The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible.

Narrowband Internet of Things (NB-IoT) is a new access technology introduced by 3GPP. This paper presents an analytical model to estimate the access success probability and average access delay of the random access channels by considering maximum number of preamble transmissions, size of backoff windows, and number of sub-carriers in each coverage enhancement (CE) levels. A joint optimization technique is proposed to configure the parameters to maximize the access success probability under a target delay constraint. The accuracy of the analysis and the effectiveness of the proposed optimization technique are verified by computer simulations and benchmarked with exhaustive search. The result shows that the proposed optimization is able to find the optimal configuration under various conditions.

The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges. This study centers on employing deep learning methodologies to detect attacks. In general, this research aims to improve the performance of existing deep learning models. To mitigate data imbalances and enhance learning outcomes, the synthetic minority over-sampling technique (SMOTE) is employed. Our approach contributes to a multistage feature extraction process where autoencoders (AEs) are used initially to extract robust features from unstructured data on the model architecture’s left side. Following this, long short-term memory (LSTM) networks on the right analyze these features to recognize temporal patterns indicative of abnormal behavior. The extracted and temporally refined features are inputted into convolutional neural networks (CNNs) for final classification. This structured arrangement harnesses the distinct capabilities of each model to process and classify IoT security data effectively. Our framework is specifically designed to address various attacks, including denial of service (DoS) and Mirai attacks, which are particularly harmful to IoT systems. Unlike conventional intrusion detection systems (IDSs) that may employ a singular model or simple feature extraction methods, our multistage approach provides more comprehensive analysis and utilization of data, enhancing detection capabilities and accuracy in identifying complex cyber threats in IoT environments. This research highlights the potential benefits that can be gained by applying deep learning methods to improve the effectiveness of IDSs in IoT security. The results obtained indicate a potential improvement for enhancing security measures and mitigating emerging threats.

From an environmentally conscious and ecological perspective, the sustainability of cities within the effects of climate change are closely related to the wise use of resources and modifications in the ecological status of the environment. In terms of the ecological environment, the sustainability of smart cities entails meeting present and future societal demands for the environment of the water, land, and air, among others. Environmental and the ecological concerns that arise from rapid climate change and monetary developments are shown in the inconsistency between ecological assets, environmental pollution, and the destruction of nature. In this study, the authors aim to develop a strategy to deal with climate change in urban areas using Remote Sensing and the Driver-Pressure-State-Impact-Response (DPSIR) Framework with a case study in Jakarta Smart City. The DPSIR framework, which will be developed and implemented in the city of Jakarta, is a smarter and more sustainable framework that is evaluated through a systematic evaluation of sustainability with quantitative research using the entropy weight method and Partial Least Square-Structural Equation Modeling (PLS-SEM). These methods evaluate 58 representative elements of environments at the urban level, including the shortcomings of earlier research such as data availability, spatial and temporal constraints, and several related ecological indicators, such as soil pH, wind speed, air quality index as well as land changes in the spatial (spatiotemporal) time series. The results of the study show that in the metropolitan city of Jakarta, the Drivers that are related to climate change are the rate of population growth and the rate of industrial growth which, although increases people’s income and GRDP in Jakarta; it also creates Pressures, namely an increase in the amount of water consumption and in the amount of wastewater. Based on these pressures, the environmental conditions (State) of Jakarta city have undergone several environmental changes, such as loss of water supply, changes in wind speed, changes in rainfall, and increasing concentrations of the Air Pollutant Standard Index. The Impact of these three elements resulted in the increase in household and industrial water consumption, an increase in annual electricity consumption, and deteriorating air quality. Hence, the Response to these four interrelated causal variables is that the Jakarta Provincial Government must increase annual funds for the construction of urban community facilities, increase the production capacity of clean water supply, build environment-friendly wastewater treatment facilities, increase the capacity of waste processing infrastructure and transportation fleets, and educate people to use water wisely to reduce the level of water use.