Computational Contributions to the Automation of Agriculture

The purpose of this paper is to explore ways that computational advancements have enabled the complete automation of agriculture from start to finish. With a major need for agricultural advancements because of food and water shortages, some farmers have begun creating their own solutions to these problems. Primarily explored in this paper, however, are current research topics in the automation of agriculture. Digital agriculture is surveyed, focusing on ways that data collection can be beneficial. Additionally, self-driving technology is explored with emphasis on farming applications. Machine vision technology is also detailed, with specific application to weed management and harvesting of crops. Finally, the effects of automating agriculture are briefly considered, including labor, the environment, and direct effects on farmers

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Locating the information: applications, technologies and future aspects

In today’s world, the demand for information is growing rapidly with respect to the human curiosity to explore the inside and the outside of our planet. In a simple analogy, the human body has thousands of sensors called receptor neurons to obtain information such as temperature or pressure from the environment. Similarly, recent developments in electronics and wireless communications lead engineers to the design of small-sized, low-power, low-cost sensor nodes which have the ability to communicate with each other over short distances and collect the information that is gathered

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table