Recently, ambient backscatter communication has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks, and thus it is expected to realize numerous Internet-of-Things applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.

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Ambient Backscatter Communications: A Contemporary Survey

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Rfid Handbook Fundamentals And Applications In Contactless Smart Cards And Identification

In the last decade, the research on and the technology for outdoor tracking have seen an explosion of advances. It is expected that in the near future, we will witness similar trends for indoor scenarios where people spend more than 70% of their lives. The rationale for this is that there is a need for reliable and high-definition real-time tracking systems that have the ability to operate in indoor environments, thus complementing those based on satellite technologies, such as the Global Positioning System (GPS). The indoor environments are very challenging, and as a result, a large variety of technologies have been proposed for coping with them, but no legacy solution has emerged. This paper presents a survey on indoor wireless tracking of mobile nodes from a signal processing perspective. It can be argued that the indoor tracking problem is more challenging than the problem on indoor localization. The reason is simple: From a set of measurements, one has to estimate not one location but a series of correlated locations of a mobile node. The paper illustrates the theory, the main tools, and the most promising technologies for indoor tracking. New directions of research are also discussed.

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Indoor Tracking: Theory, Methods, and Technologies

In the era of smart cities, there are a plethora of applications where the localization of indoor environments is important, from monitoring and tracking in smart buildings to proximity marketing and advertising in shopping malls. The success of these applications is based on the development of a cost-efficient and robust real-time system capable of accurately localizing objects. In most outdoor localization systems, global positioning system (GPS) is used due to its ease of implementation and accuracy up to five meters. However, due to the limited space that comes with performing localization of indoor environments and the large number of obstacles found indoors, GPS is not a suitable option. Hence, accurately and efficiently locating objects is a major challenge in indoor environments. Recent advancements in the Internet of Things (IoT) along with novel wireless technologies can alleviate the problem. Small-size and cost-efficient IoT devices which use wireless protocols can provide an attractive solution. In this paper, we compare four wireless technologies for indoor localization: Wi-Fi (IEEE 802.11n-2009 at the 2.4 GHz band), Bluetooth low energy, Zigbee, and long-range wide-area network. These technologies are compared in terms of localization accuracy and power consumption when IoT devices are used. The received signal strength indicator (RSSI) values from each modality were used and trilateration was performed for localization. The RSSI data set is available online. The experimental results can be used as an indicator in the selection of a wireless technology for an indoor localization system following application requirements.

RSSI-Based Indoor Localization With the Internet of Things

Several methods for enterprise systems analysis rely on flow-oriented representations of business operations, otherwise known as business process models. The Business Process Modeling Notation (BPMN) is a standard for capturing such models. BPMN models facilitate communication between domain experts and analysts and provide input to software development projects. Meanwhile, there is an emergence of methods for enterprise software development that rely on detailed process definitions that are executed by process engines. These process definitions refine their counterpart BPMN models by introducing data manipulation, application binding, and other implementation details. The de facto standard for defining executable processes is the Business Process Execution Language (BPEL). Accordingly, a standards-based method for developing process-oriented systems is to start with BPMN models and to translate these models into BPEL definitions for subsequent refinement. However, instrumenting this method is challenging because BPMN models and BPEL definitions are structurally very different. Existing techniques for translating BPMN to BPEL only work for limited classes of BPMN models. This article proposes a translation technique that does not impose structural restrictions on the source BPMN model. At the same time, the technique emphasizes the generation of readable (block-structured) BPEL code. An empirical evaluation conducted over a large collection of process models shows that the resulting BPEL definitions are largely block-structured. Beyond its direct relevance in the context of BPMN and BPEL, the technique presented in this article addresses issues that arise when translating from graph-oriented to block-structure flow definition languages.

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From business process models to process-oriented software systems

In this paper, we present a novel semi-passive radio-frequency identification (RFID) system for accurate indoor localization. The system is composed of a standard ultra high frequency (UHF) ISO-18000-6C compliant RFID reader, a set of standard passive RFID tags whose locations are known, and a newly developed tag-like RFID component, which is attached to the items that need to be localized. The new semi-passive component, referred to as sensatag (sense-a-tag), has a dual functionality: it can sense and decode communication between the reader and standard tags in its proximity, and can communicate with the reader like standard tags using backscatter modulation. Based on the information conveyed by the sensatags to the reader, localization algorithms based on binary sensor principles can be developed. We conduct a number of experiments in a laboratory to quantify the performance of our system, including two real applications, one finding the exact placement of items on shelves, and the other estimating the direction of item movement.

