Routing, the act of moving information from a source node to a destination node across any kind of network is one of the major issues in computer network literature. Ad hoc wireless networks are increasing in popularity, because of the spread of laptops, sensor devices, personal digital assistants, and other mobile electronic devices. These devices will eventually need to communicate with each other, without an adequate infrastructure to rely on. For mobile ad hoc networks, the complexity of routing increases because of its characteristics such as dynamic topology, absence of centralized authority, time varying quality of service (QoS) requirements, etc. The biggest challenge in this kind of networks is to find a path between communication end points satisfying user's QoS requirement in spite of frequent path failures because of node mobility. Recent advances in wireless technology and availability of mobile computing devices with networking capabilities have generated a lot of interest in wireless ad hoc networks for QoS-based real-time multimedia applications. In this article, we are proposing AMQR, an ant-based multiobjective on demand QoS routing algorithm for mobile ad hoc network which will be highly adaptive, efficient, scalable, and mainly reduces end-to-end delay in high mobility cases.

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Agriculture is the primary source of economic development in India. The fertility of soil, weather conditions, and crop economic values make farmers select appropriate crops for every season. To meet the increasing population requirements, agricultural industries look for improved means of food production. Researchers are in search of new technologies that would reduce investment and significantly improve the yields. Precision is a new technology that helps in improving farming techniques. Pest and weed detection and plant leaf disease detection are the noteworthy applications of precision agriculture. The main aim of this paper is to identify the diseased and healthy leaves of distinct plants by extracting features from input images using CNN algorithm. These features extracted help in identifying the most relevant class for images from the datasets. The authors have observed that the proposed system consumes an average time of 3.8 seconds for identifying the image class with more than 94.5% accuracy.

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People can store their data on servers in cloud computing and allow public users to access data via data centers. One of the most difficult tasks is to provide security for the access policy of data, which is also needed to be stored at cloud servers. The access structure (policy) itself may reveal partial information about what the ciphertext contains. To provide security for the access policy of data, a number of encryption schemes are available. Among these, CP-ABE (Ciphertext-Policy Attribute-Based Encryption) scheme is very significant because it helps to protect, broadcast, and control the access of information. The access policy that is sent as plaintext in the existing CP-ABE scheme along with a ciphertext may leak user privacy and data privacy. To resolve this problem, we hereby introduce a new technique, which hides the access policy using a hashing algorithm and provides security against insider attack using a signature verification scheme. The proposed system is compared with existing CP-ABE schemes in terms of computation and expressive policies. In addition, we can test the functioning of any access control that could be implemented in the Internet of Things (IoT). Additionally, security against indistinguishable adaptive chosen ciphertext attacks is also analyzed for the proposed work.

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