Review of auto-ID technology use in warehouse management

Warehouses play a significant role in the seamless distribution, integration, and storage of items as well as in supply chain operations. Automated identification (auto-ID) technologies that include barcode and RFID provide class- or item-level visibility to facilitate effective and efficient decisions in their respective environments. A warehouse environment benefits from auto-ID through improved cost savings, operational efficiency, and opportunities for higher revenues. It is therefore not surprising that both researchers and practitioners have considered the use of auto-ID in warehouses. We take stock of related literature to determine the state-of-the-art on auto-ID use in warehouse management, with specific focus on RFID, and identify potential directions of further research. Based on our review, we develop a conceptual framework that incorporates the primary factors that guide the decision to adopt auto-ID in warehouse management.

A comparative study of RFID adoption patterns in retail

Radio frequency identification (RFID) has been viewed as a promising technology for quite some time. Initially developed a couple of decades ago, the technology has been accompanied by predictions of imminent widespread adoption since its beginnings. A majority of retailers and other users are now using or planning to use the technology. This paper employs a combination of the technology-organization-environment (TOE) model and the 3-S (substitution, scale, structural) model to analyze the long journey of RFID adoption in retail. Top retail executives in the US and Europe were interviewed to investigate RFID adoption patterns based on differences in technological, organizational, and environmental circumstances. As the retail industry is moving into a post-adoption era, these results demonstrate the current stage of retail RFID adoption, identify factors playing important roles over time as motivators or impediments, and provide some insight into the slow pace of adoption.

Deep learning approach for UHF RFID-based indoor localization

Due to the proliferation of the IoT devices, indoor location-based service is bringing huge business values and potentials. The positioning accuracy is restricted by the variability and complexity of the indoor environment. Radio Frequency Identification (RFID), as a key technology of the Internet of Things, has became the main research direction in the field of indoor positioning because of its non-contact, non-line-of-sight and strong anti-interference abilities. This paper proposes the deep leaning approach for RFID based indoor localization. Since the measured Received Signal Strength Indicator (RSSI) can be influenced by many indoor environment factors, Kalman filter is applied to erase the fluctuation. Furthermore, linear interpolation is adopted to increase the density of the reference tags. In order to improve the processing ability of the fingerprint database, deep neural network is adopted together with the fingerprinting method to optimize the non-linear mapping between fingerprints and indoor coordinates. The experimental results show that the proposed method achieves high accuracy with a mean estimation error of 0.347 m.

Software-based shielding for real-time inventory count in different store areas: A feasibility analysis in fashion retail

RFID is an established technology and its implementation has been increasing steadily in different industries in the last decades. An important and relatively recent RFID breakthrough has been that of moving the level of tagging from pallet- or case-level, to item-level. This development has opened up a new set of use cases and benefits, especially in retail. One of these new use cases is the estimation of items’ location by positioning and tracking the tags attached to them. This problem is often seen as a classification problem, especially when tags that are read at the retail store must be located either in the sales floor or in the backroom area. The typical approach to ease this classification consists of physically shielding the interested areas via hardware installations, although this solution is expensive and lacks flexibility. In this paper, we present a different solution, namely a software-based shielding approach, to address the classification problem. Our solution makes use of item-level RFID tags and is based on the well-known logistic regression. Whenever a reading session is performed by means of a handheld reader, the classification model estimates in real-time (i.e. within a few seconds) which tagged items are in the same area of the reader and which are not, with no need of any shielding hardware installation. According to the validation preliminary tests presented in this paper, in which we simulated a fashion retail store, the proposed approach has an overall average accuracy of 95.5%.

