An Enhanced Distributed Data Aggregation Method in the Internet of Things.

"Internet of Things (IoT)" has emerged as a novel concept in the world of technology and communication. In modern network technologies, the capability of transmitting data through data communication networks (such as Internet or intranet) is provided for each organism (e.g. human beings, animals, things, and so forth). Due to the limited hardware and operational communication capability as well as small dimensions, IoT undergoes several challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation, and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph increased through restricting the degree; and network congestion reduced as a result. In addition, a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL). More specifically, each node was equipped with learning automata in order to perform data aggregation and transmissions. Simulation and experimental results indicate that the LA-RPL has better efficiency than the basic methods used in terms of energy consumption, network control overhead, end-to-end delay, loss packet and aggregation rates.

Neuro-fuzzy method for predicting the viability of stem cells treated at different time-concentration conditions.

Dental stem cells isolated for human dental pulp are an excellent source for regenerative medicine and dentistry. Simulation of clinical scenario is one of the crucial challenges for evaluation of the efficacy of DPSCs in various regenerative therapies. In this study we evaluated the viability of DPSCs after treatment with artificial bacterial lipopolysaccharides (LPS) as the main component responsible for inducing inflammatory response in majority of the inflammatory conditions in clinical scenario. Although a number of studies have previously treated stem cells with LPS from bacteria, however the accuracy level of the outcome was not established. Here we have analyzed the outcome using adaptive neuro-fuzzy inferences system (ANFIS) to predict the viability of human DPSCs after treatment with bacterial LPS.