The end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming. The primary challenges in such scenarios include (i) identifying the most energy-constrained nodes, i.e., nodes with the lowest residual energy to prevent the loss of tracking and localization functionality; (ii) avoiding reliance on the computationally intensive channel state information (CSI) acquisition process; and (iii) ensuring long-range RF wireless power transfer (LoRa-RFWPT). To address these issues, this paper introduces an adaptive and safety-aware deep reinforcement learning (DRL) framework for energy beamforming in LoRa-enabled underground disaster networks. Specifically, we develop a Safe Adaptive Deep Q-Network (SADQN) that incorporates residual energy awareness to enhance energy harvesting under mobility, while also formulating a SADQN approach with dual-variable updates to mitigate constraint violations associated with fairness, minimum energy thresholds, duty cycle, and uplink utilization. A mathematical model is proposed to capture the dynamics of post-disaster underground mine environments, and the problem is formulated as a constrained Markov decision process (CMDP). To address the inherent NP hardness of this constrained reinforcement learning (CRL) formulation, we employ a Lagrangian relaxation technique to reduce complexity and derive near-optimal solutions. Comprehensive simulation results demonstrate that SADQN significantly outperforms all baseline algorithms: increasing cumulative harvested energy by approximately 11% versus DQN, 15% versus Safe-DQN, and 40% versus PSO, and achieving substantial gains over random beamforming and non-beamforming approaches. The proposed SADQN framework maintains fairness indices above 0.90, converges 27% faster than Safe-DQN and 43% faster than standard DQN in terms of episodes, and demonstrates superior stability, with 33% lower performance variance than Safe-DQN and 66% lower than DQN after convergence, making it particularly suitable for safety-critical underground mining disaster scenarios where reliable energy delivery and operational stability are paramount.

Underground mine emergencies compromise fixed communication infrastructure exactly when situational awareness is most critical for effective rescue operations. Existing LoRa mesh protocols fail in underground mines because they ignore the structured topology of tunnel networks, specifically the waveguide effect along straight galleries, severe signal discontinuity at junctions, and the dead-end geometry of working faces. This paper presents the Topology-Aware Concurrent LoRa (TACL) mesh protocol, in which each node autonomously infers its structural role from local RF observations and packet header information, without GPS, pre-loaded mine maps, or central coordination. Role classification resolves the contender estimation problem (Nh) left open in the prior concurrent transmission literature, enabling provably bounded timing offsets before transmission. TACL assigns a spreading factor (SF)12 to dead-end source nodes for maximum link robustness and SF7–SF10 to relay nodes to create the inter-SF orthogonality margin required for concurrent decoding at junction nodes. Monte Carlo simulation of over 2000 trials yields TACL a PDR of 80.5% versus near-zero for all three baselines, confirming that topology-aware SF diversity is the necessary and sufficient mechanism to prevent junction collision collapse. Hardware deployment at the Missouri S&T Experimental Mine yields a 4.0× PDR improvement over the topology-agnostic concurrent transmission (CT)-fixed baseline, a median end-to-end latency of 1815 ms with 84× tighter latency spread than ALOHA-based protocols and 2.5× lower energy per delivered packet. These results establish that explicit exploitation of underground mine topology is essential for reliable, predictable, and energy-efficient emergency mesh communications in post-disaster underground mine scenarios.

