Security is highly challenging in today's world. A country may face lot of problems due to security such as military applications, vehicle theft. Radio Frequency Identification (RFID) is the next technology to improve the security. RFID technology is applied in national defense, Medical, Human Identification, for tracking stable and moving objects. A small RFID Tag and the Reader have Contribute their service for security. In this paper, we give a small study on RFID technology and its working of RFID system. A comparative study made on different attacks on RFID system and the recovery methods are described in detail. This article will suggest a direction for research to recover from the RFID Tag attacks and damage problem.

Radio Frequency Identification (RFID) is primarily used to resolve the problems of taking care of the majority of nodes perceived and tracking tags related to the items. Utilizing contactless radio frequency identification data can be communicated distantly using electromagnetic fields. In this paper, the comparison and analysis made between the Clustered RFID with existing protocols Ad hoc On-demand Multicast Distance Vector Secure Adjacent Position Trust Verification (AOMDV_SAPTV) and Optimal Distance-Based Clustering (ODBC) protocols based on the network attributes of accuracy, vulnerability and success rate, delay and throughput while handling the huge nodes of communication. In the RFID Network, the clustering mechanism was implemented to enhance the performance of the network when scaling nodes. Multicast routing was used to handle the large number of nodes involved in the transmission of particular network communication. While scaling up the network, existing methods may be compromised with their efficiency. However, the Clustered RFID method will give better performance without compromising efficiency. Here, Clustered RFID gives 93% performance, AOMDV_SAPTV can achieve 79%, and ODBC can reach 85% of performance. Clustered RFID gives 14% better performance than AOMDV_SAPTV and 8% better performance than ODBC for handling a huge range of nodes.

Navigating dense forest environments presents significant challenges due to obstructed GPS signals, dynamic terrain, and limited communication infrastructure. This research proposes a Smart Forest Navigation System that enables real-time, infrastructure-free navigation by integrating LoRa communication, radar-based obstacle detection, and Dynamic A* (D*) pathfinding. The system leverages Time of Arrival (ToA) measurements from LoRa nodes for long-range localization, enhanced through sensor fusion with Inertial Measurement Unit (IMU) data using an Extended Kalman Filter (EKF) on the user’s smartphone. A radar sensor provides real-time environmental perception, detecting obstacles and updating a dynamically evolving map enriched with satellite, drone, and crowdsourced data via probabilistic modeling. Path optimization is handled by the D* Lite algorithm, which adapts to terrain changes and obstacle updates, using a cost function that accounts for terrain difficulty and energy efficiency. All computations are performed on-device, ensuring low-latency operation without reliance on GPS or external servers. The proposed system offers a cost-effective, energy-efficient solution suitable for forest exploration, wildlife tracking, and search and rescue missions in GPS-denied environments.

The journal retracts the article “Improving Efficiency of Large RFID Networks Using a Clustered Method: A Comparative Analysis”