Blockchain for 5G and beyond networks: A state of the art survey

Towards blockchain-based intelligent transportation systems

Survey on blockchain-based applications in internet of vehicles

Blockchain-based batch authentication protocol for Internet of Vehicles

Object detection for autonomous driving is a huge challenge in the cross-domain adaptation scenario, especially for the time- and resource-consuming task. Distributed deep learning (DDL) has demonstrated a considerably good balance between efficiency and computation complexity. However, the reliability of DDL is low. Moreover, the cost of training data and model is not priced well. In this article, a novel blockchain-enabled model sharing approach is proposed to improve the performance of object detection with cross-domain adaptation for autonomous driving systems. Based on the blockchain and mobile-edge computing (MEC) technology, a domain-adaptive you-only-look-once (YOLOv2) model is trained across nodes, which can reduce significantly the domain discrepancy for different object categories. Furthermore, smart contracts are developed to perform data storage and model sharing tasks efficiently. The reliability of model sharing is ensured with blockchain consensus. We evaluate the proposed method under public data sets. The simulation results demonstrate that the efficiency and reliability of the proposed approach are better than the reference model.

Blockchain-Enabled Cross-Domain Object Detection for Autonomous Driving: A Model Sharing Approach

Recent proliferation in disruptive technologies has opened up a new horizon for Internet of Vehicles (IoV). The success of IoV highly depends on the robustness of vehicular information system as a dispute among the service providers on data rights or any kind of security violation could disrupt the transport services altogether. In this work we propose a blockchain based novel architecture for vehicle authentication and privacy preservation with seamless access control for IoV. Proposed architecture is decentralized, robust and scalable. Along with privacy preserving authentication and conflict-free access-log maintenance, the proposed BlockAPP protocol also supports an optional traceability feature. Performance evaluation using smart contact over Ethereum Blockchain validates the effectiveness of the proposed architecture.

BlockAPP: Using Blockchain for Authentication and Privacy Preservation in IoV

Smart interconnected vehicles generate a huge amount of data to be used by a wide range of applications. Although cloud based data management is currently in practice, for many applications serving road safety or traffic regulation, it is utmost important that applications access these data at the site itself for improved quality of service. Road side units (RSUs) play a crucial role in handling these vast amount of vehicular data and serving the running applications in turn. In this current era of edge computing, in-place data access is also proven to be advantageous from cost point of view. As multiple applications from different service providers are interested to access different fragments of these data, a robust access control mechanism is needed to ensure desired level of security as well as reliability for these data. In this paper, we introduce B2VDM, a novel architecture for vehicular data management at RSUs, that provides a seamless access control using Blockchain technology. The proposed B2VDM framework also implements a simple load distribution module, which maintains the reliability by minimizing the number of packet drops at a heavily loaded RSU during peak hours. An extensive evaluation using Etherium Blockchain validates the effectiveness of the proposed architecture.

B2VDM: Blockchain Based Vehicular Data Management

Query driven Broadcast through wireless sensor nodes also leads to the domain of event driven converge cast. A query-response based application in Wireless Sensor Networks(WSN) demands the correct delivery of data message at each sensor node. A Breadth-First Search(BFS) tree rooted at the base station offers shortest path traversal for each data message which utilizes the sensor resources efficiently. Resource constrained sensor nodes are highly prone to sudden crash. So the application demands a quick and smart approach to repair the tree when a node dies. In this paper a novel scheme has been proposed to locally repair the tree with constant round of message transmissions. Each node piggybacks a few bytes of extra information along with each query and response messages. Based on these piggybacked values each node calculates its alternate parent. When a parent node fails, its children can contact their respective alternate parents immediately to establish an alternate path to the root. Reduced communication cost in terms of extra message transmissions saves battery power at each node. Efficient query-response message handler ensures the correct delivery of messages. Fast repairing offers good Quality of Service(QoS). Simulation result shows that no message is lost except the one holding by the crashed node.

A Tree-Based Local Repairing Approach for Increasing Lifetime of Query Driven WSN

Driven by the growth in ubiquitous sensor networks and IoT, Human Activity Recognition (HAR) has emerged to be an exciting challenge. Popular machine learning approaches proposed in this direction have shown promise with modest performance. Treatment of HAR data in a recurrent form, and subsequent analysis using deep networks such as RNNs/LSTM is largely unexplored. In this work, we treat HAR data stream, generated from a smart home setup, as a series of events and apply LSTM to perform feature extraction. We use the Aruba dataset from the CASAS project [4], where we apply LSTM to extract features and perform annotations. We then apply standard classification techniques to recognize the different activities in the newly annotated data. We observe that, using our method we can achieve annotation accuracies of up to 79.5% which is 13.6% better than other state-of-the-art solutions.

Learning and Annotating Activities for Home Automation using LSTM

A reliable broadcast demands that each data packet forwarded from the source node is received by all other nodes in the network without any loss A flooding based Broadcast scheme incurs significant overhead due to explosion of packets in the network So an efficient way of routing packets in order to broadcast saves the energy of resource constrained sensor nodes The delivery of broadcast packets at each node in correct sequence is also crucial for certain applications In this paper a tree based broadcast mechanism is proposed that constructs a spanning tree out of all nodes in the network rooted at the source of the broadcast The broadcast tree eventually becomes a Breadth-First Search(BFS) tree where each node maintains the shortest path to the root So packets routed through the shortest paths reduce transmission delay and packet loss BFS tree based broadcast saves the battery power at each node by reducing the number of packet transmissions The proposed scheme for broadcast is also crash tolerant Each node computes a maintenance plan during the broadcast tree construction for future requirement If a node dies, all the nodes in neighborhood repairs the tree locally within constant round of message transmissions Each broadcast packet is assured to be delivered at each node without redundancy and maintaining the correct order

Suchetana Chakraborty

Papers

BlockAPP: Using Blockchain for Authentication and Privacy Preservation in IoV

B2VDM: Blockchain Based Vehicular Data Management

A Tree-Based Local Repairing Approach for Increasing Lifetime of Query Driven WSN

Learning and Annotating Activities for Home Automation using LSTM

A reliable and total order tree based broadcast in wireless sensor network