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.

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.

Deep lean is a novel approach that is concerned with the profound analysis for waste’s behavior at hidden layers in manufacturing processes to enhance processes’ reliability level at the upstream. Ideal Standard Co. for bathtubs suffered from defects and cost losses in the spraying section, due to differences in the painting cover thickness due to bubbles, caused by eddies, which move toward the bathtubs through hoses. These bubbles and their movement are considered as a form of lean’s waste. The spraying liquid inside the tanks and hoses must move with uniform velocity, viscosity, pressure, feed rate and suitable Reynolds circulation values to eliminate the eddy causes. These factors are tackled through the adoption Internet of Things (IoT) technologies that are aided by neural networks (NN) when an abnormal flow rate is detected using sensor data in real-time that can reduce the defects. The NN aimed at forecasting eddies’ movement lines that carry bubbles and works on being blasted before entering the hoses through using Design of Experiment (DOE). This paper illustrates a deep lean perspective as driven by the define, measure, analysis, improvement and control (DMAIC) methodology to improve reliability. The eddy moves downstream slowly with an anti-clockwise flow for some of the optimal values for the influencing factors, whereas the circulation of Ω increases, whether for vertical or horizontal travel.

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