Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern era.IoT generally refers to a network of gadgets linked via wireless network and communicates via internet.Resource management,especially energy management,is a critical issue when designing IoT devices.Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment.In this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT environment.The proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance phase.The proposed EECBRMmodel was validated through a series of experiments and the results were verified under several aspects.EECBRM model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network lifetime.When simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant manner.Thus,the efficiency of the proposed model is established.

Lightweight Direct Acyclic Graph Blockchain for Enhancing Resource-Constrained IoT Environment

Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things(IoT).In concept,blockchain has a linear structure that grows with the number of transactions entered.This growth in size is the main obstacle to the blockchain,which makes it unsuitable for resource-constrained IoT environments.Moreover,conventional consensus algorithms such as PoW,PoS are very computationally heavy.This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm.The Multi-Zone Direct Acyclic Graph(DAG)Blockchain(Multizone-DAG-Blockchain)framework is proposed for the fog-based IoT environment.In this context,fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes,thus preserving the energy consumption of the IoT devices.Both IoT and fog nodes are initially authenticated using a non-cloneable physical function-based validationmechanism(DPUF-VM)inwhichmultiple authentication certificates are verified in the blockchain.Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q-Curve algorithm.In the cloud,sensitive data is stored as ciphertext.Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes.The fog node first performs a redundancy analysis using the Jaccard Similarity(JS)measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network(N2IN)algorithm.A lightweight proof-of-authentication(PoAh)algorithm is presented and executed by the optimal consensus node selected by the bi-objective spiral optimization(BoSo)algorithm for transaction validation.The proposed work is modeled in Network Simulator 3.26(ns-3.26),and the performance is evaluated in terms of energy consumption,storage cost,response time,and throughput.

Semantic QoS Ontology and Semantic Service Ranking Approach for IoT services

Due to a rapid increase in the number of functionally equivalent web services at open and dynamic Io T service environment,Qo S has become a major discrimination factor to reflect the user's expectation and experience of using a service.There are different languages and models for expressing Qo S advertisements and requirements among service providers and consumers.Therefore,it leads to the issues of semantic interoperability of Qo S information and semantic similarity match between a semantic description of the service being requested by the service consumer,and a formal description of the service being offered by the service provider.In this paper,we propose a hierarchical two-layer semantic Qo S ontology to promote the description and declaration of Qo S-based service information in detail for any domain and application.And,we develop a semantic matchmaking algorithm to compare the web services according to their Qo S information and adopt analytical hierarchy process( AHP) to make decision for the ranked services depending on the Qo S criteria.The comparison study and experimental result show that our proposed system is superior to other service ranking approaches.

Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.