The use of wireless and Internet of Things (IoT) devices is growing rapidly. Because of this expansion, nowadays, mobile apps are integrated into low-cost, low-power platforms. Low-power, inexpensive sensor nodes are used to facilitate this integration. Given that they self-organize, these systems qualify as IoT-based wireless sensor networks. WSNs have gained tremendous popularity in recent years, but they are also subject to security breaches from multiple entities. WSNs pose various challenges, such as the possibility of numerous attacks, their innate power, and their unfeasibility for use in standard security solutions. In this paper, to overcome these issues, we propose the secure encryption random permutation pseudo algorithm (SERPPA) for achieving network security and energy consumption. SERPPA contains a major entity known as a cluster head responsible for backing up and monitoring the activities of the nodes in the network. The proposed work performance is compared with other work based on secure IoT devices. The calculation metrics taken for consideration are energy, overheads, computation cost, and time consumption. The obtained results show that the proposed SERPPA is very significant in comparison to the existing works, such as GKA (Group Key Agreement) and MPKE (Multipath Key Establishment), in terms of data transfer rate, energy consumption and throughput.

Chronic Kidney Disease (CKD) is a chronic renal problem that affects the human kidney and makes it not to function properly or causes complete renal failure. It results in dialysis or causes other related diseases and reduces the quality of living. The symptoms of this disease cannot be identified in the preliminary stage. Only very lesser people are aware of this disease and can predict the symptoms at the earlier stage. However, it leads to prolonged disruption of kidney functional and finally causes it to failure and reduces the functionality completely. This can be occurred due to prolonged diabetes and also related with other diseases like Cardio-Vascular Disease (CVD). Due to the lack of awareness and inadequate prediction approaches in the preliminary stage, there is a delay in treating the patients" at the initial phase of disease. From the various literature studies, it is identified that CKD can be predicted and treated in the earlier stage using the soft-computational techniques. Earlier CKD predictor model needs to be improved with higher prediction accuracy and precision. Therefore, there is a need for a decision support system that assists the nephrologists during the time of emergency conditions. Therefore, in this research, Naive Bayes classifier is used for classification along with the Choice based Hierarchy (NB-CbH). NB classifier works effectually with huge dataset and reduces the computational complexity. The prediction rate and the severity of the disease analysis with NB are extremely higher.

Honey encryption (HE) was proposed by Juels and Ristenpart in 2014 as a solution against brute force attacks. As honey encryption provides fake messages when a wrong password is entered, it gives an extra layer of security to the user as all these honey messages which consist of sweet words in distribution transforming encoder(DTE). As technology is progressing there are major concerns among users about security. In this paper we are providing ways for making honey encryption available for general and practical use. We have proposed an Application program interface (API) for this which would help in proper improvement in readily making honey encryption for the programmers so as to decrease the hassle of programming from scratch. We have proposed some methods for message space reduction in honey encryption and methods for its application on natural language message. Through this paper our approach for making honey encryption readily available so as to make work easy on it so that it could speed up the progress of honey encryption in research.

Resource controlled sensing devices are being used widely to build and set up self-organizing Wireless Sensor Networks for a variety of the essence applications such as smart agriculture. Smart building, smart energy, smart cities, smart healthcare. A Data Stream Manager (DSM) at the Server side gathers the information streams to perform ongoing examination and dynamic for these basic applications. A scornful rival may access or alter the information on the way. One of the difficult undertakings in such applications is to guarantee the obligation of the gathered information with the goal that any choices are made on the handling of right information. Guaranteeing high information dependability necessitates that the framework fulfills two key security properties: classification and uprightness. To guarantee the protection of gathered information, we have to keep delicate data from contacting some unacceptable individuals by guarantee that the power individuals are getting it. Senses data are constantly connected with various affectability levels dependent on the affectability of talented applications are sensed data types or the detecting Devices. Furnishing staggered information secrecy alongside information respectability for large detecting information streams with regards to approach continuous examination is a difficult issue. We proposed a Selective Encryption (SEEN) Method to secure big sensing data streams that fulfills the ideal different levels of secrecy and information genuineness. The Proposed method is based on two key concepts: common shared keys that are initialized and restructured by DataStream Manager without requiring retransmission and a flawless key refreshment. A scheme to secure a multi-hop protocol through the use of multiple one-way hash chains is proposed. The scheme is shown to be lower cost in power consumption, increased computational performance , and lower in communication costs able to secure multi-hop propagation of program.

In this age of automation, industries are trying to modernize their infrastructure by installing robotic and automated machineries in their stores and workshops. This is done in order to reduce the cost of manufacturing a product, speeding up the process and making the industry less dependent on manual labor. In retail stores and shopping malls, customers have to wait in line at the billing counter for the receipt of the items which they brought. This is due to the inefficiency of the human worker who enters the details of the product and makes bill. This is a time consuming process. To make it more efficient we propose an automated billing system which can calculate the total billing amount of the items brought without any help of cloud serves and internet. We use the concept of edge computing to make our system less dependent on internet and more dependent on local machines. We use the techniques of image classification like residual network and ImageAI to identify the product with different dimensions and color.