The first step to detect when a vineyard has any type of deficiency, pest or disease is to observe its stems, its grapes and/or its leaves. To place a sensor in each leaf of every vineyard is obviously not feasible in terms of cost and deployment. We should thus look for new methods to detect these symptoms precisely and economically. In this paper, we present a wireless sensor network where each sensor node takes images from the field and internally uses image processing techniques to detect any unusual status in the leaves. This symptom could be caused by a deficiency, pest, disease or other harmful agent. When it is detected, the sensor node sends a message to a sink node through the wireless sensor network in order to notify the problem to the farmer. The wireless sensor uses the IEEE 802.11 a/b/g/n standard, which allows connections from large distances in open air. This paper describes the wireless sensor network design, the wireless sensor deployment, how the node processes the images in order to monitor the vineyard, and the sensor network traffic obtained from a test bed performed in a flat vineyard in Spain. Although the system is not able to distinguish between deficiency, pest, disease or other harmful agents, a symptoms image database and a neuronal network could be added in order learn from the experience and provide an accurate problem diagnosis.

According to World Health Organization (WHO) estimations, one out of five adults worldwide will be obese by 2025. Worldwide obesity has doubled since 1980. In fact, more than 1.9 billion adults (39%) of 18 years and older were overweight and over 600 million (13%) of these were obese in 2014. 42 million children under the age of five were overweight or obese in 2014. Obesity is a top public health problem due to its associated morbidity and mortality. This paper reviews the main techniques to measure the level of obesity and body fat percentage, and explains the complications that can carry to the individual's quality of life, longevity and the significant cost of healthcare systems. Researchers and developers are adapting the existing technology, as intelligent phones or some wearable gadgets to be used for controlling obesity. They include the promoting of healthy eating culture and adopting the physical activity lifestyle. The paper also shows a comprehensive study of the most used mobile applications and Wireless Body Area Networks focused on controlling the obesity and overweight. Finally, this paper proposes an intelligent architecture that takes into account both, physiological and cognitive aspects to reduce the degree of obesity and overweight.

The main aim of smart cities is to achieve the sustainable use of resources. In order to make the correct use of resources, an accurate monitoring and management is needed. In some places, like underground aquifers, access for monitoring can be difficult, therefore the use of sensors can be a good solution. Groundwater is very important as a water resource. Just in the USA, aquifers represent the water source for 50% of the population. However, aquifers are endangered due to the contamination. One of the most important parameters to monitor in groundwater is the salinity, as high salinity levels indicate groundwater salinization. In this paper, we present a specific sensor for monitoring groundwater salinization. The sensor is able to measure the electric conductivity of water, which is directly related to the water salinization. The sensor, which is composed of two copper coils, measures the magnetic field alterations due to the presence of electric charges in the water. Different salinities of the water generate different alterations. Our sensor has undergone several tests in order to obtain a conductivity sensor with enough accuracy. First, several prototypes are tested and are compared with the purpose of choosing the best combination of coils. After the best prototype was selected, it was calibrated using up to 30 different samples. Our conductivity sensor presents an operational range from 0.585 mS/cm to 73.8 mS/cm, which is wide enough to cover the typical range of water salinities. With this work, we have demonstrated that it is feasible to measure water conductivity using solenoid coils and that this is a low cost application for groundwater monitoring.

The number of forest fires that occurred in recent years in different parts of the world is causing increased concern in the population, as the consequences of these fires expand beyond the destruction of the ecosystem. However, with the proliferation of the Internet of Things (IoT) industry, solutions for early fire detection should be developed. The assessment of the fire risk of an area and the communication of this fact to the population could reduce the number of fires originated by accident or due to the carelessness of the users. This paper presents a low-cost network based on Long Range (LoRa) technology to autonomously evaluate the level of fire risk and the presence of a forest fire in rural areas. The system is comprised of several LoRa nodes with sensors to measure the temperature, relative humidity, wind speed and CO2 of the environment. The data from the nodes is stored and processed in a The Things Network (TTN) server that sends the data to a website for the graphic visualization of the collected data. The system is tested in a real environment and, the results show that it is possible to cover a circular area of a radius of 4 km with a single gateway.

The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

This paper describes an scheme for automatic forest surveillance. A complete system for forest fire detection is firstly presented although we focus on infrared image processing. The proposed scheme based on infrared image processing performs early detection of any fire threat. With the aim of determining the presence or absence of fire, the proposed algorithms performs the fusion of different detectors which exploit different expected characteristics of a real fire, like persistence and increase. Theoretical results and practical simulations are presented to corroborate the control of the system related with probability of false alarm (PFA). Probability of detection (PD) dependence on signal to noise ration (SNR) is also evaluated.

Internet of Vehicle (IoV) is a sub class of vehicular ad hoc networks with more advanced cloud and Internet-enabled services. These networks offer various types of safety and infotainment services and provide comfortability and safety to passengers as well as to the drivers. Due to the high mobility of nodes, the nodes are out from its communication range and the information becomes outdated and causes link disconnections and packet dropping. Most feasible routing protocols are needed to provide in-time data communication, handle high mobility of nodes, dynamic topologies and unpredictable environments of these networks. In this paper, we proposed SACBR (Self-Assessment Cluster-based Routing) protocol in which the Cluster Heads (CHs) can communicate with other CHs. Every vehicle node initiates a self-assessment approach based on more appropriate routing metrics and elects the CH for every cluster and then collects the data from member nodes and further forward the data to other CHs. The CH is responsible to manage its own and member nodes' data forwarding process. The proposed protocol provides more stability and less overhead compared to the aggregation method where every node exchanges its data with a one-hop neighbor. Proposed protocol suites sparse and dense traffic scenarios where most of the time vehicle nodes are moving in platoons or snaking structures. The experimental results show the better performance of SACBR compared to state-of-the-art protocols. INDEX TERMS Cluster head, Internet of Vehicles, forwarding, routing, protocol.

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