RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance

One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can better understand the sources of such infections. We envision a smart hospital capable of increasing operational efficiency and improving patient care with less spending. In this paper, we propose a non-intrusive vision-based system for tracking people's activity in hospitals. We evaluate our method for the problem of measuring hand hygiene compliance. Empirically, our method outperforms existing solutions such as proximity-based techniques and covert in-person observational studies. We present intuitive, qualitative results that analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. This work is a step towards a computer-vision based smart hospital and demonstrates promising results for reducing hospital acquired infections.Comment: Machine Learning for Healthcare Conference (MLHC

Location estimation in smart homes setting with RFID systems

Indoor localisation technologies are a core component of Smart Homes. Many applications within Smart Homes benefit from localisation technologies to determine the locations of things, objects and people. The tremendous characteristics of the Radio Frequency Identification (RFID) systems have become one of the enabler technologies in the Internet of Things (IOT) that connect objects and things wirelessly. RFID is a promising technology in indoor positioning that not only uniquely identifies entities but also locates affixed RFID tags on objects or subjects in stationary and real-time. The rapid advancement in RFID-based systems has sparked the interest of researchers in Smart Homes to employ RFID technologies and potentials to assist with optimising (non-) pervasive healthcare systems in automated homes. In this research localisation techniques and enabled positioning sensors are investigated. Passive RFID sensors are used to localise passive tags that are affixed to Smart Home objects and track the movement of individuals in stationary and real-time settings. In this study, we develop an affordable passive localisation platform using inexpensive passive RFID sensors. To fillful this aim, a passive localisation framework using minimum tracking resources (RFID sensors) has been designed. A localisation prototype and localisation application that examined the affixed RFID tag on objects to evaluate our proposed locaisation framework was then developed. Localising algorithms were utilised to achieve enhanced accuracy of localising one particular passive tag which that affixed to target objects. This thesis uses a general enough approach so that it could be applied more widely to other applications in addition to Health Smart Homes. A passive RFID localising framework is designed and developed through systematic procedures. A localising platform is built to test the proposed framework, along with developing a RFID tracking application using Java programming language and further data analysis in MATLAB. This project applies localisation procedures and evaluates them experimentally. The experimental study positively confirms that our proposed localisation framework is capable of enhancing the accuracy of the location of the tracked individual. The low-cost design uses only one passive RFID target tag, one RFID reader and three to four antennas

RFID Applied to Supply Chain Logistics in Disaster Recovery

The purpose of this paper is to review recent developments to use RFID (Radio Frequency Identification) for supply chain logistics in the construction industry and identify potential applications of this technology for disaster recovery in construction operations. Natural disasters, terrorist attacks, and extreme weather conditions can cause significant damage to local communities and disruptions to the local supply chain. The effective movement of resources during disaster recovery operations is critical in a postdisaster recovery environment. RFID technology has advanced to the level that the technology could be used to improve the supply chain preparedness, people, material and equipment locating and tracking, and corresponding billing operations in many industries. Current RFID technology could be used in the construction industry in areas to automate material tracking in off-site warehouse of construction firms or suppliers, to establish initial location of material received on construction sites, to track materials that are being shared and in common areas for multiple construction sites, to restock material in laydown or warehouses, to minimize material over-ordering with better tracking of quantity information, to reduce loss from theft and support site security measures, and to assist site equipment and fuel management. Studies have shown that 3.1% of total construction costs could be saved after implementing RFID technology tagging and tracking materials on site and in warehouses. In the case of after-natural-disasters reconstruction, materials locating and tracking activities are highly valuable because disaster relief reconstruction site conditions are more challenging than regular construction site conditions. Reconstruction resources are often limited and difficult to move in and out. Significant construction cost savings can be achieved with RFI-enabled material tracking and locating systems. A review of the current technology provides many emerging applications of RFID technology for the broader construction industry. This paper examines direct applications of RFID for disaster relief reconstruction operations

Location estimation in a 3D environment using radio frequency identification tags

