Earticle

Wireless network coverage optimization is critical for improving service quality. However, optimizing large-scale networks remains challenging for both classical algorithms and quantum methods in the NISQ era. This paper proposes a quantum approach that models the problem as a covering graph, partitions it using a QUBO formulation, and solves subproblems via a filtered variational quantum eigensolver. The method is experimentally validated on real quantum hardware, including a coherent Ising machine and a superconducting quantum processor, and compared with classical methods like SA and PSO. This work introduces a divide-and-conquer strategy for large-scale network coverage optimization and expands the solution landscape.

This paper proposes a deterministic Global Optimization Algorithm (GOA) for UAV-assisted communications, developed as an enhancement to the benchmark Two-Stage Optimization Algorithm (TSOA). The algorithm simultaneously addresses the dual objectives of maximizing ground user (GU) coverage and minimizing total power consumption in multiple UAV systems. Unlike existing literature, which predominantly relies on heuristic approaches, GOA provides a more precise and systematic solution to achieve optimal performance. Comprehensive simulations demonstrate that GOA achieves a 3.68 % increase in coverage count versus SOA under clustered GU distributions while delivering energy savings approximately 2.47 % (uniform) and 2.6 % (clustered) relative to the TSOA benchmark. Crucially, these efficiency gains are realized while maintaining superior GU coverage maximization versus all benchmarked methods. Both numerical results and visual analyses conclusively validate the proposed algorithm's outperformance of existing benchmarks. ⓒ2025 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NCsingle bondND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

This paper presents a kind of coverage optimization strategy based on improved artificial fish swarm algorithm for wireless sensor networks, by adaptively adjusting the vision range and the step length of artificial fish swarm the accuracy of optimization, convergence speed and stability are improved, then combining with the performance of WSN network coverage, the network coverage can be optimized. The simulation results show that comparing to the basic artificial fish swarm algorithm, the network coverage ratio of improved artificial fish swarm algorithm improves 17%.

With regard to the subject of sensor network node optimization, a strategy for wireless sensor network coverage optimization based on improved fish swarm algorithm is proposed in this paper. The improved algorithm is targeted on network coverage, node utilization rate and energy consumption balance, which makes use of the ergodic property of chaotic motion to overcome the disadvantage that artificial fish swarm algorithm may easily lead to regional optimization. As for this, the global searching ability of algorithm is improved and the solving efficiency is optimized. Moreover, the algorithm is able to adapt to complicated environment. Shown by related simulation experiment, the improved fish swarm algorithm could effectively optimize sensor network node deployment and improve network coverage rate. Compared with basic artificial fish swarm algorithm, improved fish swarm algorithm increases network coverage rate by 8.9%.

The separate situation of research has area coverage or target coverage in wireless sensor networks. Based on directional sensing model the algorithm used the virtual potential field to make the sensor nodes shift positions and change directions automatically in monitoring area, With the completion of multiple prior coverage of hot targets which needed higher quality requirements the algorithm could maximize the coverage rate throughout the monitoring area Simulation experiments show that the algorithm has a good ability of self-organizing. It can satisfy the requirements of coverage quality of the hot targets and the whole monitoring area and save resources effectively of the network.

Maximize coverage and prolong the network lifetime for wireless sensor networks has become one of the key topics of research. For this, put forward a kind of node scheduling strategy based on covering algorithm. The algorithm by Poisson distribution model structure node density formula, according to the node density formula in the monitoring region deploying to effective coverage; On the other hand, through the node state scheduling mechanism updates as well as to the neighbor node, can be made of residual energy of nodes and monitoring the area sensor the node energy consumption balance, so as to prolong the network lifetime goal. The simulation results show that, the algorithm cans not only useless nodes completely too effective coverage, improve the coverage while optimizing the cyber source configuration, prolong the network lifetime.

Target area coverage is the most fundamental goal of WSN (Wireless Sensor Network) application, so the design of energy aware based algorithm for coverage time maximization becomes a core problem of a large network. For this reason, a DECAR (Distributed-energy and Coverage Aware Routing) protocol is proposed in this paper to realize the goal of network coverage maximization. Specifically, the rest energy and the coverage overlapping degree of the nodes are considered during CH (cluster head) selection process so as to provide more opportunities for the nodes with more rest energy or higher coverage overlapping degree to become CH; like this, the nodes with less rest energy will not be selected as CH, thus to avoid shortening the network lifetime due to early failure of these nodes and balance the network energy consumption. Meanwhile, the main data transmission line composed of CHs is constructed in the data transmission stage in order to improve data transmission efficiency. The simulation result shows: compared with CPCP-ea and EEUC protocols, the proposed DECAR protocol can obtain longer network lifetime and better data coverage ratio.

