To prolong the function of wireless sensor networks (WSNs), the lifetime of the system has to be increased. WSNs lifetime can be calculated by using a few generic parameters, such as the time until the death of the first node and other parameters according to the application. Literature indicates that choosing the most appropriate cluster head by clustering is one of the most successful ways to improve the lifespan of the WSN. The drawback of clustering protocols is based on the probabilistic model. Sometimes they select two cluster heads for two different clusters which are very close to each other and results in head situated at the edge of the cluster in some cases. This type of cluster head selection leads to a reduction in energy efficiency. Therefore, we have proposed the LEACH-Fuzzy Clustering (LEACH-FC) protocol and implemented a fuzzy logic-based cluster head selection and cluster formation to maximize the lifetime of the network. For selections of cluster head and formation of the cluster, we have used a centralized approach instead of distributed ones. We have also employed fuzzy logic in the selection of vice cluster head, which is also a centralized approach. The proposed algorithm has been found to be effective in balancing the energy load at each node thereby enhancing the reliability of WSN. It outperforms other proposed algorithms for improving network lifetime and energy consumption. INDEX TERMS Energy, fuzzy logic, centralized clustering, network lifetime.

A compact, low-profile, coplanar waveguide (CPW)-fed quad-port multiple-input–multiple-output (MIMO)/diversity antenna with triple band-notched (Wi-MAX, WLAN, and X-band) characteristics is proposed for super-wideband (SWB) applications. The proposed design contains four similar truncated–semi-elliptical–self-complementary (TSESC) radiating patches, which are excited through tapered CPW feed lines. A complementary slot matching the radiating patch is introduced in the ground plane of the truncated semi-elliptical antenna element to obtain SWB. The designed MIMO/diversity antenna displays a bandwidth ratio of 31:1 and impedance bandwidth (|S11| ≤ − 10 dB) of 1.3–40 GHz. In addition, a complementary split-ring resonator (CSRR) is implanted in the resonating patch to eliminate WLAN (5.5 GHz) and X-band (8.5 GHz) signals from SWB. Further, an L-shaped slit is used to remove Wi-MAX (3.5 GHz) band interferences. The MIMO antenna prototype is fabricated, and a good agreement is achieved between the simulated and experimental outcomes.

A planar, microstrip line-fed, quad-port, multiple-input-multiple-output (MIMO) antenna with dual-band rejection features is proposed for ultra-wideband (UWB) applications. The proposed MIMO antenna design consists of four identical octagonal-shaped radiating elements, which are placed orthogonally to each other. The dual-band rejection property (3.5 GHz and 5.5 GHz corresponding to Wi-MAX and WLAN bands) was obtained by introducing a hexagonal-shaped complementary split-ring resonator (HCSRR) in the radiators of the designed antenna. The MIMO antenna was etched on low-cost FR-4 dielectric substrate of size 58 × 58 × 0.8 mm3. Isolation higher than 18 dB and envelope correlation coefficient (ECC) lesser than 0.07 was observed for the MIMO/diversity antenna in the operating range of 3–16 GHz. The presented four-port UWB MIMO antenna configuration was fabricated, and the experimental results validate the simulation outcomes.

The application of Internet of Things (IoT) has been emerging as a new platform in wireless technologies primarily in the field of designing electric vehicles. To overcome all issues in existing vehicles and for protecting the environment, electric vehicles should be introduced by integrating an intellectual device called sensor all over the body of electric vehicle with less cost. Therefore, this article confers the need and importance of introducing electric vehicles with IoT based technology which monitors the battery life of electric vehicles. Since the electric vehicles are implemented with internet, an online monitoring system which is called Things Speak has been used for monitoring all the vehicles in a continuous manner (day-by-day). These online results will then be visualized in MATLAB after an effective boosting algorithm is integrated with objective function. The efficiency of proposed method is tested by visual analysis and performance results prove that the projected method on electric vehicle is improved when using IoT based technology. It is also observed that cost of implementation is lesser and capacity of electric vehicle is increased to about 74.3% after continuous monitoring with sensors.

Prostate cancer is the main cause of death over the globe. Earlier detection and classification of cancer is highly important to improve patient health. Previous studies utilized statistical and machine learning (ML) techniques for prostate cancer detection. However, several challenges that exist in the investigation process are the existence of high dimensionality data and less number of training samples. Metaheuristic algorithms can be used to resolve the curse of dimensionality and improve the detection rate of artificial intelligence (AI) techniques. With this motivation, this article develops an artificial intelligence based feature selection with deep learning model for prostate cancer detection (AIFSDL-PCD) using microarray gene expression data. The AIFSDL-PCD technique involves preprocessing to enhance the input data quality. In addition, a chaotic invasive weed optimization (CIWO) based feature selection (FS) technique for choosing an optimal subset of features shows the novelty of the work. Moreover, the deep neural network (DNN) model can be applied as a classification model to detect the existence of prostate cancer in the microarray gene expression data. Furthermore, the hyperparameters of the DNN model can be effectively adjusted by the use of RMSprop optimizer. The design of CIWO based FS technique helps for reducing the computational complexity and improve the classification accuracy. The experimental results highlighted the betterment of the AIFSDL-PCD approach on the other techniques with respect to distinct measures.

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.

Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant and benign tumors with improved accuracy. Results obtained using the voting classifier with convoluted features demonstrate that the highest classification accuracy of 100% is achieved. The proposed approach revealed the accuracy enhancement in comparison with the state-of-the-art approaches.

In the present scenario the depletion of conventional sources causes an energy crisis. The energy crisis causes load demand with respect to electricity. The use of renewable energy sources plays a vital role in reducing the energy crisis and in reduction of CO2 emission. The use of solar energy is the major source of power in generation as this is the root cause for the development of wind, tides, etc. However, due to climatic condition the availability of PV sources varies from time to time. Hence it is essential to track the maximum source of energy by implementing different types of MPPT algorithms. However, use of MPPT algorithms has the limitation of using the same during partial shadow conditions. The issue of tracking power under partial shadow conditions can be resolved by implementing an intelligent optimization tracking algorithm which involves a computation process. Though many of nature’s inspired algorithms were present to address real world problems, Mirjalili developed the dragonfly algorithm to provide a better optimization solution to the issues faced in real-time applications. The proposed concept focuses on the implementation of the dragonfly optimization algorithm to track the maximum power from solar and involves the concept of machine learning, image processing, and data computation.