Taxonomy of Fundamental Concepts of Localization in Cyber-Physical and Sensor Networks

Koubâa, Anis; Jamâa, Maissa Ben

doi:10.1007/s11277-013-1025-z

Cited by 12 publications

(8 citation statements)

References 76 publications

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“…The requesting drone first requests for the coordinates of the anchor drones present at a 1-hop distance. If the requesting drone receives the location from at least three anchor drones in the 1-hop, the distance between the requesting drone and the anchor drone is calculated using the Received Signal Strength Indicator (RSSI) method [100]. However, if the requesting drone does not receive three minimum responses from the neighboring anchor drones, the distance between the requesting drone and the anchor drones is calculated using the DV-Hop (Distance Vector Hop) method [101].…”

Section: Secure Localizationmentioning

confidence: 99%

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Hassija,

Chamola,

Agrawal

et al. 2021

Preprint

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Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

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Section: Secure Localizationmentioning

confidence: 99%

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Hassija,

Chamola,

Agrawal

et al. 2021

Preprint

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“…These schemes are displaying an enormous potential in interacting with the physical world and creating its management more efficient. Integrating the computation and communication competencies into the modules of the physical surrounding [8] enables this. CPSs represent nowadays the new peer group of networks and embedded structures.…”

Section: Relationship To Cyber-physical Systemsmentioning

confidence: 99%

“…The placement of CPS raises numerous difficulties, where the most significant topic that has activated massive quantity of study is localization [8]. Basically, localization goals at estimating the location of CPS modules.…”

Section: Relationship To Cyber-physical Systemsmentioning

confidence: 99%

A WLS Estimator for Target Localization in a Cooperative Wireless Sensor Network

Tomic

Beko

Dinis

et al. 2016

Technological Innovation for Cyber-Physical Systems

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This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). We employ non-traditional methodology which merges distance and angle measurements, respectively withdrawn from the received signal strength (RSS) and angle-of-arrival (AoA) information. Based on RSS measurement model and effortless geometry, a novel non-convex estimator according to the weighted least squares (WLS) criterion is obtained, which closely approximates the maximum likelihood (ML) estimator for small noise. It is shown that the devised estimator is appropriate for distributed implementation. Following the squared range (SR) approach, we propose a suboptimal SR-WLS estimator according to the generalized trust region sub-problem (GTRS) framework, to estimate the locations of all targets in the WSN. According to our simulations, the new estimator has excellent performance in a great variety of considered settings, in which the effectiveness of fusing two radio measurements is confirmed.

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“…For all these applications, localization is essential considering that these applications are typically location-aware. In fact, localization is attracting interest due to the emerging of context-aware applications in the IoT [6], [7]. However, to take benefit from localization services, applications must trust the localization data and make sure that the positions are not manipulated by malicious nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, several research works are addressing the problem of secure localization in the Internet of Things, such as [8], [9], [10], [11], [12]. In fact, many localization algorithms such as those based on triangulation [7], or RSSI-based algorithms [13], [14] are very dependent on the correctness of the location of nodes participating in the localization algorithm. If any node provides the wrong location information, the whole localization system will be compromised.…”

Section: Introductionmentioning

confidence: 99%

BlockLoc: Secure Localization in the Internet of Things using Blockchain

Cheikhrouhou

Koubâa

2019

2019 15th International Wireless Communications &Amp; Mobile Computing Conference (IWCMC)

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Several IoT applications are tightly dependent on the locations of the devices. However, localization algorithms can be easily compromised by injecting false locations. In this paper, we propose a Blockchain-based secure localization algorithm for the Internet of Things (IoT). The algorithm uses a public ledger (Blockchain) that contains nodes position and the list of their neighbor nodes. This ledger is shared among the IoT devices. Once an IoT device is localized its new position and the list of neighbor nodes are added to the Blockchain. This shared localization data will be used later by other IoT devices for their localization process. To avoid the attack where a malicious node sends a fake position, the correctness of the claimed position are verified before adding it to the Blockchain. Moreover, data exchanged between nodes (IoT devices) are signed to guarantee their authenticity and integrity. The integration of these security mechanisms into the localization process permits to exclude false data and therefore reduces the localization error. The simulation results show that adding the proposed security mechanism improves the localization accuracy of the algorithm when running in the presence of malicious nodes.

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Taxonomy of Fundamental Concepts of Localization in Cyber-Physical and Sensor Networks

Cited by 12 publications

References 76 publications

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

A WLS Estimator for Target Localization in a Cooperative Wireless Sensor Network

BlockLoc: Secure Localization in the Internet of Things using Blockchain

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