The process of verifying the modified software in the maintenance phase is called Regression Testing. The size of the regression test suite and its selection process is a complex task for regression testers because of time and budget constraints. In this research paper, the Bee Colony Optimization (BCO) algorithm for the fault coverage regression test suite prioritization has been presented. In the natural bee colony, there are of two types of worker bees; Scout bees and forager bee, who are responsible for the development and maintenance of the colony. The BCO algorithm developed for the fault coverage regression test suite is based on the behavior of these two bees. The BCO algorithm has been formulated for fault coverage to attain maximum fault coverage in minimal units of execution time of each test case, using two examples whose results are comparable to optimal solution. Average Percentage of Fault Detection (APFD) metrics and charts has been used to show the effectiveness of proposed algorithm.

In the process of coverage for multiple targets, due to the existence of a large number of redundant data make the effective monitoring area coverage decreased and force the network to consume more energy. Therefore, this paper proposes a multi-target k-coverage preservation protocol. First of all, establish the affiliation between the sensor nodes and target nodes through the network model, present a method to compute the coverage expected value of the monitoring area; secondly, in the network energy conversion, using scheduling mechanism in sensor nodes to attain the network energy balance, and achieve different network coverage quality through different nodes energy conversion. Finally, simulation results show that NMCP can effectively reduce the number of active nodes meeting certain coverage requirements and then improve the network lifetime.

According to the maximum coverage problem in wireless sensor networks, GA algorithm combined with the standard processing method can improve the network coverage based on, but there is a risk of falling into local optimum, and costs more computation time. On the condition of analysis and proof of the effectiveness by the employment of normalization processing to resolve coding redundancy of MCSDP, further details of the evaluate conditions are proposed, and new parent selection mechanism is introduced, which both are verified by compared experiments. The experiment results show that the optimization processing method proposed in this paper retains the characteristics of existing methods, and has better optimization performance and improves the network coverage rate as well as calculation speed, which verifies the effectiveness and superiority of the method proposed in this paper.

Optimal coverage of wireless sensor networks is one of the most fundamental problems for constructing efficient perception layer of the Internet of Things. On the basis of research on spatial neighborhood, the node coverage and area coverage models are analyzed, then an optimal coverage algorithm of wireless sensor networks is proposed based on particle swarm optimization with coherent velocity. Experimental results show that the algorithm can significantly improve the network coverage; in addition, the coherent velocity can effectively avoid network prematurely into a local optimal solution, so as to enhance the network coverage.

재난현장에서 신속한 애드혹 무선 백본망을 형성하기 위해서는 사전계획 없이 초동 대응요원들에 의하여 무선 애드혹 중계장치를 실시간으로 배치하는 기법이 필요하다. 그러나 이를 실현하기 위해서는 중계장치의 중복배치 최소화뿐만 아니라 통신권역 확장을 위한 중계장치의 최적 위치를 선정할 수 있어야 한다. 따라서 본 논문에서는 통신권역을 최적화하기 위한 새로운 중계장치 배치기법을 제안하고 모의실험을 통하여 알고리즘의 성능이 향상되었음을 보였다.

For rapid formed the ad hoc wireless backbone network in disaster scene, It is necessary for real-time deployment scheme of wireless ad hoc relay devices by first responders without pre-planning. However, in order to realize this scheme, redundant deployment should be minimized, as well as optimal location of relay devices should be selected to expand communication coverage. Therefore, in this paper, we propose a new deployment scheme of relay devices to optimize communication coverage and then through simulations showed that improved performance of algorithm.

In wireless sensor network, since each sensor is equipped with a limited power, efficient use of the energy is important. One possible network management scheme is to cluster the sensors into several sets, so that the sensors in each of the sets can completely perform the monitoring task. Then the sensors in one set become active to perform the monitoring task and the rest of the sensors switch to a sleep state to save energy. Therefore, we rotate the roles of the active set among the sensors to maximize the network lifetime. In this paper, we suggest an optimal algorithm for the maximum lifetime coverage problem which maximizes the network lifetime. For comparison, we implemented both the heuristic proposed earlier and our algorithm, and executed computational experiments. Our algorithm outperformed the heuristic concerning the obtained network lifetimes, and it found the solutions in a reasonable amount of time.