Accurate path-loss models are essential for planning reliable wireless networks in underground mines, yet existing characterization studies rely on specialized channel sounders and vector network analyzers costing tens of thousands of dollars, placing them beyond the reach of most mine operators. This paper demonstrates that LoRa transceivers costing approximately US $15 per node can serve as a self-contained path-loss measurement instrument, logging the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) directly to a CSV file over a standard USB serial connection. A measurement campaign conducted at the Missouri S&T Experimental Mine on 31 March 2026 collected 4801 packets across four distinct underground canonical primitives: straight tunnel, T-junction, vertical shaft, and post-bend NLoS gallery at distances of 5 to 60 m using Waveshare Pico-LoRa-SX1262 boards operating at 915 MHz. The results reveal a pronounced two-zone propagation structure, including a line-of-sight (LoS) zone with a negative path-loss exponent of -0.34, confirming tunnel waveguide gain up to 25 m, followed by a steep NLoS zone with an exponent of 13.0 after a 24.0 dB bend diffraction loss. Environment-specific measurements quantify a 5.5 dB junction excess loss and a 29.5 dB shaft excess loss relative to a straight-tunnel reference. Spreading factor sensitivity tests across SF7, SF9, and SF12 confirm that RSSI measurements are consistent to within 2 dB across all SFs, validating the measurement methodology. The resulting four-zone path-loss model provides mine network planners with parameters sufficient for LoRa link budget design and relay node placement without any specialized RF instrumentation.

A novel 1.8 GHz far-field wireless power transfer (WPT) testbed was designed to achieve long-distance energy harvesting in underground mine (UM). Prior experimental studies confirm that underground mines experience 50 dB radio frequency (RF) signal attenuation compared to a typical wireless environment. Due to this severe signal attenuation, the currently proposed radio frequency energy harvesting (RF-EH) testbed cannot be deployed in underground mines. In this study, we proposed, designed and performed an empirical study with a military-grade class A radio frequency wireless power transfer (RF-WPT) testbed. The various digital modulation schemes experimented with included: BPSK, DBPSK, QPSK, OQPSK, Pi4DQPSK, 8PSK, D8PSK,16PSK, QAM16, QAM 64, QAM 256, QAM 1024, 2ASK, 2FSK, 4FSK, 8FSK, 16FSK, and OFDM. With the proposed testbed, we successfully performed long-distance WPT in an underground mine at 20 meters by harvesting 4.2 volts DC with a Powercast P21XXCSR-EVB RF-EH receiver. We are specifically interested in understanding the optimal digital modulation scheme from the perspective of WPT efficiency in real-world deployment scenarios. Based on the outcome of this study, the optimal modulation scheme/s for WPT and wireless information transfer (WIT) in a simultaneous wireless information and power transfer (SWIPT) technology is established and amongst the various digital modulation schemes experimented with, 2ASK, 2FSK, 4FSK, 8FSK, 16 FSK, and Pi4DQPSK exhibited high performance based on the maximum power transfer distance. Therefore, we recommend them as good candidates for WPT in digital wireless communications. The research also compared the impact of using Yagi and omnidirectional antennas. It was observed that the omnidirectional antenna suffers a 5-meter performance loss compared to the Yagi antenna. Additionally, we mimicked a multipath wireless channel by introducing an N directional 15 dB coupler at the transmitter side, it was observed that there is an absence of multipath effect in UM, and hence, antenna diversity and MIMO technology will perform poorly in UM.

Underground mine emergencies destroy communication infrastructure when situational awareness is most critical. Current systems rely on centralized network infrastructure, which fails during emergencies when miners are trapped and require rescue coordination. This paper proposes an energy-harvesting LoRa mesh network that addresses self-powered operation, interference management, and adaptive physical layer optimization under severe underground propagation conditions. A dual-antenna architecture separates RF energy harvesting (860 MHz) from LoRa communication (915 MHz), enabling continuous operation with supercapacitor storage. The core contribution is a decentralized scheduler that derives optimal timing offsets by modeling concurrent transmissions as a Poisson collision process, exploiting LoRa’s capture effect while maintaining network coherence. A SINR-aware physical layer adapts spreading factor, bandwidth, and coding rate with hysteresis, controls recomputing timing parameters after each change. Experimental validation in Missouri S&T’s operational mine demonstrates far-field wireless power transfer (WPT) reaching 35 m. Simulations across 2000 independent trials show a 2.2× throughput improvement over ALOHA (49% vs. 22% delivery ratio at 10 nodes/hop), 64% collision reduction, and 67% energy efficiency gains, demonstrating resilient emergency communications for underground environments.

