Design and Development of Optic Controller for Domestic Appliances to Assist Paralytic
Contactless Shopping cart automation using RFID tags with security and inventory management During Pandemic Diseases Outbreak
Optimization of 50-Node Wireless Sensor Networks Using Centrality Measures: A Case Study with the Watts-Strogatz Model
Performance Analysis of Modified Least Mean Squares (LMS) Algorithm for Adaptive Beamforming in Smart Antennas for 5G Networks
An Optimized Routing for IoT deviecs using node features based on Machine learning techniques
Enhancing MANETs for Military Applications: A Comprehensive Review of Innovations, Challenges, and Research Gaps
Simulation with Different Ad hoc Network Scenarios of Routing Protocols in MANETS using OPNET Simulator
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
Diagnosis of Anemia using Non-Invasive Anemia Detector through Parametrical Analysis
In order to assist the patient in returning to everyday activities, newly created, high-tech gadgets are used in their bodies. Particularly for the individuals with paralysis, like quadriplegics, who endure immense suffering as a result of their physical restrictions. The development of a tool to help those with paralysis, like quadriplegics, is now imperative. In addition, individuals are keen to digitize their daily lives in an effort to engage in less physical activity. Our notion is to create an assist device that would enable someone to operate any item they use in their everyday life with less physical effort and would allow us to automate our home's electrical appliances simply by blinking our eyelids. Although there have been a number of prototypes created in the past, most of them lack originality or are not user-friendly. The project's objective is to develop a tiny, user-friendly ocular controlling webcam-based home automation system that can power electrical equipment in the house. This will also save energy usage and enable a patient who is disabled to operate the fans and lights without assistance. 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 discerned that our innovation provides a greater enhancement in the life of paralytic and also ensures a higher accuracy rate when compared with the existing devices.
Shopping malls are simply teeming with serious purchase operations. Long queue lines, on the other hand, are extremely time demanding, and may also include weariness from pushing trolleys throughout the mall and lengthy processing of payments by the cashier. These purchasing difficulties may seem onerous to the elderly, handicapped, pregnant, and nursing moms.During pandemic we faced a major problem to interact with people in shopping malls etc…, we tackled these purchasing issues by developing a self-sustaining shopping cart.When a consumer places shopping products in a cart while pushing the cart, it will automatically bill it.It requests payment from the customer and updates each stock using an original database.Our design includes an ATmega328 microcontroller, 32 KB ISP flash memory, sensor inputs for an RFID reader, a WiFi module, and a 20x4 alpha numeric LCD screen, load. RFID tags are attached to objects.To create the database, we utilised an open-source cross-platform programme called XAMPP, and we used RFID tags to charge the things that are automatically placed in the shopping cart.Using ESP8266 WiFi module transceivers, the system updates payments and broadcasts these deals to an original database.and we have a webpage. With the highly integrated development of techonology we have combined all the fields of internet of things and networking to transfer the data which is read by the sensors and then updated in the database for billing.
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. What industry and research ocean apprise as the decreasing performance ratio improvement when optimizing WSN design is valuable information-based optimization of key nodes influencing the traffic visibility and probability of connection. The research demonstrates the benefits of a combined centrality approach in strengthening the architecture and functioning of wireless sensor networks.
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).
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, i.e., signal strength, link quality indicator, noise floor, path length (no. of hops) between the ith node and the sink node, etc., 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.
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.
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.
