The Internet of Things (IoT) sustainable solutions are the next generation of methods for managing and monitoring valuable natural resources such as water. Research has focused on smart IoT-based water management and monitoring system designs for various types of applications, including agricultural, industrial, residential, and crude oil exploration sectors. To this end, unlike other surveys available in the literature, this work presents an all-encompassing analysis of 43 papers published between 2014 and 2022 for systems proposed to manage and monitor water in four different sectors: agricultural, industrial, residential, and oilfield sectors. In this work, we also propose a new optical water management system to address a gap in the literature, particularly for oilfield processes that require significant amounts of water to stimulate oil production. The proposed system employs an advanced optical technique for sensing and transmitting collected data. Overall, this work provides a seminal guide for future research efforts focused on integrating IoT in the field of water management and monitoring. 

/pdf/iot-based-water-management-systems-survey-and-future-2oxi77p0.pdf

IoT-Based Water Management Systems: Survey and Future Research Direction

The Internet of Things (IoT) sustainable solutions are the next generation of methods for managing and monitoring valuable natural resources such as water. Research has focused on smart IoT-based water management and monitoring system designs for various types of applications, including agricultural, industrial, residential, and crude oil exploration sectors. To this end, unlike other surveys available in the literature, this work presents an all-encompassing analysis of 43 papers published between 2014 and 2022 for systems proposed to manage and monitor water in four different sectors: agricultural, industrial, residential, and oilfield sectors. In this work, we also propose a new optical water management system to address a gap in the literature, particularly for oilfield processes that require significant amounts of water to stimulate oil production. The proposed system employs an advanced optical technique for sensing and transmitting collected data. Overall, this work provides a seminal guide for future research efforts focused on integrating IoT in the field of water management and monitoring.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09745489.pdf

Finding the shortest path of the traveling salesman problem (TSP) is a typical NP-hard problem and one of the basic optimization problems. TSP in three-dimensional space (3D-TSP) is an extension of TSP. It plays an important role in the fields of 3D path planning and UAV inspection, such as forest fire patrol path planning. Many existing studies have focused on the expected length of the shortest path of TSP in 2D space. The expected length of the shortest path in 3D space has not yet been studied. To fill this gap, this research focuses on developing models to estimate the expected length of the shortest path of 3D-TSP. First, different experimental scenarios are designed by combining different service areas and the number of demand points. Under each scenario, the specified number of demand points is randomly generated, and an improved genetic algorithm and Gurobi are used to find the shortest path. A total of 500 experiments are performed for each scenario, and the average length of the shortest path is calculated. The models to estimate the expected length of the shortest path are proposed. Model parameters are estimated and k-fold cross-validation is used to evaluate the goodness of fit. Results show that all the models fit the data well and the best model is selected. The developed models can be used to estimate the expected length of the shortest path of 3D-TSP and provide important references for many applications.

/pdf/expected-length-of-the-shortest-path-of-the-traveling-2hj82gh4.pdf

Expected Length of the Shortest Path of the Traveling Salesman Problem in 3D Space

Metaheuristic algorithms aim to find high performing, near-optimal solutions with reasonable computing costs. Using the process of natural selection that belongs to a set of evolutionary algorithms, are Genetic Algorithms. These algorithms intelligently exploit random search over the data in search of a solution space for better performance. Detection of forgery in legal documents by an automated system remains a valid problem of today. In this paper, the identification and detection of forgery in the monetary notes is performed by using deep neural-based Genetic Algorithms. An Artificial Neural Network was used for training on the banknote dataset and the learned weights were vectorized for Genetic Algorithm processing. Genetic Algorithm is explained in detail along with the working of the Fitness model that determines the efficiency of the trained model. The model achieved a high-performance accuracy of 94% with the fitness score of95.2. The improvement of the model with the infusion of trainable weights from a deep neural model is represented and analyzed.

Deep-Neural Genetic Algorithm Optimization Analysis for Forgery Detection of Banknotes

Planning the most energy-effective and fastest route is pivotal to reducing the windshield time of field workers, ensuring guests receive their packages at the listed time and dwindling the logistic cost. One of the numerous benefits of route optimization is that all the parcels are delivered with the most systematized use of resources. Route optimization enables the computation of the fastest and most energy-effective route while taking into account multiple stops and limited delivery time windows. It solves the vehicle route problem and travelling salesman problem. Using a collection of New York City locales represented as longitude and latitude coordinates, the algorithm searches the solution space to find the most effective travelling salesman path which takes the least amount of time to travel. By creating a regression model to unravel the trip time cost between two points, the algorithm optimizes the travelling salesman path using four biologically inspired meta-heuristics: Genetic Evolution, ant colony, grey wolf, and Artificial Bee Colony. 

Optimization Algorithms to Reduce Route Travel Time

The recent advancements in the fields of computer science and engineering have led to strides in several multi-disciplinary and trans-disciplinary scientific research areas. The Travelling Salesman Problem is one such classical optimization problem that is widely used in various fields including Social Net-work analysis, traffic flow control, human resource management, logistic planning, etc. Attempts have been made in the past to solve this problem with different computational techniques including basic dynamic programming, neural networks to several others. This paper presents a comparison of four optimization algorithms in solving the Travelling Salesman Problem. The chosen approaches are the classical pure Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant-Colony Optimization (ACO) and the Simulated Annealing algorithm (SA). The paper compares and contrasts the performance based on the problem-solving exercises.

Comparing Performance of Evolutionary Algorithms - A Travelling Salesman Perspective

Water conservation is one of the prime concerns in the current scenario where environmental conditions are deteriorating at an alarming rate. Smart cities, unlike the conventional system of living, are in the frontline of innovation in terms of both connectivity and technological advancement. The main idea is to use the available technology to make life easy with lesser harm to the environment. An Internet of Things (IoT) and data analytics (DA) based water management system will be a basic ground for implementation and future research on how data and IoT can be used to make this happen. This paper proposes an Internet of Things (IoT) and data analytics (DA) based water distribution cum management system that would help in optimal distribution of water based on user consumption at the plot holding level. The proposed system would not only save water misuse but also help in storing usage data for analysis and town planning at a macro-level.

Immanuel Savio Donbosco

Papers

Comparing Performance of Evolutionary Algorithms - A Travelling Salesman Perspective

An IoT-Based Water Management System for Smart Cities