Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery.The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN.However,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space.Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage.The Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor nodes.As the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate.Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

An Optimization Approach of IoD Deployment for Optimal Coverage Based on Radio Frequency Model

Recently,Internet of Drones(IoD)has garnered significant attention due to its widespread applications.However,deploying IoD for area coverage poses numerous limitations and challenges.These include interference between neighboring drones,the need for directional antennas,and altitude restrictions for drones.These challenges necessitate the development of efficient solutions.This research paper presents a cooperative decision-making approach for an efficient IoDdeployment to address these challenges effectively.The primary objective of this study is to achieve an efficient IoDdeployment strategy thatmaximizes the coverage regionwhile minimizing interference between neighboring drones.In deployment problem,the interference increases as the number of deployed drones increases,resulting in bad quality of communication.On the other hand,deploying a few drones cannot satisfy the coverage demand.To accomplish this,an enhanced version of a concise population-based meta-heuristic algorithm,namely Improved Particle SwarmOptimization(IPSO),is applied.The objective function of IPSO is defined based on the coverage probability,which is primarily influenced by the characteristics of the antennas and drone altitude.A radio frequency(RF)model is derived to evaluate the coverage quality,considering both Line of Sight(LOS)and Non-Line of Sight(NLOS)down-link coverage probabilities for ground communication.It is assumed that each drone is equipped with a directional antenna to optimize coverage in a given region.Extensive simulations are conducted to assess the effectiveness of the proposed approach.Results demonstrate that the proposed method achieves maximum coverage with minimum transmission power.Furthermore,a comparison is made against Collaborative Visual Area Coverage Approach(CVACA),and a game-based approach in terms of coverage quality and convergence speed.The simulation results reveal that our approach outperforms both CVACA and the gamebased schemes in terms of coverage and convergence speed.Comparisons validate the superiority of our approach over existing methods.To assess the robustness of the proposed RFmodel,we have considered two distinct ranges of noise:range1 spanning from−120 to−90 dBm,and range2 spanning from−90 to−70 dBmfor different numbers of UAVs.In summary,this research presents a cooperative decision-making approach for efficient IoD deployment to address the challenges associatedwith area coverage and achieves an optimal coveragewithminimal interference.

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.

Sensors deployment optimization in multi-dimensional space based on improved particle swarm optimization algorithm

Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment.Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system(e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature.For uncovering the optimal deployment of the sensor network, the particle swarm optimization(PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method.

Hybrid optimization of dynamic deployment for networked fire control system

With applying the information technology to the military field, the advantages and importance of the networked combat are more and more obvious. In order to make full use of limited battlefield resources and maximally destroy enemy targets from arbitrary angle in a limited time, the research on firepower nodes dynamic deployment becomes a key problem of command and control. Considering a variety of tactical indexes and actual constraints in air defense, a mathematical model is formulated to minimize the enemy target penetration probability. Based on characteristics of the mathematical model and demands of the deployment problems, an assistance-based algorithm is put forward which combines the artificial potential field （APF） method with a memetic algorithm. The APF method is employed to solve the constraint handling problem and generate feasible solutions. The constrained optimization problem transforms into an optimization problem of APF parameters adjustment, and the dimension of the problem is reduced greatly. The dynamic deployment is accomplished by generation and refinement of feasible solutions. The simulation results show that the proposed algorithm is effective and feasible in dynamic situation.

LED Adaptive Deployment Optimization in Indoor VLC Networks

Driven by the continuous penetration of high data rate services and applications,a large amount of unregulated visible light spectrum is used for communication to fully meet the needs of 6th generation(6G)mobile technologies.Visible light communication(VLC)faces many challenges as a solution that complements existing radio frequency(RF)networks.This paper studies the optimal configuration of LEDs in indoor environments under the constraints of illumination and quality of experience(QoE).Based on the Voronoi tessellation(VT)and centroidal Voronoi tessellation(CVT)theory,combined with the Lloyd’s algorithm,we propose two approaches for optimizing LED deployments to meet the illumination and QoE requirements of all users.Focusing on(i)the minimization of the number of LEDs to be installed in order to meet illumination and average QoE constraints,and(ii)the maximization of the average QoE of users to be served with a fixed number of LEDs.Monte Carlo simulations are carried out for different user distribution compared with hexagonal,square and VT deployment.The simulation results illustrate that under the same conditions,the proposed deployment approach can provide less LEDs and achieve better QoE performance.

Optimal deployment of swarm positions in cooperative interception of multiple UAV swarms

In order to prevent the attacker from breaking through the blockade of the interception,deploying multiple Unmanned Aerial Vehicle(UAV)swarms on the interception line is a new combat style.To solve the optimal deployment of swarm positions in the cooperative interception,an optimal deployment optimization model is presented by minimizing the penetration zones'area and the analytical expression of the optimal deployment positions is deduced.Firstly,from the view of the attackers breaking through the interception line,the situations of vertical penetration and oblique penetration are analyzed respectively,and the mathematical models of penetration zones are obtained under the condition of a single UAV swarm and multiple UAV swarms.Secondly,based on the optimization goal of minimizing the penetration area,the optimal deployment optimization model for swarm positions is proposed,and the analytical solution of the optimal deployment is solved by using the convex programming theory.Finally,the proposed optimal deployment is compared with the uniform deployment and random deployment to verify the validity of the theoretical analysis.

