Numerical development of high performance quasi D-shape PCF-SPR biosensor : An external sensing approach employing gold

This research was supported by the Sejong University through its Research Faculty Program ( 20192021 ).Peer reviewe

Techno-economic analysis of the hybrid solar pv/h/fuel cell based supply scheme for green mobile communication

Hydrogen has received tremendous global attention as an energy carrier and an energy storage system. Hydrogen carrier introduces a power to hydrogen (P2H), and power to hydrogen to power (P2H2P) facility to store the excess energy in renewable energy storage systems, with the facts of large-scale storage capacity, transportability, and multiple utilities. This work examines the techno-economic feasibility of hybrid solar photovoltaic (PV)/hydrogen/fuel cell-powered cellular base stations for developing green mobile communication to decrease environmental degradation and mitigate fossil-fuel crises. Extensive simulation is carried out using a hybrid optimization model for electric rnewables (HOMER) optimization tool to evaluate the optimal size, energy production, total production cost, per unit energy production cost, and emission of carbon footprints subject to different relevant system parameters. In addition, the throughput, and energy efficiency performance of the wireless network is critically evaluated with the help of MATLAB-based Monte-Carlo simulations taking multipath fading, system bandwidth, transmission power, and inter-cell interference (ICI) into consideration. Results show that a more stable and reliable green solution for the telecommunications sector will be the macro cellular basis stations driven by the recommended hybrid supply system. The hybrid supply system has around 17% surplus electricity and 48.1 h backup capacity that increases the system reliability by maintaining a better quality of service (QoS). To end, the outcomes of the suggested system are compared with the other supply scheme and the previously published research work for justifying the validity of the proposed system

Backtracking Search Algorithm for Microgrid Energy Scheduling in Day Ahead Market

Energy utilization and cost minimization of renewable energy microgrids (REM) is an essential and difficult optimization challenge. However, most works have not considered heuristic and non-heuristic algorithms of REM complex equality and inequality requirements. In response, a single-objective optimization model for REM scheduling is developed and presented in this study. This microgrid model uses a very efficient and well-liked meta-heuristic optimization method called the Backtracking Search Algorithm (BSA). The BSA method locates a solution by constructing a solution piece by piece, adding levels over time, and using recursive calling. It is a method of large-scale search, and unlike other meta-heuristic approaches, it has a different model structure. Therefore, the proposed solution strategy may provide desirable outcomes with sufficient computational effort. Furthermore, in this research, the suggested process evaluates the consequences of another well-known meta-heuristic, particle swarm optimization. Its results demonstrate the effectiveness and superiority of the BSA over other REM determination techniques

Optimal Design of a Hybrid Solar PV/BG‐Powered Heterogeneous Network

The increased penetration of renewable energy sources (RESs) along with the rise in demand for wireless communication had led to the need to deploy cellular base stations powered by locally accessible RESs. Moreover, networks powered by renewable energy sources have the ability to reduce the costs of generating electricity, as well as greenhouse gas emissions, thus maintaining the quality of service (QoS). This paper examines the techno‐economic feasibility of developing grid‐tied solar photovoltaic (PV)/biomass generator (BG)‐powered heterogeneous networks in Bangladesh, taking into account the dynamic characteristics of RESs and traffic. To guarantee QoS, each macro and micro‐base station is supplied through a hybrid solar PV/BG coupled with enough energy storage devices. In contrast, pico and femto BSs are powered through standalone solar PV units due to their smaller power rating. A hybrid optimization model for electric renewables (HOMER)‐based optimization algorithm is considered to determine the optimum system architecture, economic and environmental analysis. MATLAB‐based Monte‐Carlo simulations are used to assess the system’s throughput and energy efficiency. A new weighted proportional‐fair resource method is presented by trading power consumption and communication latency in non‐real‐time applications. Performance analysis of the proposed architecture confirmed its energy efficiency, economic soundness, reliability, and environmental friendliness. Additionally, the suggested method was shown to increase the battery life of the end devices

Machine learning‐based LoRa localisation using multiple received signal features

Abstract Low‐power localisation systems are crucial for machine‐to‐machine communication technologies. This article investigates LoRa technology for localisation using multiple features of the received signal, such as Received Signal Strength Indicator (RSSI), Spreading Factors (SF), and Signal to Noise Ratio (SNR). A novel range‐based technique to estimate the distance of a target node from a LoRa gateway using machine‐learning models that incorporates SF, SNR, and RSSI to train the models is proposed. A modified trilateration approach is then used to localise the target node from three gateways. Our experiment used three LoRaWAN gateways and two sensor nodes, on a sports oval with an approximate area coverage of 30,000 square metres. The authors also used a public LoRaWAN dataset to build a model test the proposed method and compare both range‐based distance mapping with trilateration and fingerprint‐based direct location estimation techniques. Our method achieved an average distance error of 43.97 m on our experimental dataset. The results show that the combination of RSSI, SNR, and SF‐based distance mapping provides ∼10% improvement on ranging accuracy and 26.58% higher accuracy for trilateration‐based localisation when compared with just using RSSI. Our method also achieved 50% superior localisation accuracy with fingerprint‐based direct location estimation approaches

Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network

The heterogeneous network (HetNet) is a specified cellular platform to tackle the rapidly growing anticipated data traffic. From a communications perspective, data loads can be mapped to energy loads that are generally placed on the operator networks. Meanwhile, renewable energy-aided networks offer to curtailed fossil fuel consumption, so to reduce the environmental pollution. This paper proposes a renewable energy based power supply architecture for the off-grid HetNet using a novel energy sharing model. Solar photovoltaics (PV) along with sufficient energy storage devices are used for each macro, micro, pico, or femto base station (BS). Additionally, a biomass generator (BG) is used for macro and micro BSs. The collocated macro and micro BSs are connected through end-to-end resistive lines. A novel-weighted proportional-fair resource-scheduling algorithm with sleep mechanisms is proposed for non-real time (NRT) applications by trading-off the power consumption and communication delays. Furthermore, the proposed algorithm with an extended discontinuous reception (eDRX) and power saving mode (PSM) for narrowband internet of things (IoT) applications extends the battery lifetime for IoT devices. HOMER optimization software is used to perform optimal system architecture, economic, and carbon footprint analyses while the Monte-Carlo simulation tool is used for evaluating the throughput and energy efficiency performances. The proposed algorithms are validated through the practical data of the rural areas of Bangladesh from which it is evident that the proposed power supply architecture is energy-efficient, cost-effective, reliable, and eco-friendly

Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network

The heterogeneous network (HetNet) is a specified cellular platform to tackle the rapidly growing anticipated data traffic. From a communications perspective, data loads can be mapped to energy loads that are generally placed on the operator networks. Meanwhile, renewable energy-aided networks offer to curtailed fossil fuel consumption, so to reduce the environmental pollution. This paper proposes a renewable energy based power supply architecture for the off-grid HetNet using a novel energy sharing model. Solar photovoltaics (PV) along with sufficient energy storage devices are used for each macro, micro, pico, or femto base station (BS). Additionally, a biomass generator (BG) is used for macro and micro BSs. The collocated macro and micro BSs are connected through end-to-end resistive lines. A novel-weighted proportional-fair resource-scheduling algorithm with sleep mechanisms is proposed for non-real time (NRT) applications by trading-off the power consumption and communication delays. Furthermore, the proposed algorithm with an extended discontinuous reception (eDRX) and power saving mode (PSM) for narrowband internet of things (IoT) applications extends the battery lifetime for IoT devices. HOMER optimization software is used to perform optimal system architecture, economic, and carbon footprint analyses while the Monte-Carlo simulation tool is used for evaluating the throughput and energy efficiency performances. The proposed algorithms are validated through the practical data of the rural areas of Bangladesh from which it is evident that the proposed power supply architecture is energy-efficient, cost-effective, reliable, and eco-friendly

Towards Energy Efficient Load Balancing for Sustainable Green Wireless Networks under Optimal Power Supply

The enormous growth in the cellular networks and ubiquitous wireless services has incurred momentous energy consumption, greenhouse gas (GHG) emissions and thereby, imposed a great challenge to the development of energy-efficient sustainable cellular networks. With the augmentation of harvesting renewable energy, cellular base stations (BSs) are progressively being powered by renewable energy sources (RES) to reduce the energy crisis, carbon contents, and its dependency on conventional grid supply. Thus, the combined utilization of renewable energy sources with the electrical grid system is proving to be a more realistic option for developing an energy-efficient as well as an eco-sustainable system in the context of green mobile communications. The ultimate objective of this work is to develop a traffic-aware grid-connected solar photovoltaic (PV) optimal power supply system endeavoring the remote radio head (RRH) enabled heterogeneous networks (HetNets) aiming to minimize grid energy consumption and carbon footprint while ensuring long-term energy sustainability and energy efficiency (EE). Moreover, the load balancing technique is implemented among collocated BSs for better resource blocks (RBs) utilization and thereafter, the performance of the system is compared with an existing cell zooming enabled cellular architecture for benchmarking. Besides, the techno-economic feasibility of the envisaged system has been extensively analyzed using HOMER optimization software considering the dynamic nature of solar generation profile and traffic arrival rate. Furthermore, a thorough investigation is conducted with the help of Monte-Carlo simulations to assess the wireless network performance in terms of throughput, spectral efficiency (SE), and energy efficiency as well under a wide range of design scenarios. The numerical outcomes demonstrate that the proposed grid-tied solar PV/battery system can achieve a significant reduction of grid power consumption yielding up to 54.8% and ensure prominent energy sustainability with the effective modeling of renewable energy harvesting.</p

Single Phase Fault Detection of Induction Motor using Machine Learning Approaches

The induction motor (IM), an asynchronous type of AC electric motor, plays a crucial role in operational procedures in industrial sectors, which needs to be operated sophisticatedly without any error. This research investigates the occurrence of single-phase faults, which are commonly observed in induction motors, among various other types of failures, through detection and classification employing machine learning (ML) tools. This research addresses the machine\u27s condition based on three operational modes, which include the healthy case, 5% fault and 10% fault of induction motors. In the case of generating the dataset for implementation of ML tools, simple d-axis and q-axis conversions are considered for a healthy situation of IM. However, on the other hand, Park\u27s transformation is made in modeling the faulty IM by transforming it from a phase to a two-phase system for accumulating the faulty dataset. Several electrical features of IM are considered regarding generating healthy and faulty datasets for training the ML models so that they can detect and classify the operational mode of IM. Two well-known statistical features, namely the mean and standard deviation, are chosen to measure the performance of the ML models in detecting and identifying the motor operating conditions. Several ML models are implemented to the model of the machine in testing the robustness of the fault identifying and diagnosis procedure where the Random Forest algorithm shows the best performance with 99.9% accuracy