Conference Papers

Iris: Secure reliable live-streaming with opportunistic mobile edge cloud offloading

Martins R., Correia M., Antunes L., Silva F.

Future Generation Computer Systems

2019

Abstract:

The ever-increasing demand for higher quality live streams is driving the need for better networking infrastructures, specially when disseminating content over highly congested areas, such as stadiums, concerts and museums. Traditional approaches to handle this type of scenario relies on a combination of cellular data, through 4G distributed antenna arrays (DAS), with a high count of WiFi (802.11) access points. This obvious requires a substantial upfront cost for equipment, planning and deployment. Recently, new efforts have been introduced to securely leverage the capabilities of wireless multipath, including WiFi multicast, 4G, and device-to-device communications. In order to solve these issues, we propose an approach that lessens the requirements imposed on the wireless infrastructures while potentially expanding wireless coverage through the crowd-sourcing of mobile devices. In order to achieve this, we propose a novel pervasive approach that combines secure distributed systems, WiFi multicast, erasure coding, source coding and opportunistic offloading that makes use of hyperlocal mobile edge clouds. We empirically show that our solution is able to offer a 11 fold reduction on the infrastructural WiFi bandwidth usage without having to modify any existing software or firmware stacks while ensuring stream integrity, authorization and authentication.

Joining Global Aerospace Value Networks: Lessons for Industrial Development Policies

Santos C., Abubakar S., Barrosa A.C., Mendonça J., Dalmarco G., Godsell J.

Space Policy - Available online 11 February 2019

2019

Abstract:

Governmental investments on the development of high-tech clusters are among the main policies for socioeconomic development, enabling countries to be part of global value networks. Our objective is to identify which are the strategies of countries that want to join global aerospace value networks, by means of an abductive case research. Countries were divided in 3 groups (A; B; C) according to their global aerospace exports share. The analytical framework used to identify the strategies has 3 dimensions: network structure, network governance, and network dynamics. Results show different strategies according to the country’s global exports share. While for countries in group A (exports above 1%), a strategy focused on the dimension network structure indicated a sustained high-tech sector. Countries in group C tend to focus on specialization, taking advantage of shifts in technological paradigms to upgrade their development level. The dimension network governance is mainly related to governmental efforts toward the creation of clusters and associations, promoting specialization and collaborative work. Finally, the dimension network dynamics describes the attraction of foreign companies to qualify the clusters at countries who belong to group C, while countries at group A reinforce their research and development activities. The comparison between countries is helpful for governmental representatives who want to develop strategies toward increasing participation in an industrial global value network and for supply chain managers to help selecting the locations for their operations. Keywords Global value networkAerospace industryPublic policyAbductive research

Joint Flight Cruise Control and Data Collection in UAV-aided Internet of Things: An Onboard Deep Reinforcement Learning Approach

Li K., Ni W., Tovar E., Guizani M.

IEEE Internet of Things Journal

2020

Abstract:

Employing Unmanned Aerial Vehicles (UAVs) as aerial data collectors in Internet-of-Things (IoT) networks is a promising technology for large-scale environment sensing. A key challenge in UAV-aided data collection is that UAV maneuvering gives rise to buffer overflow at the IoT node and unsuccessful transmission due to lossy airborne channels. This paper formulates a joint optimization of flight cruise control and data collection schedule to minimize network data loss as a Partial Observable Markov Decision Process (POMDP), where the states of individual IoT nodes can be obscure to the UAV. The problem can be optimally solvable by reinforcement learning, but suffers from curse-of-dimensionality and becomes rapidly intractable with the growth in the number of IoT nodes. In practice, a UAV-aided IoT network contains a large number of network states and actions in POMDP while the up-to-date knowledge is not available at the UAV. We propose an onboard Deep Q-Network based Flight Resource Allocation Scheme (DQN-FRAS) to optimize the online flight cruise control of the UAV and data scheduling given outdated knowledge on the network states. Numerical results demonstrate that DQN-FRAS reduces the packet loss by over 51%, as compared to existing non-learning heuristics.

KAVE: Building Kinect Based CAVE Automatic Virtual Environments, Methods for Surround-Screen Projection Management, Motion Parallax and Full-Body Interaction Support

Gonçalves A., Bermudez S.

Proceedings of the ACM on Human-Computer Interaction archive

2018

Abstract:

While CAVE Automatic Virtual Environments (CAVE) have been around for over 2 decades they remain complex to setup, unaffordable to most, and generally limited to data and model visualization applications for academia and industry. In this paper, we present a solution to create a monocular CAVE using the Unity 3D game engine by adding motion parallax and full-body interaction support via the use of a Kinect V2 low-cost sensor. More importantly, we provide a functional and easy to use plugin for that effect, the KAVE, and its configuration tool. Here, we describe our own low-cost CAVE setup, a range of alternative configurations to CAVE systems using this technology and example applications. Finally, we discuss the potential of such an approach considering the current advancements in VR and gaming.

