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The improvement of stable, energy-efficient mobile-based clustering and routing protocols in wireless sensor networks (WSNs) has become indispensable so as to develop large-scale, versitale, and adaptive applications. Data is gathered more efficiently and the total path length is shortened optimally by means of mobile sink (MS). Two algorithms as bacterial interaction based cluster head (CH) selection and energy and transmission boundary range cognitive routing algorithm with novel approach for heterogeneous mobile networks are proposed in this study. The more reliable and powerful CH selection is made with the greedy approach that is based on the interaction fitness value, energy node degree, and distance to adjacent nodes in a compromised manner. The best trajectories, thanks to intersection edge points of the visited CHs, are obtained in the proposed routing algorithm. In this way, the MS entry to transmission range boundaries of the CH has been a sufficient strategy to collect information. As in energy model, we adopt energy consumption costs of listening and sensing channel as well as transmit and receive costs. Comprehensive performance analyzes have been seriously carried out via the Matlab 2016a environment. We validate that the proposed scheme outperforms existing studies in terms of several performance metrics as simulations.

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SummaryThe professional design of the routing protocols with mobile sink(s) in wireless sensor networks (WSNs) is important for many purposes such as maximizing energy efficiency, increasing network life, and evenly distributing load balance across the network. Moreover, mobile sinks ought to first collect data from nodes which have very important and dense data so that packet collision and loss can be prevented at an advanced level. For these purposes, the present paper proposes a new mobile path planning protocol by introducing priority‐ordered dependent nonparametric trees (PoDNTs) for WSNs. Unlike traditional clustered or swarm intelligence topology‐based routing methods, a topology which has hierarchical and dependent infinite tree structure provides a robust link connection between nodes, making it easier to reselect ancestor nodes (ANs). The proposed priority‐ordered infinite trees are sampled in the specific time frames by introducing new equations and hierarchically associated with their child nodes starting from the root node. Hence, the nodes with the highest priority and energy that belong to the constructed tree family are selected as ANs with an opportunistic approach. A mobile sink simply visits these ANs to acquire data from all nodes in the network and return to where it started. As a result, the route traveled is assigned as the mobile path for the current round. We have performed comprehensive performance analysis to illustrate the effectiveness of the present study using NS‐2 simulation environment. The present routing protocol has achieved better results than the other algorithms over various performance metrics.

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By designing cognitive radio networks (CRNs) for low power wide area networks (LPWANs), adaptive spectrum sensing plays an important role in using frequency bands effectively. With spectrum sensing, existing spectrum holes are filled by enabling secondary users to use the licensed band that primary users do not use. Many researchers have proposed various optimization or artificial intelligence techniques in CRNs for spectrum sensing. However, there is hardly any adaptive spectrum sensing method integrated into the LoRa communication standard, which is a very popular LPWAN technology. For this purpose, an adaptive optimization based spectrum sensing methodology with an improved genetic algorithm is proposed in this study. Depending on this methodology, two fitness functions adapted to the LoRa-based network are defined. Thanks to these functions, the movement of individuals in the network population is provided. In parallel, received signal strength values of optimum LoRa nodes were determined by genetic coding, and bands to be used by SUs instead of PUs were discovered considering minimum bit errors. To verify the success of the proposed method, various simulation experiments were carried out. The performance results strikingly show that the proposed method is quite successful for LoRa-CRNs. For example, the proposed method significantly improved the bit error rate performance, and reduced the number of faulty transmissions caused by packet collisions. It also reduced the packet error rate by more than 8% per minute in a 200-node network.

The artificial intelligence-based spectrum sensing approach is extremely important in terms of effective bandwidth utilization for low power wide area networks (LPWANs) based on cognitive radio networks (CRNs). Most studies perform spectrum detection with CRNs using optimization or deep neural network methods. However, optimization-based spectrum detection approaches based on current LPWANs are scarce. For this purpose, in this study, a hybrid optimization methodology integrated with CRNs is proposed for LoRa, which is one of the most compatible LPWAN technologies in the Internet of Things (IoTs) recently. In the particle swarm optimization (PSO) part of this hybrid methodology, agent users are created so that secondary users (SUs) could use the licensed band of primary users (PUs) in cognitive radio. On the genetic algorithm side, LoRa error rates are minimized in order to further improve the performance of the proposed method. In this way, effective spectrum sensing is performed in the LoRa network. Various LoRa-CRN experiments have been carried out in the simulation environment, and the probability of detection and false alarm performances have been compared with both theoretical and proposed approaches in terms of quality estimation parameters. It is clear from the results that the proposed methods give successful results for the LoRa-CRNs.

Weather forecasting has recently gained great importance in terms of predicting potential disasters, taking precautions, and increasing the quality of life around the world. Researchers apply optimization and artificial intelligence techniques to make weather forecasts based on various nature parameters. In this study, it is proposed a weather condition forecasting scheme with time series using deep hybrid neural networks. In the proposed scheme, the essential parameters for weather forecasting, namely, relative humidity, temperature, atmospheric pressure, and wind speed are trained and predicted with long short-term memory (LSTM)-convolutional neural networks (CNNs) models in a hybrid way. The values represented by the input neurons are first passed through the CNN layers for a clearer and more accurate estimation of the data. Then, after fine-tuning, the results are sent to the LSTM block. The proposed hybrid deep method has been compared with both machine learning and deep learning methods According to the results, running the proposed method, the RMSE, MADE,