Inception

Jin, Haiming; Lu, Shan; Xiao, Houping; Nahrstedt, Klara

doi:10.1145/2942358.2942375

Cited by 151 publications

(10 citation statements)

References 38 publications

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“…First of all, because IoT elements are close to the edge nodes, the fog can act as the first front of access control and encryption as well as provide isolation and contextual integrity. Besides, it can also protect privacy-sensitive data [36,37], by not allowing it to leave the edge. In addition, sensors often generate a huge volume of raw data in burst mode, i.e., there are peaks of data transmission that fluctuate over time.…”

Section: Data Collection and Protocols At The Edgementioning

confidence: 99%

The Internet of Things, Fog and Cloud continuum: Integration and challenges

et al. 2018

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The Internet of Things needs for computing power and storage are expected to remain on the rise in the next decade. Consequently, the amount of data generated by devices at the edge of the network will also grow. While cloud computing has been an established and effective way of acquiring computation and storage as a service to many applications, it may not be suitable to handle the myriad of data from IoT devices and fulfill largely heterogeneous application requirements. Fog computing has been developed to lie between IoT and the cloud, providing a hierarchy of computing power that can collect, aggregate, and process data from/to IoT devices. Combining fog and cloud may reduce data transfers and communication bottlenecks to the cloud and also contribute to reduced latencies, as fog computing resources exist closer to the edge. This paper examines this IoT-Fog-Cloud ecosystem and provides a literature review from different facets of it: how it can be organized, how management is being addressed, and how applications can benefit from it. Lastly, we present challenging issues yet to be addressed in IoT-Fog-Cloud infrastructures. low latency, and mobile applications. The centralized cloud data centers are often physically and/or logically distant from the cloud client, implying communication and data transfers to traverse multiple hops, which introduces delays and consumes network bandwidth of edge and core networks [2].The widespread adoption of cloud computing, combined with the ever increasing ability of edge devices to run heterogeneous applications that generate and consume all kinds of data from a variety of sources, requires novel distributed computing infrastructures that can cope with such heterogeneous application requirements. Computing infrastructures that enact applications at edge devices have started to appear in recent years [3,4], improving aspects such as response time and reducing bandwidth use. Combining the ability of running smaller, localized applications at the edge with the high-capacity from the cloud, fog computing has emerged as an paradigm that can support heterogeneous requirements of small and large applications through multiple layers of a computational infrastructure that combines resources from the edge of the network as well as from the cloud [5].In this paper, we aim at identifying and reviewing the main aspects and challenges that make the combination of fog computing and cloud computing suitable for all kinds of applications leveraged by the Internet of Things. We discuss aspects from the infrastructure (processing, networking, protocols, and infrastructure for 5G support) to applications (smart cities, urban computing, and industry 4.0), passing through the management complexity of the distributed IoT-fog-cloud system (services, resource allocation and optimization, energy consumption, data management and locality, devices federation and trust, and business and service models).In the next section we introduce concepts and definitions for Internet of Things (IoT), cloud computi...

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Section: Data Collection and Protocols At The Edgementioning

confidence: 99%

The Internet of Things, Fog and Cloud continuum: Integration and challenges

et al. 2018

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“…The second mechanism assumes that the CP knows the data privacy sensitivity level of each mu, and considers the variable compensation of the mu. Jin et al [21] an incentive mechanism based on privacy protection is designed. When designing the incentive mechanism, the characteristics of data fusion and data interference in the system are taken into account.…”

Section: ) Incentive Mechanismmentioning

confidence: 99%

Method for Spatial Crowdsourcing Task Assignment Based on Integrating of Genetic Algorithm and Ant Colony Optimization

