Smart Storage System Research Articles

Design and Development of a Model Smart Storage System

Food security has become a global major problem, due to the rapid increase in population growth. This has necessity the development of an effective agricultural products’ storage system, to alleviate the problem of food wastage. This study was embarked upon to develop a prototype of universal smart storage system for farm products, by using the internet of thing (IoT). The storage structure consists of four principal constituents which were; the power source, storage chamber, central processing system, and peripheral component interconnect (PCI) heater and PCI fan. The developed model was tested at a pre-set temperature and relative humidity of 32C and 62% RH respectively. The results revealed that the developed system had an efficiency of 85%. Though, the smart model had a failure rate of 15%, this smart prototype is a major breakthrough in the production of automated storage system for agricultural products.

A Smart Storage System and Non-Invasive Technique for Spoilage Detection

Fresh vegetables are widely recognized as a vital source of essential nutrients for a healthy diet, including vegetable sprouts which are consumed globally for their high nutritional value. Only high-quality fruits and vegetables are preferred by those who are health-conscious. Consequently, it is crucial to check the quality of fruits and vegetables. However, consuming raw vegetables poses a significant risk of transmitting intestinal parasites. With a growing awareness of quality in the food and health industry, there is an increasing need for research on defined quality production, preservation during marketing, and evaluation of quality parameters. By addressing these concerns, this proposed non-invasive system presents a promising technique for detecting the quality of stored fruits and vegetables. The suggested system can help ensure the availability of safe and nutritious produce for consumers, thereby contributing to the improvement of public health.

Development and Implementation of an Innovative Smart Storage System for Fruit Quality Preservation

Development and Implementation of an Innovative Smart Storage System for Fruit Quality Preservation

Automation and Digitization in the Seed Sector: Offline to Online

As automation has become an integral part of today's world, the Internet of things (IoT) and Machine learning (ML) offers a platform for tracking and controlling different environments remotely with the help of sensors, controllers and the internet. Today IoT and ML are taking over control of agriculture industries and playing an important role in smart farming. The literature highlights the potential use of IoT and ML in agriculture and even in seed sector which is feasible for surveillance and monitoring from sowing to harvesting and similar packaging operations. In this review, we have highlighted the use of IoT applications with remote sensors such as temperature, humidity, soil moisture, water level sensors and pH value providing an idea of automation in seed sowing, seedling development and yield, which displays precision as well as practical utility to cope with difficulties in the field. It also addresses the digitalization in seed distribution and market systems. Some notable prospects by ML in the seed industry viz., seed tracking and smart storage system has also been stated.

Advances in solar energy harvesting integrated by van der Waals graphene heterojunctions.

Graphene has garnered increasing attention for solar energy harvesting owing to its unique features. However, limitations hinder its widespread adoption in solar energy harvesting, comprising the band gapless in the molecular orbital of graphene lattice, its vulnerability to oxidation in oxidative environments, and specific toxic properties that require careful consideration during development. Beyond current challenges, researchers have explored doping graphene with ionic liquids to raise the lifespan of solar cells (SCs). Additionally, they have paid attention to optimizing graphene/Si Schottky junction or Schottky barrier SCs by enhancing the conductivity and work function of graphene, improving silicon's reflectivity, and addressing passivation issues at the surface/interface of graphene/Si, resulting in significant advancements in their power conversion efficiency. Increasing the functional area of graphene-based SCs and designing efficient grid electrodes are also crucial for enhancing carrier collection efficiency. Flaws and contaminants present at the interface between graphene and silicon pose significant challenges. Despite the progress of graphene/Si-based photovoltaic cells still needs to catch up to the efficiency achieved by commercially available Si p-n junction SCs. The low Schottky barrier height, design-related challenges associated with transfer techniques, and high lateral resistivity of graphene contribute to this performance gap. To maximize the effectiveness and robustness of graphene/Si-based photovoltaic cells, appropriate interlayers have been utilized to tune the interface and modulate graphene's functionality. This mini-review will address ongoing research and development endeavors using van der Waals graphene heterojunctions, aiming to overcome the existing limitations and unlock graphene's full potential in solar energy harvesting and smart storage systems.

