Recently, Wireless Rechargeable Sensor Networks (WRSNs) have been attracting increasing attention and seen rapid development. However, in previous studies, pioneering work on charging issues and data gathering strategies are always discussed separately. In this paper, we aimed to develop a strategy which can guarantee a short data acquisition cycle and high charging efficiency. We used a new Combined Recharging and Collecting Data Model (CRCM) to set-up a WRSN. In this network, mobile chargers were used to separately collected data and charge sensor nodes. A K-means algorithm was used to group sensor nodes into different clusters, from which mobile chargers collected data from sensor nodes in the center of these clusters. The Nearest-Job-Next with Preemption (NJNP) algorithm was used to determine the charging route. The data acquisition cycle was first discussed in this model in order to ensure all data from the sensor nodes could be gathered within a certain time period. Additionally, the Periodically Restricted Dynamic Mobile Chargers (PRDMCs) algorithm was proposed to determine the number of mobile chargers. Lastly, we used the normal CRCM for comparison with our new CRCM, and the results showed that the new CRCM can effectively safeguard the data acquisition cycle without requiring the addition of more mobile chargers.
In the process of fault diagnosis and the health and safety operation evaluation of modern industrial processes, it is crucial to measure important state variables, which cannot be directly detected due to limitations of economy, technology, environment and space. Therefore, this paper proposes a data-driven soft sensor approach based on an echo state network (ESN) optimized by an improved genetic algorithm (IGA). Firstly, with an ESN, a data-driven model (DDM) between secondary variables and dominant variables is established. Secondly, in order to improve the prediction performance, the IGA is utilized to optimize the parameters of the ESN. Then, the immigration strategy is introduced and the crossover and mutation operators are changed adaptively to improve the convergence speed of the algorithm and address the problem that the algorithm falls into the local optimum. Finally, a soft sensor model of an ESN optimized by an IGA is established (IGA-ESN), and the advantages and performance of the proposed method are verified by estimating the alumina concentration in an aluminum reduction cell. The experimental results illustrated that the proposed method is efficient, and the error was significantly reduced compared with the traditional algorithm.
In wireless networks, the network coverage and sustainable operations are closely interlinked. These are the most critical problems in any wireless sensor networks (WSNs), which are based on software defined networks. However, in previous literature, these problems are always considered separately. Consequently, these problems are not addressed in an efficient manner. In this work, we focus on new network structures known as software defined wireless rechargeable sensor networks (SDWRSNs) to ensure long-term operations and full coverage of the network simultaneously. In this work, we propose the least nodes deployment and charging algorithm (LNDCA) based on the homology theory. In the proposed LNDCA, the SDN controller requests the mobile chargers to replenish the energy for the node with the lowest energy. Additionally, the algorithm fully covers the whole network by using minimum number of nodes and ensures continuous operations in the network. The simulation results and analysis conducted in this work show that the proposed algorithm performs well in terms of energy consumption, coverage, and sustainable operations.INDEX TERMS Wireless rechargeable sensor networks, software defined networks, homology theory, topology control.
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