Data-driven techniques for temperature data prediction: big data analytics approach.

For extrapolation, climate change and other meteorological analysis, a study of past and current weather events is a prerequisite. NASA (National Aeronautics and Space Administration) has been able to develop a model capable of predicting various weather data for any location on the Earth, including locations lacking weather stations, weather satellite coverage, and other weather measuring instruments. This paper evaluates the prediction accuracy of the NASA temperature data with respect to NiMet (Nigerian Meteorological Agency) ground truth measurement, using Akwa Ibom Airport as a case study. Exploratory data analysis (descriptive and diagnostic analyses) of temperature retrieved from NiMet and NASA was performed to give a clear path to follow for predictive and prescriptive analyses. Using 2783 days of weather data retrieved from NiMet as ground truth, the accuracy of NASA predictions with the corresponding resolution was calculated. Mean absolute error (MAE) of 2.184 °C and root mean square error (RMSE) of 2.579 °C, with a coefficient of determination (R2) of 0.710 for maximum temperature, then MAE of 0.876 °C, RMSE of 1.225 °C with a coefficient of determination (R2) of 0.620 for minimum temperature was discovered. There is a good correlation between the two datasets; hence, a model can be developed to generate more accurate predictions, using the NASA data as input. Predictive and prescriptive analyses were performed by employing five prediction algorithms: decision tree regression, XGBoost regression and MLP (multilayer perceptron) with LBFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno) optimizer, MLP with SGD (stochastic gradient) optimizer and MLP with Adam optimizer. The MLP LBFGS algorithm performed best, by significantly reducing the MAE by 35.35% and RMSE by 31.06% for maximum temperature, accordingly, MAE by 10.05% and RMSE by 8.00% for minimum temperature. Results obtained show that given sufficient data, plugging NASA predictions as input to an LBFGS-MLP model gives more accurate temperature predictions for the study area.

Smart deployment of IoT-TelosB service care StreamRobot using software-defined reliability optimisation design.

Intelligent service care robots have increasingly been developed in mission-critical sectors such as healthcare systems, transportation, manufacturing, and environmental applications. The major drawbacks include the open-source Internet of Things (IoT) platform vulnerabilities, node failures, computational latency, and small memory capacity in IoT sensing nodes. This article provides reliable predictive analytics with the optimisation of data transmission characteristics in StreamRobot. Software-defined reliable optimisation design is applied in the system architecture. For the IoT implementation, the edge system model formulation is presented with a focus on edge cluster log-normality distribution, reliability, and equilibrium stability considerations. A real-world scenario for accurate data streams generation from in-built TelosB sensing nodes is converged at a sink-analytic dashboard. Two-phase configurations, namely off-taker and on-demand, link-state protocols are mapped for deterministic data stream offloading. An orphan reconnection trigger mechanism is used for reliable node-to-sink resilient data transmissions. Data collection is achieved, using component-based programming in the experimental testbed. Measurement parameters are derived with TelosB IoT nodes. Reliability validations on remote monitoring and prediction processes are studied considering neural constrained software-defined networking (SDN) intelligence. An OpenFlow-SDN construct is deployed to offload traffic from the edge to the fog layer. At the core, fog detection-to-cloud predictive machine learning (FD-CPML) is used to predict real-time data streams. Prediction accuracy is validated with decision tree, logistic regression, and the proposed FD-CPML. The data streams latency gave 40.00%, 33.33%, and 26.67%, respectively. Similarly, linear predictive scalability behaviour on the network plane gave 30.12%, 33.73%, and 36.15% respectively. The results show satisfactory responses in terms of reliable communication and intelligent monitoring of node failures.