Lightweight and Scalable DAG based distributed ledger for verifying IoT data integrity
ABSTARCT :
Verifying the integrity of IoT data in cloud-based IoT architectures is crucial for building reliable IoT applications. Traditional data integrity verification methods rely on a Trusted Third Party (TTP) that has issues of risk and operational cost by centralization. Distributed Ledger Technology (DLT) has a high potential to verify IoT data integrity and overcome the problems with TTPs. However, the existing DLTs have low transaction throughput, high computational and storage overhead, and are unsuitable for IoT environments, where a massive scale of data is generated. Recently, Directed Acyclic Graph (DAG) based DLTs have been proposed to address the low transaction throughput of linear DLTs. However, the integration of IoT Gateways (GWs) into the peer to peer (P2P) DLT network is challenging because of their low storage and computational capacity. This paper proposes Lightweight and Scalable DAG based distributed ledger for IoT (LSDI) that can work with resource-constrained IoT GWs to provide fast and scalable IoT data integrity verification. LSDI uses two key techniques: Pruning and Clustering, to reduce 1) storage overhead in IoT GWs by removing sufficiently old transactions, and 2) computational overhead of IoT GWs by partitioning a large P2P network into smaller P2P networks. The evaluation results of the proof of concept implementation showed that the proposed LSDI system achieves high transaction throughput and scalability while efficiently managing storage and computation overhead of the IoT GWs.
EXISTING SYSTEM :
? Existing BC implementations typically use one of the following consensus algorithms: Proof of Work (POW) or Proof of Stake (POS).
? Micropayments were never possible in existing blockchain platforms due to the fact transaction fees were higher than the transactions themselves, but with the IOTA fee-less environment, micropayments can happen for the future M2M economy.
? We used the Ed25519 signature algorithm on the existing IOTA MAM channel to ensure the authenticity of each message based on the key pairs of IoT devices.
? Problems of reuse of addresses exist in this system. Possible solutions are described below with insights into each solution.
DISADVANTAGE :
? In this paper, we propose a Lightweight Scalable BC (LSB) for IoT security and privacy that addresses the above issues.
? Although BC has the potential to tackle the IoT problems such as security and privacy, its application to build an IoT architecture remains difficult.
? In a local community, each Home Node needs to get certification issued by the local CA to join the network.
? The various benefits afforded by BC technology as outlined earlier in this section make it an attractive solution for addressing the aforementioned problems in IoT.
? In the IoT context, one can expect serious scalability issues since the number of nodes is expected to be very large.
PROPOSED SYSTEM :
• The proposed method reduces the header size of the 6LowPAN and Host Identity protocol (HIP) from 40 bytes to a maximum of 25 bytes by eliminating unnecessary header fields and thus reduces network overhead.
• Assuming the user is authenticated, the RA generates a shared key for communication between the user and the device. Security analysis shows that the proposed method is secure against the man-inthe-middle attack.
• In LSB, we have rather proposed a tiered structure where a single public BC is managed distributedly by the overlay nodes and the devices within each smart home are managed independently by a home-specific LBM.
• The proposed architecture has three main components namely: data management protocol, data store system, and message service.
ADVANTAGE :
? Our approach differs from other solutions in the way that it applies a lightweight, scalable and high performance Tangle technology which is suitable for IoT.
? However, the unacceptable performance of the current mainstream DLT systems such as Bitcoin can hardly meet the efficiency and scalability requirements of IoT.
? However, this tremendous market growth raises new challenges such as security and privacy, scalability and data processing performance for IoT system architecture, which means that an effective solution needs to be devised.
? It is also interesting for us to conduct the research on performance evaluation such as analytical modeling and simulation for DAG-based DL systems.
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