A Heuristic and Analytical Framework for Routing Protocol Optimization in Wireless Sensor Networks: A Performance Metric-Based Analysis
QEEH: Adaptive Energy-Efficient Handover for FANETs using Q-Learning
Development of a Microstrip Rectangular Patch Phased Array Antenna for 6 GHz Wi-Fi Applications
Artificial Intelligence and Machine Learning Models with Wireless Sensors for Air Quality Prediction and Pollution Control: A Comprehensive Review
Fuzzy Logic-Based Approach to Improve Security in Wireless Ad Hoc Networks
Performance Evaluation of Advanced Congestion Control Mechanisms for COAP
Impact of Mobility on Power Consumption in RPL
Implementation of Traffic Engineering Technique in MPLS Network using RSVP
FER Performance Analysis of Adaptive MIMO with OSTBC for Wireless Communication by QPSK Modulation Technique
DGS Based MIMO Microstrip Antenna for Wireless Applications
A Review on Optimized FFT/IFFT Architectures for OFDM Systems
Balanced Unequal Clustering AlgorithmFor Wireless Sensor Network
HHT and DWT Based MIMO-OFDM for Various ModulationSchemes: A Comparative Approach
Study and Comparison of Distributed Energy Efficient Clustering Protocols in Wireless Sensor Network: A Review
Artificial Intelligence and Machine Learning Models with Wireless Sensors for Air Quality Prediction and Pollution Control: A Comprehensive Review
Wireless Sensor Networks (WSNs), composed of numerous spatially dispersed, low-power, and computationally constrained sensor nodes, are instrumental in acquiring real-time data from dynamic environments. These miniature embedded systems, though economically viable, demand judicious protocol design due to their intrinsic limitations in energy, bandwidth, and processing capacity. In this study, a rigorous evaluation of transport and MAC layer routing protocols is undertaken to ascertain their performance efficacy under resource-constrained conditions. Utilizing the NS2 (v2.35) simulation platform, three transport protocols-AODV, DSDV, and DSR-and three MAC protocols-IEEE 802.11, IEEE 802.15.4, and S-MAC-are comparatively analyzed. Key performance indices such as Packet Delivery Ratio (PDR), mean end-to-end delay, and network throughput are employed to assess their operational characteristics under controlled testbed scenarios. The empirical findings reveal that AODV and DSR both attained an ideal PDR of 100%, whereas DSDV, though relatively efficient, registered a slightly diminished delivery rate of 97.16%. DSDV demonstrated the shortest average delay at 0.3105 seconds, outperforming both AODV (0.4921 s) and DSR (0.5102 s). Throughput-wise, AODV emerged superior with 263.34 kbps, while DSR closely followed at 258.77 kbps. At the MAC layer, S-MAC delivered impeccable reliability, achieving full packet delivery without any loss, albeit at the cost of increased latency (2.516 seconds) owing to periodic sleep cycles. WSN performance and careful optimisation-guided by simulation and metric- driven analysis-are essential for tailoring protocol behavior to meet specific application demands.
Flying Ad Hoc Networks (FANETs) are a critical component of UAV-based communication systems with applications in surveillance, disaster response, and defense. Their highly dynamic topology, high mobility and limited battery capacity makes reliable and energy-efficient communication challenging for them. Traditional handover mechanisms, often adapted from Vehicular Ad Hoc Networks (VANETs), rely on static thresholds and are not suited to the three-dimensional mobility and energy constraints of FANETs. This study proposes QEEH, a Q-learning–based Energy-Efficient Handover framework designed for FANET environments. QEEH employs reinforcement learning to make adaptive handover decisions by considering signal strength, node density, residual energy, and traffic load. It also integrates multiple energy states—active, sleep, hibernate, and wake-up—to reduce power consumption without compromising connectivity. NS3-based simulations show that QEEH consistently outperforms CLEA-AODV, LFEAR, and PARouting. Compared with CLEA-AODV, QEEH achieves up to 23% higher throughput, 20% higher packet delivery ratio, 30% lower end-to-end delay, and 28% lower energy consumption, while maintaining more than 90% node survivability at the end of simulation, exceeding other protocols by 15–21%.These results demonstrate that intelligent, energy-aware handover schemes can enhance FANET performance. However, the findings are limited to NS3 simulations with moderate UAV densities. Future work will focus on testbed validation, scalability to large UAV swarms, and extending QEEH with deep reinforcement and federated learning for decentralized training.
This paper presents the design and implementation of a Rectangular Patch Phased Array (RPPA) antenna for 6 GHz Wi-Fi applications. Phased array antennas are widely utilized due to their high gain, reliability, and directivity. The rectangular patch is chosen for its unique characteristics, including low power consumption, high-quality factor, and efficiency in narrow bandwidth applications. A 4x4 rectangular patch phased array configuration is proposed to achieve high directivity, with 16 patch elements connected via conducting strips and fed by a corporate feeding network with 50-ohm impedance matching. The antenna uses FR4 substrate (1.6 mm thickness, permittivity value included) and is designed and simulated in CST Microwave Studio. Results demonstrate a gain of 17.98 dB and a highly directional radiation pattern, making the antenna suitable for 6 GHz Wi-Fi applications. The proposed design is compact, lightweight, and cost-effective, positioning it as a viable solution for 6 GHz Wi-Fi routers.
Air pollution is a significant threat to the environment and public health worldwide. In the context of rising urbanization and industrialization, accurate air quality forecasts and control have become increasingly important. Traditional statistical and deterministic models typically fail to account for the complex, nonlinear, and dynamic behavior of air contaminants. In this context, Artificial Intelligence (AI) and Machine Learning (ML) approaches have developed as useful tools for evaluating large and diverse environmental information, leading to more precise forecasts and effective pollution mitigation approaches. This comprehensive analysis focuses on the present state of AI and machine learning applications in air quality prediction and pollution mitigation. It discusses various models for temporal and geographic forecasting, source distribution, and real-time monitoring, such as regression algorithms, decision trees, neural networks, support vector machines, and deep learning approaches. The assessment also emphasizes the use of AI, IoT devices, remote sensing data, and geospatial analytics to enhance pollution control systems. Additionally, it covers issues related to data integrity, model interpretability, and scalability, while also highlighting key areas for future research and practical implementations. This review aims to serve as a valuable resource for environmental academicians, policymakers, and technologists working on resilient air quality monitoring.
Wireless Ad Hoc Networks (WANETs) have attracted considerable attention due to their decentralized nature and flexibility, but they also face critical security challenges. Traditional mechanisms often struggle to cope with the dynamic, distributed, and resource-constrained characteristics of these networks. This review examines the use of fuzzy logic- based techniques to strengthen WANET security frameworks, focusing on their effectiveness in intrusion detection, attack prevention, and network resilience. The literature highlights diverse applications of fuzzy logic, including trusted routing protocols, encryption and decryption of fuzzy matrices, parallel encryption with digit arithmetic of cover text, congestion control and QoS scheduling, multicast key distribution, data division using fuzzy logic and blockchain, malicious node eviction in vehicular ad hoc networks, biometric encryption for IoT, multi-level authentication with fuzzy logic-based quantum key distribution, and fog-based secure IoT architectures. Additional contributions include fuzzy logic applications in random number generation, performance evaluation of encryption algorithms, energy-efficient schemes, multipath routing, web spam detection, data security enhancement, key management, packet-dropping attack detection, and high-speed public-key cryptography. The findings suggest that fuzzy logic enhances WANET security and performance by enabling decision-making under uncertainty, improving attack detection, and optimizing resource utilization. However, challenges such as frequent rekeying, larger key sizes, communication and storage overheads, and network congestion remain, requiring further research for efficient deployment in resource-constrained environments.
