The present study aims to evaluate the evolution of the discourse and the way research has progressed in this incipient sphere of collaborative consumption up to the present day by means of an in-depth analysis of scientific production through bibliometrics and network analysis techniques with the VOSviewer© software and the complete database of publications obtained from the Web Of Science (2152 publications). This has transformed its development from an economic opportunity to a form of decentralised, equitable and sustainable economy with the creation of new initiatives and companies that have reduced environmental impact by decreasing the use of natural resources. The Sharing Economy has emerged as a multidimensional and multidisciplinary concept, which initially only covered areas of the economy and social sciences and which later experienced growth in business, urban planning, tourism, information technology and digital science (industry 4.0) or engineering. The Sharing Economy has been emerged in recent years as a trend with high growth potential by showing itself to be an innovative model for creating products, services and relationships based on sustainable consumption. Lastly, we evaluate a serverless implementation using a large social media dataset to simulate a production environment and compare our framework to a network flow approach to solving the problem. We illustrate this framework using three use cases. We present a scalable framework for establishing small online groups that balance two objectives, making the best group recommendations to users and guiding group hosts to the best users for their group. This makes it difficult to find compatible people that share the same goal, and are interested in niche groups on those platforms. On social media platforms, users often have large networks of connections but have very few close personal relationships within them. In the wake of the current pandemic, meeting new people that are right for a specific team is even more difficult than before due to the lack of in-person events. A distributed algorithm is presented and its correctness is proven.įor those seeking to recruit teammates for a specific purpose, like a project or study group, challenges quickly arise once they have exhausted their social circle. SelfAid uses only spare resources of player machines, following the trend of sharing economy. Furthermore, the number of designated machines adapts to the demand. After the developer creates a service algorithm defining the matchmaking behavior specific to his/hers case, the SelfAid platform designates a number of player machines to execute the service.
In order to achieve this goal, the SelfAid platform for building custom P2P matchmaking strategies is presented. This paper aims to provide a solution for the problem of matchmaking players on the scale of the Internet, without using a central server. Those range from additional costs associated with infrastructure maintenance to inability to play the game once servers become unavailabe (due to being under Denial of Service attack or being shut down after earning enough profit). Existing solutions employ client-server architecture, which induces several problems. Matchmaking players is an important problem in online multiplayer games.
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