A Data Intrusion Tolerance Model Based on an Improved Evolutionary Game Theory for the Energy Internet

Malicious attacks against data are unavoidable in the interconnected,open and shared Energy Internet(EI),Intrusion tolerant techniques are critical to the data security of EI.Existing intrusion tolerant techniques suffered from problems such as low adaptability,policy lag,and difficulty in determining the degree of tolerance.To address these issues,we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas:(1)it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights;and (2)it combines a tournament competition model with incentive weights to obtain optimal strategies for each stage of the game process.Extensive experiments are conducted in the IEEE 39-bus system,whose results demonstrate the feasibility of the incentive weights,confirm the proposed strategy strengthens the system’s ability to tolerate aggression,and improves the dynamic adaptability and response efficiency of the aggression-tolerant system in the case of limited resources.

Energy Minimization for Heterogenous Traffic Coexistence with Puncturing in Mobile Edge Computing-Based Industrial Internet of Things

Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things(IIoT), where mobile edge computing(MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore,considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks(RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm(IEMA) is proposed.Simulation results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.

An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical resources.By grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the organization.Nevertheless,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively.In this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based IoT.Three consecutive segments are suggested.For the covering of adjusted clusters,an ideal number of clusters is determined first.Then,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor hubs.Cluster heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster members.Specifically,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long time.The assessment results demonstrate that the IEPA performs better than existing conventions.

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

In this paper,the Internet ofMedical Things(IoMT)is identified as a promising solution,which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service(QoS)in the healthcare sector.However,problems with the present architectural models such as those related to energy consumption,service latency,execution cost,and resource usage,remain a major concern for adopting IoMT applications.To address these problems,this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming(MILP),with the objective of efficiently processing and placing IoMT applications in the edge-fog-cloud computing environment,while maintaining certain quality standards(e.g.,energy consumption,service latency,network utilization).A modeling environment is used to assess and validate the proposed model by considering different traffic loads and processing requirements.In comparison to the other existing models,the performance analysis of the proposed approach shows a maximum saving of 38%in energy consumption and a 73%reduction in service latency.The results also highlight that offloading the IoMT application to the edge and fog nodes compared to the cloud is highly dependent on the tradeoff between the network journey time saved vs.the extra power consumed by edge or fog resources.

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer processes.However,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy costs.This paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research process.The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers.These sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for analysis.The data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center environment.Through the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT sensors.The model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power costs.Furthermore,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and AlexNet.The NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark algorithms.These findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive capabilities of cloud computing systems.The IoT plays a critical role in driving these advancements by providing real-time data insights into the operational aspects of data centers.

NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.

Distributed Optimal Co-multi-microgrids Energy Management for Energy Internet

Unlike conventional power systems, the upcoming energy internet U+0028 EI U+0029 emphasizes comprehensive utilization of energy in the whole power system by coordinating multi-microgrids, which also brings new challenge for the energy management. To address this issue, this paper proposes a novel consensus-based distributed approach based on multi-agent framework to solve the energy management problem of the energy internet, which only requires local information exchange among neighboring agents. Correspondingly, two consensus algorithms are presented, one of which drives the incremental cost of each distributed generator U+0028 DG U+0029 converge to the state of the leader agent-energy router, and the other one is used to estimate the global power mismatch, which is a first-order average consensus algorithm modified by a correction term. In addition, in order to meet the supply-demand balance, an effective control strategy for the energy router is proposed to accurately calculate the power exchange between the microgrid and the main grid. Finally, simulation results within a 7-bus test system are provided to illustrate the effectiveness of the proposed approach. © 2014 Chinese Association of Automation.

Smart Grids with Intelligent Periphery:An Architecture for the Energy Internet

A future smart grid must fulfill the vision of the Energy Internet in which millions of people produce their own energy from renewables in their homes, offices, and factories and share it with each other. Electric vehicles and local energy storage will be widely deployed. Internet technology will be utilized to transform the power grid into an energysharing inter-grid. To prepare for the future, a smart grid with intelligent periphery, or smart GRIP, is proposed. The building blocks of GRIP architecture are called clusters and include an energy-management system （EMS）-controlled transmission grid in the core and distribution grids, micro-grids, and smart buildings and homes on the periphery; all of which are hierarchically structured. The layered architecture of GRIP allows a seamless transition from the present to the future and plug-and-play interoperability. The basic functions of a cluster consist of （1） dispatch, （2） smoothing, and （3） mitigation. A risk-limiting dispatch methodology is presented; a new device, called the electric spring, is developed for smoothing out fluctuations in periphery clusters; and means to mitigate failures are discussed.

