Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.

https://www.mdpi.com/1424-8220/20/4/1042/pdf

IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture

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Optical Networks A Practical Perspective

Traveling-wave amplification of a lambda = 1.53 microm signal with +22-dB gain is achieved at 295 K in an Er(3+)-doped single-mode fiber using a lambda = 514.5 nm pump source. The optimum fiber length for maximum gain is determined experimentally. A limit in signal-to-noise ratio that is due to concurrent amplification of spontaneous emission is observed. By cooling the fiber to 77 K, the amplifier gain is increased to +29 dB as a result of depopulation of the lower laser level.

High-gain erbium-doped traveling-wave fiber amplifier

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Security and the smart city: A systematic review

Presently, various real time applications has been developed using smart systems such as smart cities, smart homes, smart transportation, etc. The use of smart sensors in those systems leads to the generation of different kinds of multimedia data like images, videos, audios, and so on. To acquire multimedia data from smart sensor environments, Wireless Sensor Networks (WSN) has been employed, which is an integral part of smart system which helps to maintain connectivity and coverage. In WSN, the major challenging issue is to process the massive amount of multimedia data which leads to maximum energy utilization. Clustering is an energy efficient way of organizing the network in a systematic way for proper load distribution and maximize network lifetime. To facilitate the optimal selection of Cluster Heads (CHs), in this paper, we propose an Improved Artificial Bee colony optimization based ClusTering(IABCOCT) algorithm by utilizing the merits of Grenade Explosion Method (GEM) and Cauchy Operator. This incorporation of GEM and Cauchy operator prevents the Artificial Bee Colony(ABC) algorithm from stuck into local optima and improves the convergence rate. The benefits of GEM and Cauchy operator are embedded into the Onlooker Bee and scout bee phase for phenomenal improvement in the degree of exploitation and exploration during the process of CH selection. The simulation results reported that the IABCOCT algorithm outperforms the state of art methods like Hierarchical Clustering-based CH Election (HCCHE), Enhanced Particle Swarm Optimization Technique (EPSOCT) and Competitive Clustering Technique (CCT) interms of different measures such as throughput, packet loss, delay, energy consumption and network lifetime.

Improved artificial bee colony optimization based clustering algorithm for SMART sensor environments

Around the globe, agriculture plays a vital role in framing up the economy. Though, India one of the biggest producer of agricultural products, the growth in agricultural products has been stagnant for past several years. Thereby, requiring technologies to improve the efficiency of the agricultural productivity. Hence, the key objective of the proposed project is to making agriculture smart using IoT technologies. The important feature of this project includes the prevention of crops from spoilage during rain and efficiently recycling the rain water for irrigation. Secondly, it includes intruder alarm/buzzer which is used to detect any human/animal intruder into the farm. Finally, the operation will be performed by interfacing Wi-Fi module, GSM module and sensors with Arduino. With the proposed work, current problems related to farming are solved by reducing human efforts, wastage of water and updating the farmer about the live condition of the field on the mobile device.

Smart agro system using wireless sensor networks

In medical field, securing every patient’s record is main concern, ascribed to many fraudulent cases occurring in the health sector. The data of every individual must be engraved and sent into end-user without any issues. Mainly in the healthcare industry, where thoughts are often focused on saving someone’s life and rightly so, but securing access to interfaces and computer systems that store private data like medical records is also an essential factor to consider. Data security is a corresponding action between controlling access to information while allowing free and easy access to those who need that information. Still few problems are focused by the physician in the health sector. Patient’s data should be kept securely in medical provider servers so that physicians can provide proper treatments. To ensure secure storage and access management, we propose a novel hybrid lightweight encryption using swarm optimization algorithm (HLE–SO).The proposed HLE–SO technique merge Paillier encryption and KATAN algorithm, which provides the lightweight features. Generally, the lightweight encryption algorithms are affected by the key space. We introduce the swarm optimization algorithm to optimize the key space by changing the number of iteration round. Our main goal is to encrypt the medical data (EEG signal) and send to end user by utilizing proposed HLE–SO method. Finally, the implementation is done with MATLAB tool with different EEG signal data set. The simulation results of proposed HLE–SO technique is compared with the existing state-of-art techniques in terms of different performance metrics are MSE, PSNR, SSIM, PRD, encryption time and decryption time.

Medical Data Security for Healthcare Applications Using Hybrid Lightweight Encryption and Swarm Optimization Algorithm

A Distributed Denial of Service (DDoS) attack is a type of cyber-attack that attempts to interrupt regular traffic on a targeted server by overloading the target. The system under DDoS attack remains occupied with the requests from the bots rather than providing service to legitimate users. These kinds of attacks are complicated to detect and increase day by day. In this paper, machine learning algorithm is employed to classify normal and DDoS attack traffic. DDoS attacks are detected using four machine learning classification techniques. The machine learning algorithms are tested and trained using the CICDDoS2019 dataset, gathered by the Canadian Institute of Cyber Security. When compared against KNN, Decision Tree, and Random Forest, the Artificial Neural Network (ANN) generates the best results.

DDoS Detection using Machine Learning Techniques

The core objective of controlling topology, conserving energy and extending the life cycle in sensor networks is highly indispensable for maximizing its lifetime. Meta-heuristic optimization algorithms are found to be optimal in facilitating the process of cluster head election and clustering. An Improved Artificial Bee Colony Optimization-based Clustering Technique (IABCOCT) is propounded by deriving the merits of Grenade Explosion and Cauchy operator for ensuring optimal clustering process and election of cluster heads. The potential of Grenade Explosion and Cauchy operator are embedded in the Onlooker Bee and scout bee phase for phenomenal improvement in the degree of exploitation and exploration of searching that aids in the optimal election of cluster heads. This incorporation of Grenade Explosion and Cauchy operator in the clustering process maximizes the rate of cluster head election for reducing energy consumption in sensor nodes that enhances lifetime of the network. The potential of IABCOCT is studied through simulation experiments and its performance is investigated against Enhanced Particle Swarm Optimization Technique (EPSOCT), Hierarchical Clustering-based Cluster Head Election (HCCHE) and Competitive Clustering Technique (CCT). The simulation results of IABCOCT prove that the mortality rate of sensor nodes is minimized for enhancing network lifetime. The results deduce that the relative energy, dropping ratio, Mean energy consumption and Network Routing Overhead is better than EPSOCT, HCCHE and CCT under the impact of varying number of rounds.

A. Jawahar

Papers

Improved artificial bee colony optimization based clustering algorithm for SMART sensor environments

Smart agro system using wireless sensor networks

Medical Data Security for Healthcare Applications Using Hybrid Lightweight Encryption and Swarm Optimization Algorithm

DDoS Detection using Machine Learning Techniques

Improved Artificial Bee Colony Optimization-Based Clustering Technique for WSNs