Energy Prediction in IoT Systems Using Machine Learning Models

The Internet of Things (IoT) technology has been developed for directing and maintaining the atmosphere in smart buildings in real time. In order to optimise the power generation sector and schedule routine maintenance, it is crucial to predict future energy demand. Electricity demand forecasting is difficult because of the complexity of the available demand patterns. Establishing a perfect prediction of energy consumption at the building’s level is vital and significant to efficiently managing the consumed energy by utilising a strong predictive model. Low forecast accuracy is just one of the reasons why energy consumption and prediction models have failed to advance. Therefore, the purpose of this study is to create an IoT-based energy prediction (IoT-EP) model that can reliably estimate the energy consumption of smart buildings. A real-world test case on power predictions is conducted on a local electricity grid to test the practicality of the approach. The proposed (IoT-EP) model selects the significant features as input neurons, the predictable data is selected as output nodes, and a multi-layer perceptron is constructed along with the features of the Convolution Neural Network (CNN) algorithm. The analysis of the proposed IoT-EP model has higher accuracy of 90%, correlation of 89%, and variance of 16% in less training time of 29.2 s, and with a higher prediction speed of 396 (observation/sec). When compared to existing models, the results showed that the proposed (IoT-EP) model outperforms with a satisfactory level of accuracy in predicting energy consumption in smart buildings.     

六边形蜂窝芯异面类静态压缩力学行为的仿真分析

目的研究六边形蜂窝芯材异面类静态压缩载荷的数值模拟方法及相关力学行为。方法基于蜂窝单元阵列的方法,构筑了单双壁厚六边形蜂窝芯材异面类静态压缩有限元数值计算模型和分析方法。结果借助于该模拟方法,分析计算了不同结构参数条件下单双壁厚六边形蜂窝芯材的异面变形模式、变形曲线和类静态峰应力值,并绘制了相应的图、压缩力位移曲线和数据表格。结论将计算结果与现有的实验和理论计算结果作对比分析可知,计算结果与已有结果吻合较好,证明了所提出的有限元分析方法的可靠性。     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

凹六边形蜂窝纸板面内承载性能有限元仿真分析

目的 探究以凹六边形为芯层的蜂窝纸板的力学性能,为凹六边形蜂窝纸箱的运输包装设计提供理论基础.方法 运用有限元仿真,分析不同结构参数影响下的凹六边形蜂窝纸板的面内承载性能.结果 当水平胞壁的长度减小,其他结构参数保持不变,凹六边形蜂窝纸板面内方向上的平台应力增大,能量吸收平台阶段标准化应力增大,最佳吸能点也上升.结论 凹六边形蜂窝纸板结构参数的变化对其平台应力和能量吸收特性有深远影响,其性能研究促进了蜂窝纸箱的进一步发展.     

冲击速度和应变率对六边形蜂窝共面缓冲性能的影响

目的通过改变压缩方向的厚度,即共面方向的蜂窝层数,来研究冲击速度和应变率对六边形蜂窝共面缓冲性能的影响。方法借助软件Ansys/LS-DYNA来模拟六边形蜂窝在应变率恒定时冲击速度对蜂窝共面缓冲性能的影响,以及六边形蜂窝在速度值恒定时应变率对蜂窝共面缓冲性能的影响。结果设定3组应变率恒值(300,500,1000 s-1)研究不同冲击速度对蜂窝动态峰应力的影响,随着速度的增加,动态峰应力也增加。设定3组冲击速度恒值(3,50,100 m/s)研究不同应变率对蜂窝动态峰应力的影响,随着应变率增加,动态峰应力基本不变。结论在试件材料选用双线性硬化的铝基材料,壁材视为应变率不敏感的模型下,应变率对正六边形蜂窝的动态力学性能和缓冲性能基本无影响。     

六角形蜂窝网络的反馈数

一个图的反馈点集指的是在去掉时导致图无圈的节点子集.反馈集问题来源于组合电路设计,在操作系统死锁预防,人工智能的贝叶斯推断、网络理论有大量应用.六角形蜂窝网络是一种新近提出的并行计算互连网络.本文通过构造的方法求出了蜂窝网格和蜂窝圆环面网络的反馈数.     

