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This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on the genetic algorithm and numerical simulations. The authors established a new method of collecting the mining compressive stress-strain distribution data to address the problem of the number of sensors and to optimize the sensor locations in physical similarity simulations to improve the efficiency and accuracy of data collection.

First, numerical simulations were used to obtain the compressive stress distribution curve under specific mining conditions. Second, by comparing the mean square error between a fitted curve and simulation data for different numbers of sensors, a genetic algorithm was used to optimize the three-dimensional (3D) spatial deployment of sensors. Third, the authors designed an efficient synchronous acquisition module to allow distributed sensors to achieve synchronous and efficient acquisition of hundreds of data points through a built-in on-board database and a synchronous sampling communication structure.

The sensor deployment scheme was established through the genetic algorithm, A synchronous and selective data acquisition method was established for reduced the amount of sensor data required under synchronous acquisition and improved the system acquisition efficiency. The authors obtained a 3D compressive stress distribution when the advancement was 200 m on a large-scale 3D physical similarity simulation platform.

The proposed method provides a new optimization method for sensor deployment in physical similarity simulations, which improves the efficiency and accuracy of system data acquisition, providing accurate acquisition data for experimental data analysis.

Collision avoidance is considered as a crucial issue in mobile robotic navigation to guarantee the safety of robots as well as working surroundings, especially for humans. Therefore, the position and velocity of obstacles appearing in the working space of the self-driving mobile robot should be observed to help the robot predict the collision and choose traversable directions. This paper aims to propose a new approach for obstacle tracking, dubbed MoDeT.

First, all long lines, such as walls, are extracted from the 2D-laser scan and considered as static obstacles (or mapped obstacles). Second, a density-based procedure is implemented to cluster nonwall obstacles. These clusters are then geometrically fitted as ellipses. Finally, the combination of Kalman filter and global nearest-neighbor (GNN) method is used to track obstacles’ position and velocity.

The proposed method (MoDeT) is experimentally verified by using an autonomous mobile robot (AMR) named AMR SR300. The MoDeT is found to provide better performance in comparison with previous methods for self-driving mobile robots.

The robot can only see a part of the object, depending on the light detection and ranging scan view. As a consequence, geometrical features of the obstacle are sometimes changed, especially when the robot is moving fast.

This proposed method is to serve the navigation and path planning for the AMR.

(a) Proposing an extended weighted line extractor, (b) proposing a density-based obstacle detection and (c) implementing a combination of methods [in (a) and (b) constant acceleration Kalman and GNN] to obtain obstacles’ properties.

Energy is the major concern in wireless sensor networks (WSNs) for most of the applications. There exist many factors for higher energy consumption in WSNs. The purpose of this work is to increase the coverage area maintaining the minimum possible nodes or sensors.

This paper has proposed multilayer (ML) nodes deployment with distributed MAC (DS-MAC) in which nodes listen time is controlled based on communication of neighbors. Game theory optimization helps in addressing path loss constraints while selecting path toward base stations (BS).

The simulation is carried out using NS-2.35, and it shows better performance in ML DS-MAC compared to random topology in DS-MAC with same number of BS. The proposed method improves performance of network in terms of energy consumption, network lifetime and better throughput.

Energy consumption is the major problem in WSNs and for which there exist many reasons, and many approaches are being proposed by researchers based on application in which WSN is used. Node mobility, topology, multitier and ML deployment and path loss constraints are some of the concerns in WSNs.

Game theory is used in different situations like countries whose army race, business firms that are competing, animals generally fighting for prey, political parties competing for vote, penalty kicks for the players in football and so on.

WSNs find applications in surveillance, monitoring, inspections for wild life, sea life, underground pipes and so on.

Game theory optimization helps in addressing path loss constraints while selecting path toward BS.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

The miniscule wireless sensor nodes, engaged in the wide range of applications for its capability of monitoring the physical changes around, requires an improved routing strategy with the befitting sensor node arrangement that plays a vital part in ensuring a completeness of the network coverage.

This paves way for the reduced energy consumption, the enhanced network connections and network longevity. The conventional methods and the evolutionary algorithms developed for arranging of the node ended with the less effectiveness and early convergence with the local optimum respectively.

The paper puts forward the befitting arrangement of the sensor nodes, cluster-head selection and the delayless routing using the ant lion (A-L) optimizer to achieve the substantial coverage, connection, the network-longevity and minimized energy consumption.

The further performance analysis of the proposed system is carried out with the simulation using the network simulator-2 and compared with the genetic algorithm and the particle swarm optimization algorithm to substantiate the competence of the proposed routing method using the ant lion optimization.

