Energy efficient anti-collision algorithm for the RFID networks

Energy efficiency is crucial for radio frequency identification (RFID) systems as the readers are often battery operated. The main source of the energy wastage is the collision which happens when tags access the communication medium at the same time. Thus, an efficient anti-collision protocol could minimize the energy wastage and prolong the lifetime of the RFID systems. In this regard, EPCGlobal-Class1-Generation2 (EPC-C1G2) protocol is currently being used in the commercial RFID readers to provide fast tag identification through efficient collision arbitration using the Q algorithm. However, this protocol requires a lot of control message overheads for its operation. Thus, a reinforcement learning based anti-collision protocol (RL-DFSA) is proposed to provide better time system efficiency while being energy efficient through the minimization of control message overheads. The proposed RL-DFSA was evaluated through extensive simulations and compared with the variants of EPC-Class 1 Generation 2 algorithms that are currently being used in the commercial readers. The results show conclusively that the proposed RL-DFSA performs identically to the very efficient EPC-C1G2 protocol in terms of time system efficiency but readily outperforms the compared protocol in the number of control message overhead required for the operation

A Hy- brid Tag Number Estimation Scheme for Aloha Based Anti-Collision Algorithm

Simulation results showed that the proposed scheme had high estimation accuracy than the existing algorithm

Energy efficiency in short and wide-area IoT technologies—A survey

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions

Embedded Dual Band Rfid Based Blood Glucose Monitoring System For Internet Of Medical Things

Manually recorded health information could lead to errors such as inaccurate patient identification and mismatch patient data that could seriously affect patient safety. In order to reduce the risks of error for patients with diabetes, a new design of wireless blood glucose monitoring system with the embedment of dual band RFID for Internet of Medical Things is being developed. Using this method, passive RFID allows short-range communication to read automatically the patient identification number and active RFID extends long-range communication for recording and monitoring blood glucose data through multi-hop WSN. The work presented in this thesis contributes mainly to the embedded system and its application in healthcare to reduce the burden of recording, tracing and monitoring the patient‘s data by embedding blood glucose sensor, passive RFID, active RFID, WSN, M2M and IoMT into a single platform. A new design concept is established for the patient identification mechanism, where the mechanism is embedded in the source device to enhance the ability of the system to automatically assign the identification number to each blood glucose measurement (mmol/L) during multiple patients monitoring. Additionally, the results from the experiments conducted showed that the developed system produced better overall performance compared to the Bluetooth BGM and conventional BGM system in terms of the shortest recording time and the ability to retransmit data. In the reliability analysis using ANOVA and DOE statistical methods, the result validates that the number of hop and number of end node significantly affects the PDR performance of conventional CSMA/CA. These two parameters are then taken into account in experimental setup for performance evaluation of the enhanced CSMA/CA (EN-CSMA/CA) algorithm that uses an external interrupt mechanism and a cross layer approach. The PDR increased from 94% (conventional CSMA/CA) to 99.33% (EN-CSMA/CA), an improvement of 5.33%. The PDR model estimates that for the best and worst scenario, the percentage of PDR is 100.0% and 51.67%, respectively. To optimize the arrangement of routers for real implementation of the developed system in health facilities, the developed path loss model estimates that the router should be positioned at a distance of 30 m from each other, which agrees with the test results which indicate that the router should be positioned ≤ 40 m in order to achieve the best PDR performance

Spatial and Temporal Analysis on the Distribution of Active Radio-Frequency Identification (RFID) Tracking Accuracy with the Kriging Method

Radio frequency identification (RFID) technology has already been applied in a number of areas to facilitate the tracking process. However, the insufficient tracking accuracy of RFID is one of the problems that impedes its wider application. Previous studies focus on examining the accuracy of discrete points RFID, thereby leaving the tracking accuracy of the areas between the observed points unpredictable. In this study, spatial and temporal analysis is applied to interpolate the continuous distribution of RFID tracking accuracy based on the Kriging method. An implementation trial has been conducted in the loading and docking area in front of a warehouse to validate this approach. The results show that the weak signal area can be easily identified by the approach developed in the study. The optimum distance between two RFID readers and the effect of the sudden removal of readers are also presented by analysing the spatial and temporal variation of RFID tracking accuracy. This study reveals the correlation between the testing time and the stability of RFID tracking accuracy. Experimental results show that the proposed approach can be used to assist the RFID system setup process to increase tracking accuracy

Capture-aware identification of mobile RFID tags with unreliable channels

Radio frequency identification (RFID) has been widely applied in large-scale applications such as logistics, merchandise and transportation. However, it is still a technical challenge to effectively estimate the number of tags in complex mobile environments. Most of existing tag identification protocols assume that readers and tags remain stationary throughout the whole identification process and ideal channel assumptions are typically considered between them. Hence, conventional algorithms may fail in mobile scenarios with unreliable channels. In this paper, we propose a novel RFID anti-collision algorithm for tag identification considering path loss. Based on a probabilistic identification model, we derive the collision, empty and success probabilities in a mobile RFID environment, which will be used to define the cardinality estimation method and the optimal frame length. Both simulation and experimental results of the proposed solution show noticeable performance improvement over the commercial solutions

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Adjoining Internet of Things with Data Mining : A Survey

The Interactive Data Corporative (IDC) conjectures that by 2025 the worldwide data circle will develop to 163ZB (that is a trillion gigabytes) which is ten times the 16.1ZB of information produced in 2016. The Internet of Things is one of the hot topics of this living century and researchers are heading for mass adoption 2019 driven by better than-expected business results. This information will open one of a kind of user experience and another universe of business opening. The huge information produced by the Internet of Things (IoT) are considered of high business esteem, and information mining calculations can be connected to IoT to extract hidden data from information. This paper concisely discusses the work done in sequential manner of time in different fields of IOT along with its outcome and research gap. This paper also discusses the various aspects of data mining functionalities with IOT. The recommendation for the Challenges in IOT that can be adopted for betterment is given. Finally, this paper presents the vision for how IOT will have impact on changing the distant futur