Sustainability

Research

16 pages, 2067 KiB

Open AccessArticle

A Cross-Layer Media Access Control Protocol for WBANs

by Linfeng Zheng, Juncheng Hu and Yingjun Jiao

Sustainability 2023, 15(14), 11381; https://doi.org/10.3390/su151411381 - 21 Jul 2023

Viewed by 896

Wireless body area network (WBAN) is an emerging comprehensive technology that can deeply integrate with e-health and smart sports. As a wearable network, improving the quality of network service and user experience is crucial. Due to the miniaturized design of sensors, their available [...] Read more.

Wireless body area network (WBAN) is an emerging comprehensive technology that can deeply integrate with e-health and smart sports. As a wearable network, improving the quality of network service and user experience is crucial. Due to the miniaturized design of sensors, their available energy from batteries is limited, making the extension of their lifetime a key research challenge. Existing studies have proposed methods to improve energy efficiency, but there are still limitations in addressing dynamic adaptive aspects of differential energy distribution and channel conditions. In order to further extend the lifetime of sensor nodes and networks while ensuring quality of service, and to provide a reliable transmission mechanism for heterogeneous application data, this paper presents a cross-layer optimized MAC protocol mechanism. The protocol takes into account the transmission requirements of different types of data and redesigns the superframe. To improve the lifetime of nodes, we propose an energy-adaptive adjustment mechanism considering the channel conditions. At the same time, a cooperative transmission mechanism is proposed to further enhance network lifetime. In experiments conducted on two typical networks, compared to IEEE 802.15.6, the power adjustment scheme improves the network lifetime by 2.8 to 3.7 times, and the cooperative mechanism between nodes further increases the network lifetime by 17% to 44%. Our proposed scheme effectively extends the network lifetime while ensuring quality of service, avoiding frequent battery resets for users, and effectively improving the user experience quality. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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15 pages, 2599 KiB

Open AccessArticle

Hybrid Muddy Soil Fish Optimization-Based Energy Aware Routing in IoT-Assisted Wireless Sensor Networks

by Mohammed Rizwanullah, Hadeel Alsolai, Mohamed K. Nour, Amira Sayed A. Aziz, Mohamed I. Eldesouki and Amgad Atta Abdelmageed

Sustainability 2023, 15(10), 8273; https://doi.org/10.3390/su15108273 - 19 May 2023

Cited by 5 | Viewed by 1214

Abstract

The seamless operation of interconnected smart devices in wireless sensor networks (WSN) and the Internet of Things (IoT) needs continuously accessible end-to-end routes. However, the sensor node (SN) relies on a limited power source and tends to cause disconnection in multi-hop routes because [...] Read more.

The seamless operation of interconnected smart devices in wireless sensor networks (WSN) and the Internet of Things (IoT) needs continuously accessible end-to-end routes. However, the sensor node (SN) relies on a limited power source and tends to cause disconnection in multi-hop routes because of a power shortage in the WSN, eventually leading to the inefficiency of the total IoT network. Furthermore, the density of available SNs affects the existence of feasible routes and the level of path multiplicity in the WSN. Thus, an effective routing model is predictable to extend the lifetime of WSN by adaptively choosing the better route for the data transfers between interconnected IoT devices. This study develops a Hybrid Muddy Soil Fish Optimization-based Energy Aware Routing Scheme (HMSFO-EARS) for IoT-assisted WSN. The presented HMSFO-EARS technique majorly focuses on the identification of optimal routes for data transmission in the IoT-assisted WSN. To accomplish this, the presented HMSFO-EARS technique involves the integration of the MSFO algorithm with the Adaptive

β

-Hill Climbing (ABHC) concept. Moreover, the presented HMSFO-EARS technique derives a fitness function for maximizing the lifespan and minimizing energy consumption. To demonstrate the enhanced performance of the HMSFO-EARS technique, a series of experiments was performed. The simulation results indicate the better performance of the HMSFO-EARS algorithm over other recent approaches with reduced energy consumption, less delay, high throughput, and extended network lifetime. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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28 pages, 2048 KiB

Open AccessArticle

Green Requirement Engineering: Towards Sustainable Mobile Application Development and Internet of Things

by Mahrukh Tanveer, Huma Hayat Khan, Muhammad Noman Malik and Youseef Alotaibi

Sustainability 2023, 15(9), 7569; https://doi.org/10.3390/su15097569 - 5 May 2023

Cited by 2 | Viewed by 2104

Abstract

Mobile usage statistics show the one thing that cannot be overlooked, which is the overwhelming usage of smartphones. According to the statistics, there are approximately 6.4 billion users of smartphones. Considering the world population, this rate of smart phone usage is more than [...] Read more.

