Low-cost mobile personal clouds

Abstract

Abstract:We propose a mobile peer to peer personal cloud architecture which allows users to capture, store, analyse, interact with and share different types of personal and context data with no privacy leakage. Our mobile personal cloud can host multiple different services which are intelligent, distributed, dynamic and operate in real time. In this paper we describe one service that we designed and deployed on our mobile personal cloud called Mobile Wellbeing Companion Cloud (MWCC). Using low-cost, off-the-shelf hardware components and opensource software, our MWCC combines several sensor network technologies to allow users to monitor and interact with their personal data and environment in real time without privacy leakage. MWCC augments heterogeneous sensors data with state of the art machine learning algorithms for signal filtering, fast classification and analysis and provides interactive data visualisation for transparent user interaction. We show that our MWCC is easy to use and highly accurate while managing to keep resource costs low.

A novel cross-layer framework for large scale emergency communications

“…E 3 F shows high message delivery performance for two types of traffic: data dissemination and query answering while keeping energy consumption low and adaptively preserve battery life for more important agents. In our future work, we plan to integrate E 3 F to our low-cost prototyping testbed [11,26] for more accurate performance validation. In addition, we will aim to extend E 3 F to allow collaborations and cooperations between multiple vehicular clouds in order to enhance its performance.…”

“…cost of services, energy consumption, network congestion) which have not been solved due to, for example, lack of network resources, architecture scalability and heterogeneity. Our previous works [11,26] describes low-cost personal clouds (deployed on Raspberry Pis, smart-phones) which can host, store and monitor a range of information and services locally while still being able to share with others. Note that the term "node" that we use in this paper can refer both vehicular cloud and mobile personal clouds without loss of generality.…”

Cognitive Caching at the Edges for Mobile Social Community Networks: A Multi-Agent Deep Reinforcement Learning Approach

“…In previous works, we have proposed and deployed fullydistributed real-time multi-layer mobile edge cloud architectures for enabling multiple services for smart vehicles, drones, cities and agriculture applications spanning MODiToNeS [32], mobile personal edge-clouds [66] and Raspberry PI based personal clouds RasPiPCloud [32,33,34] which support multiple on-demand virtual containers (e.g. LXC, Docker) to host different services and applications that collect, store, analyse, predict and share data with other edges while retaining completed control and ownership of their data.…”

“…Content caching in content delivery networks (CDNs) [63,64], such as AWS Cloudfront [25] and Azure CDN [61], allows improvement of users QoS and QoE but it still suffers from sparse network coverage, disconnections, network congestion and highly dynamic users' mobility and query patterns [1,2,6]. Many applications, such as remote health care and mobile social networks, need to be supported by next-generation mobile edge predictive content services which allow localized content storing and processing close to the users interested in it [1,6,7,32,33,34]. State-of-the-art edge services hosted in the mobile edge devices bring local data management, computation and inference capabilities to the edges to reduce the delay and improve the performance of data transport for end-users [67].…”