Time Slotted Multiple-Hypothesis Interference Tracking in Wireless Networks

Nowadays Internet of Things and Industry 4.0 devices are often connected wirelessly. Current wireless sensor network (WSN) deployments are relying in most cases on the industrial, scientific, and medical (ISM) bands without centralized resource scheduling. Thus, each device is a potential source of interference to other devices, both within its own WSN but also to devices in other collocated WSNs. If the transmission behavior of devices from other WSNs is not random, we are able to find patterns in the time domain in their channel access. This is, for example, possible for periodic channel access, which is quite common for WSNs with demanding low-power and reliability requirements. The main goal of this work is to detect multiple sources of periodic interference in time slotted signal-level measurements and estimate the time windows of future transmissions. This gives a WSN a certain understanding of the radio surrounding and can be used to adapt the transmission behavior to thus avoid collisions. For this, the multihypothesis tracking algorithm is adapted and used together with timeslot-based interference measurements on low-cost sensor nodes. The applicability of the algorithm is shown with extensive simulations and the performance is demonstrated with measurements on a time-division multiple access-based WSN built upon the Bluetooth low-energy physical layer.

[1]  Meng Jin,et al.  Exploiting Interference Fingerprints for Predictable Wireless Concurrency , 2021, IEEE Transactions on Mobile Computing.

[2]  Akram Al-Hourani,et al.  Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands , 2021, IEEE Internet of Things Journal.

[3]  Stefano P. Coraluppi,et al.  Multiple-Hypothesis Group Tracking , 2020, 2020 IEEE Radar Conference (RadarConf20).

[4]  Yang Zhang,et al.  Iterative Multiple Hypothesis Tracking With Tracklet-Level Association , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Emil Jovanov,et al.  A Robust Algorithm for Sniffing BLE Long-Lived Connections in Real-Time , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[6]  Francisco Herrera,et al.  Deep Learning in Video Multi-Object Tracking: A Survey , 2019, Neurocomputing.

[7]  Carolynn Bernier,et al.  Design of a Low Complexity Interference Detector for LPWA Networks , 2019, 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[8]  Zhiwen Yu,et al.  Traffic-Based Side-Channel Attack in Video Streaming , 2019, IEEE/ACM Transactions on Networking.

[9]  Aamir Mahmood,et al.  Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices , 2018, IEEE Access.

[10]  Hwangnam Kim,et al.  Channel Quality Estimation for Improving Awareness of Communication Situation in the 2.4 GHz ISM Band , 2018, IEEE Transactions on Mobile Computing.

[11]  Majid Nabi,et al.  Cooperative Coexistence of BLE and Time Slotted Channel Hopping Networks , 2018, 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[12]  Gaetano Patti,et al.  Multi-Hop Real-Time Communications Over Bluetooth Low Energy Industrial Wireless Mesh Networks , 2018, IEEE Access.

[13]  Boris Bellalta,et al.  Target Wake Time: Scheduled Access in IEEE 802.11ax WLANs , 2018, IEEE Wireless Communications.

[14]  Kwangjin Yoon,et al.  Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views , 2018, IET Image Process..

[15]  Andreas Springer,et al.  Period estimation with linear complexity of sparse time varying point processes , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

[16]  Adam Wolisz,et al.  Traffic model for HTTP-based adaptive streaming , 2017, 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[17]  Andreas Springer,et al.  Life cycle of wireless sensor nodes in industrial environments , 2017, 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS).

[18]  Majid Nabi,et al.  Analysis of coexistence between IEEE 802.15.4, BLE and IEEE 802.11 in the 2.4 GHz ISM band , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[19]  James M. Rehg,et al.  Multiple Hypothesis Tracking Revisited , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[20]  Elfed Lewis,et al.  A Lightweight Classification Algorithm for External Sources of Interference in IEEE 802.15.4-Based Wireless Sensor Networks Operating at the 2.4 GHz , 2014, Int. J. Distributed Sens. Networks.

[21]  E. Lewis,et al.  2.4 GHz IEEE 802.15.4 channel interference classification algorithm running live on a sensor node , 2012, 2012 IEEE Sensors.

[22]  MengChu Zhou,et al.  Impacts of 2.4-GHz ISM Band Interference on IEEE 802.15.4 Wireless Sensor Network Reliability in Buildings , 2012, IEEE Transactions on Instrumentation and Measurement.

[23]  Wei Wang,et al.  A multiple object tracking method using Kalman filter , 2010, The 2010 IEEE International Conference on Information and Automation.

[24]  Wei Yuan,et al.  Adaptive CCA for IEEE 802.15.4 Wireless Sensor Networks to Mitigate Interference , 2010, 2010 IEEE Wireless Communication and Networking Conference.

[25]  Hao He,et al.  Spectral Analysis of Nonuniformly Sampled Data: A New Approach Versus the Periodogram , 2009, IEEE Transactions on Signal Processing.

[26]  Song Han,et al.  WirelessHART: Applying Wireless Technology in Real-Time Industrial Process Control , 2008, 2008 IEEE Real-Time and Embedded Technology and Applications Symposium.

[27]  Ramjee Prasad,et al.  Strategies for adaptive frequency hopping in the unlicensed bands , 2006, IEEE Wireless Communications.

[28]  Thomas W. Rondeau,et al.  Residential microwave oven interference on Bluetooth data performance , 2004, IEEE Transactions on Consumer Electronics.

[29]  Lang Hong,et al.  Computational complexity analysis for multiple hypothesis tracking , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[30]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[31]  D. Reid.  An algorithm for tracking multiple targets , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[32]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .