Secure and Efficient Context-Aware Localization of Drones in Urban Scenarios

Sharma, Vishal; Jayakody, Dushantha Nalin K.; You, Ilsun; Kumar, Ravinder; Li, Jun

doi:10.1109/mcom.2018.1700434

Cited by 33 publications

(22 citation statements)

References 13 publications

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“…The authors of [39] proposed the SHARK protocol for secured autonomous and heterogeneous swarm drones for ensuring the security of swarm drone applications over smart cities. Also, the authors of [126] explored a novel solution for securing information sharing (and provided a 3D visualization) between swarm drones in a smart city. The findings of the proposed technique showed the security prowess of drone context exchange validation and provided a secured path for drone to drone collaboration.…”

Section: Privacy and Security Issues In Drones And Iot Collaboramentioning

confidence: 99%

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

et al. 2019

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Smart cities contain intelligent things which can intelligently automatically and collaboratively enhance life quality, save people's lives, and act a sustainable resource ecosystem. To achieve these advanced collaborative technologies such as drones, robotics, artificial intelligence, and Internet of Things (IoT) are required to increase the smartness of smart cities by improving the connectivity, energy efficiency, and quality of services (QoS). Therefore, collaborative drones and IoT play a vital role in supporting a lot of smartcity applications such as those involved in communication, transportation, agriculture,safety and security, disaster mitigation, environmental protection, service delivery, energy saving, e-waste reduction, weather monitoring, healthcare, etc. This paper presents a survey of the potential techniques and applications of collaborative drones and IoT which have recently been proposed in order to increase the smartness of smart cities. It provides a comprehensive overview highlighting the recent and ongoing research on collaborative drone and IoT in improving the real-time application of smart cities. This survey is different from previous ones in term of breadth, scope, and focus. In particular, we focus on the new concept of collaborative drones and IoT for improving smart-city applications. This survey attempts to show how collaborative drones and IoT improve the smartness of smart cities based on data collection, privacy and security, public safety, disaster management, energy consumption and quality of life in smart cities. It mainly focuses on the measurement of the smartness of smart cities, i.e., environmental aspects, life quality, public safety, and disaster management.

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Section: Privacy and Security Issues In Drones And Iot Collaboramentioning

confidence: 99%

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

et al. 2019

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“…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. 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.…”

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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“…Similar to the vehicular networks, broadcasting among multiple unmanned aerial vehicles (UAVs) are also widely used in many applications of UAV networks, such as civilian, military, and agriculture. In [5], UAVs cooperate with each other to realise cooperative map building and cooperative task resolutions by broadcasting, which facilitates urban surveillance. In addition to surveillance, autonomous UAVs can prevent collisions in the applications of air combat by broadcasting safety messages based on UAV-to-UAV (U2U) [6].…”

Section: Introductionmentioning

confidence: 99%