Random-Deadline Missing Probability Analysis for Wireless Communications in Industrial Environments

Published: July 05, 2018

Author(s)

Mohamed T. Hany, Nader Moayeri

Abstract

The flexibility of wireless networks, and their cost-efficient setup and maintenance have motivated the deployment of wireless communications in industrial environments. Strict industrial requirements including the time-sensitive delivery of data have led to developing appropriate wireless technologies to satisfy these requirements. Time division multiple access (TDMA) protocols have been widely exploited in various technologies due to their ease of implementation and packet collision avoidance. In this work, we consider the problem of scheduling multiple data flows over a wireless network operating in an industrial environment. These flows are characterized by random strict deadlines for each packet following a given probability distribution. Each of these flows may represent the data coming from a sensor to the controller or the control commands from the controller to an actuator. A randomized frame- based scheduling scheme is analyzed where each time slot in the frame is assigned to a data flow randomly.We derive a method to calculate the average number of packets missing their deadlines per frame that can be used for flow admission control or optimization of the scheduling algorithm. Finally, we study the effects of various system parameters on the ratio of the average number of packets missing their deadlines to the average total number of packets generated.
Proceedings Title: Proceedings of the 14th IEEE International Workshop on Factory Communication Systems
Conference Dates: June 13-15, 2018
Conference Location: Imperia, -1
Conference Title: WFCS 2018: 14th IEEE International Workshop on Factory Communication Systems
Pub Type: Conferences

Keywords

wireless networks, transmission scheduling, industrial networks, ultra reliable wireless, data packets with delivery deadlines, time division multiple access protocol
Created July 05, 2018, Updated August 12, 2019