Mobile usage statistics show the one thing that cannot be overlooked, which is the overwhelming usage of smartphones. According to the statistics, there are approximately 6.4 billion users of smartphones. Considering the world population, this rate of smart phone usage is more than 80%. Mobile development is the fastest prominent trend, although web development cannot be denied. However, the fact is that mobile platforms are considered cumbersome and complex when it comes to accomplishing requirement engineering processes, especially when mobile applications are combined with the Internet of Things (IoT). These complexities result in barriers to sustainable mobile development. The difficulty and differences occur due to various limitations, either that of mobile devices or others. Some of those from mobile devices include processor, battery, and touch screens, user experience in terms of touch screens, user context, and interactive behaviors. Other limitations include the difference in the software development lifecycle and the difference in the software development process due to inconsistency in user requirements with the aforementioned limited device capabilities. The target objective of this research is to investigate and identify all possible challenges related to mobile applications and connected mobile devices (IoT) while executing the requirement engineering process. This study can further the existing state of knowledge by contributing to the list of challenges faced in the requirement gathering process of mobile application development. Furthermore, it can also help practitioners, specifically those involved in the requirement gathering process, to carefully consider these challenges before executing the requirement engineering process. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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27 pages, 5523 KiB

Open AccessArticle

Design of a Telepresence Robot to Avoid Obstacles in IoT-Enabled Sustainable Healthcare Systems

by Ali A. Altalbe, Muhammad Nasir Khan and Muhammad Tahir

Sustainability 2023, 15(7), 5692; https://doi.org/10.3390/su15075692 - 24 Mar 2023

Cited by 1 | Viewed by 2092

Abstract

In the Internet of Things (IoT) era, telepresence robots (TRs) are increasingly a part of healthcare, academia, and industry due to their enormous benefits. IoT provides a sensor-based environment in which robots receive more precise information about their surroundings. The researchers work day [...] Read more.

In the Internet of Things (IoT) era, telepresence robots (TRs) are increasingly a part of healthcare, academia, and industry due to their enormous benefits. IoT provides a sensor-based environment in which robots receive more precise information about their surroundings. The researchers work day and night to reduce cost, duration, and complexity in all application areas. It provides tremendous benefits, such as sustainability, welfare improvement, cost-effectiveness, user-friendliness, and adaptability. However, it faces many challenges in making critical decisions during motion, which requires a long training period and intelligent motion planning. These include obstacle avoidance during movement, intelligent control in hazardous situations, and ensuring the right measurements. Following up on these issues requires a sophisticated control design and a secure communication link. This paper proposes a control design to normalize the integration process and offer an auto-MERLIN robot with cognitive and sustainable architecture. A control design is proposed through system identification and modeling of the robot. The robot control design was evaluated, and a prototype was prepared for testing in a hazardous environment. The robot was tested by considering various parameters: driving straight ahead, turning right, self-localizing, and receiving commands from a remote location. The maneuverability, controllability, and stability results show that the proposed design is well-developed and cost-efficient, with a fast response time. The experimental results show that the proposed method significantly minimizes the obstacle collisions. The results confirm the employability and sustainability of the proposed design and demonstrate auto-MERLIN’s capabilities as a sustainable robot ready to be deployed in highly interactive scenarios. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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15 pages, 3251 KiB

Open AccessArticle

Telepresence Robot with DRL Assisted Delay Compensation in IoT-Enabled Sustainable Healthcare Environment

by Fawad Naseer, Muhammad Nasir Khan and Ali Altalbe

Sustainability 2023, 15(4), 3585; https://doi.org/10.3390/su15043585 - 15 Feb 2023

Cited by 9 | Viewed by 2066

Abstract

Telepresence robots have become popular during the COVID-19 era due to the quarantine measures and the requirement to interact less with other humans. Telepresence robots are helpful in different scenarios, such as healthcare, academia, or the exploration of certain unreachable territories. IoT provides [...] Read more.

