Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Residential microwave oven interference on Bluetooth data performance
IEEE Transactions on Consumer Electronics
WiSpot: fast and reliable detection of Wi-Fi networks using IEEE 802.15.4 radios
Proceedings of the 9th ACM international symposium on Mobility management and wireless access
Radio link quality estimation in wireless sensor networks: A survey
ACM Transactions on Sensor Networks (TOSN)
Low-cost interferer detection and classification using TelosB sensor motes
Proceedings of the 18th annual international conference on Mobile computing and networking
ACM SIGBED Review - Special Issue on the 3rd International Workshop on Networks of Cooperating Objects (CONET 2012)
Low-cost interferer detection and classification using TelosB sensor motes
ACM SIGMOBILE Mobile Computing and Communications Review
SoNIC: classifying interference in 802.15.4 sensor networks
Proceedings of the 12th international conference on Information processing in sensor networks
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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 2.4GHz 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.