Critical Industrial Applications Research Articles

Kink-mediated high strength and large ductility via nanocrystallization in a tough titanium alloy

Titanium (Ti) alloys, which are highly promising as structural materials in critical industrial applications, generally require high strength and ductility, particularly high fracture toughness. However, the conventional approach of enhancing strength through mechanical processing to induce dislocations often leads to a compromised ductility known as the strength-ductility trade-off. Here, we develop a new strategy of the nanocrystallized kinks to overcome this issue in Ti-11V metastable β-Ti alloys via combining industrially used cold forging (CF) and cold rolling (CR) processes. Deformation kinks are firstly activated by CF, and subsequently they are fragmented into nanograins during CR, architecting nanocrystallized kinks in the coarse-grained matrix. This unique microstructure effectively balances the strength-ductility conflict, endowing this Ti-V binary alloy with high yield strength ∼1200 MPa, appreciable ductility ∼17 % and high fracture toughness ∼ 52.0 MPa·m½, which is superior to numerous multielement engineering Ti alloys. This design strategy of nanocrystallized kinks can be extended to other engineering materials, e.g., Mg and Zr alloys, for advanced performance at large industrial scales.

AI-Based Virtual Sensing of Gaseous Pollutant Emissions at the Tailpipe of a High-Performance Vehicle

<div>This scientific publication presents the application of artificial intelligence (AI) techniques as a virtual sensor for tailpipe emissions of CO, NOx, and HC in a high-performance vehicle. The study aims to address critical challenges faced in real industrial applications, including signal alignment and signal dynamics management. A comprehensive pre-processing pipeline is proposed to tackle these issues, and a light gradient-boosting machine (LightGBM) model is employed to estimate emissions during real driving cycles. The research compares two modeling approaches: one involving a unique “direct model” and another using a “two-stage model” which leverages distinct models for the engine and the aftertreatment. The findings suggest that the direct model strikes the best balance between simplicity and accuracy. Furthermore, the study investigates two sensor setups: a standard configuration and an optimized one, which incorporates an additional lambda probe in the exhaust line after the main catalyst. The results indicate a significant enhancement in performance for NOx and CO estimations with the introduction of the third lambda probe, while HC results remain relatively unchanged. Additionally, the AI model is tested on two different electronic control unit (ECU) software calibrations, yielding excellent results in both cases. This suggests that machine learning models are robust to control software variation and can be used to optimize software calibrations in a virtual environment, reducing the reliance on extensive experimental testing. Moreover, the AI model’s performance demonstrates compatibility with real-time implementation. In conclusion, this work establishes the viability and efficiency of AI techniques in accurately estimating tailpipe emissions from an engine in an industrial context. The study showcases the potential for AI to contribute to emission estimation and optimization processes, offering a promising pathway for an innovative industrial practice.</div>

FPGA implementation of an improved envelope detection approach for bearing fault diagnosis

This study presents an enhanced envelope detection technique implemented on a field-programmable gate array (FPGA) to diagnose bearing faults in rotating machinery. Bearing faults frequently result in machinery breakdowns, incurring substantial downtime and maintenance expenses. In our approach, we employ the Teager energy operator (TEO) to extract the vibration signal envelope. Subsequently, we subject the envelope signal to the fast Fourier transform (FFT) to generate the envelope spectrum of the vibration signal. Finally, we further refine the envelope spectrum using TEO for a second time, resulting in a pronounced fault peak that facilitates early fault detection. We evaluate the effectiveness and performance of the proposed method using two distinct types of bearing vibration signals, one being simulated and the other measured. Our findings reveal that the suggested approach outperforms traditional envelope detection methods, leading to a substantially enhanced fault diagnosis capability. For instance, when we assess the characteristic frequency ratio (FCFR) for faults in the inner and outer rings of the bearing using the proposed method, we observe that the FCFR values are significantly elevated, ranging from 160 to 330% higher compared to the analysis performed by the TEO and HT methods. Consequently, this indicates that the proposed approach has the ability to detect faults at an earlier stage than other methods. Furthermore, the FPGA-based implementation makes it suitable for critical industrial applications where rapid fault detection is essential to prevent catastrophic failures.

