Optimal ThrowBoxes assignment for big data multicast in VDTNs

Liu, Peng; Ding, Yue; Fu, Tingting

doi:10.1007/s11276-019-01974-z

Cited by 22 publications

(13 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the non-negativity of each individual variable is guaranteed with constraint (14). Here, we consider the case that the above two constraints (12) and (13) are linearly independent, i.e., E Z P…”

Section: Problem Formulation and Algorithm Designmentioning

confidence: 99%

“…Particularly, AoI measures the time elapsed since the latest received update packet was generated, while PAoI provides information about the maximum value of AoI for each update and captures the extent to which the update information is stale. Unlike many conventional metrics, e.g., delay or throughput [10]- [12], AoI and PAoI are affected not only by the transmission delay but also by the update generation rate, and hence they are more essential and comprehensive for information freshness evaluation [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Information Freshness in Computing-Enabled IoT Networks

Yang

Wang

et al. 2020

IEEE Internet Things J.

View full text Add to dashboard Cite

Internet of Things (IoT) has emerged as one of the key features of the next generation wireless networks, where timely delivery of status update packets is essential for many realtime IoT applications. To provide users with context-aware services and lighten the transmission burden, the raw data usually needs to be preprocessed before being transmitted to the destination. However, the effect of computing on the overall information freshness is not well understood. In this work, we first develop an analytical framework to investigate the information freshness, in terms of peak age of information (PAoI), of a computing enabled IoT system with multiple sensors. Specifically, we model the procedure of computing and transmission as a tandem queue, and derive the analytical expressions of the average PAoI for different sensors. Based on the theoretical results, we formulate a minmax optimization problem to minimize the maximum average PAoI of different sensors. We further design a derivative-free algorithm to find the optimal updating frequency, with which the complexity for checking the convexity of the formulated problem or obtaining the derivatives of the object function can be largely reduced. The accuracy of our analysis and effectiveness of the proposed algorithm are verified with extensive simulation results.Index Terms-Internet of things, information freshness, peak age of information, data preprocessing, derivative-free optimization.

show abstract

Section: Problem Formulation and Algorithm Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Information Freshness in Computing-Enabled IoT Networks

Yang

Wang

et al. 2020

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Figure 4 illustrates the system architecture of the privacy-preserving matching scheme, which is composed of five entities: the requestor, the intermediate platform, the space provider, the server, and the CA. These entities use modern networking technologies to communicate with each other, such as wireless area network, wide area network, cellular network, and vehicular ad-hoc network [30]. Each entity is explained in detail below.…”

Section: Privacy-preserving Matching Schemementioning

confidence: 99%

Privacy-Preserving Vehicle Assignment in the Parking Space Sharing System

Liu

2020

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

Nowadays, the availability of parking spaces is far behind the quick rising number of cars. Rather than building more lots, a better way is to share private-owned parking spaces. However, this faces the challenge that users are not willing to expose their privacy to the public. To solve this problem, we propose a new architecture for parking space sharing, integrating homomorphic cryptography into the design of a secure protocol for parking space searching and booking. The proposed privacy-preserving matching scheme (PPMS) is constructed in an untrusted third-party service system including two independent entities, namely, a server and an intermediary platform. Via the participant comparison protocol (PCP), a driver can choose from the matching result and be navigated to the parking space near his destination, without knowing any information of the provider and vice versa. In the meanwhile, in order to further improve the efficiency of matching, we also propose a block algorithm based on the longitude and latitude (BABLL), which utilizes a novel partitioning scheme. The feasibility of the architecture is validated through the detailed theoretical analysis and extensive performance evaluations, including the assessment of the resilience to attacks.

show abstract

“…Placing popular contents as close as possible to users at the edge nodes can greatly reduce duplicate data transmission and service latency. As the cache capacity of edge nodes is limited compared to a huge number of contents [3], the effective utilization of the available cache capacity has a profound effect on the service performance.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Federated Deep Learning for Cooperative Hierarchical Caching in Fog Computing

Min

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

Over the past few years, Fog Radio Access Networks (F-RANs) have become a promising paradigm to support the tremendously increasing demands of multimedia services, by pushing computation and storage functionalities towards the edge of networks, closer to users. In F-RANs, distributed edge caching among Fog Access Points (F-APs) can effectively reduce network traffic and service latency as it places popular contents at local caches of F-APs rather than the remote cloud. Due to the limited caching resources of F-APs and spatio-temporally fluctuant content demands from users, many cooperative caching schemes were designed to decide which contents are popular and how to cache them. However, these approaches often collect and analyse the data from Internet-of-Things (IoT) devices at a central server to predict the content popularity for caching, which raises serious privacy issues. To tackle this challenge, we propose a Federated Learning based Cooperative Hierarchical Caching scheme (FLCH), which keeps data locally and employs IoT devices to train a shared learning model for content popularity prediction. FLCH exploits horizontal cooperation between neighbour F-APs and vertical cooperation between the BaseBand Unit (BBU) pool and F-APs to cache contents with different degrees of popularity. Moreover, FLCH integrates a differential privacy mechanism to achieve a strict privacy guarantee. Experimental results demonstrate that FLCH outperforms five important baseline schemes in terms of the cache hit ratio, while preserving data privacy. Moreover, the results show the effectiveness of the proposed cooperative hierarchical caching mechanism for FLCH.

show abstract

Optimal ThrowBoxes assignment for big data multicast in VDTNs

Cited by 22 publications

References 24 publications

Optimizing Information Freshness in Computing-Enabled IoT Networks

Optimizing Information Freshness in Computing-Enabled IoT Networks

Privacy-Preserving Vehicle Assignment in the Parking Space Sharing System

Privacy-Preserving Federated Deep Learning for Cooperative Hierarchical Caching in Fog Computing

Contact Info

Product

Resources

About