Biblio

The labor market involves several untrusted actors with contradicting objectives. We propose a blockchain based system for labor market, which provides benefits to all participants in terms of confidence, transparency, trust and tracking. Our system would handle employment data through new Wavelet blockchain platform. It would change the job market enabling direct agreements between parties without other participants, and providing new mechanisms for negotiating the employment conditions. Furthermore, our system would reduce the need in existing paper workflow as well as in major internet recruiting companies. The key differences of our work from other blockchain based labor record systems are usage of Wavelet blockchain platform, which features metastability, directed acyclic graph system and Turing complete smart contracts platform and introduction of human interaction inside the smart contracts logic, instead of automatic execution of contracts. The results are promising while inconclusive and we would further explore potential of blockchain solutions for labor market problems.

In mobile wireless sensor networks (MWSN), data imprecision is a common problem. Decision making in real time applications may be greatly affected by a minor error. Even though there are many existing techniques that take advantage of the spatio-temporal characteristics exhibited in mobile environments, few measure the trustworthiness of sensor data accuracy. We propose a unique online context-aware data cleaning method that measures trustworthiness by employing an initial candidate reduction through the analysis of trust parameters used in financial markets theory. Sensors with similar trajectory behaviors are assigned trust scores estimated through the calculation of “betas” for finding the most accurate data to trust. Instead of devoting all the trust into a single candidate sensor's data to perform the cleaning, a Diversified Trust Portfolio (DTP) is generated based on the selected set of spatially autocorrelated candidate sensors. Our results show that samples cleaned by the proposed method exhibit lower percent error when compared to two well-known and effective data cleaning algorithms in tested outdoor and indoor scenarios.