Underground mines are susceptible to occasional roof falls and cave-ins, temporarily destroying the existing wireless communications and telemetry infrastructure. During this temporary outage, intermittent provision of electrical energy wirelessly to the already deployed low-power wireless area networks (LPWAN) and Internet of Things (IoT) devices assumes a fundamental requirement. In this article, we propose and design a long-range far-field radio frequency (RF) wireless power transfer (WPT) testbed to power LPWAN and IoT devices at 35 m in an underground mines facility. Class AB external power amplifier (PA) was introduced to achieve a long-distance RF WPT, in the 880 MHz band. Thus, considerably reducing the adverse effects of the high signal attenuation power loss of about 50 dB. With the aid of a commercially available off-the-shelf (COTS) TPS61030 boost converter, a 2.5 V/25F supercapacitor was boosted to provide the energy needed to the Heltec ESP32 system-on-chip (SoC) microcontroller unit (MCU). The Heltec ESP32 integrates various components, including a dual-core CPU, LoRa, Wi-Fi, and Bluetooth radio connectivity modules. Thus, realizing far-field, IoT, and LPWAN battery-free technology in underground mines environment. This novel research exploited the concept of utilizing supercapacitors for the storage of harvested energy. To the best of our knowledge, this is the first long-range far-field WPT testbed specifically designed for underground mines facilities at 880 MHz band. In this study, we also investigated the impact of using various modulation schemes. The schemes evaluated were of amplitude modulation (AM), frequency modulation (FM), and pulse width modulation (PWM). Empirical results indicate that the most suitable scheme for RF-WPT in the underground mine is FM. In addition, we performed extensive analysis to understand the energy and current consumption profiles of the Heltec ESP32 LoRa, WiFi, and BLE transceiver using Power Profiler Kit II under different configurations and scenarios. The experimental results indicate that the WiFi connectivity is not energy efficient compared to the BLE transceiver.

Energy efficiency is a key area of research aimed at achieving sustainable and environmentally friendly networks. With the rise in data traffic and network congestion, IoT devices with limited computational power and energy resources face challenges in analyzing, processing, and storing data. To address this issue, computing technology has emerged as an effective means of conserving energy for IoT devices by providing high-performance computing capabilities and efficient storage to support data collection and processing. As such, energy-efficient computing, or ''green computing,'' has become a focal point for researchers seeking to deploy large-scale IoT networks. This study provides a comprehensive Survey of recent research efforts aimed at achieving energy-efficient computing and green computing for IoT networks. To the best of our knowledge, none of the studies in the literature have discussed all types of green computing (edge, fog, cloud) and their role in enabling massive IoT networks in terms of energy efficiency. The article starts with an overview of computing technologies and then goes with a discussion of the empowering energy-saving techniques for computing (edge, fog, and cloud) environments including, energy-aware architecture, data aggregation and compression, low-power hardware, energy-aware scheduling, task offloading, switching on/off unused resources, virtualization, energy harvesting, and cooling optimization. This article is an outline of a roadmap toward realizing the vision of a sustainable computing environment for massive IoT networks; in addition, open the door for interested researchers to follow and continue the vision of Energy-Efficient Computing.

The internet of things (IoT) has a significant economic and environmental impact owing to the billions or trillions of interconnected devices that use various types of sensors to communicate through the internet. It is well recognized that each sensor requires a small amount of energy to function; but, with billions of sensors, energy consumption can be significant. Therefore, it is crucial to focus on developing energy-efficient IoT technology and sustainable solutions. The contribution of this article is to support the implementation of eco-friendly IoT solutions by presenting a thorough examination of energy-efficient practices and strategies for IoT to assist in the advancement of sustainable and energy-efficient IoT technologies in the future. Four framework principles for achieving this are discussed, including (i) energy-efficient machine-to-machine (M2M) communications, (ii) energy-efficient and eco-sustainable wireless sensor networks (WSN), (iii) energy-efficient radio-frequency identification (RFID), and (iv) energy-efficient microcontroller units and integrated circuits (IC). This review aims to contribute to the next-generation implementation of eco-sustainable and energy-efficient IoT technologies.

