Clustered Crowd GPS for Privacy Valuing Active Localization

Yucel, Fatih; Bulut, Eyuphan

doi:10.1109/access.2018.2830300

Cited by 23 publications

(6 citation statements)

References 29 publications

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“…Such vulnerabilities have been prominently identified in smart watches 6 as well as in military cyberattacks a ecting thousands of civilian ships [31]. Despite these limitations, there is active research on building secure and privacy-preserving localization solutions based on GPS by introducing additional protocol and security mechanisms, for instance, GPS-based active crowd localization based on digital signatures and bulletin boards applied for tracking lost items [34,131].…”

Section: A Review On Proof Of Locationmentioning

confidence: 99%

“…Proofs of location are performed between witnesses and a prover, whose Bluetooth interactions verify the identities of the involved devices as well as whether the location claims of each device are reachable within the radio coverage supported by the communication technology of the devices. Spatio-temporal mobility patterns of users may influence the verification process and additional measures of verification may be required, for instance, analysis of betweeness in pseudonym correlation graphs [132] or social tracking distance metrics [131]. Periodically changing the device identifiers according to a Poisson distribution prevents the reveal of real identities by observing location proof records [132].…”

Section: A Review On Proof Of Locationmentioning

confidence: 99%

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Proof of witness presence: Blockchain consensus for augmented democracy in smart cities

Pournaras

2020

Journal of Parallel and Distributed Computing

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Smart Cities evolve into complex and pervasive urban environments with a citizens' mandate to meet sustainable development goals. Repositioning democratic values of citizens' choices in these complex ecosystems has turned out to be imperative in an era of social media filter bubbles, fake news and opportunities for manipulating electoral results with such means. This paper introduces a new paradigm of augmented democracy that promises actively engaging citizens in a more informed decision-making augmented into public urban space. The proposed concept is inspired by a digital revive of the Ancient Agora of Athens, an arena of public discourse, a Polis where citizens assemble to actively deliberate and collectively decide about public matters. The core contribution of the proposed paradigm is the concept of proving witness presence: making decision-making subject of providing secure evidence and testifying for choices made in the physical space. This paper shows how the challenge of proving witness presence can be tackled with blockchain consensus to empower citizens' trust and overcome security vulnerabilities of GPS localization. Moreover, a novel platform for collective decision-making and crowd-sensing in urban space is introduced: Smart Agora. It is shown how real-time collective measurements over citizens' choices can be made in a fully decentralized and privacy-preserving way. Witness presence is tested by deploying a decentralized system for crowd-sensing the sustainable use of transport means. Furthermore, witness presence of cycling risk is validated using o cial accident data from public authorities, which are compared against wisdom of the crowd. The paramount role of dynamic consensus, self-governance and ethically aligned artificial intelligence in the augmented democracy paradigm is outlined.

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Section: A Review On Proof Of Locationmentioning

confidence: 99%

Section: A Review On Proof Of Locationmentioning

confidence: 99%

Proof of witness presence: Blockchain consensus for augmented democracy in smart cities

Pournaras

2020

Journal of Parallel and Distributed Computing

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“…Fatih et al [13] used crowd GPS to find the localization of lost object. In order to measure the profit, new parameters were used.…”

Section: B Global Scheduler (Gs)mentioning

confidence: 99%

Heuristic Search Based Localization in Mobile Computational Grid

Sahu

Singh

Manju³

et al. 2019

IEEE Access

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In recent years, the number of cell phones in society has increased drastically and they are getting popular due to their computational ability and adaptability. Resource provisioning is important, but still remains NP-hard problem in mobile computational grid (MCG). Once the jobs are assigned to the MCG, the main challenge is how to identify the correct resource according to the job's requirement and use them to execute the sub-jobs. The heuristic methods such as Min-Min, Max-Min, and HEFT can be used to select appropriate resources from the MCG that is assigned for job execution. Since the computational nodes are static and mobile in nature, the performance of such heuristics is not as expected. Such heuristics suffers from low throughput and low speedup. The process of localization is used in a wireless sensor network with good results. The proposed model uses heuristics and localization process for optimizing the quality of service parameter localization, normalized speedup, and throughput in MCG, with the concept of grid nodes available in MCG. The observation shows significant improvement in the quality of service parameter localization, normalized speedup, and throughput in MCG. The proposed model HGLA and MIN-MIN, MAX-MIN, and HEFT are compared with respect to localization, speedup, and throughput. The results reveal that the proposed model shows better performance over MIN-MIN, MAX-MIN, and HEFT.

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“…For example, in the healthcare of a home or hospital for detecting the location and activities of the elderly people or patients; 2,3 in smart spaces such as airports, shopping centers, and tourist attractions for location-based services; 4,5 in case of fire for location detection of firefighters and survivors to realize emergency rescue; 6 for indoor environmental monitoring and building energy saving control; 7 as well as for detecting and tracking the location of terrorists in safety protection. 8 Various wireless localization technologies such as GPS, 9 infrared, 10 ultrasonic, 11 and radio frequency (RF) identification 12 have been developed to support the needs of the aforementioned scenarios. The application of these technologies, however, requires the target to be equipped with wireless devices, such as smart phones, ultrasonic receiver nodes, and tags.…”

Section: Introductionmentioning

confidence: 99%

Multi-targets device-free localization based on sparse coding in smart city

Zhao

Qin

Guo

et al. 2019

International Journal of Distributed Sensor Networks

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With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city.

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Clustered Crowd GPS for Privacy Valuing Active Localization

Cited by 23 publications

References 29 publications

Proof of witness presence: Blockchain consensus for augmented democracy in smart cities

Proof of witness presence: Blockchain consensus for augmented democracy in smart cities

Heuristic Search Based Localization in Mobile Computational Grid

Multi-targets device-free localization based on sparse coding in smart city

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