With the rapid development of Internet of Things (IoT),the issue of trust in distributed routing systems has attracted more research attention.The existing trust management frameworks,however,suffer from some possible...With the rapid development of Internet of Things (IoT),the issue of trust in distributed routing systems has attracted more research attention.The existing trust management frameworks,however,suffer from some possible attacks in hostile environments,such as false accusation,collusion,on-off,and conflicting behavior.Therefore,more comprehensive models should be proposed to predict the trust level of nodes on potential routes more precisely,and to defeat several kinds of possible attacks.This paper makes an attempt to design an attack-resistant trust management model based on beta function for distributed routing strategy in IoT.Our model can evaluate and propagate reputation in distributed routing systems.We first describe possible attacks on existing systems.Our model is then proposed to establish reliable trust relations between self-organized nodes and defeat possible attacks in distributed routing systems.We also propose a theoretical basis and skeleton of our model.Finally,some performance evaluations and security analyses are provided to show the effectiveness and robustness of our model compared with the existing systems.展开更多
The spatial distribution of soil physical properties is essential for modeling and understanding hydrological processes. In this study, the different spatial information (the conventional soil types map-based spatial ...The spatial distribution of soil physical properties is essential for modeling and understanding hydrological processes. In this study, the different spatial information (the conventional soil types map-based spatial information (STMB) versus refined spatial information map (RSIM)) of soil physical properties, including field capacity, soil porosity and saturated hydraulic conductivity are used respectively as input data for Water Flow Model for Lake Catchment (WATLAC) to determine their effectiveness in simulating hydrological processes and to expound the effects on model performance in terms of estimating groundwater recharge, soil evaporation, runoff generation as well as partitioning of surface and subsurface water flow. The results show that: 1) the simulated stream flow hydrographs based on the STMB and RSIM soil data reproduce the observed hydrographs well. There is no significant increase in model accuracy as more precise soil physical properties information being used, but WATLAC model using the RSIM soil data could predict more runoff volume and reduce the relative runoff depth errors; 2) the groundwater recharges have a consistent trend for both cases, while the STMB soil data tend to produce higher groundwater recharges than the RSIM soil data. In addition, the spatial distribution of annual groundwater recharge is significantly affected by the spatial distribution of soil physical properties; 3) the soil evaporation simulated using the STMB and RSIM soil data are similar to each other, and the spatial distribution patterns are also insensitive to the spatial information of soil physical properties; and 4) although the different spatial information of soil physical properties does not cause apparent difference in overall stream flow, the partitioning of surface and subsurface water flow is distinct. The implications of this study are that the refined spatial information of soil physical properties does not necessarily contribute to a more accurate prediction of stream flow, and the selection of appropriate soil physical property data needs to consider the scale of watersheds and the level of accuracy required.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61100219the Fundamental Research Funds for the Central Universities under Grant No.2012JBM010the Key Program of National Natural Science Foundation of China under Grant No.60833002
文摘With the rapid development of Internet of Things (IoT),the issue of trust in distributed routing systems has attracted more research attention.The existing trust management frameworks,however,suffer from some possible attacks in hostile environments,such as false accusation,collusion,on-off,and conflicting behavior.Therefore,more comprehensive models should be proposed to predict the trust level of nodes on potential routes more precisely,and to defeat several kinds of possible attacks.This paper makes an attempt to design an attack-resistant trust management model based on beta function for distributed routing strategy in IoT.Our model can evaluate and propagate reputation in distributed routing systems.We first describe possible attacks on existing systems.Our model is then proposed to establish reliable trust relations between self-organized nodes and defeat possible attacks in distributed routing systems.We also propose a theoretical basis and skeleton of our model.Finally,some performance evaluations and security analyses are provided to show the effectiveness and robustness of our model compared with the existing systems.
基金Under the auspices of Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (No. IWHR-SKL-201111)National Natural Science Foundation of China (No. 41101024)
文摘The spatial distribution of soil physical properties is essential for modeling and understanding hydrological processes. In this study, the different spatial information (the conventional soil types map-based spatial information (STMB) versus refined spatial information map (RSIM)) of soil physical properties, including field capacity, soil porosity and saturated hydraulic conductivity are used respectively as input data for Water Flow Model for Lake Catchment (WATLAC) to determine their effectiveness in simulating hydrological processes and to expound the effects on model performance in terms of estimating groundwater recharge, soil evaporation, runoff generation as well as partitioning of surface and subsurface water flow. The results show that: 1) the simulated stream flow hydrographs based on the STMB and RSIM soil data reproduce the observed hydrographs well. There is no significant increase in model accuracy as more precise soil physical properties information being used, but WATLAC model using the RSIM soil data could predict more runoff volume and reduce the relative runoff depth errors; 2) the groundwater recharges have a consistent trend for both cases, while the STMB soil data tend to produce higher groundwater recharges than the RSIM soil data. In addition, the spatial distribution of annual groundwater recharge is significantly affected by the spatial distribution of soil physical properties; 3) the soil evaporation simulated using the STMB and RSIM soil data are similar to each other, and the spatial distribution patterns are also insensitive to the spatial information of soil physical properties; and 4) although the different spatial information of soil physical properties does not cause apparent difference in overall stream flow, the partitioning of surface and subsurface water flow is distinct. The implications of this study are that the refined spatial information of soil physical properties does not necessarily contribute to a more accurate prediction of stream flow, and the selection of appropriate soil physical property data needs to consider the scale of watersheds and the level of accuracy required.