Optimum sensor selection for helicopter enhanced vision in all-weather operations is a strategic issue and has a significant impact on safety, efficiency and utility of military and Emergency Service Helicopters. On the other hand, selecting the optimal sensor among many alternatives is a multi-criteria decision-making (MCDM) problem. The sensor selection task in this paper is modelled as a stepwise Analytic Hierarchy Process (AHP) to guide the selection process, based on criteria relating to environmental conditions (fog, rain, dust) and sensor characteristics (detection range, update rate, resolution). Result of this study reveals that a combination of millimeter wave radar, passive millimeter wave camera and infrared camera is the optimal suite having the highest value among all the alternatives considered. This result will guide decision makers at the Headquarters of the Nigerian Air force and indeed other helicopter operators in their quest to equip helicopters for operation in adverse weather conditions.

The impact of daily emissions of gaseous and particulate pollutants of machines and industries on human health and the environment has attracted increasing concerns. This impact has significantly led to a notable increase in mortality in the highly industrialized zones. Therefore, monitoring air quality and creating public awareness are important for a safer future, which led the governments globally to invest multi-billion in policymaking and solution stratification to address the problem. This study aims to design a real-time Internet of Things low-cost air quality monitoring system. The system utilizes air quality and carbon monoxide sensors for monitoring gaseous pollutants. Moreover, the system utilizes an Arduino Nano development board equipped with a WiFi module to effectively send readings to a ThingSpeak online channel platform for instantaneous and real-time display of air quality. The ThingSpeak uses HTTP protocols to send emails in raising awareness of poor air quality. The level of concentration is monitored graphically through channels with the help of ThingSpeak to aid remote communication. A threshold value is set. Thus, when pollutants have become unhealthy and harmful, the system trips off an alarm, and e-mail notifications are sent to the officials. The results have shown that the work was successfully implemented a design of a low-cost air quality monitoring system using Arduino and ThingSpeak, showing that an air quality system can be implemented using a low-cost technology, Arduino and ThingSpeak.

In this study, the real-world performance analysis of four Nigerian mobile network operators (MNOs), namely MTN, GLO, Airtel, and 9Mobile long-term evolution (LTE) cellular network, were analyzed and compared. The Nigerian MNOs utilize 5 MHz, 10 MH, and 20 MHz channel bandwidths based on third-generation partnership project’s (3 GPPs) recommendation. The presented analysis shows the uplink (UL), and downlink (DL) throughputs gaps in mobility condition as well as other LTE’s system quality of service (QoS) key performance indicators (KPI’s) of: Connection drop rate, connection failure rate, peak physical downlink throughput, minimum radio link control (RLC) downlink throughput threshold and latency are not strictly followed. The reason may be due to a lack of regulatory oversight enforcement. The comparative studies showed that MTN provides the best QoS. The introduction of novel LTE QoS metrics herein referred to as national independent wireless broadband quality reporting (NIWBQR) is the significant contribution of this study. The goal of this study is to show the quality of the network as it affects the user's experience. Important observation showed that all the MNOs are not adhering to the 3 GPPs specified user plane latency of 30 ms and control plane latency of 100 ms, respectively, which makes video streaming and low latency communication a near-impossible task.

Expert systems are being utilized increasingly in medical fields for the purposes of assisting diagnosis and treatment planning. Existing systems used few symptoms for dental diagnosis. In Dentistry, few symptoms are not enough for diagnosis. In this research, a conditional probability model (Bayes rule) was developed with increased number of symptoms associated with a disease for diagnosis. A test set of recurrent cases was then used to test the diagnostic capacity of the system. The generated diagnosis matched that of the human experts. The system was also tested for its capacity to handle uncommon dental diseases and the system portrayed useful potential.

