Many Internet of Things (IoT) -based networks are being built to develop applications spanning multiple domains. Many small to large devices connected in various ways increases the risk of IoT networks failing. Small devices in the devices layer frequently fail due to their small size and high usage. Intermittent failures of the IoT networks lead to catastrophes at times. The IoT systems must be designed to be fault-tolerant. Fault tolerance of IoT networks must be computable so that the same can be considered while designing IoT networks. However, the computation of fault tolerance of IoT networks is complex, especially when heterogeneous structures are used for building a specific IoT network. Fault tree-based models are not suitable for computing fault-tolerance of complex models, which requires probability assessment. Hybrid fault tolerance computing models have been presented in this paper that consider both linear and probabilistic methods of computing the fault tolerance considering many complex networking topologies used in each layer of IoT networks. The fault-tolerance computing models are formal methods that can be used to compute the fault tolerance of any IoT network built with any internal processing. The accuracy of fault tolerance computing is 12.9% higher than other methods.

<span>Cloud computing technologies and infrastructure facilities are coming up in a big way making it cost effective for the users to implement their IT based solutions to run business in most cost-effective and economical way. Many intricate issues however, have cropped-up which must be addressed to be able to use clouds the purpose for which they are designed and implemented. Among all, fault tolerance and securing the data stored on the clouds takes most of the importance. Continuous availability of the services is dependent on many factors. Faults bound to happen within a network, software, and platform or within the infrastructure which are all used for establishing the cloud. The network that connects various servers, devices, peripherals etc., have to be fault tolerant to start-with so that intended and un-interrupted services to the user can be made available. A novel network design method that leads to achieve high availability of the network and thereby the cloud itself has been presented in this paper</span>

<span>Cloud computing technologies and infrastructure facilities are coming up in a big way making it cost effective for the users to implement their IT based solutions to run business in most cost-effective and economical way. Many intricate issues however, have cropped-up which must be addressed to be able to use clouds the purpose for which they are designed and implemented. Among all, fault tolerance and securing the data stored on the clouds takes most of the importance. Continuous availability of the services is dependent on many factors. Faults bound to happen within a network, software, and platform or within the infrastructure which are all used for establishing the cloud. The network that connects various servers, devices, peripherals etc., have to be fault tolerant to start-with so that intended and un-interrupted services to the user can be made available. A novel network design method that leads to achieve high availability of the network and thereby the cloud itself has been presented in this paper</span>

<span>Industrial organizations select the students for placement by conducting tests based on the academic content and targeting students' cognitive levels, such as the problem-solving ability. Educational institutes are mostly dependent on the students' academic performance to judge the likelihood of Employing the students. Cognitive and academic-based models are required to accurately predict the students' employment and assess the areas of improvement required. The interrelationships must be established to achieve coherence between the models. In this paper, three predictive models have been presented, which are based on: cognitive factors, Academic factors with and without anomaly correction. The models will help the educational institutions prepare the students for the highest number of placements. The models provide the basis for prediction on the individual subject/factor basis and the overall prediction considering all the subjects/cognitive factors. 98% accuracy in predicting the placement of the students has been achieved considering both the cognitive and Academic models with a built-in anomaly correction mechanism. The anomaly correction mechanism presented in the paper improved the accuracy of prediction from 92% to 98%. The positive correlation between the cognitive and Academic model helps inferencing one model from the other.</span>