# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 2 Literature Review
In this study, the author refined the energy distribution method and used fuzzy logic clustering to imitate human reasoning and cognition. Rather than strictly binary cases of truth, fuzzy logic includes 0 and 1 as extreme cases of truth but with various intermediate degrees of truth, both of which have a direct impact on scalability. In particular, the AEEF algorithm presented a Landslide Detection System (LDS), which streamlines routing paths and finds the best routes via sinks to shorten delays and be useful in the event of landslides. The Reliable Cluster Head Selection Technique (RCHST) utilizes Integrated Energy and Trust-based Semi-Markov Prediction (IETSMP) to combine energy and trust parameters as RCHST-IETSMP [40]. This method leverages short-path communication using effective distance to carry out a basic comparison between the interacting sensor nodes of the IoT network and has the ability to aid the user in data processing and sensing. It takes advantage of the Hyper-Erlang process to lengthen a sensor network's lifespan. The lifespan is a crucial element of an IoT network, whereas the Hyper-Erlang process requires more resources to extend the network's lifetime. Dynamic directional routing (DDR) is designed for Mobile Wireless Sensor Networks (MWSNs) for effective and reliable routing that anticipates the next opportunity through advanced calculations of a fitness function [41]. Furthermore, it chooses the particular specified search angle that forecasts the total potential candidate for the following hope node. The discovery phase and the data forwarding phase are the two primary aspects of this protocol. It connects certain sophisticated characteristics of prediction with the fundamental Threshold sensitive Low Energy Adaptive Clustering Hierarchy (T-LEACH) structure. Due to the fact that T-LEACH chooses cluster heads at random which has an adverse effect on the algorithm's overall efficiency, the DDR can only make limited improvements to its prediction, leading to low latency. The vulnerability and system weaknesses that are raised by the expansion of IoT networks have raised many concerns for the research community. Earlier, software-based intrusion detection systems (IDSs) were suggested as a solution to the security issues but over time, it became apparent that there is no clear and defined software methodology for IDSs. In order to extend the longevity and technology, the authors presented a Software Engineering (SE) ([[https://www.mdpi.com/1424-8220/20/5/1375](https://www.mdpi.com/1424-8220/20/5/1375)]([https://www.mdpi.com/1424-8220/20/5/1375](https://www.mdpi.com/1424-8220/20/5/1375)), accessed on 14 August 2023) based solution for IoT in which three crucial SE phases are utilized as requirements, design and software testing to design the IDS [42]. When IDS is used for an IoT network, this approach performs well in terms of energy usage and network longevity. Since the broadcast design transmits many messages at once, it requires a lot of energy to do so, making it inefficient to implement. The key disadvantage of the suggested method is that complicated designs still require a correct structure to be solved. In order to attain the energy efficiency of IoT networks, the authors presented the Gaussian elimination approach with the Distributed Energy-Efficient Clustering DEEC Gaussian (DEEC Gaussian) in [43]. The Gaussian elimination algorithm is used to increase residual energy and improve optimal energy along with aggregation of IoT operations whereas the DEEC algorithm is used to provide a high-performance level of heterogeneity and achieve energy-efficient clustering that is used to increase the network lifetime in stable ways. Overall, none of the aforementioned encryption techniques, however, cannot successfully fight against assaults initiated from within the IoT network.