Performance enhancement in blockchain based IoT data sharing using lightweight consensus algorithm.

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

Sleep efficiency in community-dwelling persons living with dementia: exploratory analysis using machine learning.

STUDY OBJECTIVES: Sleep disturbances lead to negative health outcomes and caregiver burden, particularly in community settings. This study aimed to investigate a predictive model for sleep efficiency and its associated features in older adults living with dementia in their own homes. METHODS: This was an exploratory, observational study. A total of 69 older adults diagnosed with dementia were included in this study. Data were collected via actigraphy for sleep and physical activity for 14 days, a sweat patch for cytokines for 2-3 days, and a survey of diseases, medications, psychological and behavioral symptoms, functional status, and demographics at baseline. Using 730 days of actigraphy, sweat patches, and baseline data, the best prediction model for sleep efficiency was selected and further investigated to explore its associated top 10 features using machine learning analysis. RESULTS: The CatBoost model was selected as the best predictive model for sleep efficiency. In order of importance, the most important features were sleep regularity, number of medications, dementia medication, daytime activity count, instrumental activities of daily living, neuropsychiatric inventory, hypnotics, occupation, tumor necrosis factor-alpha, and waking hour lux. CONCLUSIONS: This study established the best sleep efficiency predictive model among community-dwelling older adults with dementia and its associated features using machine learning and various sources, such as the Internet of Things. This study highlights the importance of individualized sleep interventions for community-dwelling older adults with dementia based on associated features.

Urban small-scale hydroponics: A compact, smart home-based hydroponics system.

The increasing population and urbanization have created a massive gap in the demand-supply model of food grains. The world is facing an acute problem with global warming and EI Nino effects, which have affected the equilibrium of the food chain. It is a need of the hour to introduce new reforms in farming to reap increased yields and reduce dependency on natural resources. Hydroponics cultivation is a boon to the agricultural sector, it enhances the cultivation of plants in an organic way by enabling Internet of Things (IoT) technology and combines technology and conventional nutritional mechanisms to enable the co-plant's growth without the strain of nutrient deficiency. This research suggests a system that integrates hardware and software into the traditional hydroponics system that the users can use to have their plantation setup in an urban environment within a small and confined space at home. This system will benefit hobbyist gardeners and small-scale urban farmers seeking an efficient, compact, and smart solution for hydroponic plant cultivation.

Double-edged sword of industrial internet of things: Empirical evidence from green transformation of enterprises.

The industrial revolution based on the Internet of Things brings new opportunities and challenges for enterprises' green and sustainable development. Therefore, this study explores the relationship between the Industrial Internet of Things (IIOT) and the Green Transformation of Enterprises (GT) based on the data of China's A-share listed companies from 2009 to 2023 using two-way fixed, mediated effect, and moderated effect models. The results show that for every unit increase in IIOT, the GT increases by 28.89 percent on average, indicating that IIOT can break down information barriers and effectively promote GT. However, after disaggregating the GT, it was found that the IIOT guided companies to implement the Symbolic Green Transformation (SGT) instead of adopting the Realistic Green Transformation (RGT). This strategic choice preference is more pronounced among firms that are larger, in energy-intensive industries, in regions with weaker environmental regulations and higher levels of development. The intermediary mechanism test proved that the IIOT can drive firms to implement comprehensive GT through paths that enhance investment attractiveness, productivity, technological innovation, and pay gaps. In particular, the masking effect reminds us that smart technology is a "double-edged sword". The negative effects of smart technologies hinder the implementation of RGT. Further analysis reveals an inverted U-shaped relationship between IIOT and RGT preferences. In addition, in companies that focus on Environmental, Social and Governance (ESG) performance and avoid financial speculation, the dark side of intelligent technology is suppressed. The findings of this study enrich the research scope of smart technology, GT, and provide important insights for enterprises in emerging economies to rationally utilize IIOT to seek green and sustainable development under the wave of rapid development of smart technology.

The deep learning-based physical education course recommendation system under the internet of things.

This study aims to propose a deep learning (DL)-based physical education course recommendation system by combining the Internet of Things (IoT) technology and DL, to improve the accuracy and personalization of recommendation. Firstly, IoT devices such as smart bracelets and smart clothing are used to monitor students' physiological data in real-time, and IoT sensors are utilized to sense the environment around students. Secondly, IoT devices capture students' social interactions with their peers, recommending socially oriented courses. Meanwhile, by integrating IoT data with students' academic data, course recommendations are optimized to match students' learning progress and schedule. Finally, Generative Adversarial Network (GAN) models, especially the improved Regularization Penalty Conditional Feature Generative Adversarial Network (RP-CFGAN) model, deal with data sparsity and cold start problems. The experimental results show that this model performs well in TopN evaluation and is markedly enhanced compared with traditional models. This study denotes that integrating IoT technology and GAN models can more accurately understand student needs and provide personalized recommendations. Although the model performs well, there is still room for improvement, such as exploring more regularization techniques, protecting user privacy, and extending the system to diverse platforms and scenarios.

