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Catching abnormal helpers in smart-contract based spectrum sensing

Group members: Fabien Guillou and Florian Plonsky
Supervisors: Suzan Bayhan and Anatolij Zubow

Due to the massive increase in wireless communication recently, the efficient usage of the RF spectrum as a natural and scarce resource has gained huge importance. While the spectral efficiency is constantly improved, there is a demand for alternative solutions to increase the efficient usage of the RF spectrum. Specific RF bands are licensed by authorities for exclusive usage rights to PUs. Since the PU is not active continuously, there are so-called holes in the licensed spectrum, which can be utilized by the SU network to increase RF spectrum efficiency. The SU can access the licensed spectrum band dynamically, whenever the PU is idle. To prevent interference between PU and SU, the SU network has to ensure an accurate detection of PU traffic by repeatedly performing the task of spectrum sensing. Since spectrum sensing is an energy heavy task, it is suggested to offload it to nearby cognitive radio devices, so-called helpers.

In order to increase accuracy in the detection of the PU, not a single helper, but a network of helpers should determine the state of the PU’s RF band by sensing it in equidistant time intervals. The helpers therefore offer Spectrum Sensing as a Service (Spass) to the SU, and get payed by the SU in return.

A network of multiple helpers does not necessarily increase the accuracy of spectrum sensing; it might face the issue of so-called abnormal helpers - helpers that (un-) intentionally send incorrect sensing data. In order to not depend on any third entity, a blockchain based decentralized computer platform running smart contracts (Ethereum) in used to offer and run Spass. Not only does it handle the payment of the helpers for their service ; it also handles the identification of abnormal helpers.

This goal of this project was to model different types of helpers and their sensing behavior, develop algorithms in order to catch them and consider the cost and efficiency of the suggested approach. During the course of the project four different helper types were successfully modelled. Based on the obtained models three different algorithms where developed in order to catch and exclude abnormal helpers from cooperative spectrum sensing. Moreover the aspect of cost concerning this approach to Spass was considered and an algorithm to select the most cost efficient helpers to archive the required accuracy in spectrum sensing was developed.

Finally the algorithms were implemented in Solidity - a contract-oriented programming language for writing smart contracts and deployed to the Ethereum network. In order to test the implementation a Matlab program was written to simulate PU, SU and various helper types. Using the Matlab simulation, the algorithms as well as the models were verified and the goal to catch abnormal helpers in smart contract based spectrum sensing was accomplished.

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