Litcius/Paper detail

Energy Efficient Consensus Approach of Blockchain for IoT Networks with Edge Computing

Shivani Wadhwa, Shalli Rani, Kavita Kavita, Sahil Verma, Jana Shafi, Marcin Woźniak

2022Sensors66 citationsDOIOpen Access PDF

Abstract

Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed.

Topics & Concepts

BlockchainByzantine fault toleranceComputer scienceProof-of-work systemDistributed computingBlock (permutation group theory)Enhanced Data Rates for GSM EvolutionEfficient energy useEnergy consumptionEdge computingTask (project management)DigitizationConsensusFault toleranceComputer securityArtificial intelligenceMulti-agent systemEngineeringTelecommunicationsGeometryMathematicsElectrical engineeringSystems engineeringBlockchain Technology Applications and SecurityIoT and Edge/Fog ComputingBrain Tumor Detection and Classification