Flexible Magnetoelectric Fiber for Self-Powered Human-Machine Interactive.

Flexible large strain sensors are an ideal choice for monitoring human motion, but the current use of flexible strain gauges is hindered by the need for external power sources and long-term operation requirements. Fiber-based sensors, due to their high flexibility, excellent breathability, and the ease with which they can be embedded into everyday clothing, have the potential to become a novel type of wearable electronic device. This paper proposes a flexible self-powered strain sensing material based on the electromagnetic induction effect, composed of a uniform mixture of Ecoflex and Nd2Fe14B, which has good skin-friendliness and high stretchability of over 100%. The voltage output of the magnetoelectric composite fiber remains stable over 5000 stretch-release cycles, reaching up to 969 µV. Based on this novel sensing material, a remote smart car control scheme for a human-machine interaction system was designed, enabling real-time gesture interaction.

AI security and cyber risk in IoT systems.

Internet-of-Things (IoT) refers to low-memory connected devices used in various new technologies, including drones, autonomous machines, and robotics. The article aims to understand better cyber risks in low-memory devices and the challenges in IoT risk management. The article includes a critical reflection on current risk methods and their level of appropriateness for IoT. We present a dependency model tailored in context toward current challenges in data strategies and make recommendations for the cybersecurity community. The model can be used for cyber risk estimation and assessment and generic risk impact assessment. The model is developed for cyber risk insurance for new technologies (e.g., drones, robots). Still, practitioners can apply it to estimate and assess cyber risks in organizations and enterprises. Furthermore, this paper critically discusses why risk assessment and management are crucial in this domain and what open questions on IoT risk assessment and risk management remain areas for further research. The paper then presents a more holistic understanding of cyber risks in the IoT. We explain how the industry can use new risk assessment, and management approaches to deal with the challenges posed by emerging IoT cyber risks. We explain how these approaches influence policy on cyber risk and data strategy. We also present a new approach for cyber risk assessment that incorporates IoT risks through dependency modeling. The paper describes why this approach is well suited to estimate IoT risks.

Assessment of a LPG hybrid solar dryer assisted with smart air circulation system for drying basil leaves.

The fluctuation of solar radiation throughout the day presents a significant obstacle to the widespread adoption of solar dryers for the dehydration of agricultural products, particularly those that are sensitive to high temperatures, such as basil leaf drying during the winter season. Consequently, this recent study sought to address the limitations of solar-powered dryers by implementing a hybrid drying system that harnesses both solar energy and liquid petroleum gas (LPG). Furthermore, an innovative automatic electronic unit was integrated to facilitate the circulation of air between the drying chamber and the ambient environment. Considering the solar radiation status in Egypt, an LPG hybrid solar dryer has been developed to be suitable for both sunny and cloudy weather conditions. This hybrid solar dryer (HSD) uses indirect forced convection and a controlled auxiliary heating system (LPG) to regulate both temperature and relative humidity, resulting in increased drying rates, reduced energy consumption, and the production of high-quality dried products. The HSD was tested and evaluated for drying basil leaves at three different temperatures of50, 55, and 60 °C and three air changing rates of 70, 80, and 90%, during both summer and winter sessions. The obtained results showed that drying basil at a temperature of 60 °C and an air changing rate of 90% led to a decrease in the drying time by about 35.71% and 35.56% in summer and winter, respectively, where summer drying took 135-210 min and winter drying took 145-225 min to reach equilibrium moisture content (MC). Additionally, the effective moisture diffusivity ranged from 5.25 to 9.06 × 10- 9 m2/s, where higher values of effective moisture diffusivity (EMD) were increased with increasing both drying temperatures and air change rates. Furthermore, the activation energy decreased from 16.557 to 25.182 kJ/mol to 1.945-15.366 kJ/mol for the winter and summer sessions, respectively. On the other hand, the analysis of thin-layer kinetic showed that the Modified Midilli II model has a higher coefficient of determination R2, the lowest χ2, and the lowest root mean square error (RMSE) compared to the other models of both winter and summer sessions. Finally, the LPG hybrid solar dryer can be used for drying a wide range of agricultural products, and it is more efficient for drying medicinal plants. This innovative dryer utilizes a combination of LPG and solar energy, making it efficient and environmentally friendly.