Key phrase extraction of lightly filtered broadcast news

Marujo L., Ribeiro R., De Matos D.M., Neto J.P., Gershman A., Carbonell J.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2012

Abstract:

This paper explores the impact of light filtering on automatic key phrase extraction (AKE) applied to Broadcast News (BN). Key phrases are words and expressions that best characterize the content of a document. Key phrases are often used to index the document or as features in further processing. This makes improvements in AKE accuracy particularly important. We hypothesized that filtering out marginally relevant sentences from a document would improve AKE accuracy. Our experiments confirmed this hypothesis. Elimination of as little as 10% of the document sentences lead to a 2% improvement in AKE precision and recall. AKE is built over MAUI toolkit that follows a supervised learning approach. We trained and tested our AKE method on a gold standard made of 8 BN programs containing 110 manually annotated news stories. The experiments were conducted within a Multimedia Monitoring Solution (MMS) system for TV and radio news/programs, running daily, and monitoring 12 TV and 4 radio channels.

Keyphrase cloud generation of Broadcast News

Marujo L., Viveiros M., Neto J.P.

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

2011

Abstract:

This paper describes an enhanced automatic keyphrase extraction method applied to Broadcast News. The keyphrase extraction process is used to create a concept level for each news. On top of words resulting from a speech recognition system output and news indexation and it contributes to the generation of a tag/keyphrase cloud of the top news included in a Multimedia Monitoring Solution system for TV and Radio news/programs, running daily, and monitoring 12 TV channels and 4 Radios.

Large deviations analysis of consensus+innovations detection in random networks

Bajovic D., Jakovetic D., Moura J.M.F., Xavier J., Sinopoli B.

2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011

2011

Abstract:

We study the large deviations performance of consensus+innovations distributed detection over random networks, where each sensor, at each time k, weight averages its decision variable with its neighbors decision variables (consensus), and accounts for its new observation (innovation). Sensor observations are independent identically distributed (i.i.d.) both in time and space, but have generic (non Gaussian) distributions. The underlying network is random, described by a sequence of i.i.d. stochastic, symmetric weight matrices W(k); we measure the corresponding speed of consensus by |log r|, where r is the second largest eigenvalue of the second moment of W(k). We show that distributed detection exhibits a phase transition behavior with respect to |log r|: when |log r| is above a threshold, distributed detection is equivalent to the optimal centralized detector, i.e., has the error exponent equal to the Chernoff information. We explicitly quantify the optimality threshold for |log r| as a function of the log-moment generating function Λ 0 (·) of a sensor’s log- likelihood ratio. When below the threshold, we analytically find the achievable error exponent as a function of r and Λ 0 (·). Finally, we illustrate by an example the dependence of the optimality threshold on the type of the sensor observations distribution

Large deviations performance of consensus+innovations distributed detection with non-gaussian observations

Bajovic D., Jakovetic D., Moura J.M.F., Xavier J., Sinopoli B.

IEEE Transactions on Signal Processing

2012

Abstract:

We establish the large deviations asymptotic performance (error exponent) of consensus+innovations distributed detection over random networks with generic (non-Gaussian) sensor observations. At each time instant, sensors 1) combine theirs with the decision variables of their neighbors (consensus) and 2) assimilate their new observations (innovations). This paper shows for general non-Gaussian distributions that consensus+innovations distributed detection exhibits a phase transition behavior with respect to the network degree of connectivity. Above a threshold, distributed is as good as centralized, with the same optimal asymptotic detection performance, but, below the threshold, distributed detection is suboptimal with respect to centralized detection. We determine this threshold and quantify the performance loss below threshold. Finally, we show the dependence of the threshold and of the performance on the distribution of the observations: the asymptotic performance of distributed detectors over the same random network with different observations’ distributions, for example, Gaussian, Laplace, or quantized, may be different, even though the asymptotic performance of the corresponding centralized detectors is the same.

Latent sentiment detection in Online Social Networks: A communications-oriented view

Negi R., Prabhu V.U., Rodrigues M.

2014 IEEE International Conference on Communications, ICC 2014

2014

Abstract:

In this paper, we consider the problem of latent sentiment detection in Online Social Networks such as Twitter. Modeling the underlying social network as an Ising prior, we demonstrate the effect that the underlying social network structure has on the performance of a trivial sentiment detector. In doing so, we introduce a novel communications-oriented framework for characterizing the probability of error and the associated error exponent, based on information theoretic analysis. We study the variation of the calculated error exponent for several stylized network topologies such as the complete network, the star network and the closed-chain network, and show the importance of the network structure in determining detection performance.