The main idea behind social crowdsensing is to leverage social friends as crowdWorkers to participate in crowdsensing tasks. A main challenge, however, is the identification and recruitment of well-suited Workers. This becomes especially more challenging for large-scale online social networks with potential sparseness of the friendship network which may result in recruiting participants who are not in direct friendship relations with the requester. Such recruitment may increase the possibility of collusion among participants, thus threatening the application security and affecting data quality. In this paper, we propose a collusionresistant Worker selection method which aims to prevent the selection of colluders as suitable participants. For each participant who is considered to be selected as suitable, the proposed method is aimed to prevent any possible collusion. To do so, it determines whether the selection of a new participant may result in the formation of a colluding group among the selected participants. This has been achieved through leveraging the Frequent Itemset Mining technique and defining a set of collusion behavioral indicators. Simulation results demonstrate the efficacy of our proposed collusion prevention method in terms of selecting efficient collusion indicators and detecting the colluding groups.

Regarding the necessity of employing the intellectual capital, knowledge management has established in knowledge-based economy period, and it is developing by now. One of the main parts of this type of management is the human resources-as knowledge Workers. The transition from industrial period to knowledge era and the need of establishing some knowledge-based organisations attach importance to performance of human resources, its measurement, and growth in knowledge management which evaluates such capital and the effectiveness of managerial process in organisations. As human plays the key role for developing efficiency in knowledge-based efforts, in this paper, we seek to present a method to measure the performance of women knowledge Worker in different parts of an organisation, describe reasons of present conditions, and determine their priority to be considered.Based on the best decision-making procedures, dynamic behaviour analysis, and employingtime series, we try to recommend some solutions, and according to situations, offer the best option among different ones to improve performance of women knowledge Worker.This method is verified in an Iranian organisation and validated by the content validity approach.