Mathematical Modeling and Design of RIS Deployment in a RIS-Assisted Metal Cuboid for WAIC Systems

Wireless avionics intra-communications(WAIC)is an emergent research topic,since it can improve fuel efficiency and enhance aircraft safety significantly.However,there are numerous baffles in an aircraft,e.g.,seats and cabin bulkheads,resulting in serious blockage and even destroying wireless communications.Thus,this paper focuses on the reconfigurable intelligent surface(RIS)deployment issue of RIS-assisted WAIC systems,to solve the blockage problem caused by baffles.We first propose the mirror-symmetric imaging principle for mathematically analyzing electromagnetic(EM)wave propagation in a metal cuboid,which is a typical structure of WAIC systems.Based on the mirror-symmetric imaging principle,the mathematical channel model in a metal cuboid is deduced in detail.In addition,we develop an objective function of RIS's location and deduce the optimal RIS deployment location based on the geometric center optimization lemma.A two-dimensional gravity center search algorithm is then presented.Simulation results show that the designed RIS deployment can greatly increase the received power and efficiently solve the blockage problem in the aircraft.

Cuckoo search algorithm-based optimal deployment method of heterogeneous multistatic radar for barrier coverage

This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.

Numerical Simulation of Two-Phase Flow in Glutenite Reservoirs for Optimized Deployment in Horizontal Wells

It is known that the pore media characteristics of glutenite reservoirs are different from those of conventional sandstone reservoirs.Low reservoir permeability and naturally developed microfractures make water injection in this kind of reservoir very difficult.In this study,new exploitation methods are explored.Using a real glutenite reservoir as a basis,a three-dimensional fine geological model is elaborated.Then,combining the model with reservoir performance information,and through a historical fitting analysis,the saturation abundance distribution of remaining oil in the reservoir is determined.It is shown that,using this information,predictions can be made about whether the considered reservoir is suitable for horizontal well fracturing or not.The direction,well length,well spacing and productivity of horizontal well are also obtained.

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Due to their advantages in flexibility,scalability,survivability,and cost-effectiveness,drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields.This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms.Given a set of available air defense systems,the problem determines the location of each air defense system in a predetermined region,such that the cost for enemy drones to pass through the region would be maximized.The cost is calculated based on a counterpart drone path planning problem.To solve this adversarial problem,we first propose an exact iterative search algorithm for small-size problem instances,and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances.We implement the evolutionary framework with six popular evolutionary algorithms.Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.

Optimization of the Deployment of Temperature Nodes Based on Linear Programing in the Internet of Things

The Internet of Things emphasizes the concept of objects connected with each other, which includes all kinds of wireless sensor networks. An important issue is to reduce the energy consumption in the sensor networks since sensor nodes always have energy constraints. Deployment of thousands of wireless sensors in an appropriate pattern will simultaneously satisfy the application requirements and reduce the sensor network energy consumption. This article deployed a number of sensor nodes to record temperature data. The data was then used to predict the temperatures of some of the sensor node using linear programming. The predictions were able to reduce the node sampling rate and to optimize the node deployment to reduce the sensor energy consumption. This method can compensate for the temporarily disabled nodes. The main objective is to design the objective function and determine the constraint condition for the linear programming. The result based on real experiments shows that this method successfully predicts the values of unknown sensor nodes and optimizes the node deployment. The sensor network energy consumption is also reduced by the optimized node deployment.

Empowering highway network:Optimal deployment and strategy for dynamic wireless charging lanes

Amid escalating energy crises and environmental pressures,electric vehicles(EVs)have emerged as an effective measure to reduce reliance on fossil fuels,combat climate change,uphold sustainable energy and environmental development,and strive towards carbon peaking and neutrality goals.This study introduces a nonlinear integer programming model for the deployment of dynamic wireless charging lanes(DWCLs)and EV charging strategy joint optimization in highway networks.Taking into account established charging resources in highway service areas(HSAs),the nonlinear charging characteristics of EV batteries,and the traffic capacity constraints of DWCLs.The model identifies the deployment of charging facilities and the EV charging strategy as the decision-making variables and aims to minimize both the DWCL construction and user charging costs.By ensuring that EVs maintain an acceptable state of charge(SoC),the model combines highway EV charging demand and highway EV charging strategy to optimize the DWCL deployment,thus reducing the construction cost of wireless charging facilities and user charging expenses.The efficacy and universality of the model are demonstrated using the classical Nguyen-Dupius network as a numerical example and a real-world highway network in Guangdong Province,China.Finally,a sensitivity analysis is conducted to corroborate the stability of the model.The results show that the operating speed of EVs on DWCLs has the largest impact on total cost,while battery capacity has the smallest.This comprehensive study offers vital insights into the strategic deployment of DWCLs,promoting the sustainable and efficient use of EVs in highway networks.

Placement Optimization for UAV-Enabled Wireless Networks with Multi-Hop Backhauls

Unmanned aerial vehicles(UAVs)have emerged as a promising solution to provide wireless data access for ground users in various applications(e.g.,in emergency situations).This paper considers a UAVenabled wireless network,in which multiple UAVs are deployed as aerial base stations to serve users distributed on the ground.Different from prior works that ignore UAVs’backhaul connections,we practically consider that these UAVs are connected to the core network through a ground gateway node via rate-limited multi-hop wireless backhauls.We also consider that the air-to-ground access links from UAVs to users and the air-to-air backhaul links among UAVs are operated over orthogonal frequency bands.Under this setup,we aim to maximize the common(or minimum)throughput among all the ground users in the downlink of this network subject to the flow conservation constraints at the UAVs,by optimizing the UAVs’deployment locations,jointly with the bandwidth and power allocation of both the access and backhaul links.However,the common throughput maximization is a non-convex optimization problem that is difficult to be solved optimally.To tackle this issue,we use the techniques of alternating optimization and successive convex programming to obtain a locally optimal solution.Numerical results show that the proposed design significantly improves the common throughput among all ground users as compared to other benchmark schemes.