Cloud computing and internet of things (IoT) are playing a crucial role in the present era of technological, social, and economic development. The novel models where cloud and IoT are integrated together are foreseen as disruptive and enable a number of application scenarios. The e-smart is an application system designed by leveraging cloud, IoT, and several other technology frameworks that are deployed on the agricultural farm to collect the data from the farm fields. The application extracts and collects the information about the residue levels of soil and crop details and the same data will be hosted in the cloud environment. The proposed e-smart application system is to analyze, integrate, and correlate datasets and produce decision-oriented reports to the farmer by using several machine learning techniques.

Network congestion remains one of the main barriers to the continuing success of the internet and Web based services. In this background, proxy caching is one of the most successful solutions for civilizing the performance of Web since it reduce network traffic, Web server load and improves user perceived response time. Here, the most popular Web objects that are likely to be revisited in the near future are stored in the proxy server thereby it improves the Web response time and saves network bandwidth. The main component of Web caching is it cache replacement policy. It plays a key role in replacing existing objects when there is no room for new one especially when cache is full. Moreover, the conventional replacement policies are used in Web caching environments which provide poor network performance. These policies are suitable for memory caching since it involves fixed sized objects. But, Web caching which involves objects of varying size and hence there is a need for an efficient policy that works better in Web cache environment. Moreover, most of the existing Web caching policies have considered few factors and ignored the factors that have impact on the efficiency of Web proxy caching. Hence, it is decided to propose a novel policy for Web cache environment. The proposed policy includes size, cost, frequency, ageing, time of entry into the cache and popularity of Web objects in cache removal policy. It uses the Web usage mining as a technique to improve Web caching policy. Also, empirical analyses shows that proposed policy performs better than existing policies in terms of various performance metrics such as hit rate and byte hit rate.

Web caching is a mechanism used to improve network performance by reducing network traffic, load on the Web Server and delay in accessing the Web page. This is achieved by storing frequently accessed Web pages on proxy cache placed within the network. Caching can be taken place either at client side or in the proxy Server. Web proxy cache can potentially improve network performance by reducing the number of requests that reaches the server, the volume of data transferred through the network and the delay in accessing Web page. When a requested page is not present in the cache and cache is full then removal of one or more cached documents take pace. The performance of proxy depends on page replacement algorithm. The decision by which document is evicted from the cache is depends on different kinds of replacement policies used. A number of exiting cache replacement algorithms, which attempt to reduce various cost metrics such as hit rate, byte hit rate, average latency and total cost. In this paper presents one such technique that improves the Web cache performance by modifying Least Frequently Used (LFU) algorithm. Proposed algorithm is based on recency, frequency and popularity of web page with the help of Web usage mining.

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

It is generally observed throughout the world that in the last two decades, while the average speed of computers has almost doubled in a span of around eighteen months, the average speed of the network has doubled merely in a span of just eight months! In order to improve the performance, more and more researchers are focusing their research in the field of computers and its related technologies. Data Mining is one such research area. It extracts useful information the huge amount of data present in the database. The discovered knowledge can be applied in various application areas such as marketing, fraud detections and customer retention. It discovers implicit, previously unknown and potentially useful information out of datasets. Recent trend in data mining include web mining where it discover knowledge from web based information to improve the page layout, structure and its content.