A meritocratic trust-based group formation in an IoT environment for smart cities

TL;DR: In this paper , the authors proposed a triple phase resource utilised data fusion (TPRUDF) framework for resource utilization in IoT-based systems, which employs three phases of data fusion: data in-data out, data in -feature out, and feature in -decision out.

TL;DR: Zhang et al. as discussed by the authors proposed a proximity-based group formation game model, called PBCD, to detect communities in social networks based on an empirical observation that the higher number of shared communities gives rise to the higher second-order pairwise proximity.

TL;DR: In this paper , a general reference model is designed and presented to select a friend to access group message remote processing services and minimize cloud-edge resources, and the simulation results show that for the correct communication of friends at the edge of the network and in each service discovery, according to the length of the path in the network, it is possible to establish stable communication and make better service with the least possible.

TL;DR: In this article, the authors extend earlier work by its authors on formal aspects of the processes of contracting a theory to eliminate a proposition and revising it to introduce a new proposition.

TL;DR: It is shown that BP can only converge to a fixed point that is also a stationary point of the Bethe approximation to the free energy, which enables connections to be made with variational approaches to approximate inference.

TL;DR: A framework called the contract net is presented that specifies communication and control in a distributed problem solver, and comparisons with planner, conniver, hearsay-ii, and pup 6 are used to demonstrate that negotiation is a natural extension to the transfer of control mechanisms used in earlier problem-solving systems.

TL;DR: This thesis presents an approximation technique that can perform Bayesian inference faster and more accurately than previously possible, and is found to be convincingly better than rival approximation techniques: Monte Carlo, Laplace's method, and variational Bayes.