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In this paper, a reliable algorithm is proposed, which aims at providing the desired reliability, avoiding the data loss due to probable failures, and guaranteeing the energy consumption balancing among all fog nodes (FNs) in the fog-enabled Internet of Things (IoT) networks. To this end, first, a new optimization model is defined to find the best transmission power and the best transmission rate of each terminal node (TN) based on the channel conditions and the desired reliability of the network. Then, the best FN is assigned to each TN according to the topology potential between FNs and TNs, such that there exists an energy consumption balancing among all FNs. Simulation results reveal that the proposed algorithm improves the network performance in terms of reliability and loss ratio, while maintaining the energy consumption balancing among all FNs. [ABSTRACT FROM AUTHOR]

Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make it possible to study essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centralities. In this paper, we investigate the essential proteins’ topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein’s topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based methods TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), information centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential. [ABSTRACT FROM PUBLISHER]