In wireless sensor networks energy is a very important issue because these networks consist of lowpower sensor nodes. This paper proposes a new protocol to reach energy efficiency. The protocol has a different priority in energy efficiency as reducing energy consumption in nodes, prolonging lifetime of the whole network, increasing system reliability, increasing the load balance of the network, and reducing packet delays in the network. In the new protocol is proposed an intelligent routing protocol algorithm. It is based on reinforcement learning techniques. In the first step of the protocol, a new clustering method is applied to the network and the network is established using a connected graph. Then data is transmitted using the Q-value parameter of reinforcement learning technique. The simulation results show that our protocol has improvement in different parameters such as network lifetime, packet delivery, packet delay, and network balance.

A wireless sensor network (WSN) is a collection of sensor nodes that dynamically self-organize themselves into a wireless network without the utilization of any preexisting infrastructure. One of the major problems in WSNs is the energy consumption, whereby the network lifetime is dependent on this factor. In this paper, we propose an optimal routing protocol for WSN inspired by the foraging behavior of ants. The ants try to find existing paths between the source and base station. Furthermore, we have combined this behavior of ants with fuzzy logic in order for the ants to make the best decision. In other words, the fuzzy logic is applied to make the use of these paths optimal. Our algorithm uses the principles of the fuzzy ant colony optimization routing (FACOR) to develop a suitable problem solution. The performance of our routing algorithm is evaluated by Network Simulator 2 (NS2). The simulation results show that our algorithm optimizes the energy consumption amount, decreases the number of routing request packets, and increases the network lifetime in comparison with the original AODV.

Nowadays, along with network development, due to the threats of unknown sources, information communication is more vulnerable, and thus, more secured information is required. Intrusion Detection System (IDS) is very important for cybersecurity with the presence in particular of various networked computers' foundation. An efficient IDS apply machine learning method as computational technics to increase rates of detection to gain the high accuracy and low false alarms rate within the huge amounts of data. To increase the rate of detection, researcher usually implements the optimizer. Thus, in this research, a comprehensive experimental study is presented based on various binary to optimize the rate of detection and decrease the error. Moreover, Numerous researches have been conducted about intrusion detection systems with the old dataset such as Kddcup'99 dataset, and due to this reason, most of them did not get the potential accuracy to detect intrusion with the current intrusion as the old dataset is not covering the current attacks. Therefore, this research aims to A hybrid Anomaly Classification of IDS with Deep Learning (DL) and Binary Algorithms (BA) as Optimizer with the most updated dataset named “CICIDS2017” which can be used for the intrusion detection evaluation. Moreover, this research conducts the anomaly classification of IDS based on the deep neural network (DNN) as the Deep Learning (DL) platform and Binary Algorithms (BA) in terms of Binary Bat Algorithm (BBA), Binary Genetic Algorithm (BGA), Binary Gravitational Search Algorithm (BGSA) as optimizer to enhance the rates of detection. Some of the results which had been considered and achieved for DNN and the hybrid version (DNN and Binary Algorithms) are in terms of: Accuracy, Recall, Precision, Confusion Matrix, Sensitivity, Specificity, and Cost Error.

The Proxy Mobile IPv6 or the PMIPv6 is a protocol for mobile management as established by the Internet Engineering Task Force or IETF to assist in the intense usage of mobile devices and to lower the overhead of signaling. As the inclusion of the mobile node in the signaling related to mobility is not necessary, this type of solutions based on networks optimize the performance of the handover based on signaling overhead and handover latency. Nevertheless, the PMIPv6 has several disadvantages such as issues of privacy and security. The process of authentication of users is usually needed at the time of connecting to a wireless network. The mobile users might wander away from their home networks and be approached by other network services. These network services would usually require the users’ credentials to authorize the usage of the service. In order to retain a level of anonymity, various degrees of information are required to be safe guarded including the Local Mobility Anchor ID, Media Access Gateway, and Mobile Node. Nevertheless, a few methods of authentication have been suggested to enhance the PMIPv6’s performance since 2008 when this protocol was first established [1]; however, the issues of privacy are often ignored. This study attempts to evaluate the authentication methods of the PMIPv6 according to the anonymity of several network mechanisms. The findings of this study reveal that it is important to suggest an appropriate method of enhancing the protection and privacy of network mechanisms.

The purpose of this paper is to investigate the transmission dynamics of a fractional-order mathematical model of COVID-19 including susceptible (S), exposed (E), asymptomatic infected (I-1), symptomatic infected (I-2), and recovered (R) classes named SEI1I2R model, using the Caputo fractional derivative. Here, SEI1I2R model describes the effect of asymptomatic and symptomatic transmissions on coronavirus disease outbreak. The existence and uniqueness of the solution are studied with the help of Schaeferand Banach-type fixed point theorems. Sensitivity analysis of the model in terms of the variance of each parameter is examined, and the basic reproduction number (R-0) to discuss the local stability at two equilibrium points is proposed. Using the Routh-Hurwitz criterion of stability, it is found that the disease-free equilibrium will be stable for R-0 < 1 whereas the endemic equilibrium becomes stable for R-0 > 1 and unstable otherwise. Moreover, the numerical simulations for various values of fractional-order are carried out with the help of the fractional Euler method. The numerical results show that asymptomatic transmission has a lower impact on the disease outbreak rather than symptomatic transmission. Finally, the simulated graph of total infected population by proposed model here is compared with the real data of second-wave infected population of COVID-19 outbreak in India.