Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

As millimeter waves will be widely used in the Internet of Things(IoT)and Telematics to provide high bandwidth communication and mass connectivity,the coverage optimization of base stations can effectively improve the quality of communication services.How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers.This paper proposes the Muti-Fusion Sparrow Search Algorithm(MFSSA)optimize the situation to address the problem of discrete coverage maximization and rapid convergence.Firstly,the initial swarm diversity is enriched using a sine chaotic map,and dynamic adaptive weighting is added to the discoverer location update strategy to improve the global search capability.Diverse swarms have a more remarkable ability to forage for food and avoid predation and are less likely to fall into a“precocious”state.Such a swarm is very suitable for solving NP-hard problems.Secondly,an elite opposition-based learning strategy is added to expand the search range of the algorithm,and a t-distribution-based one-fifth rule is introduced to reduce the probability of falling into a local optimum.This fusion mutation strategy can significantly optimize the adaptability and searchability of the algorithm.Finally,the experimental results show that the MFSSA algorithm can effectively improve the coverage of the deployment scheme in the base station coverage optimization problem,and the convergence speed is better than other algorithms.MFSSA is improved by more than 10%compared to the original algorithm.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Estimating the optimal vegetation coverage for the dominant tree and shrub species over China's northwest drylands

Anthropogenic revegetation is an effective way to control soil erosion and restore degraded ecosystems in China's northwest drylands(NWD).However,excessive vegetation cover expansion has long been known to increase evapotranspiration,leading to reduced local water availability,which can in turn threaten the health and services of restored ecosystems.Determining the optimal vegetation coverage(OVC)is critical for balancing the trade-off between plant growth and water consumption in water-stressed areas,yet quantitative assessments over the entire NWD are still lacking.In this study,a modified Biome BioGeochemical Cycles(Biome-BGC)model was used to simulate the long-term(1961–2020)dynamics of actual evapotranspiration(ET_(a)),net primary productivity(NPP),and leaf area index(LAI)for the dominant non-native tree(R.pseudoacacia and P.sylvestris)and shrub(C.korshinkii and H.rhamnoides)species at 246 meteorological sites over NWD.The modified model incorporated the Richards equation to simulate transient unsaturated water flow in a multilayer soil module,and both soil and eco-physiological parameters required by the model were validated using field-observed ETadata for each species.Spatial distributions of OVC(given by the mean maximum LAI,LAI_(max))for the dominant species were determined within three hydrogeomorphic sub-areas(i.e.,the loess hilly-gully sub-area,the windy and sandy sub-area,and the desert sub-area).The modified Biome-BGC model performed well in terms of simulating ET_(a) dynamics for the four plant species.Spatial distributions of mean ET_a,NPP,and LAI_(max)generally exhibited patterns similar to mean annual precipitation(MAP).In the loess hilly-gully sub-area(MAP:210 to 710 mm),the OVC respectively ranged from 1.7 to 2.9 and 0.8 to 2.9 for R.pseudoacacia and H.rhamnoides.In the windy and sandy sub-area(MAP:135 to 500 mm),the OVC ranged from 0.3 to 3.3,0.5 to 2.6 and 0.6 to 2.1for P.sylvestris,C.korshinkii and H.rhamnoides,respectively.In the desert sub-area(MAP:90 to 500 mm),the OVC ranged from 0.4 to 1.7 for H.rhamnoides.Positive differences between observed and simulated plant coverage were found over 51%of the forest-and shrub-covered area,especially in the loess hilly-gully sub-area,suggesting possible widespread overplanting in those areas.This study provides critical revegetation thresholds for dominant tree and shrub species to guide future revegetation activities.Further revegetation in areas with overplanting should be undertaken with caution,and restored ecosystems that exceed the OVC should be managed(e.g.,thinning)to maintain a sustainable ecohydrological environment in the drylands.

Modified particle swarm optimization-based antenna tilt angle adjusting scheme for LTE coverage optimization

In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is proposed based on the modified particle swarm optimization（ MPSO） algorithm.The number of mobile stations（ MSs） served by e NBs, which is obtained based on the reference signal received power（RSRP） measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s.

Coverage Optimization of LTE Networks Based on Antenna Tilt Adjusting Considering Network Load

In this paper,we investigate the coverage optimization for LTE networks considering the network load. The network coverage is defined as the number of served users of evolved Node B(eNB)which is determined by e NBs' antenna tilt angles(ATA). The coverage is optimized by optimizing the number of served users based on the Modified Particle Swarm Optimization(MPSO)algorithm. Simulation results show that both the number of served users by each e NB and the system throughput are significantly increased. As well,the average load and the bandwidth efficiency of the network are improved.

Soil moisture response to land use and topography across a semi-arid watershed: Implications for vegetation restoration on the Chinese Loess Plateau

Soil moisture is a limiting factor of ecosystem development in the semi-arid Loess Plateau. Characterizing the soil moisture response to its dominant controlling factors, such as land use and topography, and quantifying the soil-water carrying capacity for revegetation is of great significance for vegetation restoration in this region. In this study, soil moisture was monitored to a depth of 2 m in three land use types(native grassland, introduced grassland,and forestland), two landforms(hillslope and gully),and two slope aspects(sunny and shady) in the Nanxiaohegou watershed of the Loess Plateau,Northwest China. The MIKE SHE model was then applied to simulate the soil moisture dynamics under different conditions and determine the optimal plant coverage. Results showed that the average soil moisture was higher in native grassland than in introduced grassland and Platycladus orientalis forestland for a given topographic condition;however,a high soil moisture content was found in Robinia pseudoacacia forestland, with a value that was even higher than the native grassland of a sunny slope. The divergent results in the two forestlands were likely attributed to the differences in plant coverage. Gully regions and shady slopes usually had higher soil moisture, while lower soil moisture was usually distributed on the hillslope and sunny slope.Furthermore, the mean absolute relative error and Nash-Sutcliffe efficiency coefficient of the MIKE SHE model ranged between 2.8%–7.8% and 0.550–0.902,respectively, indicating that the model could effectively simulate the soil moisture dynamics. The optimal plant coverage was thus determined for hillslope P. orientalis by the model, corresponding to a leaf area index(LAI) value of 1.92. Therefore, for sustainable revegetation on the Loess Plateau,selecting suitable land use types(natural vegetation),controlling the planting density/LAI, and selecting proper planting locations(gully and shady slope regions) should be considered by local policy makers to avoid the over-consumption of soil water resources.

