# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 3 Literature Review
To use wireless connections for these UAVs, Ultra Wide Band (UWB) solutions using anchor nodes have been developed, whose range can be scaled using the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) depending on the requirements. They also designed a multi-technology, duty-cycling, time-slotted UWB MAC protocol for the optimization of power consumption by UWB radios. [PERSON] et al. [35] proposed an IoT-based architecture for order picking that involved controlling and monitoring the inventory. The architecture included process specification and domain model specification using the product ID, location, rack, etc. Information model specification introduces the information about the position of the robots, the availability of the products, the position of the products, the size of the rack, etc. Service specification provides the service of operating auto and manual modes for orders received, the identification of the location of a rack, the movement of robots towards the specified rack, and so on. The authors also described an IoT-level specification includingmeasurement, automation, innovation, and cloud utilities, making the solution a Level-4 IoT system. Furthermore, the architecture involved device and component integration, where they mapped the functionalities to the sensors and actuators. In any inventory management system, the location of goods is of great importance as it makes tracking easier and more efficient. A self-adaptive decision model for the inventory positioning, check-in, and check-out of the inventory and other event handling was proposed by [PERSON] et al. [36]. The model senses the environment, creates knowledge based on it, and trains a neural network to recognize the scenario to make a decision. The model improves itself by adjusting the knowledge base and decision-making is performed at the node level, which makes this system completely decentralized. Similarly, [PERSON] et al. [37] developed a strategy for logistics management for which they devised a framework consisting of three parts: IoT-enabled vehicle terminals, resource management, and dynamic optimization services. Information is obtained by identification sensors, e.g., RFIDs, wireless connectivity, etc., and is updated to the Enterprise Information integrated System (EIS) and Geographic Information System (GIS), which further provides optimal routes; this also involves tracking of the inventory while loading, which is specifically based on RFIDs. This creates the first layer of the architecture, i.e., the IoT-based physical sensing layer. The second layer includes resource management in which the real-time data of vehicles is uploaded to the GIS, and the last layer involves providing optimal paths for the logistics. For parts' handling in warehouses, [PERSON] et al. [38] developed a robotic arm with six Degrees of Freedom (DOF) by implementing the concepts of Industry 4.0. Object detection and semantic segmentation were used to classify the objects that were requested to complete an order. The system also involves 6D pose estimation using six degrees of freedom so that the items are picked up cautiously. This system was presented at the Amazon Picking Challenge and was highly acknowledged. The automation of a part of a warehouse was performed in [39] by keeping the record of solid and liquid stock. The system uses an ultrasonic transducer to measure inventory, and the results are propagated to the Internet through a Raspberry Pi gateway device. However, it only focuses on the quantity of goods in the inventory, and it provides automatic order placement and lacks the check-in and check-out of the items based on their Unique Identifier (UID). The flow of goods can be drastically improved by Artificial Intelligence (AI) techniques [40], as AI can possibly help in decision-making for transportation within the warehouse, the loading and unloading of inventory, and the palletized goods operations. In these papers, the IoT architectures used were categorized into two types.