PhyNetLab: An IoT-Based Warehouse Testbed

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects. This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses. However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications. Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system. In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application. The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests. We describe the hardware and software components of the nodes in addition to the overall structure of the testbed. Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse

Open-Loop Dynamic Modeling of Low-Budget Batteries with Low-Power Loads

Understanding dynamic behavior of a battery and the possibility of simulating it is necessary during design of IoT systems. This plays a critical role, especially for industrial devices with low-power demands planned for long lasting operation. While cost limitation mostly leads to use of the low-budget batteries with fast degradation, a model of these batteries supplying low-power loads is provided here. The overall model is in open-loop form because no access to the terminal measurements is available during the design phase. The identification process of the relation between the state of charge and related electromotive force as a key element of the model is discussed. Moreover, guidelines are suggested for identification of this relation. Furthermore, SoC estimation based on the Coulomb counting is modified to include a dynamic inter-cycle aging factor. This factor enables replication of the degradation within a single cycle. In spite of simplicity of this concept, it is able to reduce the model’s estimation error evaluated with two different types of loads. The overall model provides promising results with relative errors less than 0.2%

Automated Data Collection for Modelling Texas Instruments Ultra Low-Power Chargers

Some IoT designers develop their ad-hoc conversion solution specifically designed for their entity. However, having Maximum Power Point Tracking (MPPT), battery control, converter and switching logic would require a series of components. These devices will increase the initial cost and the overall energy loss overhead of this middle-ware between the EH and the storage. Nevertheless, these issues can be conquered by integrating all these elements and logics into one single chip. Currently, there are three Texas Instruments (TI) chips from the BQ255XX series and ST (SPV1050) chip available on-the-shelf, specially designed for low energy environments. Among them, TI's BQ25505 and BQ25570 chips promise a better performance out of the box and are dominant in the market. Although multiple designers have used these chips in their IoT devices, no analytical analysis on them is available. Some basic information about these devices are available through their datasheets. However, for a reliable design and fast analysis of the overall energy performance of an IoT device, these chips have to be modelled

Average Modelling of State-of-the-Art Ultra-low Power Energy Harvesting Converter IC

Micro-energy harvesting from environmental sources is a promising technology for mobile or unobtrusive battery powered Internet of Things devices with constrains in terms of size and weight. Special integrated circuits aim to manage the voltage matching and power control in between the energy harvesting transducer and the storage. Hence, models to simulate the energy behaviour of these systems are important tools for system engineers. In this paper, a neuronal network model is presented to predict the performance of two state-of-the-art integrated circuits. Required model inputs are voltage and current from the energy harvesting transducer and the voltage level of the storage element. Model provides conversion efficiency as its output. Three different datasets have been collected with in-field experiments to build and evaluate the proposed model. Evaluation of the developed model on all datasets shows efficiency prediction root mean square error of less than 2 %

Automatic identification technology

This chapter explains some basics of identification and some common methods already integrated into the industry for automatic identification. After a fundamental description of coding, a very brief explanation of barcode systems is presented. Radio Frequency Identification as the focus of this chapter is described afterwards. The basic structure of this technology and its working principle is explained. Different components of RFID systems are described in more detail. Furthermore, RFID technologies are classified based on the transponder type and frequency range. Fields of application of RFID systems are reviewed. Then, a simple guideline for the selection of a proper RFID system is mentioned. In the last part of this chapter, some challenges that RFID system designers are facing such as the use of on-metal and chipless tags are reviewed. Finally, some aspects which are currently on the edge of technology and therefore the focus of much research, are discussed

Development of a testbed for systems of intelligent load carriers

Im Rahmen dieses Artikels werden aktuelle Forschungsarbeiten zum Einsatz von Energy-Harvesting und Ultra-Low-Power-Geräten in Materialflusssystemen beschrieben. Ein besonderes Augenmerk wird auf die inBin-Plattform, das Energy-Harvesting und deren Auswirkungen auf die Leistungsverfügbarkeit gelegt. Dazu werden die Hardwareplattform und Architektur der inBin-Plattform sowie der Aufbau eines Versuchsfelds detailliert erläutert. Des Weiteren wird ein Ansatz zur Modellierung und Simulation von Systemen mit einer großen Anzahl von inBin-Plattformen vorgestellt. Darüber hinaus werden die Ergebnisse zweier simulierter Szenarien und mögliche Folgen für die Planung zukünftiger Materialflusssysteme betrachtet.This paper provides a description and analysis of research in material handling systems in regard to energy-harvesting, ultra-low-power devices. Particular attention is paid to the inBin smart device, energy-harvesting, and the performance availability of material handling systems. A detailed description of the hardware platform, architecture and testbed is provided and an approach to model systems with a large number of devices is presented. Within the proposed model, two scenarios are simulated and their implications for the architecture of future materials handling systems are discussed