Wang

Zhao

2020

IEEE Access

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With the rapid development of mobile networks and the proliferation of mobile devices, Spatial Crowdsourcing (SC) has attracted the interest of industry and research groups. In addition to considering the specific spatial constraints in the existing research spatial crowdsourcing, each task has an effective duration, operational complexity, number of workers required, and incentive budget constraints. In this scenario, we studied the MQC-TA (Maximum Quality and Minimum Cost Task Assignment) problem. Firstly, the worker incentive model is established. The MQC-GAC algorithm is designed according to the MQC-TA problem to maximize the task completion quality and minimize the incentive budget. The algorithm combined the fast convergence of Genetic Algorithm and the positive feedback mechanism of Ant Colony Optimization Algorithm. Finally, the effectiveness and efficiency of the proposed method are verified by a comprehensive experiment on the data set. INDEX TERMS Spatial crowdsourcing, task assignment, MQC-TA problem, MQC-GAC algorithm.

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“…The following are the most prominent extensions in this category, not to be discussed in depth here, and interested readers can refer to the literatures. These are k-anonymity [54], Tessellation [55], l-diversity [56], micro-aggregation [57], data aggregation [58], t-closeness [59], and historical k-anonymity [60].…”

Section: Anonymization-based Mechanismsmentioning

confidence: 99%

A Survey of System Architectures, Privacy Preservation, and Main Research Challenges on Location-Based Services

2019

KSII TIIS

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Location-based services (LBSs) have become popular in recent years due to the ever-increasing usage of smart mobile devices and mobile applications through networks. Although LBS application provides great benefits to mobile users, it also raises a sever privacy concern of users due to the untrusted service providers. In the lack of privacy enhancing mechanisms, most applications of the LBS may discourage the user's acceptance of location services in general, and endanger the user's privacy in particular. Therefore, it is a great interest to discuss on the recent privacy-preserving mechanisms in LBSs. Many existing location-privacy protection-mechanisms (LPPMs) make great efforts to increase the attacker's uncertainty on the user's actual whereabouts by generating a multiple of fake-locations together with user's actual positions. In this survey, we present a study and analysis of existing LPPMs and the state-of-art privacy measures in service quality aware LBS applications. We first study the general architecture of privacy qualification system for LBSs by surveying the existing framework and outlining its main feature components. We then give an overview of the basic privacy requirements to be considered in the design and evaluation of LPPMs. Furthermore, we discuss the classification and countermeasure solutions of existing LPPMs for mitigating the current LBS privacy protection challenges. These classifications include anonymization, obfuscation, and an encryption-based technique, as well as the combination of them is called a hybrid mechanism. Finally, we discuss several open issues and research challenges based on the latest progresses for on-going LBS and location privacy research. IntroductionRecent advances in mobile computing makes location based service (LBS) increasingly popular, which attract millions of individuals. LBS refers to a location information and convenient services provided to mobile users based on the geographic position and other information of users' mobile devices to obtain their real location data [1]- [2]. In this context, user oriented LBS applications are developed to obtain location related information relevant to their current position and surroundings. Common examples include direct location sharing with friends (e.g., uploading real-time location data and tagging services) and sharing with business sectors (e.g., search for near-by services, location check-in and search near-by friends), which help the user to share and determine their current position with friends in the communication networks [3]- [4]. Other typical examples of LBS applications include map applications (e.g., Google Maps), point of interest retrieval (e.g., Around-Me), coupons or discount offers (e.g., Group-on), GPS navigation (e.g., Tom-tom) and location-aware social networks (e.g., Face-book, Weibo, Foursquare, Wechat) [5]. A more large-scale LBS application could ask users around a region or even country to disclose their exact locations for security and safety purpose (e.g., in military, medical care, e...

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Inception

Cited by 151 publications

References 38 publications

The Internet of Things, Fog and Cloud continuum: Integration and challenges

The Internet of Things, Fog and Cloud continuum: Integration and challenges

Method for Spatial Crowdsourcing Task Assignment Based on Integrating of Genetic Algorithm and Ant Colony Optimization

A Survey of System Architectures, Privacy Preservation, and Main Research Challenges on Location-Based Services

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