Cogent and Energy Efficient Authentication Protocol for WSN in IoT

Given the accelerating development of Internet of things (IoT), a secure and robust authentication mechanism is urgently required as a critical architectural component. The IoT has improved the quality of everyday life for numerous people in many ways. Owing to the predominantly wireless nature of the IoT, connected devices are more vulnerable to security threats compared to wired networks. User authentication is thus of utmost importance in terms of security on the IoT. Several authentication protocols have been proposed in recent years, but most prior schemes do not provide sufficient security for these wireless networks. To overcome the limitations of previous schemes, we propose an efficient and lightweight authentication scheme called the Cogent Biometric-Based Authentication Scheme (COBBAS). The proposed scheme is based on biometric data, and uses lightweight operations to enhance the efficiency of the network in terms of time, storage, and battery consumption. A formal security analysis of COBBAS using Burrows–Abadi–Needham logic proves that the proposed protocol provides secure mutual authentication. Formal security verification using the Automated Validation of Internet Security Protocols and Applications tool shows that the proposed protocol is safe against man-in-the-middle and replay attacks. Informal security analysis further shows that COBBAS protects wireless sensor networks against several security attacks such as password guessing, impersonation, stolen verifier attacks, denial-of-service attacks, and errors in biometric recognition. This protocol also provides user anonymity, confidentiality, integrity, and biometric recovery in acceptable time with reasonable computational cost.     

Man Overboard Detection System Using IoT for Navigation Model

Security measures and contingency plans have been established in order to ensure human safety especially in the floating elements like ferry, ro-ro, catamaran, frigate, yacht that are the vehicles services for the purpose of logistic and passenger transport. In this paper, all processes in the event of Man overboard (MOB)are initiated for smart transportation. In MOB the falling person is totally dependent on the person who first saw the falling person. The main objective of this paper is to develop a solution to this significant problem. If a staff member or a passenger does not see the fall into the sea, undesirable situations such as disappearance, injury and death can occur during the period until the absence of the fallen person is noticed. In this paper, a comprehensive and improved solution is provided in terms of personnel and passenger security especially in all the floating elements, in which human resources are intensively involved like passengers, freight, logistics, fishing, business, yacht, leisure and naval vessels. In this case, if the ship's personnel or passengers fall into the sea in any way, it detected the fallen person into the sea by the sensors in the portable emergency device, which each person will carry. The warning system is activated via the in-ship automation system to which the information is transmitted by wireless communication. Thus, the case of MOB will be determined quickly. Internet of things (IoT) has a key role in identifying the location and information of the person falling into the sea through sensors, radio frequency, GPS and connected devices. Simultaneously, the alarm system on board will be activated and MOB flag (Oscar) will automatically be opened. This paper enables the Search and rescue (SAR) operations to be initiated and accelerated without losing time through decision-making process.     

An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

Recently, Wireless sensor networks (WSNs) have become very popular research topics which are applied to many applications. They provide pervasive computing services and techniques in various potential applications for the Internet of Things (IoT). An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism (ACMDGTM) algorithm is proposed which would mitigate the problem of “hot spots” among sensors to enhance the lifetime of networks. The clustering process takes sensors’ location and residual energy into consideration to elect suitable cluster heads. Furthermore, one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself. Related experimental results display that the presented method can avoid long distance communicate between sensor nodes. Furthermore, this algorithm reduces energy consumption effectively and improves package delivery rate.     

分布式无线传感器网络及其应用核心技术研究

提出一种分布式无线传感器网络体系结构,阐述了相关理论观点,分析其应用价值与研究现状：以减少传感器对网络的依赖为原则,增强传感器感知能力和感知精度、网络强壮性和容错性,并以传感器低功耗为研究思路;设计了以ARM体系结构32 b RISC（Reduce Instruction Computer）微处理器、uC／OS-Ⅱ嵌入式实时操作系统、轻量级TCP／IP网络协议LwIP（Lightweight TCP／IP Stack）,TR1000无线通信模块为核心的一种基于Internet的分布式微型无线网络传感器。     

