Wireless Smart Research Articles

AI-Assisted Automated Identification of Human Activities Using Ficus religiosa Leaf-Based Triboelectric Nanogenerator.

Movement monitoring and effective identification of different actions are the keys that help in fitness services, health status, clinical studies, etc. In this technological era, Internet of Things (IoT) technologies, including smart wireless devices and sensors, are very effectively used for monitoring human activities, but the demand for sustainable and green power sources is a crucial issue with these devices. Triboelectric nanogenerators (TENG) are proven to be promising applications in these devices because they harvest energy from the surrounding environment and eliminate the use of batteries as power sources. As a green energy source, this study emphasizes the fabrication of biodegradable materials-based TENGs, which are eco-friendly and are related to clean and green energy as per the UN's sustainable development goals SDG 7 (affordable and clean energy). In the present work, a natural Ficus religiosa leaf (FRL) of the F. religiosa tree is used in designing and fabricating a TENG (FRL-TENG). Also, an approach is discussed to compare the performance of FRL-TENG with TENGs fabricated from other waste biodegradable materials such as garlic tunic, onion tunic, and eggshell membrane (ESM). During the experimental study, it is observed that the FRL-based TENG produced maximum voltage in comparison to other material combinations selected in this study. The generated electric output from these TENG combinations is also used to power an array of tens of green-light-emitting diodes (LEDs). Furthermore, this paper also proposes the use of FRL-TENG as a wearable sensor to collect information and monitor the physical activities of the user, viz., walk, jump, and run. To recognize the movement status, the FRL-TENG sensor is integrated with an extra randomized tree-based machine learning model for accurately distinguishing the user's three activities with an accuracy of 96%. The work showcases an innovative approach to encourage customized uses of TENG sensors in human motion monitoring and permits the development of intelligent, self-powered systems for new applications.

Wireless sensor network-based health monitoring and smart home assistance for the older adults in sports and wellness towns

BackgroundIntegrating technology with rural development is essential for addressing the unique challenges faced by aging populations in rural areas. China’s national rural revitalization strategy emphasizes the importance of developing characteristic towns that focus on health and wellness demands, particularly for the older adults. However, there is a gap in the literature regarding the systematic use of technology to support the health and daily living needs of this demographic.ObjectiveThis study aims to bridge this gap by proposing a wireless sensor network (WSN)-based system that integrates health monitoring and smart home assistance, specifically tailored for older adults residents in sports and wellness towns.MethodsThe system’s design involves collecting and analyzing historical activity data and basic physiological parameters from older adults residents’ homes. The data are cleaned, combined and stored to prepare it for behavior analysis, which is vital for controlling smart home equipment. Preliminary health assessments are conducted using the collected physiological data. A hybrid network leveraging Sub-G and Wi-Fi technologies is optimized for the purposes of collecting and uploading data, based on a comparative analysis of wireless communication options.SignificanceThis study significantly contributes to the advancement of sports and wellness towns by promoting healthy aging through cutting-edge technological solutions.

Detection of overhead line glass insulator condition using dual function device and deep learning approach

This paper presents a design of a multifunction smart wireless device for online condition monitoring of transmission line insulators. The proposed device can measure the insulator leakage current and take images of the high-voltage insulation. Yolov5-based models and deep convolutional neural networks (DCCN) are developed to analyze and classify the measured data and estimate the insulator's health condition. We have developed and tested a prototype of the proposed device. The device can issue a real-time warning message when a sudden change takes place in the leakage current value. The control center or smartphones receive the collected data wirelessly. We analyze the transmitted data using the developed methods to detect any anomalies and take appropriate remedial action. The performance and feasibility of the developed device are assessed through extensive experimental analysis. Results attest to the robustness of the proposed device, which is easy to install for existing and future overhead transmission line insulators.

IRS assisted anti jamming and beamforming technique

Recently, the emerging intelligent reflecting surfaces (IRS) has been leveraged to establish a smart and controllable wireless environment. This paper addresses the challenge of maximizing spectral efficiency in an IRS-aided downlink multi-user system while facing jammer attacks. The optimization problem focuses on improving the available transmission rate and limiting the impact of malicious jammer signals. The efficiency of the IRS in anti-jamming communications is achieved by determining the optimal phase shifts of the IRS elements and the optimal beamforming at the base station (BS). The optimization problem is non-convex. To tackle this non-convexity, an alternating optimization algorithm is proposed, dividing the main problem into two sub-problems which are solved using the semi-definite relaxation (SDR) and Gaussian randomization techniques. The simulation results confirm the enhanced spectral efficiency achieved by the proposed anti-jamming method in an IRS-assisted system. The optimal adjustment of the phase shifts in the IRS elements has resulted in a significant enhancement of about 25% in spectral efficiency compared to the random IRS phase case.

