Wireless Sensor Networks: Blockchain and Swarm Intelligence Approach

By Muhammad OsamaMay 7 2024Reviewed by Susha Cheriyedath, M.Sc.

In a recently published paper in the journal Scientific Reports, researchers proposed a novel secure clustering routing method based on blockchain and swarm intelligence (BS-SCRM) for wireless sensor networks (WSNs). They introduced blockchain technology to enhance the security and energy efficiency of WSNs clustering routing and presented an improved cluster head (CH) election method based on an elite strategy-enhanced whale optimization algorithm (WOA).

Background

WSNs are a key component of the Internet of Things (IoT) infrastructure, enabling the collection and transmission of data from the physical world to the digital domain. They consist of sensor nodes communicating wirelessly with each other and a base station (BS). These nodes are often deployed in harsh and remote environments for monitoring parameters, such as temperature, humidity, pressure, and sound.

However, WSNs encounter several challenges, including limited energy, bandwidth, memory, and computational resources. Additionally, they are vulnerable to security threats such as eavesdropping, unauthorized access, interference, and physical tampering. These threats can compromise data reliability and integrity, highlighting the need for efficient and secure routing protocols.

One popular solution is clustering routing protocols, which organize the network into hierarchical groups of nodes, each led by a CH responsible for aggregating and forwarding data to the BS. However, traditional clustering methods often rely on centralized control or predefined rules, which may not adapt well to the dynamic nature of WSNs. Furthermore, they lack robust security and trust mechanisms to combat malicious attacks or data corruption.

About the Research

In this paper, the authors presented BS-SCRM, a new approach for WSNs that integrates swarm intelligence optimization algorithms and blockchain technology. Swarm intelligence algorithms draw inspiration from collective behaviors in nature, optimizing tasks like CH selection and routing path determination.

Meanwhile, blockchain technology offers decentralized data encryption and validation. The designed method consists of two phases: an enhanced CH election using a strategy-enhanced WOA and a secure data on-chain phase leveraging blockchain for data encryption and validation, ensuring data integrity and preventing tampering.

The first phase focuses on enhancing clustering routing in WSNs by employing an improved WOA to efficiently select CHs. WOA, inspired by the foraging behavior of whales, incorporates features like bubble-net feeding and spiral hunting. To refine WOA's effectiveness, the study introduces an elite strategy. This strategy retains the best-performing individuals across generations, preventing the loss of optimal solutions.

The objective function of the WOA algorithm considers both the node energy and the distance to the BS, to balance the energy consumption and the communication cost in the network. Ultimately, the WOA algorithm outputs a set of CHs that is either optimal or near optimal for the clustering routing problem.

Moving to the second phase, the focus shifts to establishing a secure and reliable data transmission and storage mechanism within WSNs. Here, blockchain technology emerges as the cornerstone. Blockchain encrypts and verifies CH data, ensuring its integrity. However, implementing blockchain in resource constrained WSNs presents challenges.

To address this, the study assigns distinct roles to devices. Ordinary nodes possess data viewing permissions, while accounting nodes, including the BS and auxiliary BS nodes, handle both data viewing and consensus algorithm execution. These accounting nodes execute the consensus algorithm based on fitness values derived from the WOA algorithm.

Subsequently, they select the most suitable block to append to the blockchain. Through this process, the blockchain maintains data consistency and immutability across nodes, effectively preventing unauthorized access or tampering.

Furthermore, the authors conducted extensive simulations to evaluate the effectiveness and security of their proposed method under different scenarios and compared it with three existing clustering routing methods including a modified genetic algorithm (ModifyGA), energy-efficient fuzzy clustering routing (EFCR), and linearly decreasing inertia weight particle swarm optimization (LDIWPSO). Moreover, they used four different scenarios with different numbers and distributions of nodes to measure the network performance in terms of network lifetime, energy consumption, and security performance.

Research Findings

The simulation results showed that the BS-SCRM method outperformed the other methods in terms of network lifetime, energy efficiency, and security. It achieved a longer network lifetime by 24-73% compared to the other methods, indicating that it can balance energy consumption and avoid premature node death.

Additionally, BS-SCRM consumed less energy than the other methods, especially in the later stage of the network operation, suggesting optimization in CH selection and data transmission. Moreover, it exhibited higher resistance to attacks, such as BS clustering result tampering, providing a robust security guarantee for data integrity and trustworthiness.

The developed method can be applied to various scenarios and domains that require secure and efficient data collection and transmission using WSNs, such as industrial IoT, smart cities, environmental monitoring, healthcare, and agriculture. It offers a novel solution for enhancing the security and reliability of WSNs, as well as improving their performance and scalability.

Conclusion

In summary, the novel approach effectively addressed the challenges of providing a secure and energy-efficient solution for WSNs. By leveraging blockchain technology and swarm algorithms, the method proved to be effective. Moving forward, the researchers suggested extending and integrating their method with other techniques like smart contracts, edge computing, and artificial intelligence to enable even more advanced and intelligent applications and services.