Search results for " Telecommunication"
showing 10 items of 996 documents
Implementation and Deployment Evaluation of the DMAMAC Protocol for Wireless Sensor Actuator Networks
2016
Abstract The increased application of wireless technologies including Wireless Sensor Actuator Networks (WSAN) in industry has given rise to a plethora of protocol designs. These designs target metrics ranging from energy efficiency to real-time constraints. Protocol design typically starts with a requirements specification, and continues with analytic and model-based simulation analysis. State-of- the-art network simulators provide extensive physical environment emulation, but still have limitations due to model abstractions. Deployment testing on actual hardware is therefore vital in order to validate implementability and usability in the real environment. The contribution of this article…
Efficient Hybrid Emergency Aware MAC Protocol for Wireless Body Sensor Networks
2018
International audience; In Body Sensor Networks (BSNs), two types of events should be addressed: periodic and emergency events. Traffic rate is usually low during periodic observation, and becomes very high upon emergency. One of the main and challenging requirements of BSNs is to design Medium Access Control (MAC) protocols that guarantee immediate and reliable transmission of data in emergency situations, while maintaining high energy efficiency in non-emergency conditions. In this paper, we propose a new emergency aware hybrid DTDMA/DS-CDMA protocol that can accommodate BSN traffic variations by addressing emergency and periodic traffic requirements. It takes advantage of the high delay …
Knowledge-based verification of concatenative programming patterns inspired by natural language for resource-constrained embedded devices
2020
We propose a methodology to verify applications developed following programming patterns inspired by natural language that interact with physical environments and run on resource-constrained interconnected devices. Natural language patterns allow for the reduction of intermediate abstraction layers to map physical domain concepts into executable code avoiding the recourse to ontologies, which would need to be shared, kept up to date, and synchronized across a set of devices. Moreover, the computational paradigm we use for effective distributed execution of symbolic code on resource-constrained devices encourages the adoption of such patterns. The methodology is supported by a rule-based sys…
Evaluation of Ensemble Machine Learning Methods in Mobile Threat Detection
2017
The rapid growing trend of mobile devices continues to soar causing massive increase in cyber security threats. Most pervasive threats include ransom-ware, banking malware, premium SMS fraud. The solitary hackers use tailored techniques to avoid detection by the traditional antivirus. The emerging need is to detect these threats by any flow-based network solution. Therefore, we propose and evaluate a network based model which uses ensemble Machine Learning (ML) methods in order to identify the mobile threats, by analyzing the network flows of the malware communication. The ensemble ML methods not only protect over-fitting of the model but also cope with the issues related to the changing be…
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
2019
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …
Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition
2019
Real-world data exhibiting high order/dimensionality and various couplings are linked to each other since they share some common characteristics. Coupled tensor decomposition has become a popular technique for group analysis in recent years, especially for simultaneous analysis of multi-block tensor data with common information. To address the multiblock tensor data, we propose a fast double-coupled nonnegative Canonical Polyadic Decomposition (FDC-NCPD) algorithm in this study, based on the linked CP tensor decomposition (LCPTD) model and fast Hierarchical Alternating Least Squares (Fast-HALS) algorithm. The proposed FDCNCPD algorithm enables simultaneous extraction of common components, i…
A Parallel Approach to HRTF Approximation and Interpolation Based on a Parametric Filter Model
2017
[EN] Spatial audio-rendering techniques using head-related transfer functions (HRTFs) are currently used in many different contexts such as immersive teleconferencing systems, gaming, or 3-D audio reproduction. Since all these applications usually involve real-time constraints, efficient processing structures for HRTF modeling and interpolation are necessary for providing real-time binaural audio solutions. This letter presents a parametric parallel model that allows us to perform HRTF filtering and interpolation efficiently from an input HRTF dataset. The resulting model, which is an adaptation from a recently proposed modeling technique, not only reduces the size of HRTF datasets signific…
Managing sensor data streams in a smart home application
2020
A challenge in developing an ambient activity recognition system for use in elder care is finding a balance between the sophistication of the system and a cost structure that fits within the budgets of public and private sector healthcare organisations. Much activity recognition research in the context of elder care is based on dense networks of sensors and advanced methods, such as supervised machine learning algorithms. This paper presents the data processing aspects of an activity recognition system based on a simpler, knowledge-based unsupervised approach, designed for a sparse network of sensors. By structuring sensor data management as a streaming system, we provide a simple programmi…
A Generic Approach to Scheduling and Checkpointing Workflows
2018
This work deals with scheduling and checkpointing strategies to execute scientific workflows on failure-prone large-scale platforms. To the best of our knowledge, this work is the first to target fail-stop errors for arbitrary workflows. Most previous work addresses soft errors, which corrupt the task being executed by a processor but do not cause the entire memory of that processor to be lost, contrarily to fail-stop errors. We revisit classical mapping heuristics such as HEFT and MinMin and complement them with several checkpointing strategies. The objective is to derive an efficient trade-off between checkpointing every task (CkptAll), which is an overkill when failures are rare events, …
Towards a Non-Intrusive Context-Aware Speech Quality Model
2020
Understanding how humans judge perceived speech quality while interacting through Voice over Internet Protocol (VoIP) applications in real-time is essential to build a robust and accurate speech quality prediction model. Speech quality is degraded in the presence of background noise reducing the Quality of Experience (QoE). Speech Enhancement (SE) algorithms can improve speech quality in noisy environments. The publicly available NOIZEUS speech corpus contains speech in environmental background noise babble, car, street, and train at two Signal-to-noise ratio (SNRs) 5dB and 10dB. Objective Speech Quality Metrics (OSQM) are used to monitor and measure speech quality for VoIP applications. Th…