As the IoT is moving out of its early stages, it is emerging as an area of future internet. The evolving communication paradigm among cloud servers, Fog nodes and IoT devices are establishing a multilevel communication infrastructure. Fog provides a platform for IoT along with other services like networking, storage and computing. With the tremendous expansion of IoT, security threats also arise. These security hazards cannot be addressed by mere dependence on cloud model. In this paper we present an overview of security landscape of Fog computing, challenges, and, existing solutions. We outline major authentication issues in IoT, map their existing solutions and further tabulate Fog and IoT security loopholes. Furthermore this paper presents Blockchain, a decentralized distributed technology as one of the solutions for authentication issues in IoT. We tried to discuss the strength of Blockchain technology, work done in this field, its adoption in COVID-19 fight and tabulate various challenges in Blockchain technology. At last we present the Cell Tree architecture as another solution to address some of the security issues in IoT, outlined its advantages over Blockchain technology and tabulated some future course to stir some attempts in this area.

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In this research, we provide tools to overcome the information loss limitation resulting from the requirement to estimate the results in the discrete initial expression domain. Through the use of 2-tuples, which are made up of a linguistic term and a numerical value calculated between [0.5,0.5), the linguistic information will be expressed. This model supports continuous representation of the linguistic data within its scope, permitting it to express any information counting received through an aggregation procedure. This study provides a novel approach to develop a linguistic multi-attribute group decision-making (MAGDM) approach with complex fractional orthotriple fuzzy 2-tuple linguistic (CFOF2TL) assessment details. Initially, the concept of a complex fractional orthotriple fuzzy 2-tuple linguistic set (CFO2TLS) is proposed to convey uncertain and fuzzy information. In the meantime, simple aggregation operators, such as CFOF2TL weighted average and geometric operators, are defined. In addition, the CFOF2TL Maclaurin’s symmetric mean (CFOF2TLMSM) operators and their weighted shapes are presented, and their attractive characteristics are also discussed. A new MAGDM approach is built using the developed aggregation operators to address managing economic crises under COVID-19 with the CFOF2TL information. As a result, the effectiveness and robustness of the developed method are accompanied by an empirical example, and a comparative study is carried out by contrasting it with previous approaches.

Cloud computing is a paradigm for large-scale distributed computing. The sensitive data should be outsourced to the cloud and stored in an encrypted format to keep it confidential. The existing search schemes do not suggest keywords consequently making the retrieving of documents difficult if user forgets the keyword. In this paper, we propose a multi-keyword synonym based fuzzy ranked search (MSFRS) scheme over outsourced encrypted cloud data which provides efficient search results retaining the security features of the existing schemes. It supports multi-keyword, suggests synonyms if user forgot keyword and returns results after evaluating relevance scores based on frequency of keyword in the documents bearing same rank. The performance analysis of the proposed scheme on the dataset concluded that time taken to update the index file has been reduced up to 45 % over outsourced encrypted cloud data.