Wireless devices’ energy efficiency and spectrum shortage problem has become a key concern worldwide as the number of wireless devices increases at an unparalleled speed. Wireless energy harvesting ...technique from traditional radio frequency signals is suitable for extending mobile devices’ battery life. This paper investigates a cognitive radio network model where primary users have their specific licensed band, and secondary users equipped with necessary hardware required for energy harvesting can use the licensed band of the primary user by smart sensing capability. Analytical expressions for considered network metrics, namely data rate, outage probability, and energy efficiency, are derived for uplink and downlink scenarios. In addition, optimal transmission power and energy harvesting power are derived for maximum energy efficiency in downlink and uplink scenarios. Numerical results show that outage probability improves high transmission power in the downlink scenario and high harvested power in the uplink scenario. Finally, the result shows that energy efficiency improves using optimum transmission power and energy harvesting power for downlink and uplink scenarios.
In the criminology area, to detain the serial criminal, the forthcoming serial crime time, distance, and criminal's biography are essential keys. The main concern of this study is on the upcoming ...serial crime distance, time, and suspect biographies such as age and nationality. In conjunction with having time delays, the dynamic classifier, like Time Delay Neural Network (TDNN) utilized to perform nonlinear techniques-based predictions. The TDNN classifier system, like Back Propagation Through Time (BPTT) and Nonlinear Autoregressive with Exogenous Input (NARX) are two prominent examples. However, BPTT and NARX techniques are unable to identify the dynamic system by using single-activation functions due to producing lower accuracy. Hence, during the training phase, the direct minimization of the TDNN error can further enhance the single activation function. Thus, this work introduces an enhanced NARX (eNARX) model based on the proposed activation functions of SiRBF via fusion of two functions of the hyperbolic tangent (Tansig) and Radial Basis Function (RBF), in the same hidden layer. If a fusion of activation functions can affect the TDNN error minimization, then fusing of the Tansig and RBF functions can produce a precise prediction for crime spatiotemporal. To evaluate the proposed technique and compared it with existing NARX and BPTT, we utilized five time-series datasets, namely, Dow Jones Index, Monthly River flow in cubic meters per second, Daily temperature, and UKM-PDRM datasets namely, "Suspect & Capture" and "Crime Plotting." The analysis of the results demonstrated that the proposed eNARX produce higher accuracy in comparison to other techniques of NARX and BPTT. Consequently, the proposed technique provides more effective results for the prediction of commercial serial crime.
This paper proposed a novel texture feature extraction technique for radar remote sensing image retrieval application using adaptive tetrolet transform and Gray level co-occurrence matrix. Tetrolets ...have provided fine texture information in the radar image. Tetrominoes have been employed on each decomposed radar image and best pattern of tetrominoes has been chosen which represents the better radar image geometry at each decomposition level. All three high pass components of the decomposed radar image at each level and low pass component at the last level are considered as input values for Gray level co-occurrence matrix (GLCM), where GLCM provides the spatial relationship among the pixel values of decomposed components in different directions at certain distances. The GLCMs of decomposed components are computed in (1). (0, π/2, π, 3π/2), (2). (π/4, 3π/4, 5π/4, 7π/4) (3). (0, π/4, π/2, 3π/4, π, 3π/2, 5π/4, 7π/4) directions individually and subsequently a texture feature descriptor is constructed by computing statistical parameters from the corresponding GLCMs. The retrieval performance is validated on two standard radar remote sensing image databases: 20-class satellite remote sensing dataset and 21-class land-cover dataset. The average metrices i.e., precision, recall and F-score are 61.43%, 12.29% and 20.47% for 20-class satellite remote sensing dataset while 21-class land-cover dataset have achieved 67.75%, 9.03% and 15.94% average metrices. The retrieved results show the better accuracy as compared to the other related state of arts radar remote sensing image retrieval methods.