심정지가 발생한 후 심폐소생술이나 제세동과 같은 의료 처리를 빨리할수록 환자의 생존율은 높아진다. 우리나라는 일반인의 자동심장충격기(AED) 접근 및 활용을 증대할 목적으로 공공장소에 이를 설치하고 있으며 그 수가 빠르게 증가하고 있다. 본 연구의 목적은 법률에 따라 의무적으로 설치되고 있는 AED의 서비스 적절성을 평가하고 수요에 효과적으로 대응할 수 있는 AED의 보급 방안을 모색하는 것이다. AED 수요의 공간적 분포를 재현하기 위하여 지역의 성별, 연령별 인구 및 심정지 발생률에 기초하여 공간 단위 심정지 발생 가능 인구를 추정하였다. 이미 설치된 AED는 재현된 총 수요의 35%만을 커버하여 그 효율성이 매우 낮았다. 기존 AED의 위치를 유지한 채 200개의 AED를 MCLP를 이용하여 추가할 경우 총 수요에 대한 커버리지 비율은배 가까이 향상되었다. 또한, 이미 설치된 것과 동일한 458개의 AED를 재배치할 경우 커버리지 비율은 기존 체계와 비교하여 2배 이상 향상되었다. 이러한 분석 결과들은 기존 설치된 AED의 비효율성과 수요에 기반한 공간 최적화와 같은 전략적 접근이 필요함을 함께 보여준다.

After cardiac arrest, the faster the medical treatment, such as CPR or defibrillation, the higher the patient's survival rate. Accordingly, to increase the access and use of Automated External Defibrillators (AEDs) by laypersons, they have been installed in public places, and the number is increasing rapidly. The purpose of this paper is to evaluate the service adequacy of AEDs, which is mandatory in accordance with the law, and to find an alternative dissemination method of AEDs that can effectively cover to demand. In order to represent the spatial distribution of demand for AEDs, this study estimated the possible population of cardiac arrest in a spatial unit based on the population by gender and age and the incidence of cardiac arrest by gender and age. The already installed AEDs were very low in efficiency, covering only 35% of the total demand represented. If 200 AEDs were added using maximal covering location problem (MCLP) while maintaining the location of the existing AEDs, the coverage rate to the total demand increased by nearly twice. In addition, when relocating the same 458 AEDs that have already been installed, the coverage rate is more than doubled compared to the existing system. These analysis results simultaneously show the inefficiency of existing installed AEDs and the need for a strategic approach such as spatial optimization based on demand.

사회적으로 안전에 대한 관심이 많아지고 있는 상황에서 계속되는 북한의 미사일 실험은 이를 대비한 민방위 대피시설의운영 실태를 점검하는 계기가 되었다. 본 연구에서는 공간 최적화 접근을 통하여 개별 대피시설 수준에서 서비스 커버리지와수용 능력을 평가하였다. 특히 수요를 상주인구로 재현하였을 때와 현주인구로 재현하였을 때 모델링 결과가 어떻게 달라지는지분석하였다. 사례지역으로 대구광역시 서구의 민방위 대피시설의 커버리지를 평가하였다. 그 결과, 지정된 민방위 대피시설에의해서 커버되지 않은 현주인구가 상주인구에 비하여 절대적으로 뿐만 아니라 상대적으로 많았다. 또한 기존 대피시설에 의해서커버되지 않는 상주인구와 현주인구의 위치가 달랐다. 마지막으로 개별 대피시설에 할당된 인구를 수용가능 인구와 비교해 본결과 대체로 시설의 규모가 클수록 실제 수용률이 낮게 나타났다. 이러한 공간 최적화 접근을 통하여 도출한 정보는 재난관리를보다 구체적이고 체계적으로 만드는데 기여할 것이다.

In the context of increasing social interest in safety, the continued North Korean missile tests have inspected the designation and management of civil defense evacuation facilities. This study assessed service coverage and capacity at the individual evacuation facility level using a spatial optimization approach. In particular, we analyzed how the modeling results differ when the demand is represented as de facto population as well as de jure population. The coverage of the civil defense evacuation facilities in Seo-gu, Daegu Metropolitan City was evaluated. As a result, the uncovered de facto population by the designated civil defense evacuation facilities increased not only absolutely but also relatively compared with de jure population. Also, the locations of de jure and de facto populations uncovered by the existing evacuation facilities showed a big difference. Finally, comparing the population allocated to individual evacuation facilities to the maximum accommodate population, the larger the size of the facility, the lower the actual accommodate rate. Information obtained through this spatial optimization approach will contribute to making disaster management more concrete and systematic.