A cognitive network for reliability maintenance of double-path routing in IEEE 802.15.4 networks

In order to build a reliable traffic to overcome ever-changing scenarios with different QoS requirements for WSN-based M2M applications, a cognitive technology using the causality tree is proposed for the reliability maintenance of wireless sensor networks. Routing maintenance metrics are defined as basic initiatives for device maintenance. The metrics enable to guarantee quality of services for the development of machine-to-machine communications. The causality tree performs a comprehensive survey of the metrics, and possible events are analyzed to identify routing design issues about link states, link reliability, and correlated dynamics transitions in terms of RSSI and PRR. Device maintenance metrics categorize the proposed semantic metrics into six class events. A global profile of the causality tree is finally put forward for each intelligent node. Physical experiments are used to evaluate the reliable performance of wireless nodes in the scenarios of channel and electromagnetic interference.