28,919 research outputs found
Energy-Efficient Communication over the Unsynchronized Gaussian Diamond Network
Communication networks are often designed and analyzed assuming tight
synchronization among nodes. However, in applications that require
communication in the energy-efficient regime of low signal-to-noise ratios,
establishing tight synchronization among nodes in the network can result in a
significant energy overhead. Motivated by a recent result showing that
near-optimal energy efficiency can be achieved over the AWGN channel without
requiring tight synchronization, we consider the question of whether the
potential gains of cooperative communication can be achieved in the absence of
synchronization. We focus on the symmetric Gaussian diamond network and
establish that cooperative-communication gains are indeed feasible even with
unsynchronized nodes. More precisely, we show that the capacity per unit energy
of the unsynchronized symmetric Gaussian diamond network is within a constant
factor of the capacity per unit energy of the corresponding synchronized
network. To this end, we propose a distributed relaying scheme that does not
require tight synchronization but nevertheless achieves most of the energy
gains of coherent combining.Comment: 20 pages, 4 figures, submitted to IEEE Transactions on Information
Theory, presented at IEEE ISIT 201
Droplet: A New Denial-of-Service Attack on Low Power Wireless Sensor Networks
In this paper we present a new kind of Denial-of-Service attack against the PHY layer of low power wireless sensor networks. Overcoming the very limited range of jamming-based attacks, this attack can penetrate deep into a target network with high power efficiency. We term this the Droplet attack, as it attains enormous disruption by dropping small, payload-less frame headers to its victim's radio receiver, depriving the latter of bandwidth and sleep time. We demonstrate the Droplet attack's high damage rate to full duty-cycle receivers, and further show that a high frequency version of Droplet can even force nodes running on very low duty-cycle MAC protocols to drop most of their packets
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Dynamic Voltage Scaling Techniques for Energy Efficient Synchronized Sensor Network Design
Building energy-efficient systems is one of the principal challenges in wireless sensor networks. Dynamic voltage scaling (DVS), a technique to reduce energy consumption by varying the CPU frequency on the fly, has been widely used in other settings to accomplish this goal. In this paper, we show that changing the CPU frequency can affect timekeeping functionality of some sensor platforms. This phenomenon can cause an unacceptable loss of time synchronization in networks that require tight synchrony over extended periods, thus preventing all existing DVS techniques from being applied. We present a method for reducing energy consumption in sensor networks via DVS, while minimizing the impact of CPU frequency switching on time synchronization.
The system is implemented and evaluated on a network of 11 Imote2 sensors mounted on a truss bridge and running a high-fidelity continuous structural health monitoring
application. Experimental measurements confirm that the algorithm significantly reduces network energy consumption
over the same network that does not use DVS, while requiring significantly fewer re-synchronization actions than a classic DVS algorithm.unpublishedis peer reviewe
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