Telepresence robots have become popular during the COVID-19 era due to the quarantine measures and the requirement to interact less with other humans. Telepresence robots are helpful in different scenarios, such as healthcare, academia, or the exploration of certain unreachable territories. IoT provides a sensor-based environment wherein robots acquire more precise information about their surroundings. Remote telepresence robots are enabled with more efficient data from IoT sensors, which helps them to compute the data effectively. While navigating in a distant IoT-enabled healthcare environment, there is a possibility of delayed control signals from a teleoperator. We propose a human cooperative telecontrol robotics system in an IoT-sensed healthcare environment. The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) offered improved control of the telepresence robot to provide assistance to the teleoperator during the delayed communication control signals. The proposed approach can stabilize the system in aid of the teleoperator by taking the delayed signal term out of the main controlling framework, along with the sensed IOT infrastructure. In a dynamic IoT-enabled healthcare context, our suggested approach to operating the telepresence robot can effectively manage the 30 s delayed signal. Simulations and physical experiments in a real-time healthcare environment with human teleoperators demonstrate the implementation of the proposed method. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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17 pages, 5626 KiB

Open AccessArticle

Wild Horse Optimization with Deep Learning-Driven Short-Term Load Forecasting Scheme for Smart Grids

by Abdelwahed Motwakel, Eatedal Alabdulkreem, Abdulbaset Gaddah, Radwa Marzouk, Nermin M. Salem, Abu Sarwar Zamani, Amgad Atta Abdelmageed and Mohamed I. Eldesouki

Sustainability 2023, 15(2), 1524; https://doi.org/10.3390/su15021524 - 12 Jan 2023

Cited by 7 | Viewed by 1526

Abstract

Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. [...] Read more.

Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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14 pages, 3389 KiB

Open AccessArticle

Wearables-Assisted Smart Health Monitoring for Sleep Quality Prediction Using Optimal Deep Learning

by Manar Ahmed Hamza, Aisha Hassan Abdalla Hashim, Hadeel Alsolai, Abdulbaset Gaddah, Mahmoud Othman, Ishfaq Yaseen, Mohammed Rizwanullah and Abu Sarwar Zamani

Sustainability 2023, 15(2), 1084; https://doi.org/10.3390/su15021084 - 6 Jan 2023

Cited by 9 | Viewed by 2409

Abstract

Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed for fitness and wellness as they enable people to observe their day-to-day health status. These gadgets encompass sensors to accumulate data related to user activities. Clinical act graph devices come under [...] Read more.

Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed for fitness and wellness as they enable people to observe their day-to-day health status. These gadgets encompass sensors to accumulate data related to user activities. Clinical act graph devices come under the class of wearables worn on the wrist to compute the sleep parameters by storing sleep movements. Sleep is very important for a healthy lifestyle. Inadequate sleep can obstruct physical, emotional, and mental health, and could result in several illnesses such as insulin resistance, high blood pressure, heart disease, stress, etc. Recently, deep learning (DL) models have been employed for predicting sleep quality depending upon the wearables data from the period of being awake. In this aspect, this study develops a new wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning (WSHMSQP-ODL) model. The presented WSHMSQP-ODL technique initially enables the wearables to gather sleep-activity-related data. Next, data pre-processing is performed to transform the data into a uniform format. For sleep quality prediction, the WSHMSQP-ODL model uses the deep belief network (DBN) model. To enhance the sleep quality prediction performance of the DBN model, the enhanced seagull optimization (ESGO) algorithm is used for hyperparameter tuning. The experimental results of the WSHMSQP-ODL method are examined under different measures. An extensive comparison study shows the significant performance of the WSHMSQP-ODL model over other models. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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17 pages, 5784 KiB

Open AccessArticle

Metaheuristics Based Energy Efficient Task Scheduling Scheme for Cyber-Physical Systems Environment

by Anwer Mustafa Hilal, Aisha Hassan Abdalla Hashim, Marwa Obayya, Abdulbaset Gaddah, Abdullah Mohamed, Ishfaq Yaseen, Mohammed Rizwanullah and Abu Sarwar Zamani

Sustainability 2022, 14(24), 16539; https://doi.org/10.3390/su142416539 - 9 Dec 2022

Cited by 6 | Viewed by 1107

Abstract

The widespread applicability of cyber-physical systems (CPS) necessitates efficient schemes to optimize the performance of both computing units and physical plant. Task scheduling (TS) in CPS is of vital importance to enhance resource usage and system efficiency. Traditional task schedulers in embedded real-time [...] Read more.