An Efficient Scheme for Data Pattern Matching in IoT Networks

The Internet has become an unavoidable trend of all things due to the rapid growth of networking technology, smart home technology encompasses a variety of sectors, including intelligent transportation, allowing users to communicate with anybody or any device at any time and from anywhere. However, most things are different now. Background: Structured data is a form of separated storage that slows down the rate at which everything is connected. Data pattern matching is commonly used in data connectivity and can help with the issues mentioned above. Aim: The present pattern matching system is ineffective due to the heterogeneity and rapid expansion of large IoT data. The method requires a lot of manual work and has a poor match with real-world applications. In the modern IoT context, solving the challenge of automatic pattern matching is complex. Methodology: A three-layer mapping matching is proposed for heterogeneous data from the IoT, and a hierarchical pattern matching technique. The feature classification matching, relational feature clustering matching, and mixed element matching are all examples of feature classification matching. Through layer-by-layer matching, the algorithm gradually narrows the matching space, improving matching quality, reducing the number of matching between components and the degree of manual participation, and producing a better automatic mode matching. Results: The algorithm's efficiency and performance are tested using a large number of data samples, and the results show that the technique is practical and effective. Conclusion: the proposed algorithm utilizes the instance information of the data pattern. It deploys three-layer mapping matching approach and mixed element matching and realizes the automatic pattern matching of heterogeneous data which reduces the matching space between elements in complex patterns. It improves the efficiency and accuracy of automatic matching.     

Multi-objective optimization of spectrum detection in cognitive IoT using artificial physics

In cognitive Internet of Things (C-IoT), spectrum detection aims to find the available spectrum resources for cognitive sensor nodes. However, it always consumes more energy to get higher detection rate in spectrum detection, so energy consumption and detection rate are positively correlated in C-IoT. Different from the available algorithms, we model spectrum detection in C-IoT as a multi-objective optimization problem and aim to find the trade-off points of spectrum detection. An artificial physics optimization algorithm is proposed to solve spectrum detection problems in C-IoT. The simulation results show that the proposed algorithm can effectively reduce the energy consumption and keep a high detection rate.     

LBC-IoT: Lightweight Block Cipher for IoT Constraint Devices

With the new era of the Internet of Things (IoT) technology, many devices with limited resources are utilized. Those devices are susceptible to a significant number of new malware and other risks emerging rapidly. One of the most appropriate methods for securing those IoT applications is cryptographic algorithms, as cryptography masks information by eliminating the risk of collecting any meaningful information patterns. This ensures that all data communications are private, accurate, authenticated, authorized, or non-repudiated. Since conventional cryptographic algorithms have been developed specifically for devices with limited resources; however, it turns out that such algorithms are not ideal for IoT restricted devices with their current configuration. Therefore, lightweight block ciphers are gaining popularity to meet the requirements of low-power and constrained devices. A new ultra-lightweight secret-key block-enciphering algorithm named “LBC-IoT” is proposed in this paper. The proposed block length is 32-bit supporting key lengths of 80-bit, and it is mainly based on the Feistel structure. Energy-efficient cryptographic features in “LBC-IoT” include the use of simple functions (shift, XOR) and small rigid substitution boxes (4-bit-S-boxes). Besides, it is immune to different types of attacks such as linear, differential, and side-channel as well as flexible in terms of implementation. Moreover, LBC-IoT achieves reasonable performance in both hardware and software compared to other recent algorithms. LBC-IoT’s hardware implementation results are very promising (smallest ever area “548” GE) and competitive with today’s leading lightweight ciphers. LBC-IoT is also ideally suited for ultra-restricted devices such as RFID tags.     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.     

Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics

Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances. It makes an effort to get the better of issues of health service delivery involving time factor, space and laborious terrains, validating cost-efficiency and finer ingress in both developed and developing countries. Tele health has been categorized into either real-time electronic communication, or store-and-forward communication. In recent years, a third-class has been perceived as remote healthcare monitoring or tele health, presuming data obtained via Internet of Things (IOT). Although, tele health data analytics and machine learning have been researched in great depth, there is a dearth of studies that entirely concentrate on the progress of ML-based techniques for tele health data analytics in the IoT healthcare sector. Motivated by this fact, in this work a method called, Weighted Bayesian and Polynomial Taylor Deep Network (WB-PTDN) is proposed to improve health prediction in a computationally efficient and accurate manner. First, the Independent Component Data Arrangement model is designed with the objective of normalizing the data obtained from the Physionet dataset. Next, with the normalized data as input, Weighted Bayesian Feature Extraction is applied to minimize the dimensionality involved and therefore extracting the relevant features for further health risk analysis. Finally, to obtain reliable predictions concerning tele health data analytics, First Order Polynomial Taylor DNN-based Feature Homogenization is proposed that with the aid of First Order Polynomial Taylor function updates the new results based on the result analysis of old values and therefore provides increased transparency in decision making. The comparison of proposed and existing methods indicates that the WB-PTDN method achieves higher accuracy, true positive rate and lesser response time for IoT based tele health data analytics than the traditional methods.