Enabling Industrial Internet of Things(IIoT) towards an emerging smart energy system

The increasing penetration of renewable energy on the transmission and distribution power network is driving the adoption of two-way power flow control, data and communications needed to meet the dependency of balancing generation and load. Thus, creating an environment where power and information flow seamlessly in real time to enable reliable and economically viable energy delivery, the advent of Internet of Energy(IoE) as well as the rising of Internet of Things(IoT) based smart systems.The evolution of IT to Io T has shown that an information network can be connected in an autonomous way via routers from operating system(OS) based computers and devices to build a highly intelligent eco-system. Conceptually, we are applying the same methodology to the Io E concept so that Energy Operating System(EOS) based assets and devices can be developed into a distributed energy network via energy gateway and self-organized into a smart energy eco-system.This paper introduces a laboratory based IIo T driven software and controls platform developed on the NICE Nano-grid as part of a NICE smart system Initiative for Shenhua group. The goal of this effort is to develop an open architecture based Industrial Smart Energy Consortium(ISEC) to attract industrial partners, academic universities, module supplies, equipment vendors and related stakeholder to explore and contribute into a test-bed centric open laboratory template and platform for next generation energy-oriented smart industry applications.In the meanwhile, ISEC will play an important role to drive interoperability standards for the mining industry so that the era of un-manned underground mining operation can become the reality as well as increasing safety regulation enforcement.

Energy Efficiency in Internet of Things:An Overview

Energy efficiency is very important for the Internet of Things(IoT),especially for front-end sensed terminal or node.It not only embodies the node’s life,but also reflects the lifetime of the network.Meanwhile,it is also a key indicator of green communications.Unfortunately,there is no article on systematic analysis and review for energy efficiency evaluation in IoT.In this paper,we systemically analyze the architecture of IoT,and point out its energy distribution,Furthermore,we summarized the energy consumption model in IoT,analyzed the pros and cons of improving energy efficiency,presented a state of the art the evaluation metrics of energy efficiency.Finally,we conclude the techniques and methods,and carry out a few open research issues and directions in this field.

Development of the interconnected power grid in Europe and suggestions for the energy internet in China

The European power grid is one of the largest regional interconnected power grids in the world.It realizes a multinational grid operation,which is rare.The total installed capacity of the European power grid is the largest throughout the world.In addition,the integration and utilization of renewable energy in this grid is a great benchmark for other countries and can help promote energy transformation and achieve a high proportion of renewable energy consumption.Based on the analysis of the existing status of the European interconnected power grid and the development history of this power grid,this paper summarizes four key development stages of the European power grid.In addition,the characteristics of each stage and the development prospect of the European power grid are analyzed.On this basis,this paper gives suggestions for the development and construction of China’s energy internet;this can provide valuable reference for further studies on China’s energy internet.

Trading platform for cooperation and sharing based on blockchain within multi-agent energy internet

With the release of the electricity sales side,large-scale small-capacity distributed power generation units are connected to the distribution side,forming multi-type market entities such as microgrids,integrated energy systems,and virtual power plants.With the large-scale integration of distributed energy,the energy market under the energy internet is different from a traditional transmission grid.It is currently developing in the direction of diversified entities and commodities,a flat structure,and a flexible and competitive multi-agent market mechanism.In this context,this study analyzes the value of combining blockchain and the electricity market presents the design of a blockchain trading framework for multi-agent cooperation and sharing of the energy internet.The nodes in market transactions are modeled through power system modeling in the physical layer and the transaction consensus strategy in the cyber layer;moreover,the nodes are verified in a modified IEEE 13 testing feeder of a distribution network.A transaction example is demonstrated using the multi-agent cooperation and sharing transaction platform based on the Ethereum private blockchain.

Development of Wi-Fi Based Home Energy Monitoring System for Green Internet of Things

Green Internet of things （loT） has been heralded as the ＂next big thing＂ waiting to be realized in energy-efficient ubiquitous computing. Green IoT revolves around increased machine-to-machine communications and encompasses energy-efficient wireless embedded sensors and actuators that assist in monitoring and controlling home appliances. Energy efficiency in home applications can be achieved by better monitoring of the specific energy consumption by the appliances. There are many wireless standards that can be adopted for the design of such embedded devices in loT. These communication technologies cater to different requirements and are classified as the short-range and long-range ones. To select the best communication method, this paper surveys various loT communication technologies and discusses the advantages and disadvantages to develop an energy monitoring system. An IoT device based on the Wi-Fi technology system is developed and tested for usage in the home energy monitoring environment. The performance of this system is then evaluated by the measurement of power consumption metrics. In the efficient deep-sleep mode, the system saves up to 0.3 W per cycle with an average power dissipation of less than 0.1 W/s.