(Invited) Piezocatalysis for Treating a Massive Industrial Dye Solution and Hydrogen Production

As a fossil fuel substitute, among the various renewable alternative energy sources, hydrogen fuel is a clean energy source being developed in advanced industrial nations worldwide. Photocatalytic water splitting yields hydrogen through redox reactions underlying direct water decomposition. Although water splitting is a promising method of yielding clean energy from the sunlight without contaminating byproducts, sunlight is a desultory source of energy, which limits the amount of solar radiation owing to its dependence on time (day or night), geographical position, and season.Redox reactions remain one of the most promising methods of producing hydrogen and decomposing industrial wastewater. However, for most critical practical industrial applications, the robust light attenuation of wastewater results in meager quantum yield, thus significantly limiting the commercial applications of photocatalysis. Therefore, novel approaches to evaluating parameters, including cost-effectivity, high efficiency, and long-time stability, are highly desirable for developing next-generation catalysts. In particular, such technology would be up-and-coming for simultaneously producing hydrogen gas and decomposing industrial wastewater without light irradiation. This talk will report how 2D transition metal dichalcogenides can be used for piezocatalyts for highly efficient wastewater treatment of a massive industrial dye solution and hydrogen production without applied light irradiation by utilizing a closed-loop system with a low-frequency natural water flow. Our newly developed piezoelectrocatalysts system provides the first evidence of the effective production of hydrogen gas and the simultaneous decomposition of dye molecules in wastewater without light irradiation, thus providing a promising method for treating industrial wastewater and producing clean energy.

Effect of bio-mineral oil blend quenchant on the mechanical properties of carburized mild-steel

In this study, the effect of bio-mineral oil blend quenchants on the mechanical properties of carburized mild steel was experimentally studied and reported. The tensile, hardness, impact, and microstructural test specimens were prepared in line with ASTM standards. Prepared specimens were then buried in a 50:50% ratio mixtures of eggshell/date-seed particulates as carburizing medium in a sealed packed cylindrical crucible. The carburization was then carried out in a muffle furnace at 950 oC for 3 h soaking time at 5 °C/min heating rate and thereafter quenched in different percentage blends of bio-mineral oils. Before the mechanical test and microstructural examination, samples were tempered at 200 oC for 1 h.Results from the experimental findings revealed that water and bio-mineral oil blend quenchants significantly influenced the mechanical properties and microstructure of carburized mild steel in varying degrees depending on the quenching media. Specimen quenched in 100% groundnut oil yielded the maximum yield tensile strength (805.43 MPa) and hardness at the surface edge (173.8 HV) equivalent to 106.7 and 87.66 percentage increment however, the best combination of mechanical properties (tensile strength 738.66 MPa, strain 17.12%, hardness 169.5 HV and impact strength 51.1 J) was obtained in the specimen quenched in 60/40% groundnut oil and SAE40 oil blends respectively. The enhancement in the mechanical property was due to the grain refinement in the microstructure of the bio-mineral oils quenched specimen. The 60/40 groundnut/SAE40 oil blend is therefore recommended for metallurgical heat treatment of mild steel for critical industrial applications.

Review of Next-Generation Wireless Devices with Self-Energy Harvesting for Sustainability Improvement

Wireless methodologies are the focal point of electronic devices, including telephones, computers, sensors, mobile phones, laptops, and wearables. However, wireless technology is not yet utilized extensively in underwater and deep-space communications applications, and it is also not applied in certain critical medical, military, and industrial applications due to its limited battery life. Self-energy-harvesting techniques overcome this issue by converting ambient energy from the surroundings into usable power for electronic devices; devices that use such techniques are next-generation wireless devices that can operate without relying on external power sources. This methodology improves the sustainability of the wireless device and ensures its prolonged operation. This article gives an in-depth analysis of the recent techniques that are implemented to design an efficient energy-harvesting wireless device. It also summarizes the most preferred energy sources and generator systems in the present trends. This review and its summary explore the common scope of researchers in narrowing their focus in designing new self-energy-harvesting wireless devices.

TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications

Time series databases are essential for the large-scale deployment of many critical industrial applications. In infrastructure monitoring, for instance, a database system should be able to process large amounts of sensor data in real-time, execute continuous queries, and handle complex analytical queries such as anomaly detection or forecasting. Several benchmarks have been proposed to evaluate and understand how existing systems and design choices handle specific use cases and workloads. Unfortunately, none of them fully covers the peculiar requirements of monitoring applications. Furthermore, they fall short of providing an automated way to generate representative real-world data and workloads for testing and evaluating these systems. We present TSM-Bench, a benchmark tailored for time series database systems used in monitoring applications. Our key contributions consist of (1) representative queries that meet the requirements that we collected from a water monitoring use case, and (2) a new scalable data generator method based on Generative Adversarial Networks (GAN) and Locality Sensitive Hashing (LSH). We demonstrate, through an extensive set of experiments, how TSM-Bench provides a comprehensive evaluation of the performance of seven leading time series database systems while offering a detailed characterization of their capabilities and trade-offs.

Experimental insights into energy savings and future directions of drag reducing polymers in multiphase flow pipelines

Frictional pressure drop has been grasping the attention of many industrial applications associated with multi-phase and academia. Alongside the United Nations, the 2030 Agenda for Sustainable Development calls for the exigency of giving attention to economic growth, a considerable reduction in power consumption is necessary to co-up with this vision and to adhere to energy-efficient practices. Thereinto, drag-reducing polymers (DRPs), which do not require additional infrastructure, are a much better option for increasing energy efficiency in a series of critical industrial applications. Therefore, this study evaluates the effects of two DRPs—polar water-soluble polyacrylamide (DRP-WS) and nonpolar oil-soluble polyisobutylene (DRP-OS)—on energy efficiency for single-phase water and oil flows, two-phase air–water and air-oil flows, and three-phase air–oil–water flow. The experiments were conducted using two different pipelines; horizontal polyvinyl chloride with an inner diameter of 22.5 mm and horizontal stainless steel with a 10.16 mm internal diameter. The energy-efficiency metrics are performed by investigating the head loss, percentage saving in energy consumption (both per unit pipe length), and throughput improvement percentage (%TI). The larger pipe diameter was used in experiments for both DRPs, and it was discovered that despite the type of flow or variations in liquid and air flow rates, there was a reduction in head loss, an increase in energy savings, and an increase in the throughput improvement percentage. In particular, DRP-WS is found to be more promising as an energy saver and the consequent savings in the infrastructure cost. Hence, equivalent experiments of DRP-WS in two-phase air–water flow using a smaller pipe diameter show that the head loss drastically increases. However, the percentage saving in power consumption and throughput improvement percentage is significantly compared with that found in the larger pipe. Thus, this study found that DRPs can improve energy efficiency in various industrial applications, with DRP-WS being particularly promising as an energy saver. However, the effectiveness of these polymers may vary depending on the flow type and pipe diameter.