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Novel Semi-Passive RFID System for Indoor Localization

Particle filtering is a statistical signal processing methodology that has recently gained popularity in solving several problems in signal processing and communications. Particle filters (PFs) have been shown to outperform traditional filters in important practical scenarios. However their computational complexity and lack of dedicated hardware for real-time processing have adversely affected their use in real-time applications. In this paper, we present generic architectures for the implementation of the most commonly used PF, namely, the sampling importance resampling filter (SIRF). These provide a generic framework for the hardware realization of the SIRF applied to any model. The proposed architectures significantly reduce the memory requirement of the filter in hardware as compared to a straightforward implementation based on the traditional algorithm. We propose two architectures each based on a different resampling mechanism. Further, modifications of these architectures for acceleration of resampling process are presented. We evaluate these schemes based on resource usage and latency. The platform used for the evaluations is the Xilinx Virtex II pro FPGA. The architectures presented here have led to the development of the first hardware (FPGA) prototype for the particle filter applied to the bearings-only tracking problem.

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Generic hardware architectures for sampling and resampling in particle filters

Disclosed is an apparatus and system for monitoring drug regimen compliance, the system utilizing a Radio Frequency Identification (RfID) tag affixed to a pharmaceutical agent and a wearable RFID reader that identifies a patient. The RFID tag identifies the pharmaceutical agent and the RFID reader wirelessly communicates with a central monitoring system upon ingestion of the pharmaceutical agent.

Rfid monitoring of drug regimen compliance

We present the vision of BARNET (Backscattering Activity Recognition NEtwork of Tags), a network of passive RF tags that use RF backscatter for tag-to-tag communication. BARNET not only provides identification of tagged objects but also can serve as a 'device-free' activity recognition system. BARNET's key innovation is the concept of backscatter channel state information (BCSI) which can be measured via systematic multiphase probing of the backscatter tag-to-tag channel using innovative processing on the passive tags. So far such measurements were only possible using active radio receivers that consume much higher power. Changes in BCSI provide signatures for different activities in the environment that can be learned using suitable machine learning tools. We develop the BARNET tag architecture which shows that an ASIC implementation can run on harvested RF power. We develop a printed circuit board (PCB) prototype using discrete components to evaluate activity recognition performance. We show that the prototype can recognize human daily activities with an average error around 6%. Overall, BARNET uses passive tags to achieve the same level of performance as systems that use powered, active radios.

https://dl.acm.org/doi/pdf/10.1145/3210240.3210336

BARNET: Towards Activity Recognition Using Passive Backscattering Tag-to-Tag Network

Radio frequency (RF)-powered backscatter communication between passive tags holds tremendous potential as an enabling technology for a ubiquitous “Internet of Things.” We develop a backscattering tag-to-tag network (BTTN), comprised of passive tags capable of large-scale, passive, and multihop communication with each other via backscatter modulation of an external RF excitation signal. The low sensitivity and lack of active demodulator on passive tags present significant challenges to the communication, including a unique phase cancellation problem, which significantly affects the range and robustness of a passive tag-to-tag link. We overcome these challenges using innovative tag architecture and also develop a novel multiphase backscatter modulation technique with a learning mechanism that overcomes the phase cancellation problem. This improves the link performance bringing passive tag-to-tag communication closer to practical use. The additional hardware compared to the conventional radio frequency identification tag architecture includes one more terminating impedance in the modulator. The data rate is reduced due to backscatter at two different phases while the increase in the power consumption is negligible. We develop prototype BTTN tag hardware and firmware and evaluate its performance. The prototype achieves link ranges of up to 3 m at 5 kb/s with an excitation power level of only -20 dBm while successfully overcoming phase cancellation. We further extend BTTN operation to a multihop network where we demonstrate a four hop link capable of communicating over 12 m under similar conditions.

https://ieeexplore.ieee.org/ielaam/6488907/8430490/8364537-aam.pdf

Akshay Athalye

Papers

Novel Semi-Passive RFID System for Indoor Localization

Generic hardware architectures for sampling and resampling in particle filters

Rfid monitoring of drug regimen compliance

BARNET: Towards Activity Recognition Using Passive Backscattering Tag-to-Tag Network

Design and Evaluation of “BTTN”: A Backscattering Tag-to-Tag Network