Mobile ad hoc network (MANET) can be described as a group of wireless mobile nodes that form a temporary dynamic and independent infrastructure network or a central administration facility. High energy consumption is one of the main problems associated with the MANET technology. The wireless mobile nodes used in this process rely on batteries because the network does not have a steady power supply. Thus, the rapid battery drain reduces the lifespan of the network. In this paper, a new Bat Optimized Link State Routing (BOLSR) protocol is proposed to improve the energy usage of the Optimized Link State Routing (OLSR) protocol in the MANET. The symmetry between OLSR of MANET and Bat Algorithm (BA) is that both of them use the same mechanism for finding the path via sending and receiving specific signals. This symmetry resulted in the BOLSR protocol that determines the optimized path from a source node to a destination node according to the energy dynamics of the nodes. The BOLSR protocol is implemented in a MANET simulation by using MATLAB toolbox. Different scenarios are tested to compare the BOLSR protocol with the Cellular Automata African Buffalo Optimization (CAABO), Energy-Based OLSR (EBOLSR), and the standard OLSR. The performance metric consists of routing overhead ratios, energy consumption, and end-to-end delay which is applied to evaluate the performance of the routing protocols. The results of the tests reveal that the BOLSR protocol reduces the energy consumption and increases the lifespan of the network, compared with the CAABO, EBOLSR, and OLSR.

Mobile Ad-hoc Network (MANETs) is a wireless network topology with mobile network nodes and movable communication routes. In addition, the network nodes in MANETs are free to either join or leave the network. Typically, routing in MANETs is multi-hop because of the limited communication range of nodes. Then, routing protocols have been developed for MANETs. Among them, energy-aware location-aided routing (EALAR) is an efficient reactive MANET routing protocol that has been recently obtained by integrating particle swarm optimization (PSO) with mutation operation into the conventional LAR protocol. However, the mutation operation (nonuniform) used in EALAR has some drawbacks, which make EALAR provide insufficient exploration, exploitation, and diversity of solutions. Therefore, this study aims to propose to apply the Optimized PSO (OPSO) via adopting a mutation operation (uniform) instead of nonuniform. The OPSO is integrated into the LAR protocol to enhance all critical performance metrics, including packet delivery ratio, energy consumption, overhead, and end-to-end delay.

Modern technology is turning into an essential element in the financial trade. We focus the emphasis of this review on the research on the E-wallet and online payment, which is an element of an electric payment system, to get the pattern of using this service. This research presents a review of 131 research articles published on electronic payment between 2010 and 2020 that uses a qualitative method of answering the research questions (RQ): RQ1: “What are the major security issues regarding using electronic payments”? and RQ2: “What security properties need to comply for secure electronic payments?” With the systematic literature review approach, the results show that interest in E-wallet and online payment has grown significantly during this period, and it was found that for the increasing uses of electronic payments, researchers are more focused on security issues. The results show that, to conquer the key gaps, electronic payment must have some protection properties, namely, availability, authorization, integrity, non-repudiation, authentication, and confidentiality. Nowadays, security problems in electronic payment are usually more demanding than the present security problems on the web. These findings can enable electric transaction providers to strengthen their security methods by boosting their security gaps, as required for relevant services.

A Mobile Ad-Hoc Network (MANET) is made up of wireless mobile nodes that do not require a central infrastructure or administration to establish a network. It is possible for the MANET nodes to function as a router or host. MANET works with an independent multi-hop mobile network which can be used in several real-time applications. Thus, an important issue associated with MANET is the identification of paths with high-level Quality of Service (QoS), like topology. The purpose of having a QoS-aware protocol in MANETs is to enable the discovery of paths that are more efficient between the source and destination nodes of the network and hence, the need for QoS. In this paper, a novel algorithm which can be used in the African Buffalo Optimization (ABO) to improve the QoS of routing protocol MANETs. With ABO, path selection is optimized in the Ad-hoc On-demand Distance Vector (AODV) routing protocol. Results of the test revealed that when ABO is used in AODV, delay and energy-aware routing protocol is manifested.

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Lightning mapping systems based on perpendicular crossed baseline interferometer (ITF) technology have been developed rapidly in recent years. Several processing methods have been proposed to estimate the temporal location and spatial map of lightning strikes. In this paper, a single very high frequency (VHF) ITF is used to simulate and augment the lightning maps. We perform a comparative study of using different processing techniques and procedures to enhance the localization and mapping of lightning VHF radiation. The benchmark environment involves the use of different noise reduction and cross-correlation methods. Moreover, interpolation techniques are introduced to smoothen the correlation peaks for more accurate lightning localization. A positive narrow bipolar event (NBE) lightning discharge is analyzed and the mapping procedure is confirmed using both simulated and measured lightning signals. The results indicate that a good estimation of lightning radiation sources is achieved when using wavelet denoising and cross-correlations in wavelet-domain (CCWD) with a minimal error of 3.46°. The investigations carried out in this study confirm that the ITF mapping system could effectively map the lightning VHF radiation source.

Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively.

Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset (SemCor 3.0, SensEval-2, and SensEval-3). Results show that the proposed method has superior performance in comparison with state-of-the-art approaches.