184 research outputs found
A Hierarchical and Location-aware Consensus Protocol for IoT-Blockchain Applications
Blockchain-based IoT systems can manage IoT devices and achieve a high level
of data integrity, security, and provenance. However, incorporating existing
consensus protocols in many IoT systems limits scalability and leads to high
computational cost and consensus latency. In addition, location-centric
characteristics of many IoT applications paired with limited storage and
computing power of IoT devices bring about more limitations, primarily due to
the location-agnostic designs in blockchains. We propose a hierarchical and
location-aware consensus protocol (LH-Raft) for IoT-blockchain applications
inspired by the original Raft protocol to address these limitations. The
proposed LH-Raft protocol forms local consensus candidate groups based on
nodes' reputation and distance to elect the leaders in each sub-layer
blockchain. It utilizes a threshold signature scheme to reach global consensus
and the local and global log replication to maintain consistency for blockchain
transactions. To evaluate the performance of LH-Raft, we first conduct an
extensive numerical analysis based on the proposed reputation mechanism and the
candidate group formation model. We then compare the performance of LH-Raft
against the classical Raft protocol from both theoretical and experimental
perspectives. We evaluate the proposed threshold signature scheme using
Hyperledger Ursa cryptography library to measure various consensus nodes'
signing and verification time. Experimental results show that the proposed
LH-Raft protocol is scalable for large IoT applications and significantly
reduces the communication cost, consensus latency, and agreement time for
consensus processing.Comment: Published in IEEE Transactions on Network and Service Management (
Volume: 19, Issue: 3, September 2022). arXiv admin note: text overlap with
arXiv:2305.1696
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction
Federated Learning (FL) is a privacy-preserving machine learning (ML)
technology that enables collaborative training and learning of a global ML
model based on aggregating distributed local model updates. However, security
and privacy guarantees could be compromised due to malicious participants and
the centralized FL server. This article proposed a bi-level blockchained
architecture for secure federated learning-based traffic prediction. The bottom
and top layer blockchain store the local model and global aggregated parameters
accordingly, and the distributed homomorphic-encrypted federated averaging
(DHFA) scheme addresses the secure computation problems. We propose the partial
private key distribution protocol and a partially homomorphic
encryption/decryption scheme to achieve the distributed privacy-preserving
federated averaging model. We conduct extensive experiments to measure the
running time of DHFA operations, quantify the read and write performance of the
blockchain network, and elucidate the impacts of varying regional group sizes
and model complexities on the resulting prediction accuracy for the online
traffic flow prediction task. The results indicate that the proposed system can
facilitate secure and decentralized federated learning for real-world traffic
prediction tasks.Comment: Paper accepted for publication in IEEE Transactions on Services
Computing (TSC
BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction
Accurate real-time traffic flow prediction can be leveraged to relieve
traffic congestion and associated negative impacts. The existing centralized
deep learning methodologies have demonstrated high prediction accuracy, but
suffer from privacy concerns due to the sensitive nature of transportation
data. Moreover, the emerging literature on traffic prediction by distributed
learning approaches, including federated learning, primarily focuses on offline
learning. This paper proposes BFRT, a blockchained federated learning
architecture for online traffic flow prediction using real-time data and edge
computing. The proposed approach provides privacy for the underlying data,
while enabling decentralized model training in real-time at the Internet of
Vehicles edge. We federate GRU and LSTM models and conduct extensive
experiments with dynamically collected arterial traffic data shards. We
prototype the proposed permissioned blockchain network on Hyperledger Fabric
and perform extensive tests using virtual machines to simulate the edge nodes.
Experimental results outperform the centralized models, highlighting the
feasibility of our approach for facilitating privacy-preserving and
decentralized real-time traffic flow prediction.Comment: Published in 2022 22nd IEEE International Symposium on Cluster, Cloud
and Internet Computing (CCGrid
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