SoNIC

Abstract

Sensor networks that operate in the unlicensed 2.4 GHz frequency band suffer cross-technology radio interference from a variety of devices, e.g., Bluetooth headsets, laptops using WiFi, or microwave ovens. Such interference has been shown to significantly degrade network performance. We present SoNIC, a system that enables resource-limited sensor nodes to detect the type of interference they are exposed to and select an appropriate mitigation strategy. The key insight underlying SoNIC is that different interferers disrupt individual 802.15.4 packets in characteristic ways that can be detected by sensor nodes. In contrast to existing approaches to interference detection, SoNIC does not rely on active spectrum sampling or additional hardware, making it lightweight and energy-efficient.In an office environment with multiple interferers, a sensor node running SoNIC correctly detects the predominant interferer 87% of the time. To show how sensor networks can benefit from SoNIC, we add it to a mobile sink application to improve the application's packet reception ratio under interference.

A Lightweight Classification Algorithm for External Sources of Interference in IEEE 802.15.4-Based Wireless Sensor Networks Operating at the 2.4 GHz

“…Hermans et al [20] and Rensfelt et al [11] propose SoNIC, a system consisting of a classification method and countermeasures to mitigate the effects of interference. The classification method uses individual corrupted IEEE 802.15.4 packets and gathers the data only in periods of normal operation.…”

“…Hence, the system can be more energy conserving than active sensing. However, as described in [20], packets sent under interference can be either lost (not received) or received. The received packets can be correct (which means that the interference did not affected the link) or corrupted.…”

“…The corrupted packets can also be partly (leading to less accurate classification) or fully interfered with by the interferer. Hermans et al [20] state that the ratio of corrupted packets is 10% to 20% if a link has a packet error rate above 20%. Further, only packets with a payload greater than or equal to 64 bytes are used for classification.…”

“…However, to avoid misclassifications, multiple classification results are combined. A time span of 30 s or 10 s is monitored until the final decision about the mitigation strategy is made [20]. SoNIC collects features based on RSSI, link quality indication (LQI), and corrupted bits from the corrupted packets.…”

“…SoNIC collects features based on RSSI, link quality indication (LQI), and corrupted bits from the corrupted packets. These features were then used to build a neural network (feed-forward artificial neural network) [11] or a decision tree to classify the data [11, 20]. Rensfelt et al [11] report a decision tree consisting of 749 nodes using ten features.…”