Session C-3

Mobile Edge/Cloud

10:00 AM — 11:30 AM EDT
May 12 Wed, 10:00 AM — 11:30 AM EDT

Distributed Threshold-based Offloading for Large-Scale Mobile Cloud Computing

Xudong Qin and Bin Li (University of Rhode Island, USA); Lei Ying (University of Michigan, USA)

Mobile cloud computing enables compute-limited mobile devices to perform real-time intensive computations such as speech recognition or object detection by leveraging powerful cloud servers. An important problem in large-scale mobile cloud computing is computational offloading where each mobile device decides when and how much computation should be uploaded to cloud servers by considering the local processing delay and the cost of using cloud servers. In this paper, we develop a distributed threshold-based offloading algorithm where it uploads an incoming computing task to cloud servers if the number of tasks queued at the device reaches the threshold, and processes it locally otherwise. The threshold is updated iteratively based on the computational load and the cost of using cloud servers. We formulate the problem as a symmetric game, and characterize the sufficient and necessary conditions for the existence and uniqueness of the Nash Equilibrium (NE) assuming exponential service times. Then, we show the convergence of our proposed distributed algorithm to the NE when the NE exists. Finally, we perform extensive simulations to validate our theoretical findings and demonstrate the efficiency of our proposed distributed algorithm under various practical scenarios such as general service times, imperfect server utilization estimation, and asynchronous threshold updates.

EdgeDuet: Tiling Small Object Detection for Edge Assisted Autonomous Mobile Vision

Xu Wang, Zheng Yang, Jiahang Wu and Yi Zhao (Tsinghua University, China); Zimu Zhou (Singapore Management University, Singapore)

Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance, situational awareness, and safety inspection, where it is crucial to detect both small and large objects in crowded scenes. Prior studies either perform object detection locally on-board or offload the task to the edge/cloud. Local object detection yields low accuracy on small objects since it operates on low-resolution videos to fit in mobile memory. Offloaded object detection incurs high latency due to uploading high-resolution videos to the edge/cloud. Rather than either pure local processing or offloading, we propose to detect large objects locally while offloading small object detection to the edge. The key challenge is to reduce the latency of small object detection. Accordingly, we develop EdgeDuet, the first edge-device collaborative framework for enhancing small object detection with tile-level parallelism. It optimizes the offloaded detection pipeline in tiles rather than the entire frame for high accuracy and low latency. Evaluations on drone vision datasets under LTE, WiFi 2.4GHz, WiFi 5GHz show that EdgeDuet outperforms local object detection in small object detection accuracy by 233.0%. It also improves the detection accuracy by 44.7% and latency by 34.2% over the state-of-the-art offloading schemes.

To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

Liang Li (Xidian University, China); Dian Shi (University of Houston, USA); Ronghui Hou and Hui Li (Xidian University, China); Miao Pan and Zhu Han (University of Houston, USA)

Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training, raising great challenges to battery constrained mobile devices. In this work, we target at improving the energy efficiency of FL over mobile edge networks to accommodate heterogeneous participating devices without sacrificing the learning performance. To this end, we develop a convergence-guaranteed FL algorithm enabling flexible communication compression. Guided by the derived convergence bound, we design a compression control scheme to balance the energy consumption of local computing (i.e., "working") and wireless communication (i.e., "talking") from the long-term learning perspective. In particular, the compression parameters are elaborately chosen for FL participants adapting to their computing and communication environments. Extensive simulations are conducted using various datasets to validate our theoretical analysis, and the results also demonstrate the efficacy of the proposed scheme in energy saving.

TiBroco: A Fast and Secure Distributed Learning Framework for Tiered Wireless Edge Networks

Dong-Jun Han (KAIST, Korea (South)); Jy-yong Sohn (Korea Advanced Institute of Science and Technology, Korea (South)); Jaekyun Moon (KAIST, Korea (South))

Recent proliferation of mobile devices and edge servers (e.g., small base stations) strongly motivates distributed learning at the wireless edge. In this paper, we propose a fast and secure distributed learning framework that utilizes computing resources at edge servers as well as distributed computing devices in tiered wireless edge networks. A fundamental lower bound is derived on the computational load that perfectly tolerates Byzantine attacks at both tiers. TiBroco, a hierarchical coding framework achieving this theoretically minimum computational load is proposed, which guarantees secure distributed learning by combating Byzantines. A fast distributed learning is possible by precisely allocating loads to the computing devices and edge servers, and also utilizing the broadcast nature of wireless devices. Extensive experimental results on Amazon EC2 indicate that our TiBroco allows significantly faster distributed learning than existing methods while guaranteeing full tolerance against Byzantine attacks at both tiers.