Wireless Distributed Consensus in Vehicle to Vehicle Networks for Autonomous Driving

Vital societal and industrial autonomous components are increasingly interconnected through communication networks to complete critical tasks cooperatively. However, as the reliability and trust requirements for connected autonomous systems continue to grow, the centralized communication and decision approaches that are in use today are reaching their limits. Focusing on autonomous driving applications, this paper proposes a resilient and trustworthy framework on wireless distributed consensus networks, where the communication links are less reliable or are even in the presence of incorrect local sensor readings/decisions. To accomplish that, a novel three stages consensus mechanism is proposed based on the practical Byzantine fault tolerance (PBFT), where the veto collection and gossip stages are designed to meet the stringent and complex requirements for a vehicle's maneuvers. A plan tree synthesis is also proposed to make consensus on a series of decisions while adopting network members' decision preferences. A detailed protocol including the distributed consensus, plan tree synthesis, dynamic grouping, etc. is proposed. Simulation results show that the proposed consensus mechanism is able to be reached and propagated through the network under poor wireless communication conditions and the presence of faulty vehicles with incorrect sensor readings. The result can be extended to other autonomous systems to significantly enhance safety in critical industrial applications.

An Efficient Fault Diagnosis Framework for Digital Twins Using Optimized Machine Learning Models in Smart Industrial Control Systems

In recent times, digital twins (DT) is becoming an emerging and key technology for smart industrial control systems and Industrial Internet of things (IIoT) applications. The DT presently supports a significant tool that can generate a huge dataset for fault prediction and diagnosis in a real-time scenario for critical industrial applications with the support of powerful artificial intelligence (AI). The physical assets of DT can produce system performance data that is close to reality, which delivers remarkable opportunities for machine fault diagnosis for effective measured fault conditions. Therefore, this study presents an intelligent and efficient AI-based fault diagnosis framework using new hybrid optimization and machine learning models for industrial DT systems, namely, the triplex pump model and transmission system. The proposed hybrid framework utilizes a combination of optimization techniques (OT) such as the flower pollination algorithm (FPA), particle swarm algorithm (PSO), Harris hawk optimization (HHO), Jaya algorithm (JA), gray wolf optimizer (GWO), and Salp swarm algorithm (SSA), and machine learning (ML) such as K-nearest neighbors (KNN), decision tree (CART), and random forest (RF). The proposed hybrid OT–ML framework is validated using two different simulated datasets which are generated from both the mechanized triplex pump and transmission system models, respectively. From the experimental results, the hybrid FPA–CART and FPA–RF models within the proposed framework give acceptable results in detecting the most relevant subset of features from the two employed datasets while maintaining fault detection accuracy rates exemplified by the original set of features with 96.8% and 85.7%, respectively. Therefore, the results achieve good and acceptable performance compared to the other existing models for fault diagnosis in real time based on critical IIoT fields.

Characterization of Polyurethane Shape Memory Polymer and Determination of Shape Fixity and Shape Recovery in Subsequent Thermomechanical Cycles.

Multifunctional polyurethane shape memory polymers (PU-SMPs) have been of increasing interest in various applications. Here we report structure characterization, detailed methodology, and obtained results on the identification of functional properties of a thermoset PU-SMP (MP4510) with glass transition temperature of 45 °C. The stable, chemically crosslinked network of this thermoset PU-SMP results in excellent shape memory behavior. Moreover, the proximity of the activation temperature range of this smart polymer to room and body temperature enables the PU-SMP to be used in more critical industrial applications, namely fast-response actuators. The thermomechanical behavior of a shape memory polymer determines the engineering applications of the material. Therefore, investigation of the shape memory behavior of this class of commercial PU-SMP is of particular importance. The conducted structural characterization confirms its shape memory properties. The shape fixity and shape recovery properties were determined by a modified experimental approach, considering the polymer's sensitivity to external conditions, i.e., the temperature and humidity variations. Three thermomechanical cycles were considered and the methodology used is described in detail. The obtained shape fixity ratio of the PU-SMP was approximately 98% and did not change significantly in the subsequent cycles of the thermomechanical loading due to the stability of chemical crosslinks in the thermoset materials structure. The shape recovery was found to be approximately 90% in the first cycle and reached a value higher than 99% in the third cycle. The results confirm the effect of the thermomechanical training on the improvement of the PU-SMP shape recovery after the first thermomechanical cycle as well as the effect of thermoset material stability on the repeatability of the shape memory parameters quantities.