Radio Frequency Identification (RFID) based wireless manufacturing systems, a review

Radio frequency identification (RFID) is one of the most promising technological innovations in order to track and trace products as well as material flow in manufacturing systems. High Frequency (HF) and Ultra High Frequency (UHF) RFID systems can track a wide range of products in the part production process via radio waves with level of accuracy and reliability. As a result, quality and transparency of data across the supply chain can be accurately obtained in order to decrease time and cost of part production. Also, process planning and part production scheduling can be modified using the advanced RFID systems in part manufacturing process. Moreover, to decrease the cost of produced parts, material handling systems in the advanced assembly lines can be analyzed and developed by using the RFID. Smart storage systems can increase efficiency in part production systems by providing accurate information from the stored raw materials and products for the production planning systems. To increase efficiency of energy consumption in production processes, energy management systems can be developed by using the RFID-sensor networks. Therefore, smart factories and intelligent manufacturing systems as industry 4.0 can be introduced by using the developed RFID systems in order to provide new generation of part production systems. In this paper, a review of RFID based wireless manufacturing systems is presented and future research works are also suggested. It has been observed that the research filed can be moved forward by reviewing and analyzing recent achievements in the published papers.

Machine learning-driven automatic storage space recommendation for object-based cloud storage system

An object-based cloud storage system is a storage platform where big data is managed through the internet and data is considered as an object. A smart storage system should be able to handle the big data variety property by recommending the storage space for each data type automatically. Machine learning can help make a storage system automatic. This article proposes a classification engine framework for this purpose by utilizing a machine learning strategy. A feature selection approach wrapped with a classifier is proposed to automatically predict the proper storage space for the incoming big data. It helps build an automatic storage space recommendation system for an object-based cloud storage platform. To find out a suitable combination of feature selection algorithms and classifiers for the proposed classification engine, a comparative study of different supervised feature selection algorithms (i.e., Fisher score, F-score, Lll21) from three categories (similarity, statistical, sparse learning) associated with various classifiers (i.e., SVM, K-NN, Neural Network) is performed. We illustrate our study using RSoS system as it provides a cloud storage platform for the healthcare data as experimental big data by considering its variety property. The experiments confirm that Lll21 feature selection combined with K-NN classifier provides better performance than the others.

Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces.

Impact on railway infrastructure of wayside energy storage systems for regenerative braking management: a case study on a real Italian railway infrastructure

Today, in the railway sector there is considerable interest in studying the best ways of exploiting train braking energy, in order to achieve a reduction in energy costs and better stabilisation of grid voltage. Among the various on-board or wayside measures proposed, one of the most promising solutions is based on using wayside energy storage systems (WESSs). A WESS is a storage installation which can be integrated into mass transit systems in urban areas as well as into long-distance railway lines. It can operate as a smart storage system able to provide relevant benefits in terms of recovering surplus regeneration braking energy, voltage stabilisation, reduction of peak power demand. For these purposes, an effective and flexible simulator is essential to provide all the elements needed to focus and compare feasible configurations and operations. This study examines the problem of introducing the WESS technology in real railway infrastructure. It proposes a method based on a simulator of the WESS system integrated into the infrastructure and carries out the results of dynamic simulations referring to real operating data of the system and vehicles. The results highlight the performance of the WESS in terms of energy and power exchange, also discussing economic aspects.

Blind Reconstruction of Reed-Solomon Encoder and Interleavers Over Noisy Environment

Blind estimation of code and interleaver parameters is useful in smart storage systems and ubiquitous communication applications such as adaptive modulation and coding, reconfigurable radio systems, non-cooperative radio systems, etc. In this paper, we analyze Reed-Solomon (RS) encoded data stream and propose blind estimation algorithms to identify RS code parameters. We also provide algorithms to estimate block interleaver parameters from RS coded and block interleaved data stream. In addition, synchronization compensation through appropriate bit/symbol positioning is integrated with the proposed code and interleaver parameter estimation algorithms. Simulation results validating the proposed algorithms are given for various test cases involving both erroneous and non-erroneous scenarios. Moreover, the accuracy of estimation of RS code and block interleaver parameters are also given with detailed inferences for different modulation schemes, codeword length, and code dimension values. It has been inferred that the accuracy of parameter estimation improves with decrease in code dimension and codeword length values of RS codes. Further, the accuracy of estimation of lower modulation order schemes is better when compared to higher modulation order schemes as expected. It has also been noted that the proposed code and interleaver parameter estimation algorithms for noisy environment consistently outperform the algorithms proposed in the prior works.