Resource Dependency and Multi-tenancy in Mobile Edge Clouds (MEC) present major resource orchestration challenges owing to limited and interdependent resources and dynamic deployment environments. Traditional cloud computing solutions are resource-dependency-agnostic, failing to maintain fair resource allocation and achieve high Service Level Objectives (SLOs) rates. In this paper, we present EdgeCore, a Resource Dependency (RD)-aware multi-tenant orchestrator for MEC. EdgeCore includes three integral components: 1) RD Resolver, which resolves the RD problem ignored by prior orchestrators; 2) EdgeDRF establishes multi-resource fairness among multiple tenants in MEC where prior solutions fall short; 3) EdgePilot utilizes the Resource Monitor, Resource Recommender, and Screening Algorithm, to continuously maintain the fair resource allocation across various deployments. EdgeCore is the first resource dependency-aware orchestrator outperforming the state-of-the-art AutoPilot and H-DRF with 20% higher SLO satisfaction rates evaluated in disaster response scenarios. Its lightweight implementation can be generalized to a multitude of MEC environments. EdgeCore code is available at https://github.com/LENSS/EdgeCore-SEC2024.

Mobile Edge Computing (MEC) has been gaining a major interest for use in Cyber-Physical Systems (CPS) for Disaster Response and Tactical applications. These CPS generate a very large amount of mission-critical and personal data that require resilient and secure storage and sharing. In this article, we present the design, implementation, and evaluation of a framework for resilient data storage and sharing for MEC in CPS targeting the aforementioned applications. Our framework is built on the resiliency of three main components: EdgeKeeper, which ensures resilient coordination of the framework’s components; RSock, which provides resilient communication among CPS’s nodes; and R-Drive/R-Share which, leveraging EdgeKeeper and RSock, provides resilient data storage and sharing. EdgeKeeper employs a set of replicas and a consensus protocol for storing critical meta-data and ensuring fast reorganization of the CPS; RSock decides an optimal degree for replicating data that is communicated over lossy links. R-Drive employs an adaptive erasure-coded and encrypted resilient data storage; R-Share, leveraging RSock provides resilient peer-to-peer data sharing. We implemented our proposed framework on rapidly deployable systems (e.g., manpacks, testMobile Edge Clouds) and on Android devices, and integrated it with existing MEC applications. Performance evaluation results from three real-world deployments show that our framework provides resilient data storage and sharing in MEC for CPS.

Accurate and prompt delivery of Emergency Medical Services (EMS) is critical in emergency incidents, e.g., man-made or natural disaster areas. However, quickly selecting the correct EMS protocol(s) (which dictate the medical procedures to be administered to patients) in complex medical scenarios, remains a key, demanding task for Emergency Medical Technicians (EMT). In this paper, we present EMSAssist, the first end-to-end mobile voice assistant at the edge for EMS. EMSAssist consists of three major components that address technical challenges present in state-of-the-art solutions: 1) For the first time, EMSAssist proposes and applies a few-sample fine-tuning technique in medical speech recognition task, that achieves a faster and more accurate speech transcription on our EMS audio dataset, when compared to Google Cloud Speech-to-Text; 2) A WordPiece tokenizer helps boosting the end-to-end EMS protocol selection accuracy by retrieving useful information from incorrect transcriptions; 3) A novel data customization framework that enables our data-driven EMSMobileBERT model to become the new state-of-the-art for EMS protocol selection. Extensive end-to-end evaluation results at the edge show EMSAssist can more accurately select EMS protocols (Top-5 accuracy above 96%) for EMTs, with end-to-end latencies of around 4.2 seconds.

We present EMSAssist, the first end-to-end mobile voice assistant for emergency medical services (EMS). EMSAssist allows Emergency Medical Technicians (EMT) to verbally describe patients' signs and symptoms and uses EMTs' voice input to recommend top-5 EMS protocols. Through this demo, we allow the attendees to evaluate EMSAssist through a pair of Google Glass and a mobile phone. Both mobile devices can collect users' voices as input and output top-5 recommended protocols. A companion youtube video of using EMSAssist on the Google Glass is provided: https://www.youtube.com/watch?v=bj7aQJKf4aE

Mobile Edge Computing (MEC) is gaining significant interest from first responders and tactical teams. Typical cloud-based coordination (e.g., service discovery and coordination, device naming, authentication) does not work in MEC due to high user mobility. We design and implement EdgeKeeper to provide cloud-like service coordination to distributed edge computing applications for MEC systems. It maintains an edge cluster among devices and intelligently stores data on a group of replicas to guard against node failures/disconnections. We provide a full-system implementation of Edge-Keeper for Android and Linux platforms and evaluate it with MEC applications in a real-world wide-area search and rescue operation conducted by first responders.