RFID tag location estimation in a 3D environment is investigated. The location of the tag with unknown coordinates can be estimated with certain accuracy. However, accuracy can be improved using the knowledge based on measurement of additional reference tags with known location. This thesis studies the mathematical formulation and practical realization of location sensing using RFID tags. Deviating from the standard use of RFID technology which employs one tag reader to identify the presence of tag, here multiple tag readers with known location are used to estimate the physical location of an individual tag, with/without the help of few reference tags with known locations. Mathematical model of this concept has been developed based on distance variations in terms of signal strength. Experimental approach with limited range passive tags has been carried out. Since the range of the RFID system was limited only to a few inches, signal strength variations were insignificant. Instead, time domain measurements with the help of an external antenna were conducted. The composite signal width including of the wake up signal of the interrogator, travel time between the interrogator and tag, and the tag\u27s response was measured and quantified. It was observed that the width of the signal was proportional to the distance between the tag reader and the tag. It was noticed that the use of four RFID tag readers yielded fairly accurate results to identify the location the tag based on the mathematical formulation developed here. Additionally, concept of trilateration has also been extended for tracking the tag of unknown location without the use of reference tags. Archival data set corresponding to all tag location due to four different tag readers was compiled. The unknown tag was probed with four tag readers and matching the data to the archival data set yielded unique and accurate results for its unknown location. It was demonstrated that both approaches were proved to be cost-effective techniques and estimation of the location of a specific tag has been achieved with sufficient accuracy

A review of RFID based solutions for indoor localization and location-based classification of tags

Wireless communication systems are very used for indoor localization of items. In particular, two main application field can be identified. The former relates to detection or localization of static items. The latter relates to real-time tracking of moving objects, whose movements can be reconstructed over identified timespans. Among the adopted technologies, Radio-Frequency IDentification (RFID), especially if based on cheap passive RFID tags, stands out for its affordability and reasonable efficiency. This aspect makes RFID suitable for both the above-mentioned applications, especially when a large number of objects need to be tagged. The reason lies in a suitable trade-off between low cost for implementing the position sensing system, and its precision and accuracy. However, RFID-based solutions suffer for limited reading range and lower accuracy. Solutions have been proposed by academia and industry. However, a structured analysis of developed solutions, useful for further implementations, is missing. The purpose of this paper is to highlight and review the recently proposed solutions for indoor localization making use of RFID passive tags. The paper focuses on both precise and qualitative location of objects. The form relates to (i) the correct position of tags, namely mapping their right position in a 2D or 3D environment. The latter relates to the classification of tags, namely the identification of the area where the tag is regardless its specific position

A Model for Automated Construction Materials Tracking

Materials management is a critical factor in construction project performance, particularly in the industrial sector. Research has shown that construction materials and installed equipment may constitute more than 50% of the total cost for a typical industrial project. Therefore the proper management of this single largest component can improve the productivity and cost efficiency of a project and help ensure its timely completion. One of the major problems associated with construction materials management is tracking materials in the supply chain and tracking their locations at job sites. Identification is integral to this process. Research projects conducted during the last decade to automate the identification and tracking of materials have concluded that such automation can increase productivity and cost efficiency as well as improve schedule performance, reduce the number of lost items, improve route and site optimization, and improve data entry. However, these technologies have been rapidly evolving, and knowledge concerning their implementation is sparse. One new approach enables locating of components within a few meters at a cost at least a magnitude lower than preceding technologies. It works by combining GPS located reads of RFID tags read at a rate of several thousand Hertz in order to estimate the location of these inexpensive tags which are attached to key construction materials. This technology was rapidly prototyped and deployed on two large industrial construction projects in 2007 and 2008. This thesis analyzes and synthesizes the data and experiences from these unique and large scale field trials as well as the literature in order to develop a general implementation model for automated construction materials tracking for industrial projects. It is concluded from the model that this new automated construction materials tracking technology is likely to be successful if implemented full scale on well selected future projects. This conclusion is supported by subsequent industry decisions

RF-compass: Robot object manipulation using RFIDs

Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the object's orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches). This paper presents RF-Compass, an RFID-based system for robot navigation and object manipulation. RFIDs are low-cost and work in non-line-of-sight scenarios, allowing them to address the limitations of existing solutions. Given an RFID-tagged object, RF-Compass accurately navigates a robot equipped with RFIDs toward the object. Further, it locates the center of the object to within a few centimeters and identifies its orientation so that the robot may pick it up. RF-Compass's key innovation is an iterative algorithm formulated as a convex optimization problem. The algorithm uses the RFID signals to partition the space and keeps refining the partitions based on the robot's consecutive moves.We have implemented RF-Compass using USRP software radios and evaluated it with commercial RFIDs and a KUKA youBot robot. For the task of furniture assembly, RF-Compass can locate furniture parts to a median of 1.28 cm, and identify their orientation to a median of 3.3 degrees.National Science Foundation (U.S.