Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

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Content replication and placement is an area that has been well explored in the scope of Content Delivery Networks (CDNs), but has received relatively less attention from the research community when it comes to Wireless Mesh Networks (WMNs). There are a number of Replica Placement Algorithms (RPAs) that are specifically designed for CDNs. But they do not consider the special features of wireless networks. In this paper, we propose a new heuristic called MP-DNA (Multiple Partitions per Delegate Node Assignment). We study the problem of optimal content replication and placement in WMNs. In our model, each mesh router acts as a replica server with limited storage capacity. The challenge is to replicate content as close as possible to the requesting clients and thus reduce the access latency per object, while minimizing the number of replicas. We formulate this problem in terms of combinatorial optimization and propose a novel, distributed, scalable heuristic for content replication. Using simulation tests, we demonstrate that MP-DNA is scalable, performing well with respect to the number of replica servers and the number of objects. Furthermore, MP-DNA can achieve a performance close to the Greedy-Global heuristic, with a significant reduction in running time, mean throughput and storage space required.

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Content replication has gained much popularity in recent years both in the wired and wireless infrastructures. A key challenge faced by Wireless Mesh Networks (WMNs) is to determine the number and locations of content replicas (e.g. video clip) such that the mesh clients access cost is minimized. Furthermore, the dynamic nature of the wireless environment favors a distributed and adaptive solution to this problem. In this paper, we present an efficient, lightweight and scalable object replication and placement scheme for WMNs. Since the placement problem is NP-Complete, the scheme decomposes the problem into smaller sub-problems to facilitate the distributed approach in a P2P fashion. Moreover, it exploits the long-term link-quality routing metrics to augment the replica placement decision and the instantaneous link-quality metrics for replica server selection. The effectiveness of our scheme is evaluated through extensive simulation studies.

Cloud, edge and Internet of Things (IoT) technologies have emerged to overcome the challenges involved in sharing computational resources and information services. Within generic cloud systems, two models have been identified as having widespread applicability: computation clouds and data clouds. A data cloud is cloud computing that aims to manage, unify and operate multiple data workloads. Many current applications generate datasets consisting of petabytes (PB) of information. Managing large datasets is a complex issuel; in particular, datasets associated with many applications can be distributed widely in geographical terms, particularly in IoT systems. Edge and IoT systems are facing new challenges with increased complexity, making scalability an important issue that will affect the performance of the system. Data replication services are widely accepted techniques to improve availability and fault tolerance, and to improve the data access time. Current replication services, however, often exhibit an increase in response time, reflecting the problems associated with the ever-increasing size of databases. This paper proposes a prediction model to predict replica locations using the files’ access profile, which feeds the neural networks with the access and location behavior (file profile) to minimize the overhead of transferring large volumes of data, which slows down the system and requires careful management. This new model has shown high accuracy and low overheads. The result shows a significant improvement in total task execution time using the proposed model for locating files by 16.34% and 30.45%; in addition, the results show bandwidth improvement by 24.7% and 49.4% compared to the user profile prediction model and replica service model without prediction, respectively. Consequently, the proposed algorithm can improve data access speed, reduce data access latency and decrease bandwidth consumption.