For the betterment of human life, smart Internet of Things (IoT)-based systems are needed for the new era. IoT is evolving swiftly for its applications in the smart environment, including smart ...airports, smart buildings, smart manufacturing, smart homes, etc. A smart home environment includes resource-constrained devices that are interlinked, monitored, controlled, and analyzed with the help of the Internet. In a distributed smart environment, devices with low and high computational power work together and require authenticity. Therefore, a computationally efficient and secure protocol is needed. The authentication protocol is employed to ensure that authorized smart devices communicate with the smart environment and are accessible by authorized personnel only. We have designed a novel, lightweight secure protocol for a smart home environment. The introduced novel protocol can withstand well-known attacks and is effective with respect to computation and communication complexities. Comparative, formal, and informal analyses were conducted to draw the comparison between the introduced protocol and previous state-of-the-art protocols.
In everyday life, electricity is necessary, and proper use is critical. To strengthen home electricity control, the existing systems have been examined over the years. However, the existing PMAS ...method’s error ratio is higher and does not allow for a remote monitoring system. Therefore, this study proposes a smart monitoring and control system (SMACS) for household appliances. The application’s significance is to monitor household appliances’ electricity usage using hardware and the Internet of Things (IoT) methods. The prototype of the proposed system is designed and developed considering Arduino UNO, a liquid crystal display (LCD), an ACS712 current sensor module, relays, and AC sources. The components are selected from the software library, and the simulation results are found the same as the prototype. WiFi module ESP8266 is not included in the design because it is not provided in the system. The data is recorded in cloud storage using Thing-speak. A mobile application (Virtuino) also accesses the data to visualize it through the graphical and numerical display. This study provides users with an easy system to monitor and control household appliances’ power consumption using mobile applications. Results show that the proposed system provides 0.6% current errors for the hairdryer appliance, whereas the existing Power Monitoring and Switching (PMAS) system provides 7.8% current errors.
A Review on Electronic Payments Security Hassan, Md Arif; Shukur, Zarina; Hasan, Mohammad Kamrul ...
Symmetry (Basel),
08/2020, Letnik:
12, Številka:
8
Journal Article
Recenzirano
Odprti dostop
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.
Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly ...depended on the use of non-machine learning-based and machine learning-based counting methods and so was unable to provide precise results. In this work, we used a robotic eye camera to capture shrimp photos on a shrimp farm to train the model. The image data were classified into three categories based on the density of shrimps: low density, medium density, and high density. We used the parameter calibration strategy to discover the appropriate parameters and provided an improved Mask Regional Convolutional Neural Network (Mask R-CNN) model. As a result, the enhanced Mask R-CNN model can reach an accuracy rate of up to 97.48%.
High-Speed Network DDoS Attack Detection: A Survey Haseeb-Ur-Rehman, Rana M Abdul; Aman, Azana Hafizah Mohd; Hasan, Mohammad Kamrul ...
Sensors (Basel, Switzerland),
08/2023, Letnik:
23, Številka:
15
Journal Article
Recenzirano
Odprti dostop
Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal ...harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber-Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.
The Internet of Things (IoT) is susceptible to several identities, primarily based on attacks. However, these attacks are controlling for IoT due to extraordinary growth in consumers’ density and ...slight analysis with low power access nodes. In this work, we explore the possible flaws associated with security for IoT environment insensitively meant for transfer conditions. We proposed a novel design aimed at detecting a spoofing attack that inspects the probability distributions of received power founded for the regions designed for mobile (moving) users. Additionally, we examine the influence on the Confidentiality Scope of targeted consumers in the absence and presence of observer. Our approaches were done through simulation results used for three diverse regions. Grounded on outcomes, we suggest an algorithm called MTFLA, which will guarantee detection and protection techniques intended to protect vastly sensitive areas, i.e., wherever the chance of an attack is maximized. We provide a comparison among various security algorithms prepared for the energy consumption of different patterns. Simulation results revealed that the proposed algorithm for protection (MTFL) is verified to be energy-proficient (secure garnering). It decreases the energy prerequisite for encrypting the data. We evaluated our techniques over simulation results for sensitive region information built on fuzzy logic.
PDF