Smart Building, Nuisance Electrical Failure, Remote Monitoring and Fault Recovery System

Smart Buildings are the fastest growing buildings compared to conventional buildings and are expected to grow at a rate of 8.3% for the next decay. The global commercial building automation market was valued at US$32.96 billion in 2020 and is expected to reach US76.49 billion by the year 2031. The implementation of technological advancement, demand for energy-efficient construction, low operational cost, and occupant comfortability have helped Smart Building to grow. However, Smart Buildings are still facing issues, from various sensor network diversity, clear definitions of Smart Buildings, and continuous smartness during full or partial electrical internal fault occurrence. This indicates the building is smart as long as a continuous power supply is present. Thus, this research study investigated Smart Building's drawbacks and designed a circuit to be a solution to one of the drawbacks to rectify the issues. The design provides full monitoring for real and nuisance failures and enables the performance of fault recovery procedures remotely. The circuit interfaces with the safety Residual Current Devices (RCD) and the wireless smart Wi-Fi-controlled socket outlets. Food industries, hotels, and restaurants could be the beneficiaries of this circuit. This is due to cold stores and fridges, which can lead to significant production loss/reduction when failure time increases.

Development of a wireless smart sensor system and case study on lifting risk assessment

With the widespread adoption of Industry 4.0 and smart manufacturing concepts across industries, sensor development, system integration, and data analysis have become important aspects of efficient manufacturing operations. In addition to monitoring the performance of machines, significant importance is given to human condition monitoring in factories, using body-worn sensors to ensure the well-being of workers and for injury prevention. This research presents the development of a body-worn sensor system capable of sampling acceleration and rotation data up to 400 Hz and wirelessly transmitting the data over Bluetooth Low Energy (BLE). Further, the communication protocols for data acquisition, data communication within the device, Real Time Operating System (RTOS) programming, and multi-threading are described. This system is designed in such a way that multiple devices can be connected to the Data acquisition (DAQ) system simultaneously, and data is collected from the sensors in a synchronized manner. This information is valuable for the wider adoption of sensor systems for human condition monitoring in industry. Lastly, to test the system’s capabilities, a case study of lifting risk assessment is presented, where data collected from the accelerometer and gyroscope are used to determine a relative estimate of the physical stress associated with a manual lifting task by using different machine learning (ML) algorithms. The case study highlights how sensor placement, feature extraction, and sensor types influence machine learning models. As the sensor system can perform computations on the edge, a framework to carry out real-time lifting risk assessment using lightweight algorithms and the most important data features is proposed.

Application of a specialist nurse–led wireless smart monitoring system in elderly patients with persistent atrial fibrillation

Background: With the increase in the elderly population, the prevalence of persistent atrial fibrillation (AF) among older adults has significantly risen, becoming one of the crucial cardiovascular diseases affecting the health and quality of life of the elderly. Patients with persistent AF require continuous cardiac function monitoring to prevent possible complications such as stroke and heart failure. Traditional monitoring methods often necessitate frequent hospital visits, increasing the economic burden and psychological stress on patients. With advancements in medical technology, wireless smart monitoring systems have emerged as a new remote health care technology, offering a more convenient and effective monitoring option for patients with persistent AF. Specialist nurses play a pivotal role in monitoring and managing patients using these systems. Their professional knowledge and skills are vital for improving monitoring efficiency and patient compliance. Therefore, this study aims to explore the application of wireless smart monitoring systems led by specialist nurses in elderly patients with persistent AF, in hopes of providing a scientific basis for improving the monitoring and management of this patient group. Methods: A total of 88 elderly patients with persistent atrial fibrillation admitted to the Cardiology Ward of a tertiary hospital in Weifang City from October 2021 to April 2023 were selected as research subjects. They were divided into an intervention group and a control group with conventional care, with 44 cases in each group, according to the order of admission. The control group received conventional hospitalization guidance, while the intervention group utilized a wireless smart monitoring system led by specialist nurses. The effects before and after the intervention in both groups were compared. Results: The intervention group patients had better scores in terms of mental stress, sleep quality, and comfort compared with the control group, and the nursing shift change time during hospitalization for the intervention group was shorter than that of the control group (P<0.05). Conclusions: The wireless smart monitoring system led by specialist nurses can improve the mental stress and sleep conditions of elderly patients with persistent atrial fibrillation, increase comfort, and enhance the efficiency of nursing shift changes.

Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts on railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data are transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events, like impact detection, that require a rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine-learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

Exploring charge regeneration effect in interdigitated array electrodes-based TENGs for a more than 100-fold enhanced power density

The vast potential of harnessing high entropy and abundantly available mechanical energy through triboelectric nanogenerators (TENGs) has attracted significant attention in recent years. However, the cost of harvesting this energy has often outweighed the energy collected. Recent advancements in TENGs for blue energy harvesting from water flow have shown great promise. In this study, we present a novel approach to optimize the performance of interdigitated electrode array-based TENGs operating in free-standing mode (IDA-FTENG) by introducing a gap-to-width ratio (GWR) relationship for the electrodes and its impact on the charge regeneration effects. We investigate the dependence of the charge regeneration effect on GWRs and the number of electrode pairs to enhance the performance of IDA-FTENGs, employing a rapid and industrially scalable laser scribing process for fabricating the devices. An optimized device, featuring a maximum of 34 interdigitated electrode grids, demonstrates a 140-fold increase in power density compared to conventional single electrode pair TENGs (SEP-TENGs). Furthermore, power density projections indicate that the optimized IDA-FTENGs can compete with current solar cells, if designed suitably. We showcase the applicability of the proposed IDA-FTENG devices in self-powered sensors, autonomous wireless operations, security monitoring, and smart home systems.

Smart wireless flexible bandage containing drug loaded polycaprolactone microparticles for real-time monitoring and treatment of chronic wounds.

The quality of life is negatively impacted by chronic wounds for more than 25 million people in the US. They are quite prone to infection, which may lead to the eventual loss of a limb. By exposing the ulcers to treatment agents at the appropriate time, the healing rate is increased. On-demand drug release in a closed-loop system will aid us in reaching our goal. In this study, we have developed a platform capable of real-time diagnosis of bacterial infection by wirelessly reading wound pH, as well as slow and on-demand local administration of antibiotics. The drug carrier microparticles, an electrical patch, a thermoresponsive hydrogel with an integrated microheater, and a flexible pH sensor comprised the closed-loop patch. Here it is reported that slow and smart release of cefazolin can be addressed by incorporation of drug encapsulated hydrophobic microparticles embedded into a thermo-responsive hydrogel. The utilization of a programmable bandage to provide antibiotic medication highlights the need of not only choosing appropriate therapeutic substances but also the controlled release of the medicine and its rate of release within the wound area. The results of our study indicate that the use of cefazolin encapsulated polycaprolactone (PCL) microparticles can effectively regulate the application of antibiotic treatment for chronic skin wounds. The results also showed a substantial gradual release of cefazolin from the thermo-responsive Pnipam hydrogel when the wound dressing was subjected to a temperature of 37°C. We believe that the developed flexible smart bandage can have a significant impact on chronic wound healing.

Sensitive strain sensors with wide-range detection and high stability for intelligent human motion monitoring

Miniaturized wearable electronic devices with strain sensors as core sensing elements hold significant applications in health monitoring and human-machine interaction. However, it remains a challenge to develop strain sensors with optimal response linearity, a wide detection range, and stability. In this study, to satisfy the above requirements, we construct high-performance wearable strain sensors (rGO-CNTOH@PSE) by a strategy of hybridizing conductive fillers with stretchable substrates. In detail, a synergistic conductive network is developed by adopting OH-containing carbon nanotube (CNTOH) and reduced graphene oxide (rGO) fillers with an appropriate mass ratio. After the network is integrated into a porous silicone elastomer substrate formed by the salt templating method, the developed strain sensor exhibits distinct resolution, consistent repeatability, and superior response linearity across a wide strain detection range of 0.3–303.03 %. Furthermore, the sensor also maintains good response stability over 15000 cycles of strain. We also demonstrate that this strain sensor showing outstanding exceptional detection performance has the capability for full-range human motion detection. A wireless smart electronic glove is developed based on the sensor, which can accurately recognize gesture movements and control a bionic robotic hand to replicate human hand actions, offering the potential for replacing manual tasks (such as, grasping in hazardous environments) in the future.