: Water is one of the needs with remarkable significance to man and other living things. Water quality management is a concept based on the continuous monitoring of water quality. The monitoring scheme aims to accumulate data to make decisions on water resource descriptions, identify real and emergent issues involving water pollution, formulate priorities, and plan for water quality management. The regularly considered parameters when conducting water quality monitoring are turbidity, pH, temperature, conductivity, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, ammonia, and metal ions. The usual method employed in capturing these water parameters is the manual collection and sending of samples to a laboratory for detection and analysis. However, this method is impractical in the long run because it is laborious and consumes a considerable amount of human resources. Sensors integrated into a mobile phone application interface can address this issue. This paper aims to design and implement an Internet of Things-based system comprising pH, temperature, and turbidity sensors, which are all integrated into a mobile phone application interface for a water monitoring system. This project utilizes the Bluetooth Standard (IEEE 802.15.1) for communication/transfer of data, while the water quality monitoring system relies on the pH, turbidity, and temperature of the test water.

Machine learning (ML) techniques will benefit immensely from the avalanche of data readily available from various (IoT) applications considered as the major contributor of new data for future intelligent network. Based on this new concept, network systems will further magnify their capacity to exploit variety of experimental data across a plethora of network devices, study the data information, obtain knowledge and make informed decisions based on the dataset at their disposal. Smart IoT data analysis are performed utilizing supervised learning, unsupervised learning and reinforced learning. This study is limited to supervised and unsupervised ML techniques. In other to achieve the set objectives, reviews and discussions of substantial issues related to supervised or unsupervised machine learning techniques were executed, highlighting the advantages and limitations of each algorithm as well presenting the recent research trends and recommendations for future study.

Unmanned aerial vehicle (UAV) antenna tracking system is an electromechanical component designed to track and steer the signal beams from the ground control station (GCS) to the airborne platform for optimum signal alignment. In a tracking system, an antenna continuously tracks a moving target and records their position. A UAV tracking antenna system is susceptible to signal loss if omnidirectional antenna is deployed as the preferred design. Therefore, to achieve longer UAV distance communication, there is a need for directional high gain antenna. From design principle, directional antennas are known to focus their signal energy in a particular direction viewed from their radiation pattern which is concentrated in a particular azimuth direction. Unfortunately, a directional antenna is limited by angle, thus, it must always be directed to the target. The other limitation of a UAV mechanical beam steering system is that the system is expensive to maintain and with low reliability. To solve this problem, we are proposing the use of MIMO technology as a readily available technology for UAV beyond line of sight technology. Although UAV antenna tracking is domiciled in the mechanical beam steering arrangement, this study shows that this native technology could be usurped by MIMO beam forming.

Non-orthogonal multiple access (NOMA) is a Fifth Generation (5G) technique that allows many users to simultaneously access the same time–frequency separating channels via successive interference cancellation (SIC) receiver. Cooperative NOMA (CNOMA) is an effective tool to prevent performance degradation of far users by allocating minimal power to users with good channel conditions. In this paper, we proposed a fair power and channel allocation scheme based on the Nash bargaining solution (NBS) game solution in full-duplex, cooperative beamforming (BF) for multicarrier (MC) NOMA. The proposed NBS scheme assigns optimal power and channel allocation according to channel conditions while maintaining a fair rate amongst cooperative users. NBS provides a fair and optimum approach for maximizing the total rate of CNOMA. The signal-to-leakage (SLR) ratio precoding technique is considered as a design performance criterion for beamforming vector towards achieving power domain CNOMA players. Simulation results show that at BER = 10^-5 , the NBS power allocation (proposed scheme) improved by 2 dB in terms of Signal-to-Noise Ratio (SNR), compared with the non-cooperative scheme, and 3 dB compared with the multiple-input multiple-output NOMA (MIMO-NOMA). Both improvements were as a result of interference reduction and information sharing in the network. In terms of fairness, the proposed NBS scheme shown a high level of fairness at 0.8401, compared to the other similar approaches in the literature.

Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today's high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI.

Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study.

The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research directions.

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT.