Contactless Shopping Cart Automation using Rfid Tags with Security and Inventory Management during Pandemic Diseases Outbreak
Simulation with Different Ad Hoc Network Scenarios of Routing Protocols in MANETs using OPNET Simulator
An Optimized Routing for IoT Devices using Node Features Based on Machine Learning Techniques
Optimization of 50-Node Wireless Sensor Networks using Centrality Measures: A Case Study with the Watts-Strogatz Model
Design and Development of Optic Controller for Domestic Appliances to Assist Paralytic
Performance Analysis of Modified Least Mean Squares Algorithm for Adaptive Beamforming in Smart Antennas for 5G Networks
Enhancing MANETs for Military Applications: A Comprehensive Review of Innovations, Challenges, and Research Gaps
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
Simulation with Different Ad Hoc Network Scenarios of Routing Protocols in MANETs using OPNET Simulator
Shopping malls are typically teeming with intense purchasing activity; however, long queue lines can be extremely time- consuming and may cause fatigue from pushing trolleys and waiting for payment processing. These challenges are particularly burdensome for the elderly, individuals with disabilities, pregnant women, and nursing mothers. During the pandemic, interacting with people in crowded malls became a major issue. To address these problems, a self-sustaining shopping cart was developed. As a customer adds items to the cart while moving, the system automatically generates a bill, requests payment, and updates inventory in real-time using a database. The design features an ATmega328 microcontroller with 32 KB ISP flash memory, an RFID reader, a WiFi module, and a 20 x 4 alphanumeric LCD screen. RFID tags are attached to products, and the billing process is managed using XAMPP, an open-source cross-platform software. The ESP8266 WiFi module transmits payment and product information to the original database. A dedicated webpage is also available for user interaction. This system integrates technologies from the Internet of Things and networking to ensure seamless data transfer from sensors to the database, enabling efficient and automated billing.
Mobile Ad hoc Networks (MANETs) are dynamic wireless networks with no fixed infrastructure, where mobile nodes operate as both hosts and routers. The absence of centralized infrastructure, frequent topology changes, and limited bandwidth resources present challenges for routing. Various routing protocols have been developed to address these challenges, notably AODV, DSR, and OLSR. This study presents a comparative performance analysis of AODV, DSR (reactive protocols), and OLSR (a proactive protocol), using the OPNET Modeler simulation tool. Performance is evaluated under varying traffic loads, network sizes, and node mobility, with FTP traffic used to mimic realistic applications. Key performance metrics include average end-to-end delay and throughput. The results show that throughput improves, and end-to-end delay increases with larger network sizes and higher traffic loads. However, mobility does not significantly impact performance in larger networks. Among the protocols, OLSR shows superior performance in terms of end-to-end delay, while AODV outperforms others in throughput. DSR exhibits inconsistent delay behavior, particularly under heavy load and larger networks.
The process of choosing a network path to transfer a packet from a source to a destination node is known as routing. Successful message delivery is difficult; thus, this paper presents an algorithm for Internet of Things (IoT) devices called Optimized Routing in IoT Using Machine Learning (ORuML). This algorithm predicts the network type of the source and destination nodes using machine learning named KNN, Decision Tree, and Support Vector Machine. The unique attributes of a node such as signal strength, link quality indicator, noise floor, and path length or number of hops between the ith node and the sink node are gathered from wireless sensor network (WSN) measurements conducted in an industrial environment used to train the ML model. Using these datasets, three machine learning techniques KNN, DT, and SVM were employed to predict the network type of the nodes to find the best path for data transmission between source and destination. The results of the simulation show that the DT method predicts the best among the other machine learning algorithms used, outperforming KNN and SVM in terms of accuracy and AUC.
This study provides a comprehensive understanding of optimizing a 50-node Wireless Sensor Network generated by the Watts-Strogatz model. The six-centrality metrics applied to node ranking and identification are Degree, Betweenness, Closeness, Eigenvector, Katz and Subgraph to determine which nodes can improve the efficacy of communication, pathways within the network, and survivability. Combining these centrality measures is another way to boost the performance of the WSN. From both industry and research perspectives, understanding the decreasing performance ratio during WSN optimization is crucial, as it provides valuable insights into the information-based optimization of key nodes that significantly influence traffic visibility and connection probabilities. The research demonstrates the benefits of a combined centrality approach in strengthening the architecture and functioning of wireless sensor networks.
High tech, newly developed devices are being inserted into patients' bodies to assist them in resuming normal activities, particularly individuals with paralysis, such as quadriplegics, who face significant challenges due to physical limitations. The development of a device to support those affected by paralysis has become essential. Moreover, many individuals are increasingly inclined to digitize their daily routines to minimize physical exertion. The concept involves designing an assistive device that enables users to control everyday items with minimal physical effort, allowing home electrical appliances to be automated through a simple eyelid blink. Although various prototypes have been developed in the past, most have proven either unoriginal or difficult to operate. This initiative aims to develop a compact, user-friendly, webcam-based home automation system capable of controlling household electrical appliances efficiently. Additionally, this will reduce energy use and allow a disabled patient to control the lights and fans on their own. The setup uses a webcam which is programmed using AI technology to trace the blink count. The blink count is transferred to both the appliances through Wi-fi connection in which it checks for its compatible programmed values and the respective appliance is controlled by the relay module accordingly. It is to be noted that this innovation offers significant improvement in the lives of individuals with paralysis and ensures a higher accuracy rate compared to existing devices.
Adaptive beamforming is a crucial technique for enhancing the performance of 5G networks by mitigating interference and improving signal quality. This paper investigates the performance of a modified Least Mean Squares (LMS) algorithm with a variable step size for adaptive beamforming in smart antennas. The proposed algorithm dynamically adjusts the step size based on the instantaneous error, leading to improved convergence and reduced steady-state error compared to the standard LMS algorithm. The performance of the modified LMS algorithm is evaluated through simulations, considering various scenarios with different signal-to-noise ratios (SNRs) and angles of arrival. Simulation is done by using MATLAB software with uniform linear array as array geometry. The results demonstrate the effectiveness of the proposed algorithm in achieving faster convergence, better beam pattern formation, and lower mean squared error (MSE).
Mobile Ad Hoc Networks (MANETs) have become indispensable in modern military operations; they provide decentralized, adaptive, and resilient communication frameworks in dynamic battlefield environments. This paper presents a comprehensive review of MANET innovations, challenges, and research gaps, focusing on advancements in security mechanisms, energy efficiency, routing optimization, interoperability, and AI-driven management systems. MANETs enhance military communication by enabling self-forming and self-healing networks, improving situational awareness, tactical coordination, and mission success. However, security vulnerabilities, energy constraints, and performance instability remain critical concerns that must be addressed to ensure operational resilience. Emerging technologies such as AI-powered security frameworks, blockchain authentication protocols, cognitive radio-based spectrum allocation, and energy-efficient routing strategies provide promising solutions to these challenges. The integration of autonomous optimization models, predictive analytics, and quantum cryptography further reinforces the robustness of military MANETs in contested and high-risk environments. Despite these innovations, research gaps persist, particularly in interoperability with legacy systems, cyber security frameworks, and large-scale deployment strategies. This review highlights the strategic importance of MANETs in military applications and provides insights into ongoing research aimed at enhancing their reliability and efficiency. Future advancements should prioritize intelligent, self- organizing, and cyber-secured MANET architectures, ensuring seamless communication, operational efficiency, and superior defense capabilities in next-generation warfare.