ETM-IoT:Energy-Aware Threshold Model for Heterogeneous Communication in the Internet of Things

The internet of things(IoT)has a wide variety of applications,which in turn raisesmany challenging issues.IoT technology enables devices to closely monitor their environment,providing context-aware intelligence based on the real-time data collected by their sensor nodes.The IoT not only controls these devices but also monitors their user’s behaviour.One of the major issues related to IoT is the need for an energy-efficient communication protocol which uses the heterogeneity and diversity of the objects connected through the internet.Minimizing energy consumption is a requirement for energyconstrained nodes and outsourced nodes.The IoT nodes deployed in different geographical regions typically have different energy levels.This paper focuses on creating an energy-efficient protocol for IoTwhich can deal with the clustering of nodes and the cluster head selection process.An energy thresholdmodel is developed to select the appropriate cluster heads and also to ensure uniform distribution of energy to those heads andmember nodes.The proposed model envisages an IoT network with three different types of nodes,described here as advanced,intermediate and normal nodes.Normal nodes are first-level nodes,which have the lowest energy use;intermediate nodes are second-level nodes,which have a medium energy requirement;and the advanced class are thirdlevel nodes with the highest energy use.The simulation results demonstrate that the proposed algorithm outperforms other existing algorithms.In tests,it shows a 26%improvement in network lifetime compared with existing algorithms.

Cooperative Differential Game for Model Energy-Bandwidth Efficiency Tradeoff in the Internet of Things

Internet of Things （IoT） refers to an infrastructure which enables the forms of com- munication and collaboration between people and things, and between things themselves. In order to improve its performance, we present a tradeoff between bandwidth and energy con- sumption in the loT in this paper. A service providing model is built to find the relation- ship between bandwidth and energy consump- tion using a cooperative differential game mo- del. The game solution is gotten in the condi- tion of grand coalition, feedback Nash equili- brium and intermediate coalitions and an allo- cation policy is obtain by Shapley theory. The results are shown as follows. Firstly, the per- formance of IoT decreases with the increasing of bandwidth cost or with the decreasing of en- ergy cost; secondly, all the nodes in the IoT com- posing a grand coalition can save bandwidth and energy consumption; thirdly, when the fac- tors of bandwidth cost and energy cost are eq- ual, the obtained number of provided services is an optimised value which is the trade-off between energy and bandwidth consumption.

How to Promote the New Energy Vehicles under China’s “Internet plus” Initiative: A Review

Under the background of the rapid development of the Internet, new energy vehicles (NEVs) have ushered in an excellent development opportunity. With the subsidy policy exiting, new energy vehicles are facing unprecedented challenges. One purpose of stimulating the NEVs through “Internet plus” initiative is to promote diversification of vehicle energy systems and advance industrial upgrading and transformation. On the premise of “Internet plus”, the paper analyses the obstacles and opportunities of new energy vehicles from four aspects, and proposes a promoting development model which includes management mode and profit mode, and constructs a promoting development framework which is about three stages of new energy vehicles. Finally, from the perspective of the new energy vehicle charging, the paper puts forward some policy advice to promote industrialization and popularization of new energy vehicles in China.

Research on task scheduling and concurrent processing technology for energy internet operation platform

The energy Internet operation platform provides market entities such as energy users,energy enterprises,suppliers,and governments with the ability to interact,transact,and manage various operations.Owing to the large number of platform users,complex businesses,and large amounts of data-mining tasks,it is necessary to solve the problems afflicting platform task scheduling and the provision of simultaneous access to a large number of users.This study examines the two core technologies of platform task scheduling and multiuser concurrent processing,proposing a distributed task-scheduling method and a technical implementation scheme based on the particle swarm optimization algorithm,and presents a systematic solution in concurrent processing for massive user numbers.Based on the results of this study,the energy internet operation platform can effectively deal with the concurrent access of tens of millions of users and complex task-scheduling problems.

Energy-Efficient Content-Based Routing in Internet of Things

The?convergence of the Internet, sensor networks, and Radio Frequency Identification (RFID) systems has ushered to the concept of Internet of Things (IoT) which is capable of connecting daily things, making them smart through sensing, reasoning, and cooperating with other things. Further, RFID technology enables tracking of an object and assigning it a unique ID. IoT has the potential for a wide range of applications relating to healthcare, environment, transportation, cities… Moreover, the middleware is a basic component in the IoT architecture. It handles heterogeneity issues among IoT devices and provides a common framework for communication. More recently, the interest has focusing on developing publish/subscribe middleware systems for the IoT to allow asynchronous communication between the IoT devices. The scope of our paper is to study routing protocols for publish/subscribe schemes that include content and context-based routing. We propose an Energy-Efficient Content-Based Routing (EECBR) protocol for the IoT that minimizes the energy consumption. The proposed algorithm makes use of a virtual topology that is constructed in a centralized manner and then routes the events from the publishers to the intended interested subscribers in a distributed manner. EECBR has been simulated using Omnet++. The simulation results show that EECBR has a significant performance in term of the energy variance compared to the other schemes.