Context-aware and pro-active queue management systems in intelligent environments

Abstract

Abstract-The Ambient intelligence (AmI) paradigm refers to electronic environments which are sensitive and responsive to the presence of people. Queue systems are practically used in various institutions and commercial enterprises constituting a challenge for the intelligent environments in smart cities. The management of the customer flows guarantees elimination or reduction of the queues as well as the economic benefits which follow the clients' satisfaction of the better service quality. There has been proposed the intelligent queue management system designed as the pro-active and context-aware system basing on multiple lowlevel sensors and devices constituting the IoT (Internet of Things) network. The designed context-driven system is characterized by user friendliness, as well as the client behavior understanding to generate actions that support clients. There has been proposed the conceptual version of the system. The selected aspects of the prototype version has been simulated. This prototype can be used as the necessary experience for building the target system meeting the precise needs and assumptions typical for context-aware and pro-active system basing on IoT networks.

Systematic literature review of context-awareness applications supported by smart cities’ infrastructures

“…Moreover, as shown by the diagram of Fig. 2, 74% of the studies (i.e., 20 studies [89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108]) were published since 2017 and there is a trend towards an increasing number of publications over the years.…”

Sensor-Enabled Context-Aware and Pro-Active Queue Management Systems in Intelligent Environments

“…This work follows up on the paper [ 48 ], where the system for intelligent queue management was first proposed. However, the changes and improvements currently made are significant and go much further.…”

“…The “Client monitoring” use case is another important part of the system which ensures the current observation of the identified client and the recognition of the moment when directing them to the appropriate cash desk as the place of trade finalisation. Paper [ 48 ] provides some general scenarios for selected use cases. The “System maintenance” use case enables the current system management, typical administrative tasks, switching the new devices on and off and deleting the outdated data, keeping it in order etc.…”

“…Figure 7 shows an important part of the system which is the organisation of the queue sub-system. The variable is responsible for the cash desk area which is located close to the physical cash registers (both serviced and those which require self-service), see also [ 48 ]. It is supposed that every client who enters this area wants to finish their shopping and is moving closer to the physical cash registers.…”

“…Even if the prototype has been simplified, it still portrays precisely how the system works. Moreover, it is much more accurate compared to the environment and experiments presented in article [ 48 ]. After the basic operational logic of agents was implemented for the proposed system, the next step was to perform the simulation of the system in order to verify the basic assumptions as well as to explore the possibilities and viability of the queue management system together with the analysis of context data coming from sensory networks.…”