QuLa: service selection and forwarding table population in service-centric networking using real-life topologies

The amount of services located in the network has drastically increased over the last decade which is why more and more datacenters are located at the network edge, closer to the users. In the current Internet it is up to the client to select a destination using a resolution service (Domain Name System, Content Delivery Networks ...). In the last few years, research on Information-Centric Networking (ICN) suggests to put this selection responsibility at the network components; routers find the closest copy of a content object using the content name as input. We extend the principle of ICN to services; service routers forward requests to service instances located in datacenters spread across the network edge. To solve this problem, we first present a service selection algorithm based on both server and network metrics. Next, we describe a method to reduce the state required in service routers while minimizing the performance loss caused by this data reduction. Simulation results based on real-life networks show that we are able to find a near-optimal load distribution with only minimal state required in the service routers

Privacy Aware Offloading of Deep Neural Networks

Deep neural networks require large amounts of resources which makes them hard to use on resource constrained devices such as Internet-of-things devices. Offloading the computations to the cloud can circumvent these constraints but introduces a privacy risk since the operator of the cloud is not necessarily trustworthy. We propose a technique that obfuscates the data before sending it to the remote computation node. The obfuscated data is unintelligible for a human eavesdropper but can still be classified with a high accuracy by a neural network trained on unobfuscated images.Comment: ICML 2018 Privacy in Machine Learning and Artificial Intelligence worksho

Collective sampling of environmental features under limited sampling budget

Exploration of an unknown environment is one of the most prominent tasks for multi-robot systems. In this paper, we focus on the specific problem of how a swarm of simulated robots can collectively sample a particular environment feature. We propose an energy-efficient approach for collective sampling, in which we aim to optimize the statistical quality of the collective sample while each robot is restricted in the number of samples it can take. The individual decision to sample or discard a detected item is performed using a voting process, in which robots vote to converge to the collective sample that reflects best the inter-sample distances. These distances are exchanged in the local neighbourhood of the robot. We validate our approach using physics-based simulations in a 2D environment. Our results show that the proposed approach succeeds in maximizing the spatial coverage of the collective sample, while minimizing the number of taken samples. (C) 2019 Elsevier B.V. All rights reserved

Decoupled Learning of Environment Characteristics for Safe Exploration

Reinforcement learning is a proven technique for an agent to learn a task. However, when learning a task using reinforcement learning, the agent cannot distinguish the characteristics of the environment from those of the task. This makes it harder to transfer skills between tasks in the same environment. Furthermore, this does not reduce risk when training for a new task. In this paper, we introduce an approach to decouple the environment characteristics from the task-specific ones, allowing an agent to develop a sense of survival. We evaluate our approach in an environment where an agent must learn a sequence of collection tasks, and show that decoupled learning allows for a safer utilization of prior knowledge.Comment: 4 pages, 4 figures, ICML 2017 workshop on Reliable Machine Learning in the Wil

A component-based approach towards mobile distributed and collaborative PTAM

Having numerous sensors on-board, smartphones have rapidly become a very attractive platform for augmented reality applications. Although the computational resources of mobile devices grow, they still cannot match commonly available desktop hardware, which results in downscaled versions of well known computer vision techniques that sacrifice accuracy for speed. We propose a component-based approach towards mobile augmented reality applications, where components can be configured and distributed at runtime, resulting in a performance increase by offloading CPU intensive tasks to a server in the network. By sharing distributed components between multiple users, collaborative AR applications can easily be developed. In this poster, we present a component-based implementation of the Parallel Tracking And Mapping (PTAM) algorithm, enabling to distribute components to achieve a mobile, distributed version of the original PTAM algorithm, as well as a collaborative scenario