Despite the fast progress in structural health monitoring (SHM), the efficient use in practice of emerging techniques for large civil engineering structures is still a challenge. This paper outlines a practical framework to optimize both the number and the locations of sensors to measure frequency response functions (FRFs) that will be processed and used to predict the location and the damage level in a model of an existing suspension bridge.

Sensors number and placement (SNPO) procedure is proposed and carried out on a 3D FE model of the 502 m long Oued Dib suspension bridge (Algeria) to determine the degrees of freedom (DOFs) that will receive the sensors. For this purpose, accessible candidate positions on the model are first determined and then reduced by taking the DOFs with the lowest values of the Fisher information matrix (FIM) associated with each of the DOFs taken individually. A genetic algorithm with an objective function equal to the square root of the sum of the squares of the non-diagonal elements of the MAC matrix and a mutation function that allows increasing and decreasing the number of the chromosomes (sensors) of the individuals showed stable convergence to optimal solutions. FRFs at sensor positions generated from the 3D FE model and altered with artificial noise to simulate experimental conditions have been used to constitute a database to train and test a feed-forward neural network.

A framework for SHM integrating a genetic algorithm to optimize both the number and placement of the sensors on the structure.

The procedure can be applied only for single predefined/potential damage detection.

The evidence from this study suggests that the proposed procedure provides a consistent framework to implement a SHM scheme for existing large infrastructures.

Vital infrastructures require special structural protection that can be achieved through effective SHM. This study contributes to the deployment of SHM for existing civil engineering structures.

In addition to the integrated SHM framework proposed in this study, the latter includes an efficient genetic algorithm capable to optimize both the number and the placement of the sensors.

Continuous exposure and over‐utilization of sensors in harsh environments can lead some sensors to fail, and thereby not covering the service area effectively and efficiently. The purpose of this paper is to propose a two‐level coverage restoration scheme for the failing sensors by the existing sensors deployed in the immediate neighborhood of the failing sensors. The restoration scheme extends the search process to the set of failed sensors' corner neighbors at a second stage, with non‐available immediate active neighboring sensors at its first stage. Thus, the coverage restoration scheme attempts to sustain a maximum area of coverage with failed sensors.

The authors have considered a wireless sensor network (WSN), comprised of sensors deployed in a grid‐based arrangement in an inaccessible arena. The authors have formulated the coverage restoration problem as an optimization problem, to find the nearest and most apt neighbor sensors to reach solutions of maximizing the coverage area with failed sensors, while minimizing the energy consumption. Simulated annealing has been utilized as a search algorithm to find out the neighboring sensors with maximal energy in the vicinity of the failed node to cover its area.

The experimental results within the optimization algorithm have demonstrated that the restoration scheme shows a better trade‐off in maximizing the coverage area up to 90 per cent with a decrease of 26 per cent lifespan. The performance of the algorithm is further improved with extended search space including the corner neighbors in addition to the immediate neighbors.

The proposed coverage restoration can be embedded within applications using WSN to restore the coverage and maintain its functionality with optimized energy consumption.

The paper employs a novel framework to restore the coverage of the failed sensors by doubling the sensing area of the neighborhood sensors, and it utilizes an optimization scheme to search for neighborhood sensors with maximal energy to extend the lifespan of WSN.

The unmanned aerial vehicles (UAVs) entered into their development stage when different applications became real. One of those application areas is agriculture. Agriculture and transport currently follow infrastructure as the top industries in the world UAV market. The agricultural UAV can be acquired as a ready-made, built by its future user or UAV-as-a-service (UaaS) way. This paper aims to help the UAVs’ users to choose the right sensors for agricultural purposes. For that sake, the overview of the types and application areas of onboard sensors is presented and discussed. Some conclusions and suggestions should allow readers to choose the proper onboard sensors set and the right way of acquiring UAVs for their purposes related to the agricultural area.

The agricultural UAVs’ onboard specialised sensors have been analysed, described and evaluated from the farmer’s operational point of view. That analysis took into consideration the agricultural UAVs’ types of missions, sensor characteristics, basics of the data processing software and the whole set of UAV-sensor-software operational features. As the conclusions, the trends in the onboard agricultural UAVs’ sensors, their applications and operational characteristics have been presented.