Applications of Blockchain and Smart Contracts to Address Challenges of Cooperative, Connected, and Automated Mobility.

Population growth and environmental burden have turned the efforts of cities globally toward smarter and greener mobility. Cooperative and Connected Automated Mobility (CCAM) serves as a concept with the power and potential to help achieve these goals building on technological fields like Internet of Things, computer vision, and distributed computing. However, its implementation is hindered by various challenges covering technical parameters such as performance and reliability in tandem with other issues, such as safety, accountability, and trust. To overcome these issues, new distributed and decentralized approaches like blockchain and smart contracts are needed. This paper identifies a comprehensive inventory of CCAM challenges including technical, social, and ethical challenges. It then describes the most prominent methodologies using blockchain and smart contracts to address them. A comparative analysis of the findings follows, to draw useful conclusions and discuss future directions in CCAM and relevant blockchain applications. The paper contributes to intelligent transportation systems' research by offering an integrated view of the difficulties in substantiating CCAM and providing insights on the most popular blockchain and smart contract technologies that tackle them.

Enhancing Lessons on the Internet of Things in Science, Technology, Engineering, and Medical Education with a Remote Lab.

Integrating remote Internet of Things (IoT) laboratories into project-based learning (PBL) in higher education institutions (HEIs) while exploiting the approach of technology-enhanced learning (TEL) is a challenging yet pivotal endeavor. Our proposed approach enables students to interact with an IoT-equipped lab locally and remotely, thereby bridging theoretical knowledge with practical application, creating a more immersive, adaptable, and effective learning experience. This study underscores the significance of combining hardware, software, and coding skills in PBL, emphasizing how IoTRemoteLab (the remote lab we developed) supports a customized educational experience that promotes innovation and safety. Moreover, we explore the potential of IoTRemoteLab as a TEL, facilitating and supporting the understanding and definition of the requirements of remote learning. Furthermore, we demonstrate how we incorporate generative artificial intelligence into IoTRemoteLab's settings, enabling personalized recommendations for students leveraging the lab locally or remotely. Our approach serves as a model for educators and researchers aiming to equip students with essential skills for the digital age while addressing broader issues related to access, engagement, and sustainability in HEIs. The practical findings following an in-class experiment reinforce the value of IoTRemoteLab and its features in preparing students for future technological demands and fostering a more inclusive, safe, and effective educational environment.

Indeterministic Data Collection in UAV-Assisted Wide and Sparse Wireless Sensor Network.

The widespread adoption of Internet of Things (IoT) applications has driven the demand for obtaining sensor data. Using unmanned aerial vehicles (UAVs) to collect sensor data is an effective means in scenarios with no ground communication facilities. In this paper, we innovatively consider an indeterministic data collection task in a UAV-assisted wide and sparse wireless sensor network, where the wireless sensor nodes (SNs) obtain effective data randomly, and the UAV has no pre-knowledge about which sensor has effective data. The UAV trajectories, SN serve scheduling and UAV-SN association are jointly optimized to maximize the amount of collected effective sensing data. We model the optimization problem and address the indeterministic effective indicator by introducing an effectiveness probability prediction model. The reformulated problem remains challenging to solve due to the number of constraints varying with the variable, i.e., the serve scheduling strategy. To tackle this issue, we propose a two-layer modified knapsack algorithm, within which a feasibility problem is resolved iteratively to find the optimal packing strategy. Numerical results demonstrate that the proposed scheme has remarkable advantages in the sum of effective data blocks, reducing the completion time for collecting the same ratio of effective data by nearly 30%.

Smart monitoring solution for dengue infection control: A digital twin-inspired approach.