Session Chair

Stephen Lee (University of Pittsburgh)

Session C-6

Robotic Applications

4:30 PM — 6:00 PM EDT
May 12 Wed, 4:30 PM — 6:00 PM EDT

POLO: Localizing RFID-Tagged Objects for Mobile Robots

Dianhan Xie, Xudong Wang, Aimin Tang and Hongzi Zhu (Shanghai Jiao Tong University, China)

In many Internet-of-Things (IoT) applications, various RFID-tagged objects need to be localized by mobile robots. Existing RFID localization systems are infeasible, since they either demand bulky RFID infrastructures or cannot achieve sufficient localization accuracy. In this paper, a portable localization (POLO) system is developed for a mobile robot to locate RFID-tagged objects. Besides a single RFID reader on board, POLO is distinguished with a tag array and a lightweight receiver. The tag array is designed to reflect the RFID signal from an object into multi-path signals. The receiver captures such signals and estimates their multi-path channel coefficients by a tag-array-assisted channel estimation (TCE) mechanism. Such channel coefficients are further exploited to determine the object's direction by a spatial smoothing direction estimation (SSDE) algorithm. Based on the object's direction, POLO guides the robot to approach the object. When the object is in proximity, its 2D location is finally determined by a near-range positioning (NRP) algorithm. POLO is prototyped and evaluated via extensive experiments. Results show that the average angular error is within 1.6 degrees when the object is in the far-range (2∼6 m), and the average location error is within 5 cm while the object is in the near-range (∼1 m).

SILoc: A Speed Inconsistency-Immune Approach to Mobile RFID Robot Localization

Jiuwu Zhang and Xiulong Liu (Tianjin University, China); Tao Gu (Macquarie University, Australia); Xinyu Tong, Sheng Chen and Keqiu Li (Tianjin University, China)

Mobile RFID robots have been increasingly used in warehousing and intelligent manufacturing scenarios to pinpoint the locations of tagged objects. The accuracy of state-of-the-art RFID robot localization systems depends much on the stability of robot moving speed. However, in reality this assumption can hardly be guaranteed because a Commercial-Off-The-Shelf (COTS) robot typically has an inconsistent moving speed, and a small speed inconsistency will cause a large localization error. To this end, we propose a Speed Inconsistency-Immune approach to mobile RFID robot Localization (SILoc) system, which can accurately locate RFID tagged targets when the robot moving speed varies or is even unknown. SILoc employs multiple antennas fixed on the mobile robot to collect the phase data of target tags. We propose an optimized unwrapping method to maximize the use of the phase data, and a lightweight algorithm to calculate the locations in both 2D and 3D spaces based on the unwrapped phase profile. By utilizing the characteristics of tag-antenna distance and combining the phase data from multiple antennas, SILoc can effectively eliminate the side effects of moving speed inconsistency. Extensive experimental results demonstrate that SILoc can achieve a centimeter-level localization accuracy in the scenario with an inconsistent or unknown robot moving speed.

Multi-Robot Path Planning for Mobile Sensing through Deep Reinforcement Learning

Yongyong Wei and Rong Zheng (McMaster University, Canada)

Mobile sensing is an effective way to collect environmental data such as air quality, humidity and temperature at low costs. However, mobile robots are typically battery powered and have limited travel distances. To accelerate data collection in large geographical areas, it is beneficial to deploy multiple robots to perform tasks in parallel. In this paper, we investigate the Multi-Robot Informative Path Planning (MIPP) problem, namely, to plan the most informative paths in a target area subject to the budget constraints of multiple robots. We develop two deep reinforcement learning (RL) based cooperative strategies: independent learning through credit assignment and sequential rollout based learning for MIPP. Both strategies are highly scalable with the number of robots. Extensive experiments are conducted to evaluate the performance of the proposed and baseline approaches using real-world WiFi Received Signal Strength (RSS) data. In most cases, the RL based solutions achieve superior or similar performance as a baseline genetic algorithm (GA)-based solution but at only a fraction of running time during inference. Furthermore, when the budgets and initial positions of the robots change, the pre-trained policies can be applied directly.

Enabling Edge-Cloud Video Analytics for Robotics Applications

Yiding Wang and Weiyan Wang (Hong Kong University of Science and Technology, Hong Kong); Duowen Liu (Hong Kong University of Science & Technology, Hong Kong); Xin Jin (Peking University, China); Junchen Jiang (University of Chicago, USA); Kai Chen (Hong Kong University of Science and Technology, China)

Emerging deep learning-based video analytics tasks demand computation-intensive neural networks and powerful computing resources on the cloud to achieve high accuracy. Due to the latency requirement and limited network bandwidth, edge-cloud systems adaptively compress the data to strike a balance between overall analytics accuracy and bandwidth consumption. However, the degraded data leads to another issue of poor tail accuracy, which means the extremely low accuracy of a few semantic classes and video frames. Autonomous robotics applications especially value the tail accuracy performance but suffer using the prior edge-cloud systems.

We present Runespoor, an edge-cloud video analytics system to manage the tail accuracy and enable emerging robotics applications. We train and deploy a super-resolution model tailored for the tail accuracy of analytics tasks on the server to significantly improves the performance on hard-to-detect classes and sophisticated frames. During online operation, we use an adaptive data rate controller to further improve the tail performance by instantly adjusting the data rate policy according to the video content. Our evaluation shows that Runespoor improves class-wise tail accuracy by up to 300%, frame-wise 90%/99% tail accuracy by up to 22%/54%, and greatly improves the overall accuracy and bandwidth trade-off.

Session Chair

Shan Lin (Stony Brook University)

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