Coverage Optimization for Defect-Tolerance Logic Mapping on Nanoelectronic Crossbar Architectures

Emerging nano-devices with the corresponding nano-architectures are expected to supplement or even replace conventional lithography-based CMOS integrated circuits, while, they are also facing the serious challenge of high defect rates. In this paper, a new weighted coverage is defined as one of the most important evaluation criteria of various defecttolerance logic mapping algorithms for nanoelectronic crossbar architectures functional design. This new criterion is proved by experiments that it can calculate the number of crossbar modules required by the given logic function more accurately than the previous one presented by Yellambalase et al. Based on the new criterion, a new effective mapping algorithm based on genetic algorithm （GA） is proposed. Compared with the state-of-the-art greedy mapping algorithm, the proposed algorithm shows pretty good effectiveness and robustness in experiments on testing problems of various scales and defect rates, and superior performances are observed on problems of large scales and high defect rates.

Coverage Optimization for UAV-Aided Internet of Things with Partial Channel Knowledge

Due to the high maneuverability of unmanned aerial vehicles(UAVs),they have been widely deployed to boost the performance of Internet of Things(IoT).In this paper,to promote the coverage performance of UAV-aided IoT communications,we maximize the minimum average rate of the IoT devices by jointly optimizing the resource allocation strategy and the UAV altitude.Particularly,to depict the practical propagation environment,we take the composite channel model including both the small-scale and the large-scale channel fading into account.Due to the difficulty in acquiring the random small-scale channel fading,we assume that only the large-scale channel sate information is available.On this basis,we formulate an optimization problem,which is not convex and challenging to solve.Then,an efficient iterative algorithm is proposed using block coordinate descent and successive convex optimization tools.Finally,simulation results are presented to demonstrate the significant performance gain of the proposed scheme over existing ones.

Coverage Optimization of Field Observation Instrument Networking Based on an Improved ABC Algorithm

The severe conditions of cold and arid areas seriously affect the progress of data collection and analysis for field observation instruments.Therefore,this study adopted the modified artificial bee colony(ABC)algorithm to optimize the coverage of nodes and designed an energy-efficient node coverage optimization method.In the coverage optimization,the coverage rate and the number of working nodes are considered comprehensively,and the fitness value calculation is improved.The experimental results reveal that the modified ABC algorithm has better coverage optimization performance than the original ABC algorithm,genetic algorithm(GA),and particle swarm optimization(PSO)algorithm.

Canopy deposition characteristics of different orchard pesticide dose models

Pesticide dose model based on canopy characteristics is the guidance basis for spray parameters adjustment.In this study,the calculation formula and canopy deposition characteristics of leaf wall area(LWA)model,tree row volume(TRV)model,and optimal coverage method(OCM)model were described and compared.A tower air-assisted spray test bench was applied to provide fine quality droplets,suitable wind speed and demand spray flow rate for corresponding models,an electric flat board vehicle was applied to drive tree in a straight line to simulate the sprayer movement speed,and droplet deposition distribution were tested in different leaf area density canopy.The results showed that the spray flow rates of three pesticide dose models decreased gradually.LWA model was only related to canopy height,TRV model was related to canopy height and canopy diameter,while OCM model was related to canopy height,canopy diameter and leaf area density.Whether dense or sparse canopy,TRV model basically satisfied the requirement of coverage rate greater than 33%in the entire canopy,OCM model met the requirement of coverage density greater than 70 droplets/cm^(2).However,LWA model,for dense canopy,unit area deposition of outermost leaves near sprayer was 3.6 times of the apple leaf maximum retention,which had a high loss risk;for sparse canopy,penetration rates of outermost leaves far away sprayer,that is,the drift rate was 21.4%.The discussion leads to the conclusion that for conventional spraying,TRV model represented a substantial improvement compared to LWA model,and OCM model was a reasonable low volume spraying model.This study provides a reference to different growth seasons spray amount adjustments in orchard.

Harris Hawks Algorithm Incorporating Tuna Swarm Algorithm and Differential Variance Strategy

Because of the low convergence accuracy of the basic Harris Hawks algorithm,which quickly falls into the local optimal,a Harris Hawks algorithm combining tuna swarm algorithm and differential mutation strategy(TDHHO)is proposed.The escape energy factor of nonlinear periodic energy decline balances the ability of global exploration and regional development.The parabolic foraging approach of the tuna swarm algorithm is introduced to enhance the global exploration ability of the algorithm and accelerate the convergence speed.The difference variation strategy is used to mutate the individual position and calculate the fitness,and the fitness of the original individual position is compared.The greedy technique is used to select the one with better fitness of the objective function,which increases the diversity of the population and improves the possibility of the algorithm jumping out of the local extreme value.The test function tests the TDHHO algorithm,and compared with other optimization algorithms,the experimental results show that the convergence speed and optimization accuracy of the improved Harris Hawks are improved.Finally,the enhanced Harris Hawks algorithm is applied to engineering optimization and wireless sensor networks(WSN)coverage optimization problems,and the feasibility of the TDHHO algorithm in practical application is further verified.