BACKGROUND AND OBJECTIVE: In the realm of smart healthcare, precise monitoring and prediction services are crucial for mitigating the impact of infectious diseases. This study introduces an innovative digital twin technology-inspired monitoring architecture that employs a similarity-based hybrid modeling scheme to significantly enhance accuracy in the smart healthcare domain. The research also delves into the potential of IoT technology in delivering advanced technological healthcare solutions, with a specific focus on the rapid expansion of dengue fever. METHODS: The proposed digital twin-inspired healthcare system is designed to proactively combat the spread of dengue virus by enabling ubiquitous monitoring and forecasting of individuals' susceptibility to dengue infection. The system utilizes digital twin technology to observe the status of healthcare and generate likely predictions about the vulnerability to the virus by employing k-means Clustering and Artificial Neural Networks. RESULTS: The proposed system has been validated and its effectiveness has been demonstrated through experimental evaluation using carefully defined methods. The results of the experimental assessment confirm that the system performs optimally in terms of Temporal Delay (14.15 s), Classification Accuracy (92.86%), Sensitivity (92.43%), Specificity (91.52%), F-measure (90.86%), and Prediction Effectiveness. Moreover, by integrating a hybrid model that corrects errors in physics-based predictions employing a model for error correction driven by data, this approach has demonstrated a noteworthy 48% reduction in prediction errors, particularly in health monitoring scenarios. CONCLUSIONS: The digital twin-inspired healthcare system proposed in this study can assist healthcare providers in assessing the health vulnerability of the dengue virus, thereby reducing the likelihood of long-term or catastrophic health consequences. The integration of a hybrid modeling approach and the utilization of IoT technology has shown promising results in enhancing the accuracy and effectiveness of smart health monitoring and prediction services.

Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention.

BACKGROUND: The challenge of preventing in-patient falls remains one of the most critical concerns in health care. OBJECTIVE: This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention. METHODS: A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system. RESULTS: In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047). CONCLUSIONS: The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety.

Enhancing data security and privacy in energy applications: Integrating IoT and blockchain technologies.

The integration of blockchain technology with the IoToffers numerous opportunities to enhance the privacy, security, and integrity. This study comprehensively analyze the challenges, scope, and potential solutions associated with integrating blockchain technology and the IoT, with a specific emphasis on nuclear energy applications. We discuss the roles and various aspects of blockchain and the IoT, highlighting their multiple dimensions and applications. Our study develops a secure data management framework that incorporates encryption, integrity verification, an integrated communication network, and a robust data flow architecture. We explore the several aspects of data security, privacy, and integrity, along with the potential solutions in the integration of blockchain and IoT. The study also investigates the secure transaction process, with a specific focus on cryptographic, mathematical, and algorithmic perspectives. We demonstrated the use of blockchain technology in the nuclear energy sector using flow charts, comprehensively addressing the associated security and privacy concerns. While emphasizing the applicability of our methodology to the nuclear sector, we also acknowledge limitations such as requirements for practical validation, challenges with resource-constrained IoT environments, increasing cyberthreats, and limited real-time data availability. The future scope of our study focuses on standardization, scalable blockchain, post-quantum cryptography, privacy, regulations, real-world testbeds, and deep learning for nuclear sector security. Our findings highlight that the integration of blockchain and IoT can significantly enhance the security and privacy of nuclear energy applications, although practical validation and optimization are necessary.

Evaluating challenges of circular economy and Internet of Things in renewable energy supply chain through a hybrid decision-making framework.

Due to industrial development, expansion of communities, and attention to sustainable development, sustainable energy supply has become a big challenge for communities. In this regard, the development and use of Renewable Energy (RE) are considered due to reducing the harmful environmental effects of fossil fuels. Improving the efficiency of the Renewable Energy Supply Chain (RESC) is important for using RE. To improve the performance and efficiency of RESC, it is necessary to use emerging technologies such as the Internet of Things (IoT) and its integration with the principles of the Circular Economy (CE). Therefore, this study proposes integrating IoT and CE for sustainable development and resource management in RESC. Also, this research provides a hybrid decision framework to assess the challenges of IoT and CE in the RESC of Iran. The CRiteria Importance Through Intercriteria Correlation (CRITIC) technique is used to specify the importance of the criteria. The Fuzzy Evaluation Based on Distance from Average Solution (FEDAS) technique ranks the challenges. The findings indicated that considering the cost of investment, the rate of return on investment, and the productivity rate were the most important sub-criteria with values of 0.149, 0.129, and 0.106 respectively. Then, the sensitivity of the results is examined and the validation of the findings is analyzed with decision-making methods. The results indicate the high priority of the challenge related to transparency in the implementation procedures of IoT and RE projects and information dissemination protocols, the development of guidelines for the integration of IoT in other systems in the information network, and the amount of investment and lack of access to financial resources. This study provided practical insights for RE development based on IoT and CE capabilities for energy planning.

Digital Twin in Managing Hypertension Among People With Type 2 Diabetes: 1-Year Randomized Controlled Trial.

