Mobile Device Clouds Research Articles

Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach

The ubiquitous nature of Internet of Things (IoT) devices has posited many challenges that need innovative solutions in the 5G era. Software defined networks (SDNs) are becoming indispensable in managing several aspects of next-generation IoT networking that arise from the need to control highly heterogeneous, geographically dispersed, mobile IoT devices. One such aspect is cache management at the edge. Recently, multiple forms of edge resources, including mobile device clouds and micro-edge data centers have emerged to provide scalable cache placement locations that reduce the costs for the mobile network operator (MNO). As all of these service locations are registered with the MNO (or links established after registration with the 5G base station, BS), content should be placed according to the user's demand and the cost the user is willing to pay to receive the desired level of QoS. To this end, it is important to understand the future popularity of the content for its optimal placement considering the highly dynamic user mobility. In this article, we address two key aspects of a mobile IoT network: security and seamless connectivity for data delivery. We rely on the federated learning (FL) architecture, which enables harnessing data and computational capabilities at end-user devices to train machine learning models. We study FL concepts in the domain of edge computing for IoT use cases, such as caching. We draw conclusions from various state-of-the-art models and posit several challenges that can be overcome via a novel proposed control algorithm.

Robust orchestration of concurrent application workflows in mobile device clouds

A hybrid mobile/fixed device cloud that harnesses sensing, computing, communication, and storage capabilities of mobile and fixed devices in the field as well as those of computing and storage servers in remote datacenters is envisioned. Mobile device clouds can be harnessed to enable innovative pervasive applications that rely on real-time, in-situ processing of sensor data collected in the field. To support concurrent mobile applications on the device cloud, a robust distributed computing framework, called Maestro, is proposed. The key components of Maestro are (i) a task scheduling mechanism that employs controlled task replication in addition to task reallocation for robustness and (ii) Dedup for task deduplication among concurrent pervasive workflows. An architecture-based solution that relies on task categorization and authorized access to the categories of tasks is proposed for different levels of trust. Experimental evaluation through prototype testbed of Android- and Linux-based mobile devices as well as simulations is performed to demonstrate Maestro’s capabilities.

Towards resource sharing in mobile device clouds

Despite the increased capabilities of mobile devices, mobile application resource requirements can often transcend what can be accomplished on a single device. This has been addressed through several proposals for efficient computation offloading from mobile devices to remote cloud resources or closely located computing resources known as cloudlets. In this paper we consider an environment in which computational offloading is performed among a set of mobile devices. We call this environment a Mobile Device Cloud (MDC). We are interested in MDCs where nodes are {\em highly collaborative}. We develop computational offloading schemes that {\em maximize the lifetime} of the ensemble of mobile devices where we consider the network to be alive as long as no device has depleted its battery. As a secondary contribution in this work, we develop and use an experimentation platform that allows us to evaluate a range of computational models and profiles derived from a realistic testbed. We use this platform as a first step in an evaluation exercise that demonstrates the effectiveness of our computation offloading algorithms in extending the lifetime of an MDC.