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Mobile sensor nodes such as hopping sensors are of critical importance in data collection. However, the occurrence of sensing holes is unavoidable due to the energy limitation of the nodes. Thus, it is evident that the relocation of mobile sensors is the most desirable method to recover the sensing holes. The previous research conducted by the authors so far demonstrated the most realistic hopping sensor relocation scheme, which is suitable for the distributed environment. In previous studies, the cluster header plays an essential role in detecting the sensing hole and requesting the neighboring cluster to recover the sensing hole that occurred in the sensor node. However, the limitations of the cluster header in the previously proposed relocation protocol are not fully considered. Because the cluster header jumps more frequently than non-header nodes, its energy consumption is relatively high compared to other nodes. Therefore, it is most likely to lead to header node failure and can lead to data loss on the network. In this paper, the jumping ability and energy consumption of the cluster header are seriously considered. Additional ability to replace cluster headers in case of failure is also implemented. Simulation results show that the data collection time can be further increased, which demonstrates the validity of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The finite element method is a key player in computational electromagnetics for designing RF (Radio Frequency) components such as waveguides. The frequency-domain analysis is fundamental to identify the characteristics of the components. For the conventional frequency-domain electromagnetic analysis using FEM (Finite Element Method), the system matrix is complex-numbered as well as indefinite. The iterative solvers can be faster than the direct solver when the solver convergence is guaranteed and done in a few steps. However, such complex-numbered and indefinite systems are hard to exploit the merit of the iterative solver. It is also hard to benefit from matrix factorization techniques due to varying system matrix parts according to frequency. Overall, it is hard to adopt conventional iterative solvers even though the system matrix is sparse. A new parallel iterative FEM solver for frequency domain analysis is implemented for inhomogeneous waveguide structures in this paper. In this implementation, the previous solution of the iterative solver of Matlab (Matrix Laboratory) employing the preconditioner is used for the initial guess for the next step's solution process. The overlapped parallel stage using Matlab's Parallel Computing Toolbox is also proposed to alleviate the cold starting, which ruins the convergence of early steps in each parallel stage. Numerical experiments based on waveguide structures have demonstrated the accuracy and efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

The occurrence of 'sensing holes' not only hinders seamless data collection but also leads to misinterpretation of information in certain areas under extensive data analysis. In order to overcome this, various sensor relocation strategies have been proposed, but the existing relocation strategies revealed problems such as the ping-pong, shaded area, network disconnection, etc. This paper conducted research on relocation protocols in a distributed environment that is very suitable for real-world situations and efficiently recovering the problem of sensing holes. First, a simulation was performed on the distribution of the shaded area for data collection, which is a problem with the existing representative relocation protocol. After that, a data collection capability was newly added to the relay node, which had been in charge of only communication between cluster zones so far, and with this additional functionality, the performance of the revised sensor relocation algorithm was dramatically improved to overcome the existing problems. In addition, the performance and validity of the proposed algorithm were verified through various simulations. [ABSTRACT FROM AUTHOR]

With the recent development of big data technology that collects and analyzes various data, the technology that continuously collects and analyzes the observed data is also drawing attention. Moreover, its importance is growing in data collection in areas where people cannot access. In general, it is not easy to properly deploy IoT wireless devices for data collection in these areas, and it is also inappropriate to use general wheelbased mobile devices for relocation. Recently, researches have been actively carried out on hopping moving models in place of wheel-based movement for the inaccessible regions. The majority of studies, however, so far have unrealistic assumptions that all IoT devices know the overall state of the network and the current state of each device. Moreover, various physical terrain environments, such as coarse gravel and sand, can change from time to time, and it is impossible for all devices to recognize these changes in real-time. In this paper, with the migration success rate of IoT hopping devices being relocated, the method of estimating the varying environment is proposed. This method can actively reflect the changing environment in real-time and is a realistic distributed environment-based relocation protocol on behalf of non-realistic, theory-based relocation protocols. Also, one of the significant contributions of this paper is to evaluate its performance using the OMNeT++ simulation tool for the first time in the world to reflect actual physical environmental conditions. Compared to previous studies, the proposed protocol was able to actively reflect the state of the surrounding environment, which resulted in improved migration success rates and higher energy efficiency. [ABSTRACT FROM AUTHOR]