Services performed by the UAVs for the agriculture businesses are the second in the UAV services world market, and their growth potential is around 17% compound annual growth rate in the next years. As one of the quickest developing businesses, it will attract substantial investments in all related areas. They will be done in the research, development and market deployment stages of that technology development. The authors can expect the new business models of the equipment manufacturers, service providers and sellers of the equipment, consumables and materials. The world agricultural UAVs’ services market will be divided between the following two main streams: the UAVs’ solutions dedicated to the individual farmers, systems devoted to the companies giving the specialised services to individual farmers, in the form of UaaS. It will be followed by the two directions of the agriculture UAV set optimisation, according to each of the above streams’ specific requirements and expectations. Solutions for the individual users will be more straightforward, universal and more comfortable to operate but less effective and less accurate than systems dedicated to the agricultural service provider. UAVs are becoming important universal machines in the agriculture business. They are the newcomers in that business but can change the processes performed traditionally. Such an example is spraying the crops. UAVs spray the rice fields in Japan on at least half of them every year. The other is defoliating the cotton leaves, which only in one China province takes place on a few million hectares every year (Kurkute et al., 2018). That trend will extend the range of applications of UAVs. The agricultural UAV will take over process after process from the traditional machines. The types and number of missions and activities performed by agricultural UAVs are growing. They are strictly connected with the development of hardware and software responsible for those missions’ performance. New onboard sensors are more reliable, have better parameters and their prices are reasonable. Onboard computers and data processing and transmitting methods allow for effective solutions of automatisation and autonomy of the agricultural UAVs’ operation. Automatisation and autonomous performance of the UAVs’ agricultural missions are the main directions of the future development of that technology. Changing the UAV payload allows for its application to a different mission. Changing the payload, like effectors, is quite simple and does not require any special training or tooling. It can be done in the field during the regular operation of the agricultural UAV. Changing the sensor set can be more complicated, because of the eventually required calibrating of those sensors. The same set of sensors gives a possibility to perform a relatively broad range of missions and tasks. The universal setup consists of the multispectral and RGB camera. The agricultural UAV equipped with such a set of sensors can effectively perform most of the crop monitoring missions. The agriculture business will accept the optimised sensor-computer-software UAV payload set, where its exploitation cost and operational simplicity are the critical optimisation factors. Simplicity, reliability and effectiveness of the everyday operation are the vital factors of accepting the agricultural UAV technology as a widespread working horse.

Performed research studies have been done taking into consideration the factors influencing the real operational decisions made by the farmers or companies offering UAV services to them. In that case, e.g. the economical factors have been considered, which could prevail the technical complexity or measuring accuracy of the sensors. Then, drawn conclusions can be not accurate from the scientific research studies point of view, where the financing limits are not so strict.

The main goal of the paper is to present the reasons and factors influencing the “optimised” solution of the configuration of agricultural UAV onboard sensors set. It was done at the level useful for the readers understanding the end-users expectations and having a basic understanding of the sensors-related technologies. The paper should help them to configure an acceptable agricultural UAV for the specific missions or their servicing business.

Understanding the technology implications related to the applying of agricultural UAVs into everyday service is one of the main limits of that technology market deployment. The conclusions should allow for avoiding the misunderstanding of the agricultural UAVs’ capabilities and then increasing their social acceptance. That acceptance by the farmers is the key factor for the effective introduction of that technology into the operation.

Presented conclusions have been drawn on the base of the extensive research of the existing literature and web pages, and also on the own experience in forestry and agriculture and other technical applications of the onboard sensors. The experience in practical aspects of the sensors choosing and application into several areas have been also used, e.g. manned and unmanned aeroplanes and helicopters applied in similar and other types of missions.

Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system.

First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly.

Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance.

In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

Simulation-based optimization is a decision-making tool for identifying an optimal design of a system. Here, optimal design means a smart system with sensing, computing and control capabilities with improved efficiency. As compared to testing the physical prototype, computer-based simulation provides much cheaper, faster and lesser time-and resource-consuming solutions. In this work, a comparative analysis of heuristic simulation optimization methods (genetic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed.

In this work, a comparative analysis of heuristic simulation optimization methods (genertic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed. Further, a novel simulation annealing-based heuristic approach is proposed for critical infrastructure.

A small scale network of 50–100 nodes shows that genetic simulation optimization with multi-criteria and multi-dimensional features performs better as compared to other simulation optimization approaches. Further, a minimum of 3.4 percent and maximum of 16.2 percent improvement is observed in faster route identification for small scale Internet-of-things (IoT) networks with simulation optimization constraints integrated model as compared to the traditional method.

In this work, simulation optimization techniques are applied for identifying optimized Quality of service (QoS) parameters for critical infrastructure which in turn helps in improving the network performance. In order to identify optimized parameters, Tabu search and ant-inspired heuristic optimization techniques are applied over QoS parameters. These optimized values are compared with every monitoring sensor point in the network. This comparative analysis helps in identifying underperforming and outperforming monitoring points. Further, QoS of these points can be improved by identifying their local optimum values which in turn increases the performance of overall network. In continuation, a simulation model of bus transport is taken for analysis. Bus transport system is a critical infrastructure for Dehradun. In this work, feasibility of electric recharging units alongside roads under different traffic conditions is checked using simulation. The simulation study is performed over five bus routes in a small scale IoT network.