The widespread applicability of cyber-physical systems (CPS) necessitates efficient schemes to optimize the performance of both computing units and physical plant. Task scheduling (TS) in CPS is of vital importance to enhance resource usage and system efficiency. Traditional task schedulers in embedded real-time systems are unable to fulfill the performance requirements of CPS because of the task diversity and system heterogeneities. In this study, we designed a new artificial rabbit optimization enabled energy-efficient task-scheduling scheme (ARO-EETSS) for the CPS environment. The presented ARO-EETSS technique is based on the natural survival practices of rabbits, comprising detour foraging and arbitrary hiding. In the presented ARO-EETSS technique, the TS process is performed via the allocation of

n

autonomous tasks to

m

different resources. In addition, the objective function is based on the reduction of task completion time and the effective utilization of resources. In order to demonstrate the higher performance of the ARO-EETSS system, a sequence of simulations was implemented. The comparison study underlined the improved performance of the ARO-EETSS system in terms of different measures. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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18 pages, 6119 KiB

Open AccessArticle

Smart Water Quality Prediction Using Atom Search Optimization with Fuzzy Deep Convolutional Network

by Mesfer Al Duhayyim, Hanan Abdullah Mengash, Mohammed Aljebreen, Mohamed K Nour, Nermin M. Salem, Abu Sarwar Zamani, Amgad Atta Abdelmageed and Mohamed I. Eldesouki

Sustainability 2022, 14(24), 16465; https://doi.org/10.3390/su142416465 - 8 Dec 2022

Cited by 4 | Viewed by 1444

Abstract

Smart solutions for monitoring water pollution are becoming increasingly prominent nowadays with the advance in the Internet of Things (IoT), sensors, and communication technologies. IoT enables connections among different devices with the capability to gather and exchange information. Additionally, IoT extends its ability [...] Read more.

Smart solutions for monitoring water pollution are becoming increasingly prominent nowadays with the advance in the Internet of Things (IoT), sensors, and communication technologies. IoT enables connections among different devices with the capability to gather and exchange information. Additionally, IoT extends its ability to address environmental issues along with the automation industry. As water is essential for human survival, it is necessary to integrate some mechanisms for monitoring water quality. Water quality monitoring (WQM) is an efficient and cost-effective system intended to monitor the quality of drinking water that exploits IoT techniques. Therefore, this study developed a new smart water quality prediction using atom search optimization with the fuzzy deep convolution network (WQP-ASOFDCN) technique in the IoT environment. The WQP-ASOFDCN technique seamlessly monitors the water quality parameters using IoT devices for data collection purposes. Data pre-processing is carried out at the initial stage to make the input data compatible for further processing. For water quality prediction, the F-DCN model was utilized in this study. Furthermore, the prediction performance of the F-DCN approach was improved by using the ASO algorithm for the optimal hyperparameter tuning process. A sequence of simulations was applied to validate the enhanced water quality prediction outcomes of the WQP-ASOFDCN method. The experimental values denote the better performance of the WQP-ASOFDCN approach over other approaches in terms of different measures. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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27 pages, 5128 KiB

Open AccessArticle

Integrating Blockchain with Artificial Intelligence to Secure IoT Networks: Future Trends

by Shatha Alharbi, Afraa Attiah and Daniyal Alghazzawi

Sustainability 2022, 14(23), 16002; https://doi.org/10.3390/su142316002 - 30 Nov 2022

Cited by 5 | Viewed by 4357

Abstract

Recently, the Internet of Things (IoT) has gained tremendous popularity in several realms such as smart cities, healthcare, industrial automation, etc. IoT networks are increasing rapidly, containing heterogeneous devices that offer easy and user-friendly services via the internet. With the big shift to [...] Read more.

Recently, the Internet of Things (IoT) has gained tremendous popularity in several realms such as smart cities, healthcare, industrial automation, etc. IoT networks are increasing rapidly, containing heterogeneous devices that offer easy and user-friendly services via the internet. With the big shift to IoT technology, the security of IoT networks has become a primary concern, especially with the lack of intrinsic security mechanisms regarding the limited capabilities of IoT devices. Therefore, many studies have been interested in enhancing the security of IoT networks. IoT networks need a scalable, decentralized, and adaptive defense system. Although the area of development provides advanced security solutions using AI and Blockchain, there is no systematic and comprehensive study talking about the convergence between AI and Blockchain to secure IoT networks. In this paper, we focus on reviewing and comparing recent studies that have been proposed for detecting cybersecurity attacks in IoT environments. This paper address three research questions and highlights the research gaps and future directions. This paper aims to increase the knowledge base for enhancing IoT security, recommend future research, and suggest directions for future research. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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23 pages, 2361 KiB

Open AccessArticle

Intelligent Adaptive Optimisation Method for Enhancement of Information Security in IoT-Enabled Environments

by Shailendra Pratap Singh, Youseef Alotaibi, Gyanendra Kumar and Sur Singh Rawat

Sustainability 2022, 14(20), 13635; https://doi.org/10.3390/su142013635 - 21 Oct 2022

Cited by 5 | Viewed by 1858

Abstract

The usage of the Internet increased dramatically during the start of the twenty-first century, entangling the system with a variety of services, including social media and e-commerce. These systems begin producing a large volume of data that has to be secured and safeguarded [...] Read more.

