In mobile wireless sensor network (MWSN), the lifetime of the network largely depends on energy efficient routing protocol. In the literature, cluster leader (CL) is selected based on remaining energy of mobile sensor nodes to enhance sensor network lifetime. In this study, a novel connectivity-based Low-Energy Adaptive Clustering Hierarchy-Mobile Energy Efficient and Connected (LEACH-MEEC) routing protocol was proposed, where CL is selected based on connectivity among neighboring nodes and the remaining energy of mobile sensor nodes. Consequently, it improves data delivery, network lifetime and balances the energy consumption. We studied various performance metrics including the number of alive nodes (NAN), remaining energy (RE) and packet delivery ratio (PDR). Our proposed LEACH-MEEC outperforms all other algorithms due to the connectivity metric. Moreover, the performance of mobility models was investigated through graphical and statistically tabulated results. The results show that Reference Point Group Mobility model (RPGM) is better than other mobility models.

Nowadays, spam has become serious issue for\ud computer security, because it becomes a main source for\ud disseminating threats, including viruses, worms and\ud phishing attacks. Currently, a large volume of received\ud emails are spam. Different approaches to combating these\ud unwanted messages, including challenge response model,\ud whitelisting, blacklisting, email signatures and different\ud machine learning methods, are in place to deal with this\ud issue. These solutions are available for end users but due\ud to dynamic nature of Web, there is no 100% secure\ud systems around the world which can handle this problem.\ud In most of the cases spam detectors use machine learning\ud techniques to filter web traffic. This work focuses on\ud systematically analyzing the strength and weakness of\ud current technologies for spam detection and taxonomy of\ud known approaches is introduced

Effective transportation status surveillance imposes critical challenges for the Intelligent Transportation System (ITS) design. In this paper, the microscopic congestion detection protocol (MCDP) is proposed to make the vehicle-to-vehicle (V2V) communication capable of monitoring vehicle density and identifying traffic jam. By introducing transportation control domain in the existing network protocol header, each vehicle can count its neighbors and estimate the time spacing among vehicles. MCDP provides an infrastructure-less solution to the estimate of vehicle density, flow, and average velocity in a microscopically manner. Moreover, the safety speed limit is introduced to make each vehicle calculate its time to cover the intervehicle distance, such that every vehicle is able to assess the transportation congestion by comparing with some predefined safety time threshold. Monte Carlo simulations of the MCDP over four typical Chinese highways are presented to compare the MCDP scheme with the existing Green-Shield congestion detection scheme. In addition, real road traces generated by SUMO over NS2 are utilized to show the achieved performance in terms of throughput, end-to-end delay, and packet delivery rate (PDR) in comparison to DSR and AOMDV in IEEE 802.11p and IEEE 802.11ac scenarios. On the basis of all the results, we conclude that MCDP is an inexpensive transport congestion detection technique for Vehicular Ad Hoc Networks (VANETs).