Analysis of the Relationship between Land Surface Temperature and Vegetation and Built-Up Indices in Upper-Hill, Nairobi

Designing “liveable” cities as climate change effects are felt all over the world has become a priority to city authorities as ways are sought to reduce rising temperatures in urban areas. Urban Heat Island (UHI) effect occurs when there is a difference in temperature between rural and urban areas. In urban areas, impervious surfaces absorb heat during the day and release it at night, making urban areas warmer compared to rural areas which cool faster at night. This Urban Heat Island effect is particularly noticeable at night. Noticeable negative effects of Urban Heat Islands include health problems, air pollution, water shortages and higher energy requirements. The main objective of this research paper was to analyze the spatial and temporal relationship between Land Surface Temperature (LST) and Normalized Density Vegetation Index (NDVI) and Built-Up Density Index (BDI) in Upper-Hill, Nairobi Kenya. The changes in land cover would be represented by analyzing the two indices NDVI and BDI. Results showed the greatest increase in temperature within Upper-Hill of up to 3.96&#176C between the years 2015 and 2017. There was also an increase in impervious surfaces as indicated by NDVI and BDI within Upper-Hill and its surroundings. The linear regression results showed a negative correlation between LST and NDVI and a positive correlation with BDI, which is a better predictor of Land Surface Temperature than NDVI. Data sets were analyzed from Landsat imagery for the periods 1987, 2002, 2015 and 2017 to determine changes in land surface temperatures over a 30 year period and it’s relation to land cover changes using indices. Visual comparisons between Temperature differences between the years revealed that temperatures decreased around the urban areas. Minimum and maximum temperatures showed an increase of 1.6&#176C and 3.65&#176C respectively between 1987 and 2017. The comparisons between LST, NDVI and BDI show the results to be significantly different. The use of NDVI and BDI to study changes in land cover due to urbanization, reduces the time taken to manually classify moderate resolution satellite imagery.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.