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1. Artificial Intelligence Enabled Distributed Edge Computing for Internet of Things Applications

   https://doi.org/10.1109/DCOSS49796.2020.00077  

   Fragkos, Georgios ; Tsiropoulou, Eirini Eleni ; Papavassiliou, Symeon ( May 2020 , 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS))

   null (Ed.)

   Artificial Intelligence (AI) based techniques are typically used to model decision making in terms of strategies and mechanisms that can result in optimal payoffs for a number of interacting entities, often presenting antagonistic behaviors. In this paper, we propose an AI-enabled multi-access edge computing (MEC) framework, supported by computing-equipped Unmanned Aerial Vehicles (UAVs) to facilitate IoT applications. Initially, the problem of determining the IoT nodes optimal data offloading strategies to the UAV-mounted MEC servers, while accounting for the IoT nodes' communication and computation overhead, is formulated based on a game-theoretic model. The existence of at least one Pure Nash Equilibrium (PNE) point is shown by proving that the game is submodular. Furthermore, different operation points (i.e. offloading strategies) are obtained and studied, based either on the outcome of Best Response Dynamics (BRD) algorithm, or via alternative reinforcement learning approaches (i.e. gradient ascent, log-linear, and Q-learning algorithms), which explore and learn the environment towards determining the users' stable data offloading strategies. The corresponding outcomes and inherent features of these approaches are critically compared against each other, via modeling and simulation. 

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2. U-CIMAN: Uncover Spectrum and User Information in LTE Mobile Access Networks

   https://doi.org/10.1109/INFOCOM41043.2020.9155245  

   Zou, Rui ; Wang, Wenye ( July 2020 , IEEE INFOCOM)

   Abstract: With the proliferation of Dynamic Spectrum Access (DSA), Internet of Things (IoT), and Mobile Edge Computing (MEC) technologies, various methods have been proposed to deduce key network and user information in cellular systems, such as available cell bandwidths, as well as user locations and mobility. Not only is such information dominated by cellular networks of vital significance on other systems co-located spectrum-wise and/or geographically, but applications within cellular systems can also benefit remarkably from inferring such information, as exemplified by the endeavours made by video streaming to predict cell bandwidth. Hence, we are motivated to develop a new tool to uncover as much information used to be closed to outsiders or user devices as possible with off-the-shelf products. Given the wide-spread deployment of LTE and its continuous evolution to 5G, we design and implement U-CIMAN, a client-side system to accurately UnCover as much Information in Mobile Access Networks as allowed by LTE encryption. Among the many potential applications of U-CIMAN, we highlight one use case of accurately measuring the spectrum tenancy of a commercial LTE cell. Besides measuring spectrum tenancy in unit of resource blocks, U-CIMAN discovers user mobility and traffic types associated with spectrum usage through decoded control messages and user data bytes. We conduct 4-month detailed accurate spectrum measurement on a commercial LTE cell, and the observations include the predictive power of Modulation and Coding Scheme on spectrum tenancy, and channel off-time bounded under 10 seconds, to name a few. 

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3. Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks

   Poularakis, K. ; Llorca, J. ; Tulino, A. ; Taylor, I. ; Tassiulas, L. ( April 2019 , IEEE International Conference on Computer Communications)

   The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers. 

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4. Personalized Activity Recognition using Partially Available Target Data

   https://doi.org/10.1109/TMC.2021.3071434  

   Fallahzadeh, Ramin ; Esna Ashari, Zhila ; Alinia, Parastoo ; Ghasemzadeh, Hassan ( January 2021 , IEEE Transactions on Mobile Computing)

   null (Ed.)

   Recent years have witnessed a growing body of research on autonomous activity recognition models for use in deployment of mobile systems in new settings such as when a wearable system is adopted by a new user. Current research, however, lacks comprehensive frameworks for transfer learning. Specifically, it lacks the ability to deal with partially available data in new settings. To address these limitations, we propose {\it OptiMapper}, a novel uninformed cross-subject transfer learning framework for activity recognition. OptiMapper is a combinatorial optimization framework that extracts abstract knowledge across subjects and utilizes this knowledge for developing a personalized and accurate activity recognition model in new subjects. To this end, a novel community-detection-based clustering of unlabeled data is proposed that uses the target user data to construct a network of unannotated sensor observations. The clusters of these target observations are then mapped onto the source clusters using a complete bipartite graph model. In the next step, the mapped labels are conditionally fused with the prediction of a base learner to create a personalized and labeled training dataset for the target user. We present two instantiations of OptiMapper. The first instantiation, which is applicable for transfer learning across domains with identical activity labels, performs a one-to-one bipartite mapping between clusters of the source and target users. The second instantiation performs optimal many-to-one mapping between the source clusters and those of the target. The many-to-one mapping allows us to find an optimal mapping even when the target dataset does not contain sufficient instances of all activity classes. We show that this type of cross-domain mapping can be formulated as a transportation problem and solved optimally. We evaluate our transfer learning techniques on several activity recognition datasets. Our results show that the proposed community detection approach can achieve, on average, 69%$ utilization of the datasets for clustering with an overall clustering accuracy of 87.5%. Our results also suggest that the proposed transfer learning algorithms can achieve up to 22.5% improvement in the activity recognition accuracy, compared to the state-of-the-art techniques. The experimental results also demonstrate high and sustained performance even in presence of partial data. 

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5. Efficient Algorithms for Stochastic Ride-Pooling Assignment with Mixed Fleets

   https://doi.org/10.1287/trsc.2021.0349  

   Luo, Qi ; Nagarajan, Viswanath ; Sundt, Alexander ; Yin, Yafeng ; Vincent, John ; Shahabi, Mehrdad ( July 2023 , Transportation Science)

   Ride-pooling, which accommodates multiple passenger requests in a single trip, has the potential to substantially enhance the throughput of mobility-on-demand (MoD) systems. This paper investigates MoD systems that operate mixed fleets composed of “basic supply” and “augmented supply” vehicles. When the basic supply is insufficient to satisfy demand, augmented supply vehicles can be repositioned to serve rides at a higher operational cost. We formulate the joint vehicle repositioning and ride-pooling assignment problem as a two-stage stochastic integer program, where repositioning augmented supply vehicles precedes the realization of ride requests. Sequential ride-pooling assignments aim to maximize total utility or profit on a shareability graph: a hypergraph representing the matching compatibility between available vehicles and pending requests. Two approximation algorithms for midcapacity and high-capacity vehicles are proposed in this paper; the respective approximation ratios are [Formula: see text] and [Formula: see text], where p is the maximum vehicle capacity plus one. Our study evaluates the performance of these approximation algorithms using an MoD simulator, demonstrating that these algorithms can parallelize computations and achieve solutions with small optimality gaps (typically within 1%). These efficient algorithms pave the way for various multimodal and multiclass MoD applications.

   History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics.

   Funding: This work was supported by the National Science Foundation [Grants CCF-2006778 and FW-HTF-P 2222806], the Ford Motor Company, and the Division of Civil, Mechanical, and Manufacturing Innovation [Grants CMMI-1854684, CMMI-1904575, and CMMI-1940766].

   Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0349 .

    

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