Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Contiki - A Lightweight and Flexible Operating System for Tiny Networked Sensors
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
ICN '07 Proceedings of the Sixth International Conference on Networking
Mutual interference analysis of IEEE 802.15.4 and IEEE 802.11b
Computer Networks: The International Journal of Computer and Telecommunications Networking
Minimising the effect of WiFi interference in 802.15.4 wireless sensor networks
International Journal of Sensor Networks
Experimental Study of the Impact of WLAN Interference on IEEE 802.15.4 Body Area Networks
EWSN '09 Proceedings of the 6th European Conference on Wireless Sensor Networks
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Minimizing 802.11 interference on ZigBee medical sensors
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
Empirical study of a medical sensor application in an urban emergency department
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
RFDump: an architecture for monitoring the wireless ether
Proceedings of the 5th international conference on Emerging networking experiments and technologies
A multifrequency MAC specially designed for wireless sensor network applications
ACM Transactions on Embedded Computing Systems (TECS)
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Surviving wi-fi interference in low power ZigBee networks
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
ARCH: Practical Channel Hopping for Reliable Home-Area Sensor Networks
RTAS '11 Proceedings of the 2011 17th IEEE Real-Time and Embedded Technology and Applications Symposium
Proceedings of the Nineteenth International Workshop on Quality of Service
Quantifying the channel quality for interference-aware wireless sensor networks
ACM SIGBED Review - Special Issue on the 10th International Workshop on Real-time Networks (RTN 2011)
Mitigating the effects of RF interference through RSSI-Based error recovery
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
Making sensornet MAC protocols robust against interference
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
Repeatable Experiments with Mobile Nodes in a Relocatable WSN Testbed
The Computer Journal
Low power or high performance? a tradeoff whose time has come (and nearly gone)
EWSN'12 Proceedings of the 9th European conference on Wireless Sensor Networks
Low-cost interferer detection and classification using TelosB sensor motes
Proceedings of the 18th annual international conference on Mobile computing and networking
Bit error distribution and mutation patterns of corrupted packets in low-power wireless networks
Proceedings of the 8th ACM international workshop on Wireless network testbeds, experimental evaluation & characterization
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Tracking smartphones using low-power sensor nodes
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Hi-index | 0.00 |
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.