A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

Abstract

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-ofwork in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

Energy optimization with authentication and cost effective storage in the wireless sensor IoTs using blockchain

“…The nodes that store a large amount of data are incentivized with cryptocurrency. Similarly, an authentication mechanism is proposed in Reference 26 for WSNs. Authentication is performed on the basis of nodes' credentials to prevent malicious activities in the network.…”

NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

“…In this section, the EBLSOA's 42 equations are utilized for creating the original colorful ray, after defining the way of the rays, are enhanced to advance their capability in exploring numerous discrete solutions, potentially involving the near-finest solution to the knapsack issue. The objective function for tuning 𝜔 1 t , 𝜔 2 t and E of the introduced topology is provided by Equation (15). These parameters play crucial roles in shaping the behavior and effectiveness of the optimization process.…”

“…These optimized techniques involve model compression, and quantization, which aim to diminish the computational complexity and memory footprint of DL models. 15 By employing optimized techniques, researchers have successfully mitigated the computational burden associated with deploying DL models in WSNs. Consequently, this research work introduces a NHAPMAD framework applicable to both NADS and HADS.…”

Cycle‐Consistent Generative Adversarial Network and Crypto Hash Signature Token‐based Block chain Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks

“…Khan et al 34 have presented a Block chain-Based Deep-Learning-Driven Structure for Quality Routing in WSN. Four deep learning models were utilized for the recognition of malevolent nodes.…”