27,142 research outputs found
Deterministic Secure Positioning in Wireless Sensor Networks
Properly locating sensor nodes is an important building block for a large
subset of wireless sensor networks (WSN) applications. As a result, the
performance of the WSN degrades significantly when misbehaving nodes report
false location and distance information in order to fake their actual location.
In this paper we propose a general distributed deterministic protocol for
accurate identification of faking sensors in a WSN. Our scheme does \emph{not}
rely on a subset of \emph{trusted} nodes that are not allowed to misbehave and
are known to every node in the network. Thus, any subset of nodes is allowed to
try faking its position. As in previous approaches, our protocol is based on
distance evaluation techniques developed for WSN. On the positive side, we show
that when the received signal strength (RSS) technique is used, our protocol
handles at most faking sensors. Also, when the
time of flight (ToF) technique is used, our protocol manages at most misbehaving sensors. On the negative side, we prove
that no deterministic protocol can identify faking sensors if their number is
. Thus our scheme is almost optimal with respect
to the number of faking sensors. We discuss application of our technique in the
trusted sensor model. More precisely our results can be used to minimize the
number of trusted sensors that are needed to defeat faking ones
Secure positioning in wireless networks
So far, the problem of positioning in wireless networks has been studied mainly in a nonadversarial setting. In this paper, we analyze the resistance of positioning techniques to position and distance spoofing attacks. We propose a mechanism for secure positioning of wireless devices, that we call verifiable multilateration. We then show how this mechanism can be used to secure positioning in sensor networks. We analyze our system through simulations
Deterministic Secure Positioning in Wireless Sensor Networks
Properly locating sensor nodes is an important building block for a large subset of wireless sensor networks (WSN) applications. As a result, the performance of the WSN degrades significantly when misbehaving nodes report false location and distance information in order to fake their actual location. In this paper we propose a general distributed deterministic protocol for accurate identification of faking sensors in a WSN. Our scheme does \emph{not} rely on a subset of \emph{trusted} nodes that are not allowed to misbehave and are known to every node in the network. Thus, any subset of nodes is allowed to try faking its position. As in previous approaches, our protocol is based on distance evaluation techniques developed for WSN. On the positive side, we show that when the received signal strength (RSS) technique is used, our protocol handles at most faking sensors. Also, when the time of flight (ToF) technique is used, our protocol manages at most misbehaving sensors. On the negative side, we prove that no deterministic protocol can identify faking sensors if their number is . Thus our scheme is almost optimal with respect to the number of faking sensors. We discuss application of our technique in the trusted sensor model. More precisely our results can be used to minimize the number of trusted sensors that are needed to defeat faking ones
Effect of Location Accuracy and Shadowing on the Probability of Non-Interfering Concurrent Transmissions in Cognitive Ad Hoc Networks
Cognitive radio ad hoc systems can coexist with a primary network in a scanning-free region, which can be dimensioned by location awareness. This coexistence of networks improves system throughput and increases the efficiency of radio spectrum utilization. However, the location accuracy of real positioning systems affects the right dimensioning of the concurrent transmission region. Moreover, an ad hoc connection may not be able to coexist with the primary link due to the shadowing effect. In this paper we investigate the impact of location accuracy on the concurrent transmission probability and analyze the reliability of concurrent transmissions when shadowing is taken into account. A new analytical model is proposed, which allows to estimate the resulting secure region when the localization uncertainty range is known. Computer simulations show the dependency between the location accuracy and the performance of the proposed topology, as well as the reliability of the resulting secure region
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
Strategic Deployment of Swarm of UAVs for Secure IoT Networks
Security provisioning for low-complex and constrained devices in the Internet
of Things (IoT) is exacerbating the concerns for the design of future wireless
networks. To unveil the full potential of the sixth generation (6G), it is
becoming even more evident that security measurements should be considered at
all layers of the network. This work aims to contribute in this direction by
investigating the employment of unmanned aerial vehicles (UAVs) for providing
secure transmissions in ground IoT networks. Toward this purpose, it is
considered that a set of UAVs acting as aerial base stations provide secure
connectivity between the network and multiple ground nodes. Then, the
association of IoT nodes, the 3D positioning of the UAVs and the power
allocation of the UAVs are obtained by leveraging game theoretic and convex
optimization-based tools with the goal of improving the secrecy of the system.
It is shown that the proposed framework obtains better and more efficient
secrecy performance over an IoT network than state-of-the-art greedy algorithms
for positioning and association
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