A real-time management system for the indoor environmental quality and energy efficiency in a hotel guestroom

With the concept of green hotel, the hotel industry has started to consider a sustainable design and operation to obtain more competitiveness. It is needed to implement a convenient way to recognize a real-time situation about the indoor environmental quality (e.g., temperature, relative humidity, CO2, TVOCs, PM2.5, etc.) and the relevant energy efficiency in a hotel guestroom. This study aimed to develop a scalable integrated platform for providing real-time monitoring, alert notification, and analytics so as to satisfy the level of the occupants’ comfort and satisfaction. Facility managers could gain insight from the real-time hierarchical and historical information and take actions at a point of time. The novel approach could create a comfortable and healthy environment for the occupants in a hotel guestroom while realizing the energy efficiency in real time. The proposed platform was validated with three guestrooms in a hotel, and the scalability of the system was fully confirmed. In the future studies, it is expected to deploy the proposed platform to a larger physical entity of the hotel, providing flexibility and expandability in accordance with the strategic facility planning of the hotel. Furthermore, it is planned to develop various kinds of occupant-centered services in a hotel guestroom level.

Super lightweight mobile RFID authentication protocol for bit replacement operation

In the traditional RFID (Radio Frequency IDentification) system, a secure wired channel communication is used between the reader and the server. The newly produced mobile RFID system is different from the traditional RFID system, the communication between the reader and the server is based on a wireless channel, and the authentication protocol is suitable for traditional RFID systems, but it cannot be used in mobile RFID systems. To solve this problem, a mutual authentication protocol MSB (Most Significant Bit) for super lightweight mobile radio frequency identification system is proposed based on bit replacement operation. MSB is a bitwise operation to encrypt information and reduce the computational load of communication entities. Label, readers, and servers authenticate first and then communicate, MSB may be used to resistant to common attacks. The security analysis of the protocol shows that the protocol has high security properties, the performance analysis of the protocol shows that the protocol has the characteristics of low computational complexity, the formal analysis of the protocol based on GNY logic Gong et al. (1990) provides a rigorous reasoning proof process for the protocol.

An IOT based rail track condition monitoring and derailment prevention system

This paper proposes a new method for monitoring the irregularities in railway tracks by updating the status of the tracks in the cloud. The IoT based Railway Track Monitoring System (IoT-RMS) is proposed for monitoring the health of the railway track. The system identifies any abnormality in the tracks at an early stage. These abnormalities are rectified before they develop for smoother transportation. The micro electro mechanical system (MEMS) accelerometers are placed in the axle box for measuring the signal. It becomes hard to identify the exact location of abnormalities when the global positioning system (GPS) falters due to signalling issues. In this paper, a new hybrid method is proposed for locating irregularities on a track; even in the absence of a GPS signal. Pre-processing of the GPS signal is carried out effectively because the sensors used in IoT-RMS are capable of functioning in a high noise environment. The IoT-RMS updates the location of the abnormality in the cloud and shares it with other trains that will be passing through that location. As a result, the drivers of trains respond accordingly and avoid derailment. An experimental setup has been developed for a study of the performances for four different abnormal cases, and the result shows the effectiveness of the proposed system.

Optimizing a RFID-UAV cattle search tour

We present a path determination method for an unmanned aerial vehicle (UAV) equipped with a radio frequency identification (RFID) interrogation system. UAV path design is one of the main factors influencing the cost of UAV inventory management systems. A case study for using a path-optimized UAV-RFID system is presented. In this case study the RFID-UAV travels above the pastures of an extensive cattle ranch scanning the RFID tags attached to the cattle in order to detect their locations. Since the UAV battery limits the flight time, the UAV path must be segmented and landing locations for recharging or battery replacement must be determined. Scanning a cattle RFID tag requires the UAV to fly within a certain distance from the animal, as opposed to approaching the animal’s exact location. The reading distance (RD) of the RFID equipment determines the allowable distance. We use an easy-to-explain method to determine the UAV path. We generate a RD-based hexagonal tiling of the pasture and minimize the UAV path such that it visits all the tile center points. Based on the current UAV system configuration and environmental conditions, the optimal UAV tour allows for the scan of each pasture segment’s animals to be completed within the battery-limited flight time. A case study was performed at the California Polytechnic (Cal Poly) State University’s Escuela and Walters Ranches. The RFID-UAV system solution is estimated to be 62%faster than the current cattle scanning practice, which requires ranch employees to search for and scan each animal from close proximity.