A group of individuals, organizations or things in internet of things (IoT) often dynamically self-organizes in small groups to accomplish certain tasks. This is common in virtual organization, social networks and the evolving field of IoT. These small groups have different behavioral characteristics than large groups. Members individually have some requirements and contribute some resources to the group. The organization and operation of such a group requires dynamic identification of group requirements that can be fulfilled by available resources and is approved by the group. We apply design science methods to develop an artifact that helps in conciliation of collective requirements and resources of small groups while maintaining each member’s satisfaction. The mechanism also supports dynamic conciliation as members leave and new members join the group. Each member’s requirement is specified as an explicit/implicit objective that is feasible/not feasible based on resources available to the group and whether the requirement is in alignment with other members’ objectives. We validate the artifact by using it for a manufacturing service group and simulating the change in collective group requirements and resources as group membership changes dynamically.

The process of K-medoids algorithm is that it first selects data randomly as initial centers to form initial clusters. Then, based on PAM (partitioning around medoids) algorithm, centers will be sequential replaced by all the remaining data to find a result has the best inherent convergence. Since PAM algorithm is an iterative ergodic strategy, when the data size or the number of clusters are huge, its expensive computational overhead will hinder its feasibility. The authors use the fixed-point iteration to search the optimal clustering centers and build a FPK-medoids (fixed point-based K-medoids) algorithm. By constructing fixed point equations for each cluster, the problem of searching optimal centers is converted into the solving of equation set in parallel. The experiment is carried on six standard datasets, and the result shows that the clustering efficiency of proposed algorithm is significantly improved compared with the conventional algorithm. In addition, the clustering quality will be markedly enhanced in handling problems with large-scale datasets or a large number of clusters.

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<p>In this article, in order to solve the problem of the current social endowment, promote economic transformation and sustainable social development in China, by studying the current population aging speed in Yuexiu of Guangzhou in China and the social background of rapid rise of the Internet industry. It combines the advanced Internet informatization technology, intelligent technology and pension industry to innovate on the basis of aged-care at home, institution endowment, forming an “Internet + endowment” wisdom of community service pension mode. By analyzing the relevant data of aging population, this article concludes the feasibility, necessity and practicality of this pension model through describing the current situation of home-based smart pension in Yuexiu District, Guangzhou in China.</p>