Background: Digital twin (DT)-guided lifestyle changes induce type 2 diabetes (T2D) remission but effects on hypertension (HTN) in this population are unknown. Objectives: The purpose of this study was to assess effects of DT vs standard of care (SC) on blood pressure (BP), anti-HTN medication, HTN remission, and microalbuminuria in participants with T2D. Methods: This is a secondary analysis of a randomized controlled trial in India of 319 participants with T2D. Participants were randomized to DT group (N = 233), which used artificial intelligence-enabled DT technology, or SC group (N = 86). A Home Blood Pressure Monitoring system guided anti-HTN medication adjustments. BP, anti-HTN medications, HTN remission rates, and microalbuminuria were compared between groups. Results: Among the 319 participants, 44 in DT and 15 in SC group were on anti-HTN medications, totaling 59 (18.4%) participants. DT group achieved significant reductions in systolic blood pressure (-7.6 vs -3.2 mm Hg; P < 0.007) and diastolic blood pressure (-4.3 vs -2.2 mm Hg; P = 0.046) after 1 year compared with SC group. 68.2% of DT group remained off anti-HTN medications compared to none in SC group. Among participants with HTN, DT subgroup achieved higher rates of normotension (40.9% vs 6.7%; P = 0.0009) and HTN remission (50% vs 0%; P < 0.0001) than SC subgroup. DT group had a higher rate of achieving normoalbuminuria (92.4% vs 83.1%; P = 0.018) at 1 year compared with SC group. Conclusions: Artificial intelligence -enabled DT technology is more effective than SC in reducing BP and anti-HTN medications and inducing HTN remission and normoalbuminuria in participants with HTN and T2D. (A Novel WholeBody Digital Twin Enabled Precision Treatment for Reversing Diabetes; CTRI/2020/08/027072).

Optimal control strategies for high-efficiency non-isolated DC-DC buck converters in IoT applications: A comparative study.

A DC-DC buck converter (DDBC) plays a crucial role in facilitating the rapid evolution of Internet of Things (IoT) applications across a broad spectrum of load requirements. Achieving high efficiency under diverse load conditions necessitates a meticulous exploration of modulation and control methods. This paper aims to explore literature concerning modulation and control techniques employed in buck converters for IoT applications, with the goal of achieving optimal efficiency. The most often used control methods in the DDBC for power conversion efficiently are adaptive controlled pulse skip modulation (APSM), pulse frequency modulation (PFM), digital pulse width modulation (DPWM), and adaptive on time control (AOT). Based on the major drawbacks of high quiescent current, large ripple, and low efficiency, the control methods used in IoT applications to achieve high efficiency are discussed. The structure of DDBC with the unique controlling method and their capability of suppressing the output ripple voltage and minimizing quiescent current are briefly addressed. Comparison among the methods exhibits how control methods can achieve high efficiency. This paper outlines the major challenges in power converter control for future research and development.

Design of agricultural product traceability system based on blockchain and RFID.

Ensuring the traceability of agricultural products is essential for quality control and food safety. Recent technological advances have provided new ways to enhance traceability systems. This study aims to use blockchain technology, centralized database and RFID tags to develop a secure agricultural product traceability system, retain the detailed information of agricultural products traceability, ensure that the summary information of agricultural products on the chain cannot be modified, and optimize the SM3 algorithm to effectively summarize the traceability data and improve the efficiency of the system. The aggregated data is time-stamped, recorded on the blockchain, and written into an RFID tag. The optimization of the SM3 algorithm improved the efficiency by 30% and reduced the execution time of 192-byte messages to 210µs. The system ensures accurate linking of traceability data through secure data retention and unalterable summaries on the blockchain. The integrated use of blockchain, centralized database and RFID technology, as well as the enhanced SM3 algorithm, allows the system to meet the standards for data accuracy and performance requirements in agricultural traceability applications.

Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System.

The integration of IoT and cloud services enhances communication and quality of life, while predictive analytics powered by AI and deep learning enables proactive healthcare. Deep learning, a subset of machine learning, efficiently analyzes vast datasets, offering rapid disease prediction. Leveraging recurrent neural networks on electronic health records improves accuracy for timely intervention and preventative care. In this manuscript, Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System (IOT-CC-DD-OICAN-SHS) is proposed. Initially, an Internet of Things (IoT) device collects diabetes, chronic kidney disease, and heart disease data from patients via wearable devices and intelligent sensors and then saves the patient's large data in the cloud. These cloud data are pre-processed to turn them into a suitable format. The pre-processed dataset is sent into the Improved Generative Adversarial Network (IGAN), which reliably classifies the data as disease-free or diseased. Then, IGAN was optimized using the Flamingo Search optimization algorithm (FSOA). The proposed technique is implemented in Java using Cloud Sim and examined utilizing several performance metrics. The proposed method attains greater accuracy and specificity with lower execution time compared to existing methodologies, IoT-C-SHMS-HDP-DL, PPEDL-MDTC and CSO-CLSTM-DD-SHS respectively.