FRAMEWORK FOR IMPROVING PERFORMANCE OF PROTOCOLS FOR READING RADIO FREQUENCY IDENTIFICATION TAGS

Radio-frequency Identification (RFID) is a highly sought-after wireless technology used to track and manage inventory in the supply chain industry. It has varied applications ranging from automated toll collection and security access management to supply chain logistics. Miniaturization and low tag costs of RFID tags have lead to item-level tagging, where not just the pallet holding products is tagged but each product inside has a tag attached to it. Item-level tagging of goods improves the accuracy of the supply chain but it significantly increases the number of tags that an RFID reader must identify and track. Faster identification is crucial to cutting cost and improving efficiency. Existing RFID protocols were designed to primarily handle static scenarios with both RFID tags and readers not being in motion. This research addresses the problem of inventory tracking within a warehouse in multitude of scenarios that involves mobile tags, multiple readers and high density environments. Mobility models are presented and frameworks are developed for the following scenarios: a) mobile tags on a conveyor belt with multiple fixed readers; b) mobile reader in a warehouse with stationary tags in shelves; and c) high density tag population with Near-Field (NF) communication. The proposed frameworks use information sharing among readers to facilitate protocol state handoff and segregation of tags into virtual zones to improve tag reading rates in mobile tag and mobile reader scenarios respectively. Further, a tag’s ability to listen to its Near-Field neighboring tags transmissions is exploited to assist the reader in resolving collisions and hence enhancing throughput. The frameworks discussed in this research are mathematically modeled with a probabilistic analysis of protocols employed in conjunction with framework. With an increased number of tags to be identified, mathematically understanding the performance of the protocol in these large-scale RFID systems becomes essential. Typically, this analysis is performed using Markov-chain models. However, these analyses suffer from the common state-space explosion problem. Hence, it is essential to come up with a scalable analysis, whose computation model is insensitive to the number of tags. The following research analyzes the performance of tag identification protocols in highly dense tag scenarios, and proposes an empirical formula to estimate the approximate time required to read all the tags in a readers range without requiring protocol execution

Systematical research on the aerodynamic noise of the high-lift airfoil based on FW-H method

In numerical computation of aerodynamic noises, the solution accuracy of flow fields has an obvious impact on detailed computation of eddy turbulence and acoustic results. In this paper, LES (Large Eddy Simulation) was used to conduct numerical simulation of flow fields of three-dimensional high-lift L1T2 airfoil. Unsteady flow field data on the solid wall face was extracted as the noise source. The integration method FW-H (Ffowcs Williams-Hawkings) was used to compute far-field noises. The numerical computation method was verified by experiments. Results show that: the numerical computation method used in this paper can provide an accurate solution for computing far-field aerodynamic noises. Finally, based on the verified numerical model, contribution amounts made by each high-lift airfoil component to noises as well as major factors affecting aerodynamic noises were analyzed. Computational results show that: the leading edge slats generated aerodynamic noises mainly because of the unsteady waves which were caused by the grooves between the slat and main wing, as well as small wake eddies generated on the trailing edge of slats; flaps generated aerodynamic noises mainly because of mixing between high-frequency small-scale eddies and low-frequency large-scale eddies caused by flow separation around the wing flaps. Acoustic directivity of leading edge slats and trailing edge flaps showed an obvious dipole characteristic. For both of them, the sound pressure levels reached the maximum value in the direction perpendicular to the chord line

Collective Communications and Computation Mechanisms on the RF Channel for Organic Printed Smart Labels and Resource-limited IoT Nodes

Radio Frequency IDentification (RFID) and Wireless Sensor Networks (WSN) are seen as enabler technologies for realizing the Internet of Things (IoT). Organic and printed Electronics (OE) has the potential to provide low cost and all-printable smart RFID labels in high volumes. With regard to WSN, power harvesting techniques and resource-efficient communications are promising key technologies to create sustainable and for the environment friendly sensing devices. However, the implementation of OE smart labels is only allowing printable devices of ultra-low hardware complexity, that cannot employ standard RFID communications. And, the deployment of current WSN technology is far away from offering battery-free and low-cost sensing technology. To this end, the steady growth of IoT is increasing the demand for more network capacity and computational power. With respect to wireless communications research, the state-of-the-art employs superimposed radio transmission in form of physical layer network coding and computation over the MAC to increase information flow and computational power, but lacks on practicability and robustness so far. With regard to these research challenges we developed in particular two approaches, i.e., code-based Collective Communications for dense sensing environments, and time-based Collective Communications (CC) for resource-limited WSNs. In respect to the code-based CC approach we exploit the principle of superimposed radio transmission to acquire highly scalable and robust communications obtaining with it at the same time as well minimalistic smart RFID labels, that can be manufactured in high volume with present-day OE. The implementation of our code-based CC relies on collaborative and simultaneous transmission of randomly drawn burst sequences encoding the data. Based on the framework of hyper-dimensional computing, statistical laws and the superposition principle of radio waves we obtained the communication of so called ensemble information, meaning the concurrent bulk reading of sensed values, ranges, quality rating, identifiers (IDs), and so on. With 21 transducers on a small-scale reader platform we tested the performance of our approach successfully proving the scalability and reliability. To this end, we implemented our code-based CC mechanism into an all-printable passive RFID label down to the logic gate level, indicating a circuit complexity of about 500 transistors. In respect to time-based CC approach we utilize the superimposed radio transmission to obtain resource-limited WSNs, that can be deployed in wide areas for establishing, e.g., smart environments. In our application scenario for resource-limited WSN, we utilize the superimposed radio transmission to calculate functions of interest, i.e., to accomplish data processing directly on the radio channel. To prove our concept in a case study, we created a WSN with 15 simple nodes measuring the environmental mean temperature. Based on our analysis about the wireless computation error we were able to minimize the stochastic error arbitrarily, and to remove the systematic error completely

Decentralised Algorithms for Wireless Networks.

Designing and managing wireless networks is challenging for many reasons. Two of the most crucial in 802.11 wireless networks are: (a) variable per-user channel quality and (b) unplanned, ad-hoc deployment of the Access Points (APs). Regarding (a), a typical consequence is the selection, for each user, of a different bit-rate, based on the channel quality. This in turn causes the so-called performance “anomaly”, where the users with lower bit-rate transmit for most of the time, causing the higher bit-rate users to receive less time for transmission (air time). Regarding (b), an important issue is managing interference. This can be mitigated by selecting different channels for neighbouring APs, but needs to be carried out in a decentralised way because often APs belong to different administrative domains, or communication between APs is unfeasible. Tools for managing unplanned deployment are also becoming important for other small cell networks, such as femtocell networks, where decentralised allocation of scrambling codes is a key task

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