Polymeric nanocomposite membranes for gas separation: Performance, applications, restrictions and future perspectives

In recent decades, anthropogenic industrial-based activities (i.e., energy production and its conveyance) has led to the emission of deleterious greenhouse gases such as CO 2 , H 2 S, and NO 2 as an important environmental concern. Membrane-based gas separation processes have been currently of paramount attention in critical industrial applications such as purification, biogas recovery, water treatment, gas processing and H 2 recovery. Polymeric nanocomposite membranes (PNM) have illustrated indisputable potential of utilization to mitigate the release of gaseous pollutants to the ecosystem and reduce their hazardous environmental effects. Incorporation of novel nanoparticles including activated carbon, nanofibers and carbon nanotubes have revolutionized the efficiency of polymeric membranes for gas separation. This review paper aims to present disparate applications of polymeric nanocomposite membranes for mitigating the emission of toxic/acidic gases to environment. Moreover, performance, applications and challenges towards the use of nanocomposite membranes are reviewed comprehensively and future perspectives towards their development in industrial applications are recommended. Finally, prevalent nanocomposite membranes for the separation of various gaseous mixtures are presented.

The Effect of Multi-Walled Carbon Nanotubes Additives on the Tribological Properties of Austempered AISI 4340 Steel

Due to a combination of optimal properties such as great strength, high hardness, good process ability, and good mechanical properties, AISI 4340 steel is widely used in many critical industrial applications such as nuclear, military, defense, and aerospace. It is also widely used in hydraulic forged machine tools, forged automotive crankshaft systems, shafts and gears, because of their improved characteristics, and its good tribological properties. The purpose regarding this work is to check the tribological characteristics of austempered AISI 4340 steel while dry and lubricated with machinery oil of SAE 30 grade as base oil. As received, AISI 4340 steel samples have been austempered to four definitely austenitic phase temperatures (850℃, 900℃, 1000℃, and 1050℃) for 90 minutes before being immersed in a mixture of potassium nitrite and sodium nitrite at 400℃ for 45 minutes. Friction and wear tests were then performed on austempered samples. Multi-walled carbon nanotube particles were blended at weight concentrations of 0.055, 0.1, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, and 0.45 with typical machinery oil of 30 grade as base lubricant oil. A pin on the disc wear configuration was used in the experimental investigation. The use of Multi-Walled Carbon Nanotube (MWCNTs) additives in the base oil resulted in a decrease in both friction coefficients and wear rates values when compared to typical base oil lubricant. The results also showed a reduction in both friction coefficients and wear rates as the sample's austempering temperatures were raised. Sliding surfaces were also photo micro graphed, and when the volume concentrations of Multi-Walled Carbon Nanotube particles in the normal base oil lubricant were increased, smoother surfaces with less damage were shown.

Recent advances in monovalent ion selective membranes towards environmental remediation and energy harvesting

Advanced membranes are highly necessitated for critical industrial applications for alleviating contemporary environmental and energy issues coupled with increasing climate change. Especially, monovalent ion selective membranes (MISMs) with exceptionally high selectivity for mono/multivalent ions under low-pressure or low-voltage environments are appealing for precise ion separations with reduced energy consumption. Most recently, the rapid development of emerging materials (such as carbon nanomaterials, porous frameworks and polyelectrolytes) is conducive to enhancing the separation performance of MISMs. Therein, emerging materials with desirable structure and properties can supply supernumerary sub-nano pore sizes, wettability, charge density, and functional groups. With their unique virtues, MISMs are promising to improve separation efficiency and energy efficiency in diverse environmental and energy applications, including water treatment, lithium extraction, vanadium redox flow batteries (VRFBs) and reverse electrodialysis (RED). Herein, this review highlights ion transport mechanisms of MISMs and the structure–function relationship between the structure of membrane materials and membrane separation performance. The combination between conventional methods and emerging materials results in much higher ion selectivity of MISMs, which can be applied in both traditional fields and emerging areas. At last, the current challenges are discussed and future perspectives are also proposed.

