A framework for traffic management in IoT networks

Abstract

Wireless networks for IoT applications support different types or classes of end devices. Each such class results in different uplink and downlink traffic behavior. It is important to identify suitable class for each end device. We first propose a generic framework for this purpose. We propose an element, called Software Controller, which learns device profile using variety of means such as information provided by the device itself, information provided by the associated IoT operator and contextual information using other sources. It can also use machine learning techniques to learn how a device might behave during certain period. Suitable resource management methods are to be associated with such classification schemes. We propose one such resource management method for 802.11ah type of networks. Next, we look at some traffic scenarios that may not be supported well by the existing device classes in some of these networks. Some IoT devices may always communicate low amount of data sporadically but some may need to communicate large amount of uplink or downlink (or bi-directional) data during certain time intervals. For example, an IoT device may need to measure (and report) certain parameters more frequently on detection of certain events, or a network server may want to set certain parameters or upgrade software at an IoT device during some time interval. It becomes important to control uplink / downlink communication opportunities and sleep interval at IoT devices in the network effectively. We propose a new device class and dynamic switching mechanism to handle such traffic scenarios effectively. We also include a software defined controller that provides for dynamic management of these communication opportunities at IoT devices and Access Points in the network.