The IoT networks for implementing mission-critical applications need a layer to effect remote communication between the cluster heads and the microcontrollers. Remote communication is affected through base stations using cellular technologies. Using a single base station in this layer is risky as the fault tolerance level of the network will be zero when the base stations break down. Generally, the cluster heads are within the base station spectrum, making seamless integration possible. Implementing a dual base station to cater for a breakdown of the first base station creates huge remoteness as the cluster heads are not within the spectrum of the second base station. Furthermore, using the remote base station involves huge latency affecting the performance of the IoT network. In this paper, a relay-based network is presented with intelligence to fetch the shortest path for communicating to reduce latency and sustain the fault tolerance capability of the IoT network. The results demonstrate that the technique improved the fault tolerance of the IoT network by 14.23%.

Most IoT systems designed for the implementation of mission-critical systems are multi-layered. Much of the computing is done in the service and gateway layers. The gateway layer connects the internal section of the IoT to the cloud through the Internet. The failure of any node between the servers and the gateways will isolate the entire network, leading to zero tolerance. The service and gateway layers must be connected using networking topologies to yield 100% fault tolerance. The empirical formulation of the model chosen to connect the service’s servers to the gateways through routers is required to compute the fault tolerance of the network. A rectangular and interstitial mesh have been proposed in this paper to connect the service servers to the gateways through the servers, which yields 0.999 fault tolerance of the IoT network. Also provided is an empirical approach to computing the IoT network’s fault tolerance. A rectangular and interstitial mesh have been implemented in the network’s gateway layer, increasing the IoT network’s ability to tolerate faults by 11%.

The global healthcare system faces significant challenges posed by diabetes and its complications, highlighting the need for innovative strategies to improve early diagnosis and treatment. Machine learning models help in the early detection of diseases and recommendations for taking safety measures and treating the disease. A comparative analysis of existing machine learning (ML) models is necessary to identify the most suitable model while uniformly fixing the model parameters. Assessing risk based on biomarker measurement and computing overall risk is important for accurate prediction. Early prediction of complications that may arise, based on the risk of diabetes and biomarkers, using machine learning models, is key to helping patients. In this paper, a comparative model is presented to evaluate ML models based on common model characteristics. Additionally, a risk assessment model and a prediction model are presented to help predict the occurrence of complications. Random Forest (RF) is the best model for predicting the occurrence of Type 2 Diabetes (T2D) based on biomarker input. It has also been shown that the prediction of diabetes complications using neural networks is highly accurate, reaching a level of 98%.

Many data mining studies have focused on mining positive associations among frequent and regular item sets. However, none have considered time and regularity bearing in mind such associations. The frequent and regular item sets will be huge, even when regularity and frequency are considered without any time consideration. Negative associations are equally important in medical databases, reflecting considerable discrepancies in medications used to treat various disorders. It is important to find the most effective negative associations. The mined associations should be as small as possible so that the most important disconnections can be found. This paper proposes a mining method that mines medical databases to find regular, frequent, closed, and maximal item sets that reflect minimal negative associations. The proposed algorithm reduces the negative associations by 70% when the maximal and closed properties have been used, considering any sample size, regularity, or frequency threshold.

A primary issue in black-box testing is how to generate adequate test cases from input domain of the system under test on the basis of user's requirement specification. However, for some types of systems including embedded systems, developing test cases from output domain is more suitable than developing from input domain, especially, when the output domain is smaller. This approach ensures better reliability of the system under test. In this paper, the authors present a new approach to automate the generation of test cases from output domain of a pilot project “Temperature Monitoring and Controlling of Nuclear Reactor System” (TMCNRS) which is an embedded system developed using modified Cleanroom Software Engineering methodology. An Automated Test Case Generator (ATCG) that uses Genetic algorithms (GAs) extensively and generates test cases from output domain is proposed. The ATCG generates test cases which are useful to conduct pseudo - exhaustive testing to detect single, double and several multimode faults in the system. The generator considers most of the combinations of outputs, and finds the corresponding inputs while optimizing the number of test cases generated. In order to investigate the effectiveness of this approach, test cases were generated by ATCG and the tests were conducted on the target embedded system at a minimum cost and time. Experimental results show that this approach is very promising.