68 research outputs found
Towards Energy Neutrality in Energy Harvesting Wireless Sensor Networks: A Case for Distributed Compressive Sensing?
This paper advocates the use of the emerging distributed compressive sensing
(DCS) paradigm in order to deploy energy harvesting (EH) wireless sensor
networks (WSN) with practical network lifetime and data gathering rates that
are substantially higher than the state-of-the-art. In particular, we argue
that there are two fundamental mechanisms in an EH WSN: i) the energy diversity
associated with the EH process that entails that the harvested energy can vary
from sensor node to sensor node, and ii) the sensing diversity associated with
the DCS process that entails that the energy consumption can also vary across
the sensor nodes without compromising data recovery. We also argue that such
mechanisms offer the means to match closely the energy demand to the energy
supply in order to unlock the possibility for energy-neutral WSNs that leverage
EH capability. A number of analytic and simulation results are presented in
order to illustrate the potential of the approach.Comment: 6 pages. This work will be presented at the 2013 IEEE Global
Communications Conference (GLOBECOM), Atlanta, US, December 201
Joint Sensing Matrix and Sparsifying Dictionary Optimization for Tensor Compressive Sensing.
Tensor compressive sensing (TCS) is a multidimensional framework of compressive sensing (CS), and it is
advantageous in terms of reducing the amount of storage, easing
hardware implementations, and preserving multidimensional
structures of signals in comparison to a conventional CS system.
In a TCS system, instead of using a random sensing matrix and
a predefined dictionary, the average-case performance can be
further improved by employing an optimized multidimensional
sensing matrix and a learned multilinear sparsifying dictionary.
In this paper, we propose an approach that jointly optimizes
the sensing matrix and dictionary for a TCS system. For the
sensing matrix design in TCS, an extended separable approach
with a closed form solution and a novel iterative nonseparable
method are proposed when the multilinear dictionary is fixed.
In addition, a multidimensional dictionary learning method that
takes advantages of the multidimensional structure is derived,
and the influence of sensing matrices is taken into account in the
learning process. A joint optimization is achieved via alternately
iterating the optimization of the sensing matrix and dictionary.
Numerical experiments using both synthetic data and real images
demonstrate the superiority of the proposed approache
EFFECTIVE CHANNEL CODING OF SERIALLY CONCATENATED ENCODERS AND CPM OVER AWGN AND RICIAN CHANNELS
ABSTRACT A generic M-ary continuous phase modulation (CPM) schem
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How to Measure the Average and Peak Age of Information in Real Networks?
The Age of information (AoI) was proposed in the literature to quantify the freshness of information. The majority of the work done in this area has theoretically evaluated AoI and its Peak (PAoI). In this paper, a method for obtaining the value of AoI and PAoI from experiments is proposed. We conducted an experiment emulating an M/M/1 queue and used the proposed method to evaluate AoI and PAoI. The values were compared to the expressions presented previously in the literature. Our results show that the proposed method is accurate for the M/M/1 queue. A statistical test was conducted to confirm the reliability of this conclusion
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Modelling IoT devices communication employing representative operation modes to reveal traffic generation characteristics
Several traffic models for the Internet of Things (IoT) have been proposed in the literature. However, they can be considered as heuristic models since they only reflect the stochastic characteristic of the generated traffic. In this paper, we propose a model to represent the communication of IoT devices. The model was used to obtain the traffic generated by the devices. Therefore, the proposed model is able to capture a wider understanding of device behaviour than existing, state-of-the-art traffic models. The proposed model illustrates the behaviour of Machine-to-Machine uplink communication in a network with multiple-access limited information capacity shared channels. In this paper, we analysed the number of transmitted packets using the traffic model extracted from our proposed communication model and compared it with the state-of-the-art traffic models using simulations. The simulation results show that the proposed model has significantly higher accuracy in estimating the number of transmitted packets compared with the current models in the literature
Deriving Machine to Machine (M2M) Traffic Model from Communication Model
© 2018 IEEE. The typical traffic models proposed in literature can be considered as heuristic models since they only reflect the stochastic characteristic of the generated traffic. In this paper, we propose a model for M2M communications that generates the traffic. Therefore, the proposed model is able to capture a wider picture than the state-of-the-art traffic models. The proposed model illustrates the behaviour of M2M uplink communication in a network with multiple-access limited information capacity shared channels. In this paper, we analyzed the number of transmitted packets using the traffic model extracted from our proposed communication model and compared it with the state-of- the-art traffic models using simulations. The simulation results show that the proposed model has a significantly higher accuracy in estimating the number of transmitted packets compared with the liteature model
6G Opportunities Arising from Internet of Things Use Cases: A Review Paper
The race for the 6th generation of wireless networks (6G) has begun. Researchers around the world have started to explore the best solutions for the challenges that the previous generations have experienced. To provide the readers with a clear map of the current developments, several review papers shared their vision and critically evaluated the state of the art. However, most of the work is based on general observations and the big picture vision, and lack the practical implementation challenges of the Internet of Things (IoT) use cases. This paper takes a novel approach in the review, as we present a sample of IoT use cases that are representative of a wide variety of its implementations. The chosen use cases are from the most research-active sectors that can benefit from 6G and its enabling technologies. These sectors are healthcare, smart grid, transport, and Industry 4.0. Additionally, we identified some of the practical challenges and the lessons learned in the implementation of these use cases. The review highlights the cases’ main requirements and how they overlap with the key drivers for the future generation of wireless networks
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