Cloud-Assisted Remote Sensor Network Virtualization for Distributed Consensus Estimation

We develop cloud-assisted remote sensing techniques for enabling distributed consensus estimation of unknown parameters in a given geographic area. We first propose a distributed sensor network virtualization algorithm that searches for, selects, and coordinates Internet-accessible sensors to perform a sensing task in a specific region. The algorithm converges in linearithmic time for large-scale networks, and requires exchanging a number of messages that is at most linear in the number of sensors. Second, we design an uncoordinated, distributed algorithm that relies on the selected sensors to estimate a set of parameters without requiring synchronization among the sensors. Our simulation results show that the proposed algorithm, when compared to conventional ADMM (Alternating Direction Method of Multipliers), reduces communication overhead significantly without compromising the estimation error. In addition, the convergence time, though increases slightly, is still linear as in the case of conventional ADMM.

[1]  Stephen P. Boyd,et al.  Randomized gossip algorithms , 2006, IEEE Transactions on Information Theory.

[2]  Xavier Hesselbach,et al.  Virtual Network Embedding: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[3]  Djamal Zeghlache,et al.  A Distributed Virtual Network Mapping Algorithm , 2008, 2008 IEEE International Conference on Communications.

[4]  Seth Pettie,et al.  Linear-Time Approximation for Maximum Weight Matching , 2014, JACM.

[5]  J. Munkres.  ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[6]  Devavrat Shah,et al.  Gossip Algorithms , 2009, Found. Trends Netw..

[7]  Daniel Pérez Palomar,et al.  A tutorial on decomposition methods for network utility maximization , 2006, IEEE Journal on Selected Areas in Communications.

[8]  P. Priyanga,et al.  Enabling Smart Cloud Services Through Remote Sensing : An Internet of Everything Enabler , 2015 .

[9]  Asuman E. Ozdaglar,et al.  On the O(1=k) convergence of asynchronous distributed alternating Direction Method of Multipliers , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[10]  Ahmed Karmouch,et al.  Resource Discovery and Allocation in Network Virtualization , 2012, IEEE Communications Surveys & Tutorials.

[11]  Xavier Hesselbach,et al.  A distributed, parallel, and generic virtual network embedding framework , 2013, 2013 IEEE International Conference on Communications (ICC).

[12]  Dirk Wübben,et al.  Fast Distributed Consensus-based Estimation (Fast-DiCE) for Cooperative Networks , 2014, WSA.

[13]  Arkady B. Zaslavsky,et al.  Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[14]  Prem Prakash Jayaraman,et al.  Sensor discovery and configuration framework for the Internet of Things paradigm , 2013, 2014 IEEE World Forum on Internet of Things (WF-IoT).

[15]  Luca Schenato,et al.  A Survey on Distributed Estimation and Control Applications Using Linear Consensus Algorithms , 2010 .

[16]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[17]  Armin Dekorsy,et al.  In-Network-Processing: Distributed Consensus-Based Linear Estimation , 2013, IEEE Communications Letters.

[18]  Ken Birman,et al.  The promise, and limitations, of gossip protocols , 2007, OPSR.

[19]  Xiang Cheng,et al.  Virtual network embedding through topology-aware node ranking , 2011, CCRV.

[20]  Ana M. Bernardos,et al.  Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization , 2011, Sensors.

[21]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[22]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[23]  Mark Lerner,et al.  The Promise. , 2019, Radiology management.

[24]  Stefano Ferretti,et al.  Gossiping for resource discovering: An analysis based on complex network theory , 2013, Future Gener. Comput. Syst..