Interferer Classification, Channel Selection and Transmission Adaptation for Wireless Sensor Networks

Wireless sensor networks (WSNs) are being increasingly deployed in office blocks or residential areas for commercial applications, such as home automation, meter reading, surveillance, among others. At these locations, the WSNs experience interference in the 2.4 GHz unlicensed band due to wireless LANs (WLANs) and commercial microwave devices, leading up to 92% packet losses. In this paper, an algorithmic framework is proposed, that allows the sensor nodes to identify the type of the interferer and its operational channel, so that the former may adapt their own transmission to reduce packet losses in the network. Our proposed interference classification approach comprises of an (i) offline measurement of the spectral characteristics of the WLAN and microwave devices to obtain a reference spectrum shape, and (ii) matching the observed spectral pattern during network operation with the stored reference shape The knowledge of the interferer characteristics is then leveraged by the sensor nodes to decide their transmission channel, packet scheduling times and sleep-awake cycles. Results reveal that our approach incurs up to 50 - 70% energy savings in the WSN, by reducing interference related packet losses.