Cloud computing allows the users to store their data in its storage and use them whenever they need. While the data of user is traveling outside its physical infrastructure through Internet, it needs to use a very strong encryption to protect them against the hackers who attempt to steal or alter the data. Thus, the data security in transmission is so important. Most of the users send the data to cloud, but all the data do not have a high sensitivity. In this paper, K nearest neighbors algorithm is used to decide whether the data is normal sensitive or high sensitive, and then according to the level of sensitivity we proposed a framework to do data encryption. In order to ensure user authentication, we used one time password to authenticate the user, and for the data which belong to normal sensitivity level we have applied AES (advanced encryption standard)-192 algorithm. Finally, for the data which belong to high sensitivity level, AES-256 algorithm has been applied, and RSA (Rivest–Shamir–Adleman) algorithm is used to encrypt the key of AES 256, then we use hash-based massage authentication code to be attached in the end of message to ensure integrity and authenticity of message.

Wireless Sensor network (WSN) is regarded as the most potential approach for keeping track of the physical location wherein monitoring and checking is critical. The efficiency of sensor networks strongly depends on the routing protocols used. In this paper, we examine and evaluate the impact of the divergent sink location on one of the earliest and most significant hierarchical routing protocol, LEACH. Various simulations such as the packets send to the sink, number of dead nodes in each round and the overall energy dissipation of the network are carried out to observe the performance of the protocol such as energy consumption and overall performance of the network. The result of simulation indicates that the location of base station in the centre of the network and the centre of the network's portion having highest number of nodes are the optimum choices to increase the efficiency and overall performance of the of the network.

People are nowadays opting news search engines for searching news instead of traditional web search engines as, number of specialized news search services have been developed. So it becomes necessary to evaluate these news search systems and help users to select the best one. Lots of work has been done to measure the traditional effectiveness of web search engines, major work has been done for relevance based evaluation using precision based measures, where topical relevance is often the main selection criteria, but less work has been done to measure the time-sensitive effectiveness of the news search systems where freshness matters. In this paper we used a scheme using mathematical statistics to measure the time-sensitive effectiveness of four news search systems, i. e., how well they retrieve the fresh documents. To our knowledge there is a lack of a good measure that combines both time-independent effectiveness and the relative freshness of news items so our scheme, using top ten results for 100 news queries on four news search engines with the basic idea to pull all the relevant results from the news search systems we want to compare together into a single ranked list based on their recency and analyze the relative positions of these results, will be useful in stuffing this gap.

Internet of Things (IoT)-based devices are extensively utilized for data transmission to the cloud across various organizations. Nonetheless, there are notable limitations in the conventional approach, such as in critical situations, where transmitting sensitive data, secure communication across the cloud is not guaranteed, and the memory, processing power, and bandwidth constraints in these IoT devices present significant challenges. The suggested model employs a bespoke Convolutional Neural Network (CNN) to categorize sensitive and non-sensitive images on the device, utilizes Elliptic Curve Cryptography (ECC) for safe session key sharing, implements SHA-512 hashing for integrity verification, and applies the ChaCha20 stream cipher for rapid, random encryption for sensitive images. The mean entropy of the proposed technique is 7.9976, and the correlation coefficients approximate zero. The histogram distributions are balanced, rendering statistical attacks exceedingly difficult. This approach surpasses AES + RSA-1024, SPECK, and PRESENT by reducing the average encryption time by up to 99%, enhancing throughput by over 647%, and consuming up to 99.79% less energy. This proposed solution offers a robust, efficient, and secure framework for managing sensitive government data, effectively addressing both the resource constraints of IoT devices and the necessity for privacy in governmental communication systems.