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A Blockchain-Based Secure Data Storage and Trading Model for Wireless Sensor Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1151)

Abstract

Data storage on local devices provides fast, secure and complete access to the users. However, it needs sufficient storage, which is not feasible for light-weight clients’ environments. In such scenarios, the usage of external devices makes the system vulnerable to data tampering, privacy leakage, and other data security issues. Wireless Sensor Networks (WSNs) consist of resource constrained devices, where external storage is preferred due to the lack of storage capacity in local devices. Therefore, using a centralized storage mechanism in WSNs causes slow data retrieval, which affects further operations on data. Relying on trusted parties also cause certain privacy and trust issues. Various data trading mechanisms are introduced to efficiently utilize the stored data. However, they still lack in certain aspects as discussed above. Therefore, we propose and implement a Secure Incentive-based Data Storage and Trading Model (SIDSTM), which provides a secure and efficient distributed storage mechanism. The proposed scheme uses AES-128 encryption scheme to encrypt the data for privacy purposes. An elliptic curve Diffie-Hellman key exchange is used for securely exchanging the private keys among the network peers. In WSNs, there are fewer data trading models so far. Anyone can access the data if he has access to the hash provided by the IPFS in the InterPlanetary File System (IPFS). Thus, IPFS provides efficient data access and storage mechanism. However, it deletes the data after a certain time interval due to the limited storage. Therefore, the proposed model provides incentives to motivate IPFS’s peers for data storage. It also eliminates the need for the third-party involvement in data trading. The simulations are conducted to prove the effectiveness of the proposed model by evaluating its efficiency and scalability.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.Changchun University of Science and TechnologyChangchunChina

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