     

IoT & AI Enabled Three-Phase Secure and Non-Invasive COVID 19 Diagnosis System

Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage. To minimize the spread of the pandemic, IoT-enabled devices can be used to collect patient’s data remotely in a secure manner. Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus. In this work, the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot, IoT, and deep learning technology. In phase one, an artificially assisted chatbot can guide an individual by asking about some common symptoms. In case of detection of even a single sign, the second phase of diagnosis can be considered, consisting of using a thermal scanner and pulse oximeter. In case of high temperature and low oxygen saturation levels, the third phase of diagnosis will be recommended, where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body. The proposed model reduces human intervention through chatbot-based initial screening, sensor-based IoT devices, and deep learning-based X-ray analysis. It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage.     

Smart-City-based Data Fusion Algorithm for Internet of Things

Increasingly, Wireless Sensor Networks (WSNs) are contributing enormous amounts of data. Since the recent deployments of wireless sensor networks in Smart City infrastructures, significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to transportation. Using wireless sensor nodes, a Smart City environment may now be shown for the benefit of residents. The Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society, including the elderly and others. Weak, Quality of Service (QoS) and poor data performance are common problems in WSNs, caused by the data fusion method, where a small amount of bad data can significantly impact the total fusion outcome. In our proposed research, a WSN multi-sensor data fusion technique employing fuzzy logic for event detection. Using the new proposed Algorithm, sensor nodes will collect less repeated data, and redundant data will be used to increase the data's overall reliability. The network's fusion delay problem is investigated, and a minimum fusion delay approach is provided based on the nodes’ fusion waiting time. The proposed algorithm performs well in fusion, according to the results of the experiment. As a result of these discoveries, It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.     

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique for WSN. The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.     

图的度量维数问题的0-1蚁群条件着色分辨算法研究

图的度量维数问题（MDP）是一类在机器导航、声呐系统布置、化学、数据分类等领域有重要应用的组合优化问题．针对该问题,本文通过引入图的分辨表存储结构,建立了非线性求解模型;同时,通过改进现有蚁群算法的参数设计,利用全局搜索和局部搜索相结合的策略,建立了求解模型的改进型蚁群算法．数值对比分析验证了算法的有效性：全局搜索和局部搜索的结合较大程度的改进了算法求解质量;在规则图上提高算法求解质量具有一定挑战;与遗传算法计算结果相比较,本文提出的算法不仅在求解质量方面有所提升,而且在最坏的情况下能为图提供极小分辨集. 最后,本文探索了部分算法参数对算法求解质量的影响,并给出了进一步研究课题．     

Experimental Characterisation and Numerical Modelling of Hexagonal Honeycomb Cellular Solids under Multi‐axial Loading

Abstract: Cellular solids are becoming increasingly popular for sandwich core and energy‐absorbing applications in many automotive and other transportation structures. This paper investigates experimentally and numerically the strength and post‐failure energy absorption of a popular hexagonal aluminium honeycomb material under multi‐axial loading conditions. For the experimental work, an improved Arcan test apparatus is used so that interaction of multi‐axial compression and shear loading on failure and crushing may be studied; optical measuring methods are used to extract deformation data. In addition, experimental work to characterise the material with pre‐deformation in the in‐plane directions has also been conducted. This experimental work provides input for computational modelling of the material and two alternative modelling approaches have been investigated. First, a three‐dimensional anisotropic, elastic–plastic model, with coupling of loading components is used to represent the material at the macro‐level and, second, a meso‐modelling approach using a fine shell representation of the thin‐walled honeycomb cellular structure is applied. For practical analysis of large‐scale structures, the former approach is computationally efficient and can reasonably treat the most important failure and crush characteristics of the material. However, for more accurate analysis, particularly in the case of complex non‐proportional loading, the meso‐shell model may provide a more realistic solution.