Investigation on Effect of Shape of HTS Pole Coil on Airgap Magnetic Field of HTS Synchronous Motor

The High Temperature Superconducting (HTS) synchronous motors offer considerably higher torque and power densities along with higher efficiencies for strategic and critical industrial applications. The most widely explored airgap based topology for HTS synchronous motors has conventional copper windings in stator and HTS pole coils in the rotor. The shape of these rotor pole coils affects various electrical parameters of the motor. In this paper, a detailed investigation has been carried out on effect of shape of HTS pole coil on airgap magnetic field and rated open circuit characteristic (OCC) of an 8 MW HTS synchronous motor along with its other parameters like pole coil excitation current, peak magnetic field inside the motor, maximum perpendicular magnetic field on HTS tape and operating temperature of HTS pole coils through FEM based electromagnetic analysis. Based on the results of electromagnetic analysis, the optimum shape of HTS pole coil has been finalised.

Digital Twin Network for the IIoT using Eclipse Ditto and Hono

Digital twins are beginning to revolutionize many industries in the last decade, offering a plethora of benefits to optimize the performance of industrial systems. They aim to create a continuously synchronized model of the physical system that allows for rapid adaptation to dynamics, primarily to unpredicted and undesired changes. A vast range of industrial domains have already benefited from digital twin technology, such as aerospace, manufacturing, healthcare, city management and shipping. In addition, recent research is beginning to explore the integration of digital twins into computer networks to enable more innovation and intelligent management. One of the building blocks of digital twin technology is the Internet of Things, where wireless sensors and actuators are deployed to provide interaction between the physical and digital worlds. This type of network is complex to manage due to its strong constraints, especially when controlling critical industrial applications, which gave rise to the Industrial Internet of Things (IIoT). We believe that optimizing IIoT will lead to effective integration of digital twins in Industry 4.0. In this paper, we design a Digital Twin Network (DTN) for IIoT where sensors, actuators, and communication infrastructure are replicated in the digital twin to enable real-time intelligent management of these networks. By taking advantage of Eclipse Hono which allows an efficient connectivity for the network devices and Eclipse Ditto for representing the devices states in a digital form and also providing easy access to these states for the DTN. In this way, cognitive network services such as predictive maintenance, sustainability features, network diagnostics, security management, resource allocation, energy optimization can be efficiently integrated and operated in the network lifecycle. We validate the proposed architecture by providing a resource allocation case study where we explain how the Time Slotted Channel Hopping mechanism is exploited in our architecture.

Coded excitation for low-power guided ultrasonic wave inspection in safety-critical industries: case studies

High-power ultrasonic non-destructive evaluation (NDE) poses significant threats to intrinsic safety. It may lead to hazards in critical industrial applications, especially in oil & gas refineries, high-energy material technologies and the aerospace and aviation industries. Typically, industries employ various certifications and undertake several safety protocols to suppress the likelihood of industrial hazards. In order to satisfy safety standards for operating high-power equipment close to potential explosives and inflammable substances, industries direct large sums of investment into making these inspection systems intrinsically safe by designing complex structures and devising procedures to isolate such equipment from the system or process entirely. However, the uncertainty regarding the effectiveness of such protective measures results in a persisting difficulty in obtaining plant safety certifications and approvals. In this paper, the application of a coded excitation method to make inspection systems intrinsically safe and easily certifiable is explored. Using a pulse compression-based signal processing technique called coded excitation, it has been made possible to achieve non-contact transduction (electromagnetic acoustic transduction and air-coupled transduction) in transmitreceive mode with excitation as low as 0.5 Vpp (peak-to-peak supply voltage). This work reports on the application of coded excitation in bringing down the transduction power requirements for guided ultrasonic wave inspection, thereby making it possible to formulate new inspection applications at very low power, particularly in safety-critical industries.

Exploring on a three-fluid Eulerian-Eulerian-Eulerian approach for the prediction of liquid jet atomization

Predicting the atomization of a liquid jet in its applications, such as in fuel combustion and nuclear safety systems and in many other critical industrial applications, remains a challenging task. Using a low-computer consumption coarse-grid, this work presents a 3-D numerical method with three phases, which respectively represent the gas, continuous liquid and dispersed liquid. A Eulerian description is used for each of these phases. In addition, an algebraic interfacial area density (AIAD) model is used to consider the continuous liquid and gas phases. Meanwhile, a discrete population balance model is applied in order to take the droplet breakup and coalescences into account. The present Eulerian approach is then tested for the different liquid jets used in atomization regimes and then this is validated against the experimental data. The comparison reveals reasonable agreement in aspects such as jet spreading, droplet coalescence and the distribution of droplet sizes. Based on the simulation, the disintegration rates are found to increase from 175 kg/m3/s for case 1 to 310 kg/m3/s for case 2, which is due to increases in ejection velocity and turbulence intensity. In both the simulation and the experiment, larger-sized droplets form as the jet evolves downstream. This indicates that the coalescence between droplets overwhelms the possible breakup, meaning therefore that the diameter of the droplets increases streamwise, as shown in the comparison between D10 and D32 at the axial positions of 200 mm and 400 mm.

An Innovative Multiple Attribute Based Distributed Clustering with Sleep/Wake Scheduling Mechanism for WSN

Wireless sensor network is a dynamic field of networking and communication because of its increasing demand in critical Industrial and Robotics applications. Clustering is the technique mainly used in the WSN to deal with large load density for efficient energy conservation. Formation of number of duplicate clusters in the clustering algorithm decreases the throughput and network lifetime of WSN. To deal with this problem, advance distributive energy-efficient adaptive clustering protocol with sleep/wake scheduling algorithm (DEACP-S/W) for the selection of optimal cluster head is presented in this paper. The presented sleep/wake cluster head scheduling along with distributive adaptive clustering protocol helps in reducing the transmission delay by properly balancing of load among nodes. The performance of algorithm is evaluated on the basis of network lifetime, throughput, average residual energy, packet delivered to the base station (BS) and CH of nodes. The results are compared with standard LEACH and DEACP protocols and it is observed that the proposed protocol performs better than existing algorithms. Throughput is improved by 8.1% over LEACH and by 2.7% over DEACP. Average residual energy is increased by 6.4% over LEACH and by 4% over DEACP. Also, the network is operable for nearly 33% more rounds compared to these reference algorithms which ultimately results in increasing lifetime of the Wireless Sensor Network.

AODR: An Automatic On-Demand Retransmission Scheme for WIA-FA Networks

In Industrial Wireless Sensor Networks (IWSNs), monitoring data generated by field devices are supposed to be delivered to the gateway with high reliability and low latency. However, most of the existing industrial wireless standards are based on IEEE 802.15.4, offering a very limited data rate, which prevents their adoption in critical industrial applications. Based on IEEE 802.11, a recent international standard WIA-FA is proposed to address higher communication requirements in factory automation. In this paper, we study the timeslot allocation for retransmissions in the WIA-FA network. Specifically, we first design a novel superframe to support the Negative ACKnowledgment (NACK)-based retransmission mode in the WIA-FA network, and build a network model which considers the uplink and downlink packet losses simultaneously. Then, we analyze the three major drawbacks of the default NACK-based retransmission scheme in WIA-FA and propose an Automatic On-Demand Retransmission (AODR) scheme. Finally, we give a detailed reliability analysis of the proposed AODR scheme, and prove the relationship between the transmission reliability and critical factors (e.g., the channel condition and the length of the superframe) in the WIA-FA network. Simulation results show that the proposed AODR scheme outperforms existing works in terms of transmission reliability and timeliness for different channel conditions.