Shaping the future: Improvements in the EV charging Infrastructure: A comparative analysis of Germany and India

The shift towards sustainable transportation has accelerated the deployment of electric vehicles (EVs), demanding advancements in EV charging infrastructure. [1] This thesis, "Shaping the Future: Improvements in EV Charging Infrastructure," explores the critical role of enhancing charging networks to boost EV adoption and usage. It delves into the status, challenges, technological innovations, regulatory frameworks, and evaluates the collective impact on future mobility. In-depth investigations reveal the challenges facing the existing EV charging infrastructure, such as grid capacity limitations, interoperability issues, and regulatory complexities. [2] It also compares global strategies from countries like Germany and India to evaluate the impact of government policies, subsidies, and incentives on infrastructure deployment. The research identifies emerging trends, including wireless charging, bidirectional capabilities, and smart, connected charging stations, emphasizing their potential to revolutionize the EV charging experience. It assesses the economic and environmental sustainability of integrating renewable energy sources into charging networks, as well as the scalability and adaptability of the infrastructure to evolving demands. [3] By synthesizing these findings, the thesis provides insights into future directions for the development of EV charging infrastructure. It offers recommendations for overcoming current barriers, fostering technological innovation, and harmonizing regulations to create a seamless, sustainable, and universally accessible EV charging environment.

Iterative hybrid compressive sensing-based channel estimation method for intelligent reflecting surface-supported millimeter wave systems

The Intelligent reflecting surfaces (IRSs) have been established to show the capability to enhance spectral and energy efficiency by using the passive beamforming at the IRS point and joint optimization of the active beamforming at the base station (BS). However, in the smart wireless environments in which mostly passive RISs are deplored the estimation of the channel state estimation is challenging. This paper, therefore, aims to propose a channel estimation scheme with an improved performance in comparison with some other recently proposed estimation schemes. The proposed channel estimation scheme employs the hybrid strategy to combine the improved versions of traditional Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP) compressive sensing algorithms as a basis for the proposed estimator. The proposed estimator, through computer simulations, shows improved performance when compared with the other four channel estimators that were recently documented in literature though with a slightly high computational complexity cost.

Real-time spatial-temporal mapping and visualization of thermal comfort and HVAC control by integrating immersive augmented reality technologies and IoT-enabled wireless sensor networks: Towards immersive human-building interactions

Existing methods to assess and visualize thermal comfort mainly focused on either individual users (e.g., through wearables) or for specific locations where sensors are installed, rather than continuously for the entire indoor space. Also, most of the existing thermal comfort visualization methods and HVAC system control approaches rely on phones and mobile devices and lack a fully immersive/interactive experience and human-building interaction. Thus, this paper develops an immersive human-building interaction framework for real-time spatial-temporal mapping and visualization of indoor thermal comfort by integrating immersive augmented reality (AR) technologies and IoT-enabled smart wireless sensor networks (WSNs) (i.e., temperature and relative humidity data) to provide thermal comfort assessment for the entire space (rather than at localized/individual locations/points) and to enable users to interact with and adjust the HVAC systems from a distance using fully immersive and realistic environments. First, a smart WSN comprised of four IoT-enabled sensing stations was developed to collect real-time thermal comfort-related information (i.e., temperature and relative humidity). Second, an AR environment was created using the Unity engine, where virtual objects were overlayed into, and aligned with, the real-world environment. Third, the real-time data from the IoT-enabled WSN was integrated into the AR environment using various programming scripts that add multiple functionalities to the users, including controlling the HVAC system and visualizing the thermal comfort along the entire indoor space or room of interest through a spatial-temporal map. Fourth, the developed framework was validated and evaluated by 118 participants using a survey questionnaire. The findings showed that the graphical satisfaction achieved an overall average score of 4.75 out of 5, the user's sense of spatial presence was rated at an average score of 4.68 out of 5, the involvement aspects of the developed approach received an overall average score of 4.80 out of 5, and experienced realism was rated at an average score of 4.58 out of 5. Ultimately, this paper contributes to enhanced and intelligent human-building interaction and visualization through the use of emerging technologies by allowing the building users to control and interact with the HVAC systems using AR capabilities to improve the health and well-being of the building occupants.

