Decentralized partitioning over adaptive networks

There arises the need in many wireless network applications to infer and track different models of interest. Some nodes in the network are informed, where they observe the different models and send information to the uninformed ones. Each uninformed node responds to one informed node and joins its group. In this work, we suggest an adaptive and distributed clustering and partitioning approach that allows the informed nodes in the network to be clustered into many groups according to the observed models; then we apply a decentralized strategy to part the uninformed nodes into groups of approximately equal size around the informed nodes.

[1]  Marc Moonen,et al.  Distributed Adaptive Node-Specific Signal Estimation in Fully Connected Sensor Networks—Part I: Sequential Node Updating , 2010, IEEE Transactions on Signal Processing.

[2]  Kostas Berberidis,et al.  Distributed diffusion-based LMS for node-specific parameter estimation over adaptive networks , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Ali H. Sayed,et al.  Distributed Decision-Making Over Adaptive Networks , 2013, IEEE Transactions on Signal Processing.

[4]  J. Liu,et al.  Multitarget Tracking in Distributed Sensor Networks , 2007, IEEE Signal Processing Magazine.

[5]  Ali H. Sayed,et al.  Diffusion LMS Strategies for Distributed Estimation , 2010, IEEE Transactions on Signal Processing.

[6]  Ali H. Sayed,et al.  Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks , 2011, IEEE Transactions on Signal Processing.

[7]  Xi Zhang,et al.  Adaptive Control and Reconfiguration of Mobile Wireless Sensor Networks for Dynamic Multi-Target Tracking , 2011, IEEE Transactions on Automatic Control.

[8]  Ali H. Sayed,et al.  Mobile Adaptive Networks , 2011, IEEE Journal of Selected Topics in Signal Processing.

[9]  Ali H. Sayed,et al.  Incremental Adaptive Strategies Over Distributed Networks , 2007, IEEE Transactions on Signal Processing.

[10]  Y. Bar-Shalom,et al.  Multiple-model estimation with variable structure , 1996, IEEE Trans. Autom. Control..

[11]  Abdelhak M. Zoubir,et al.  Optimal Area Coverage in Autonomous Sensor Networks , 2014 .

[12]  Ali H. Sayed,et al.  Honeybee swarming behavior using diffusion adaptation , 2011, 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE).

[13]  Ali H. Sayed,et al.  A unified approach to the steady-state and tracking analyses of adaptive filters , 2001, IEEE Trans. Signal Process..

[14]  Ali H. Sayed,et al.  On the Influence of Informed Agents on Learning and Adaptation Over Networks , 2012, IEEE Transactions on Signal Processing.

[15]  Ivor Francis,et al.  Classification and Estimation of Several Multiple Regressions , 1974 .

[16]  Jie Chen,et al.  Multitask Diffusion Adaptation Over Networks , 2013, IEEE Transactions on Signal Processing.

[17]  Ali H. Sayed,et al.  Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior , 2013, IEEE Signal Processing Magazine.

[18]  Ali H. Sayed,et al.  Decentralized clustering over adaptive networks , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).