As the demand for wireless communications has increased, Mobile Ad hoc Networks (MANETs) have acquired their own analysis locations. Due to its rapidly changing technology, the potential application of MANET is expanding. The wireless network can be established in two modes: "Ad hoc mode" where the nodes are self-organizing, and the other is the basic mode where the nodes are controlled by the central coordinator. However, routing plays an important role in the more complex process of MANET and requires considerable attention. In order to deal with this situation, different solutions have been proposed in the literature. Analysis of different mobility models such as group mobility and file mobility is done. This paper attempts to evaluate the performance of the Dynamic Source Routing (DSR) Protocol, Optimized Link State Routing (OLSR) and Zone Routing Protocol (ZRP) having different node density using NetSim Simulator. Different Performance metrics such as Throughput, Packets Delivered, Packets Collided and Packets Errored have been measured, and the comparative results are also presented in this paper.
Internet of Things (IoT), Big Data (BD), Artificial Intelligence (AI) and Machine Learning (ML) are the novel approaches where communication happens between man-made machines. Machines interact and acquire knowledge by implementing learning algorithms. Data analytics, prediction and classification methods are machine learning approaches applied on Big data for processing various unstructured data patterns. MapReduce is a widely used programming framework to parallelize these machine learning algorithms. To accomplish best outcomes, the algorithms are fine tuned using parallel practice. This technique uses MapReduce model for processing datasets multiple times by tuning the parameters as per the requirement. But this existing MapReduce model endures with high disk rates resulting in low throughput and inefficient time complexity. To achieve the minimal time consumption for tuning the jobs, Apache Spark framework replaces the MapReduce model. This is examined in this paper by evaluating the prediction on "Demand and Supply of India" dataset. A comparative analytical study is proposed in this paper to predict the demand for forecasting by training the existing data using tree based machine learning techniques. The prediction outcomes computed are compared on tree structured ML methods with respect to time and space utilization.
In the recent years, a lot of research has been carried out in Mobile Ad-hoc Networks (MANETs). The main goals and challenges of security are which characterizes the routing protocol of MANET. Any node can join and leave the network routing protocol which is addressed for only efficient path formation, making the same network vulnerable to various attacks. Associativity-based Routing (ABR) is an on-demand routing protocol, i.e., routes are created only as and when needed. Normally, the routes will be available immediately and the routing tables will be updated constantly among the routes. On-Demand routing is chosen here, because it reduces the control packet traffic which is suitable for wireless network due to limited bandwidth. Protocol's dynamic nature enables MANET operation ensuring deployment in extreme/volatile circumstances. This paper proposes an Extended Public Key Cryptography (EPKCH) system. This cryptosystem is mainly designed for securing data during packet forwarding operation and also to detect malicious and selfish nodes during network initializing and packet forwarding operation. Confidentially is the basic feature provided by the public key cryptography, which also provides authentication, non-repudiation and integrity. The protocol is designed to protect the network from malicious and selfish nodes. An extended public key cryptography mechanism is implemented in ABR, in order to achieve security goals for the following parameters Path-Division Routing (PDR), Routing overhead, delay, on-demand routing, packet loss and throughput. The proposed model is stimulated using Network Simulated (NS2).
Internet of Things (IoT) has become a buzz word in the society, as it is emerging in different sectors with broad applications such as smart cities, smart home, connected cars, industries and so on. Already many devices have merged into the market. The industries have turned into the fourth revolution, i.e. Industry 4.0, where the departments such as manufacturing, production, and deployment have already automated with the invention of IoT. Coming to the Industries, Safety is the main measure which should be considered by the industrialists. Presently followed prediction models depend on the previous data of the accidents and other health issues related to employee which are collected from the industries. From that, precautions are taken for reducing the death and injuries of employees in future. In this paper, a new approach has been proposed with the invention of IoT in Industrial Safety, where the live information is tracked and immediate measures are taken on demand so that the loss that occurs due to accidents or other health related issues can be reduced to the maximum extent. The implementation is shown in two models; Monitoring Employee Health (MEH), Safety Automation Model (SAM).
Today websites are used for various purposes. There is a crime named website phishing which comes under Cybercrime. A website phishing tries to steal your account password or other private information by misleading you into believing that you're on a legitimate website. Several conventional techniques for detecting phishing websites have been suggested to cope with this problem. One could even land on a phishing site by mistyping a URL. In this study, a Multilayer Perceptron Learning approach is used after applying 10-fold cross validation as a preprocessing for website phishing classification which gives almost 100% accuracy. The experimental results show that the performance of the multilayer perceptron learning classifiers improved the results up to a greater extent.
A New Novel Hybrid Security Algorithm (NHSA) for Rivest-Shamir-Adleman (RSA) cryptosystem was proposed in this paper, which is based on Encryption algorithm using Dual Modulus and Enhanced method for RSA (ERSA) cryptosystem. Here, the computation of public keys and private keys depends on modulus values, each computed using three different prime integers. Thus complexity involved in factorizing the modulus value increases. It improves the security of RSA scheme against Brute Force Attack using double mod operation based encryption and decryption. Therefore, it is not possible to retrieve the original message for the cipher text even after determining a single public key. Also it is difficult to factorize the modulus value into its three prime factors. Thus it enhances the security of encrypted data two times. In this paper, the proposed algorithm is compared with “Encryption algorithm using dual modulus” in terms of key generation time and security of data.
A comparative study of secure index based on Context Based Fertility (CBF) for ranked search over encrypted data is performed in this paper. In daily requirement, there is an increasing in need for secure searched encrypted data in cloud computing. This paper mainly provides an approach to overcome the attacks and challenges that are occurred in the process of encryption by mentioning various methodologies proposed till date. In accordance with the frequent updating of technology in today's world, the secure index based on CBF for ranked search over encrypted data with few goals like Multiple Ranked Keywords Search, Guaranteed Security, Data Confidentiality, Indexed Privacy, Keyword Privacy and Efficiency faces many challenges in fulfilling their goals.