Smart Wireless Transducer Dedicated for Use in Aviation Laboratories.

Reliable testing of aviation components depends on the quality and configuration flexibility of measurement systems. In a typical approach to test instrumentation, there are tens or hundreds of sensors on the test head and test facility, which are connected by wires to measurement cards in control cabinets. The preparation of wiring and the setup of measurement systems are laborious tasks requiring diligence. The use of smart wireless transducers allows for a new approach to test preparation by reducing the number of wires. Moreover, additional functionalities like data processing, alarm-level monitoring, compensation, or self-diagnosis could improve the functionality and accuracy of measurement systems. A combination of low power consumption, wireless communication, and wireless power transfer could speed up the test-rig instrumentation process and bring new test possibilities, e.g., long-term testing of moving or rotating components. This paper presents the design of a wireless smart transducer dedicated for use with sensors typical of aviation laboratories such as thermocouples, RTDs (Resistance Temperature Detectors), strain gauges, and voltage output integrated sensors. The following sections present various design requirements, proposed technical solutions, a study of battery and wireless power supply possibilities, assembly, and test results. All presented tests were carried out in the Components Test Laboratory located at the Łukasiewicz Research Network-Institute of Aviation.

Multi-agent Iot-based System for Intelligent Vehicle Traffic Management Using Wireless Sensor Networks

Aims: Integrated computing technologies such as the Internet of Things (IoT), Multi- Agent Systems (MAS), and automatic networking to deliver Internet of Vehicles (IoV) applications. Methodology: The main objective of this paper is to combine MAS with IoT or IoV a new paradigm within its Cypher Physical System (CPS) for intelligent car applications. We proposed the MAS algorithm and applied it to control traffic lights at multiple intersections. When using MAS together with scattered computing architectures, IoV can achieve higher efficiency. The proposed combination is based on the independent knowledge, adaptability, assertiveness, and responsiveness that can be used in wireless sensor paradigms to bring new remedies. Smart products will explore further advancements and diverse mobility capabilities. Results: IoT provides an appropriate atmosphere for connecting with MAS concepts and programs in addition to providing reliable, adaptable, efficient, and intelligent solutions in the automotive network. In addition, the combination of MAS with IoT and cognitive conditions could result in scalable, automated, and smart wireless sensor solutions. Conclusion: We conduct experiments on three different datasets, and the results demonstrate that MAS outperforms several state-of-the-art algorithms in alleviating traffic congestion with shorter training time.

Sunlight-Driven Smart Windows with a Wide Temperature Range of Optical Switching Based on Chiral Nematic Liquid Crystals.

Photoresponsive liquid crystals are promising materials for sunlight-driven smart windows, which can automatically change their optical states in response to sunlight and control energy flow between the inside and outside of a building. Herein, liquid-crystalline systems are developed that show a transparent-scattering transition upon irradiation with sunlight in a wide temperature range. Push-pull azobenzenes with axial chirality have been newly developed as photochromic chiral dopants to allow changes in mesostructures of liquid crystals in response to sunlight. To realize optical switching, photochromic and photoinert chiral compounds with opposite handedness of helical twisting are doped in liquid crystals. This liquid crystalline sample with a compensated nematic phase is transparent in its initial state. Upon irradiation with sunlight, this sample transforms to a scattering state due to the formation of helical mesostructures along with photoisomerization of azobenzene moieties and the change in the helical twisting power. After the cease of irradiation, the sample reverts to the transparent state through thermal back isomerization of azobenzene moieties. This system significantly improves the operating temperature range of sunlight-driven smart windows based on liquid crystals: the transparent-scattering transition is observed at 4-42 °C. The present mechanism allows development of autonomous and wireless smart windows adaptable to various environments.

