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.

The use of precision agriculture is becoming more and more necessary to provide food for the world’s growing population, as well as to reduce environmental impact and enhance the usage of limited natural resources. One of the main drawbacks that hinder the use of precision agriculture is the cost of technological immersion in the sector. For farmers, it is necessary to provide low-cost and robust systems as well as reliability. Toward this end, this paper presents a wireless sensor network of low-cost sensor nodes for soil moisture that can help farmers optimize the irrigation processes in precision agriculture. Each wireless node is composed of four soil moisture sensors that are able to measure the moisture at different depths. Each sensor is composed of two coils wound onto a plastic pipe. The sensor operation is based on mutual induction between coils that allow monitoring the percentage of water content in the soil. Several prototypes with different features have been tested. The prototype that has offered better results has a winding ratio of 1:2 with 15 and 30 spires working at 93 kHz. We also have developed a specific communication protocol to improve the performance of the whole system. Finally, the wireless network was tested, in a real, cultivated plot of citrus trees, in terms of coverage and received signal strength indicator (RSSI) to check losses due to vegetation.

Deploying wireless sensor networks (WSN) in rural environments such as agricultural fields may present some challenges that affect the communication between the nodes due to the vegetation. These challenges must be addressed when implementing precision agriculture (PA) systems that monitor the fields and estimate irrigation requirements with the gathered data. In this paper, different WSN deployment configurations for a soil monitoring PA system are studied to identify the effects of the rural environment on the signal and to identify the key aspects to consider when designing a PA wireless network. The PA system is described, providing the architecture, the node design, and the algorithm that determines the irrigation requirements. The testbed includes different types of vegetation and on-ground, near-ground, and above-ground ESP32 Wi-Fi node placements. The results of the testbed show high variability in densely vegetated areas. These results are analyzed to determine the theoretical maximum coverage for acceptable signal quality for each of the studied configurations. The best coverage was obtained for the near-ground deployment. Lastly, the aspects of the rural environment and the deployment that affect the signal such as node height, crop type, foliage density, or the form of irrigation are discussed.

Multimedia traffic has drastically grown in the last few years. In addition, some of the last paradigms proposed, like the Internet of Things (IoT), adds new types of traffic and applications. Software-defined networks (SDNs) improve the capability of network management. Combined with SDN, artificial intelligence (AI) can provide solutions to network problems based on classification and estimation techniques. In this paper, we propose an artificial intelligence system for detecting and correcting errors in multimedia transmission in a surveillance IoT environment connected through a SDN. The architecture, algorithm, and messages of the SDN are detailed. The AI system design is described, and the test-bed and the data set are explained. The AI module consists of two different parts. The first one is a classifying part, which detects the type of traffic that is sent through the network. The second part is an estimator that informs the SDN controller on which kind of action should be executed to guarantee the quality of service and quality of experience. Results show that with the actions performed by the network, like jitter can be reduced up to 70% of average and losses can be reduced from 9.07% to nearly 1.16%. Moreover, the presented AI module is able to detect critical traffic with 77% accuracy.

For the last few years, Cloud computing has been considered an attractive high-performance computing platform for individuals as well as organizations. The Cloud service providers (CSPs) are setting up data centers with high performance computing resources to accommodate the needs of Cloud users. The users are mainly interested in the response time, whereas the Cloud service providers are more concerned about the revenue generation. Concerning these requirements, the task scheduling for the users' applications in Cloud computing attained focus from the research community. Various task scheduling heuristics have been proposed that are available in the literature. However, the task scheduling problem is NP-hard in nature and thus finding optimal scheduling is always challenging. In this research, a resource-aware dynamic task scheduling approach is proposed and implemented. The simulation experiments have been performed on the Cloudsim simulation tool considering three renowned datasets, namely HCSP, GoCJ, and Synthetic workload. The obtained results of the proposed approach are then compared against RALBA, Dynamic MaxMin, DLBA, and PSSELB scheduling approaches concerning average resource utilization (ARUR), Makespan, Throughput, and average response time (ART). The DRALBA approach has revealed significant improvements in terms of attained ARUR, Throughput, and Makespan.This fact is endorsed by the average resource utilization results (i.e., 98 % for HCSP dataset, 75 % for Synthetic workload (improve ARUR by 72.00 %, 77.33 %, 78.67 %, and 13.33 % as compared to RALBA, Dynamic MaxMin, DLBA and PSSELB respectively), and 77 % for GoCJ (i.e., the second best attained ARUR)).