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Internet of things (IoT) technology have evolved tremendously over the past few years. Owing to the recent research and development in IoT and M2M communication. Wi-Fi, LiFi, Bluetooth, Cellular technologies and LPWANS are widely used for communication globally, to stay connected, with the main focus to interconnect things, devices altogether allowing people to manage and monitor the behavior of devices from anywhere in the world. Long-range (LoRa) is a communication technology operating in unlicensed frequency spectrum. However, modest data rates, extensively wide range and low-power requirement, makes it favorable technology for majority of the internet of things verticals. LoRa allows End Devices to dynamically select LoRa radio resources in vicinity of LoRa networks. Though, LoRaWAN communication technology is has revolutionized over past few years, most broadly used and fastest growing networks of IoT due to its attractive characteristics like low power consumption, low cost, fast long range bidirectional communication, less bandwidth requirement, License free ISM standard. Clearly, it's a very assuring technology and will be universally accepted in near future, yet we are not witnessing huge IoT deployment, this is, due to issues, such as networking, signal interference, massive deployment, security concerns and concurrent transmission collisions, which need to be addressed effectively. This paper aims at summarizing various wireless technologies, background of LoRa and LoRaWAN, use cases, literature review of the existing solutions to examine recent research works on LoRaWAN performance aspects like networking, scalability, inference, state of art solutions, and classifying the security issues. In addition, a brief summary on, future research directions for potential challenges, problem formulation and proposed methodology based on machine learning approach for dynamic resource allocation, minimize collision and enhance performance of LoRaWAN for effective massive IoT deployment under secure environment, is discussed. [ABSTRACT FROM AUTHOR]

On several image comprehension activities, convolutional neural networks achieve approaching precision. As a result, they're constantly being used to combine objects and organized data in multimodal data analytics. Transfer learning was becoming common because teaching deep CNNs to scratch is costly. By employing the CNN (and several other technologies), one "reads off" a layer of Deep cnn and then combines these characteristics with numerous additional downstream ML applications to help users perform a task. No layer can have the highest rated accuracy, which demands the use of many layers to get a comparable degree of precision. This methodology is inefficient because of the reiterative CNN induction and possible process failures owing to resource management difficulties.. Vista is the first data structure to address these problems by boosting the function migration workload to a propositional level and formalizing the CNN estimation data model.Vista allows for automatic function materialization commerce management, shared data storage, and mode of governance optimization. Experiments in the real world reveal that Vista significantly increases device stability and decreases runtimes in addition to allowing smooth feature conversion. [ABSTRACT FROM AUTHOR]