A Passive Wireless Smart Washer for Locking Force Monitoring on the Orthopedic Pedicle Screw

A pedicle screw is a component for fixation in spine fusion surgery, often for patients with osteoporosis. Spine fusion condition highly depends on whether the pedicle screw is tightly fixed on spine, if not, spine fusion will not work properly. After the surgery, the first 3 months is the most crucial period, and bone healing situation cannot be shown through X-ray before the first radiologic images taken around the sixth week. Therefore, it is helpful to have a nonradiative method to monitor the locking force of the pedicle screw after surgery, especially during the early stage. Here a passive wireless force sensor is developed for monitoring the locking force of the pedicle screw, as we call it a smart washer. By integrating a capacitive ring-shape force sensor with an inductor, a passive LC sensor can be built by measuring the resonant frequency wirelessly. The smart washer is designed and calibrated to establish the relation between the locking force and resonant frequency, and then it is fixed with a screw in a porcine femur, with and without medium between the reader and inductor. When the locking force decreases from 8.3 to 0.9 N, the error is less than 0.5 N, and the maximum wireless sensing distance is 72 mm. However, the medium between the reader and the sensor inductor will affect the resonant frequency, but not the sensitivity. Therefore, the locking force variation can still be calculated by the resonant frequency shift accurately. Furthermore, by designing another five LC sensors with different operating resonant frequency ranges, it is possible to identify locking forces at different locations for more pedicle screws. To our knowledge, no LC force sensor was proposed to monitor the locking force of pedicle screws after surgery in the past.

EVALUATING THE IMPACT OF CROSS-CORRELATION PROPERTIES OF COMPLEX SIGNALS ON THE CHARACTERISTICS OF SMART RADIO SYSTEMS

The article discusses modern problems and prospects for the development of "smart radio" systems using complex signals for effective use of the radio frequency spectrum. Cognitive radio systems use complex signals to efficiently use the radio frequency spectrum. It is emphasized that the ensemble size of complex signals is a key factor that affects various system characteristics such as immunity, data transmission efficiency, and spectral efficiency. Various methods can be used to increase the ensemble size of complex signals, including the use of pseudo-random sequences with low levels of sidelobes in the cross-correlation function, new complex signal generation algorithms (e.g., based on artificial intelligence), and new coding techniques that allow for increased ensemble size. without compromising data transmission efficiency. The choice of a specific approach to increasing the size of the ensemble of complex signals depends on the specific requirements of the cognitive telecommunication system. An important task is the development of new methods of generating complex signals with high interference-resistant properties, which allow to increase the size of the ensemble without compromising the quality of service. This task is important for the development of next-generation "smart radio" systems. The analysis of various methods of increasing the size of the ensemble of complex signals is presented. The maximum correlation immunity is calculated for different lengths of sequences and the results of research for five sequences are given: pseudorandom noise, nonlinear sequence, Galois sequence, Gold sequence and Kiyosaki sequence. This information can be used to select the optimal sequence taking into account the specific requirements of "smart radio" systems. The conclusions of the article can make a valuable contribution to the development of effective and productive telecommunication systems for future generations of "smart radio".

Delay-sensitive task offloading and efficient resource allocation in intelligent edge–cloud environments: A discretized differential evolution-based approach

The number of smart wireless devices (WDs) has enormously increased over the last few years due to the advancement of 5G/B5G networks. The advanced applications of such smart WDs, e.g., augmented reality, virtual reality, online gaming, etc., demand excessive resources. Although the WDs are equipped with limited resources, the evolution of edge computing and offloading techniques enables the WDs to offload their resource-intensive tasks to the nearby edge node. These edge nodes might experience higher loads and delays when WDs generate a huge number of tasks. Moreover, the wireless channel bandwidth and transmission data rate of the wireless channels are also limited. Therefore, optimizing the use of available bandwidth as a valuable resource and reducing latency emerge as crucial objectives while offloading tasks. In this paper, a delay-aware resource-constrained offloading problem for edge–cloud systems is mathematically formulated as a 0–1 integer linear programming, and it is shown to be NP-complete. Then, a delay-aware resource-constrained offloading algorithm based on a discretized differential evolution (DARC-DE) is designed. The objectives of the DARC-DE are to maximize the utilization of the resources as bandwidth and minimize the delay. The vectors are efficiently encoded along with the decoding technique. The fitness function is designed by considering execution, offloading, queuing, transmission delay, and bandwidth utilization. The DARC-DE is shown to be executed in polynomial time. To evaluate DARC-DE, extensive simulation is performed in two different scenarios with varying numbers of tasks and edges. Simulation results demonstrate that the proposed DARC-DE can minimize total delay by 15% to 40% in comparison to particle swarm optimization, genetic algorithm, and bees algorithm, respectively. Simulation results also indicate a significant improvement in bandwidth utilization. Taguchi method and alternative average convergence rate are conducted. The statistical tests—analysis of variance, post-hoc, and Friedman tests—are also performed.