The usage of the Internet increased dramatically during the start of the twenty-first century, entangling the system with a variety of services, including social media and e-commerce. These systems begin producing a large volume of data that has to be secured and safeguarded from unauthorised users and devices. In order to safeguard the information of the cyber world, this research suggests an expanded form of differential evolution (DE) employing an intelligent mutation operator with an optimisation-based design. It combines a novel mutation technique with DE to increase the diversity of potential solutions. The new intelligent mutation operator improves the security, privacy, integrity, and authenticity of the information system by identifying harmful requests and responses and helping to defend the system against assault. When implemented on an e-commerce application, the performance of the suggested technique is assessed in terms of confidentiality, integrity, authentication, and availability. The experimental findings show that the suggested strategy outperforms the most recent evolutionary algorithm (EA). Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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12 pages, 2479 KiB

Open AccessArticle

A Novel COVID-19 Detection Technique Using Deep Learning Based Approaches

by Waleed Al Shehri, Jameel Almalki, Rashid Mehmood, Khalid Alsaif, Saeed M. Alshahrani, Najlaa Jannah and Someah Alangari

Sustainability 2022, 14(19), 12222; https://doi.org/10.3390/su141912222 - 27 Sep 2022

Cited by 7 | Viewed by 1610

Abstract

The COVID-19 pandemic affects individuals in many ways and has spread worldwide. Current methods of COVID-19 detection are based on physicians analyzing the patient’s symptoms. Machine learning with deep learning approaches applied to image processing techniques also plays a role in identifying COVID-19 [...] Read more.

The COVID-19 pandemic affects individuals in many ways and has spread worldwide. Current methods of COVID-19 detection are based on physicians analyzing the patient’s symptoms. Machine learning with deep learning approaches applied to image processing techniques also plays a role in identifying COVID-19 from minor symptoms. The problem is that such models do not provide high performance, which impacts timely decision-making. Early disease detection in many places is limited due to the lack of expensive resources. This study employed pre-implemented instances of a convolutional neural network and Darknet to process CT scans and X-ray images. Results show that the proposed new models outperformed the state-of-the-art methods by approximately 10% in accuracy. The results will help physicians and the health care system make preemptive decisions regarding patient health. The current approach might be used jointly with existing health care systems to detect and monitor cases of COVID-19 disease quickly. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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17 pages, 5667 KiB

Open AccessArticle

Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management

by Mesfer Al Duhayyim, Heba G. Mohamed, Mohammed Aljebreen, Mohamed K. Nour, Abdullah Mohamed, Amgad Atta Abdelmageed, Ishfaq Yaseen and Gouse Pasha Mohammed

Sustainability 2022, 14(18), 11704; https://doi.org/10.3390/su141811704 - 18 Sep 2022

Cited by 9 | Viewed by 2024

Abstract

Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to [...] Read more.

Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to controlling the bad odor of blowout gases and the spreading of overspills by using an IoT-based solution. The inadequate and poor dumping of waste produces radiation and toxic gases in the environment, creating an adversarial effect on global warming, human health, and the greenhouse system. The IoT and deep learning (DL) confer active solutions for real-time data monitoring and classification, correspondingly. Therefore, this paper presents an artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management, called the AEOIDL-SWM technique. The presented AEOIDL-SWM technique exploits IoT-based camera sensors for collecting information and a microcontroller for processing the data. For waste classification, the presented AEOIDL-SWM technique applies an improved residual network (ResNet) model-based feature extractor with an AEO-based hyperparameter optimizer. Finally, the sparse autoencoder (SAE) algorithm is exploited for waste classification. To depict the enhancements of the AEOIDL-SWM system, a widespread simulation investigation is performed. The comparative analysis shows the enhanced outcomes of the AEOIDL-SWM technique over other DL models. Full article

(This article belongs to the Special Issue IoT Quality Assessment and Sustainable Optimization)

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