the pilot data enables theedge server to reflect the temporal and spatial variations ofthe environment that the users will encounter, leading toincreasing the corresponding fit probability. Depending onthe complexity of the local models, the training may not

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be possible at a single edge server, which may have to bedone collaboratively with other servers [2], or offloaded tothe cloud.

b) AI model management: A server always trains a copy of itsAI model, because prematurely updating the trained versionmay lead to performance drop. When the older version ofan AI model is clearly obsolete, the server replaces it witha newer version. Aside from managing the version of themodels, the server also maintains different models with thesame objective to maximize the AI model diversity gain. Ifnecessary, a full universal model can be downloaded fromthe cloud to the edge server.

IV. DESIGN OF ENERGY EFFICIENT COMMUNICATION FORFIT PROBABILITY LEARNING

To gain sufficient level of confidence in a fit probability,it is required for an edge user to upload a large amount ofpilot data to the edge while consuming its limited energy. It isthus vital to learn fit probability in an energy efficient manner,which is the main theme of this section. Although the designof energy efficient edge computing has been widely exploredin the literature, our main focus is fundamentally different.Firstly, in the conventional MEC problems, the computationresult of the offloaded task is assumed to be always exact.In contrary, the AI models in MoDNet introduce randomnessto the results, which necessitates fit probability learning.Secondly, the number of tasks that need to be offloaded hasbeen considered as a given value. Consequently, the problemsare mostly concerned with deciding the proportions of locallycomputed and offloaded tasks. In contrast, the amount of pilotdata is a control variable for problems regarding MoDNet,because it is directly related to the level of confidence in an AImodel. These reasons call for designing new communicationdesigns, which align well with the new trend of learning drivencommunication [12].

A. Learning-Energy TradeoffFor learning fit probability, an edge user transmits a number

of pilot data to the edge, which is used to sample the AImodel’s response as explained in Sec. III-D. This acquisitionof knowledge is mathematically expressed as the reductionof confidence intervals, which is the range of values that theactual fit probability lies with high likelihood. With moreknowledge on the AI models, it becomes more likely to selectthe best AI model correctly. However, it is detrimental whentoo much resource is used for learning the fit probabilitiesof AI models that are worse than the best AI model. Thisdilemma is commonly referred to as exploration-exploitationtradeoff, which is highlighted by multi-armed bandits (MAB)problems [13].

It is noteworthy that learning to explore and exploit wellrequires wireless transmission. According to Chernoff bound,the number of pilot data transmissions needed proportion-ally scales with the required confidence ε, at the speed ofO(log(1/ε)) [13]. Furthermore, the energy consumption super-linearly increases as the amount of transmitted data increases[14]. Combing the two makes the learning-energy tradeoff thatwe are exploring.

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Figure 5. Graphical example of batch learning and selective elimination forfit probability learning.

B. Batch Learning and Selective EliminationThere are two ways for achieving higher energy efficiency.

One is to increase the available transmission time to decreasethe required data rate. The other is to decrease the total amountof data that have to be transmitted. The former is available bydecreasing the signaling overheads, such as the feedbacks fromthe server containing the sampling results of the concerned AImodel. Note that frequent feedbacks from the server inducesmore delay. Reducing the iterations of fit probability updateleads to increasing the available transmission time. For thesereasons, learning policies that transmit pilot data in batchesare preferred [13]. However, if the batch size is too large,energy is wasted for learning the fit probabilities for all AImodels, but the highest one is only required for the user. Itis in conflict with the latter. Thus, dividing the batches intoappropriate partitions is recommended. As illustrated in Fig. 5,energy can be saved by observing intermediate learning resultsbetween batches and sequentially eliminating the AI modelsthat perform seemingly poorly, i.e. its upper confidence boundis lower than the current best model’s lower confidence bound.In consequence, energy efficiency is determined by how ac-curately and quickly the inferior AI models are ruled out.

C. New Research DirectionsFollowing the design guidelines mentioned above leads to

revisiting several communication criteria regarded as de factostandards, to provide new research directions.

• Transmission mode selection: Consider an extendededge network where multiple edge servers exist in thecoverage of an edge user. To find a better AI model,the edge user can simultaneously broadcast the pilotdata to multiple servers. However, it may result in ex-cessive energy consumption, since the transmission rateis determined by the server whose channel gain is theworst. Things become worse if the AI models thereinare unlikely to be selected due to their unfitness touser demand. As a result, the transmission mode shouldbe adaptively controlled depending on the intermediateresults of batched fit probability learning.

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• Opportunistic resource allocation: In the case withmultiple users, only a limited number of users are allowedto transmit the pilot data due to the shared radio resource.The opportunistic resource allocation that grants moreresources to a user with a better channel gain may nothelp find the user with the best fit probability if thecorresponding channel condition is bad. It is thus requiredto jointly consider channel gains and learning results toavoid wasting transmission energy.

• User cooperation: As the number of AI models or thenumber of edge servers increases, it could be a heavyburden for a user to learn the fit probabilities of all pos-sible AI models. This burden is relieved by cooperatingwith several users whose AI model preference is similar.Specifically, by observing another user with similar modelpreference, a user can predict the fit probabilities of AImodels that have not been learned yet. Measuring usersimilarity has been well studied in the recommendationsystem (see, e.g., [15]). More interestingly, a new tradeoffexists between users’ cooperation and similarity measure-ment thereof: for the users to cooperate efficiently, it isbeneficial to make each user learn different AI models,but their similarity measurement becomes less accuratedue to the decrease of overlapping knowledge.

V. CONCLUDING REMARKS

Uncertainty may be deemed as a dire issue for the edge.Nevertheless, devising progressive architectures grounded onthe article’s principle and approach can turn the issue intoa promising opportunity for the area of wireless communica-tions. We start with guidelines for energy-efficient communica-tion designs, but there are other promising research directionsrelated to the efficient usage of different communication andcomputation resources. Seizing this opportunity depends onthe novel efforts that are yet to come.

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BIOGRAPHY

Sejin Seo is with the School of Electrical and ElectronicEngineering (EEE), Yonsei University. His current researchinterests include mobile edge computing, AI-assisted wirelesscommunication, and related implementations.Sang Won Choi is a senior researcher with the KoreaRailroad Research Institute (KRRI). His research interestsinclude mission-critical communications, mobile communi-cations, communication signal processing, and informationtheory.Sujin Kook is with the School of EEE, Yonsei University.Her current research interests include local 5G networks andAI-assisted wireless communication.Seong-Lyun Kim is a Professor of wireless networks with theSchool of EEE, Yonsei University, Seoul, and the Head of theRobotic and Mobile Networks Laboratory and the Center forFlexible Radio. His research interests include radio resourcemanagement, information theory in wireless networks, collec-tive intelligence, and robotic networks.Seung-Woo Ko (M’17) is an assistant professor with KoreaMaritime and Ocean University. His research interests includeintelligent communications and computing, and localization.