# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 1 Introduction
### Background and Previous Work
#### 1.1.5 AmI, IoT and Supply Chains  
Advances can be made to the Supply Chain organisation by adopting it into a smart space. Smart spaces allow for a connection of devices to share resources and access to all information on a distributed system [27]. Smart spaces are deployed in an Internet of Things (IoT) environment. Implementing sensors in the development of smart spaces allows for the continuous monitoring of the characteristics of the smart space. Once the sensors have collected enough data from the smart space, different actions can be taken which can automate the services, like automating the scaling of containers based on over or under-provision of resources from the analysis of the smart space [28]. Smart spaces deliver a value-added service, which can only further improve the efficiency and effectiveness of a Supply Chain [1].  
AmI is a set of interconnecting devices sharing information about users and their environments using intelligence. AmI applies reasoning to the collected data and makes decisions that will benefit users in the environment [29]. AmI services can be developed in a smart space [1]. By applying context awareness to the data, AmI is responsive, sensitive and adaptive to the user. Applying context awareness to these EDI messages can build an intelligent reasoning model that supports queueing systems in a Supply Chain organisation. The reasoning model can act as a decision point when stress testing an environment.  
Different AmI services are being developed in smart space environments. Smart-M3 is one such service. Smart-M3 is an information-sharing open-source platform which was developed in 2006 by NOKIA and is being further advanced by different universities [1]. The Smart-M3 platform provides a shared view of dynamic knowledge and services in a ubiquitous computing environment within distributed applications. Two components of M-3 are a semantic information broker (SIB) and knowledge processor (KP) [30]. Fog computing was developed to support smart space applications. It integrates resources at edge devices and cloud platforms. Where low latency is required in a distributed smart space application, Fog Computing is important. Fog computing uses the sense-process-actuate model. Once streaming data is generated from the sensing device, the fog device subscribes to and processes the data. The processed data is then translated into actions and sent to actuators [31]. In B2B systems where customers need near-real-time information about their transactions, latency is an important metric. A Supply Chain organisation would benefit from Fog Computing.  
With smart spaces being an important factor for AmI in an IoT environment, one needs to be able to describe all the sensors and devices connected to a smart space. Peer-to-Peer (P2P) network models are one such solution. Every sensor or device needs to be represented in some way. Ref. [32] proposes a P2P model that can virtualise physical objects by representing them as a network of interacting information objects in the smart space.