Many rural areas along Spain do not have access to the Internet. Despite the huge spread of technology that has taken place during recent years, some rural districts and isolated villages have a lack of proper communication infrastructures. Moreover, these areas and the connected regions are notably experiencing a technological gap. As a consequence of this, the implementation of technological health solutions becomes impracticable in these zones where demographic conditions are especially particular. Thus, inhabitants over 65 suppose a large portion of such population, and many elderly people live alone at their homes. These circumstances also impact on local businesses which are widely related to the agricultural and livestock industry. Taking into account this situation, this paper proposes a solution based on an opportunistic network algorithm which enables the deployment of technological communication solutions for both elderly healthcare and livestock industrial activities in rural areas. This way, two applications are proposed: a presence detection platform for elderly people who live alone and an analytic performance measurement system for livestock. The algorithm is evaluated considering several simulations under multiple conditions, comparing the delivery probability, latency, and overhead outcomes with other well‐known opportunistic routing algorithms. As a result, the proposed solution quadruples the delivery probability of Prophet, which presents the best results among the benchmark solutions and greatly reduces the overhead regarding other solutions such as Epidemic or Prophet. This way, the proposed approach provides a reliable mechanism for the data transmission in these scenarios.

In the mobile device market there is a large number of applications to help people monitor intake or provide suggestions to lose weight and manage a healthy diet. However, the vast majority of these apps consume a lot of time by having to introduce food one by one. This paper presents the work to develop and pilot test a new Android application, FoodScan, aimed at people over 70, specially those from rural environments or with limited technical knowledge, to manage their food from the items that appear on their grocery receipts, avoiding the obligation to introduce one by one those foods, and generating recommendations. To achieve this final objective, specific objectives have been completed as indicated in the methods section. We conducted a review of current calorie control applications to learn about their weaknesses and strengths. Different algorithms were tested to expedite the introduction of food into the application and the most suitable for the FoodScan application was selected. Likewise, several options were taken into account to create the knowledge base of food, taking into account dietary recommendations for people over 70 years. Once developed, a pilot evaluation was carried out with a convenience sample of 109 volunteers in rural areas of Caceres and Valladolid (Spain) and Alentejo (Portugal). They tested FoodScan for a month after which they completed a user satisfaction survey. 93 % (101/109) believed that the app was easy to download and install, 66 % (72/109) thought that it was easy to use, 47 % (51/109) noted that the charts with the recommendations helped them with diet control and 49 % (53/109) indicated that FoodScan helped them improve healthy eating habits. One-month pilot evaluation data suggested that most users found the app somewhat helpful for monitoring food intake, easy to download and easy to use.

The massive deployment of Internet-connected devices has led to an increase in the collection of data that are then used by companies to improve their decision-making processes. This growing trend demands more and more cloud and communications infrastructure. The limited resources, the need for sharing them, and the fact that many consumers are interested in the same data, call for an efficient management of the available resources. The cloud-to-things continuum can be used to execute different analytics closer to the data source so that infrastructure consumption and data circulation can be optimized. In this article, different dimensions for achieving elastic analytics and a framework for dynamically modifying their behavior are proposed.

Smartphones have become a central element in modern society with their widespread adoption driven by technological advancements and their ability to facilitate everyday tasks. A critical feature influencing user satisfaction and smartphone adoption is battery life, as the intensive use of mobile devices can significantly drain battery power. This paper addresses the challenge of predicting smartphone battery consumption using artificial intelligence techniques, specifically deep learning, to optimize energy efficiency. By collecting and analyzing data from mobile devices, such as application usage, screen time, network type, network usage, and battery temperature among others, we developed a predictive model tailored to user-specific behavior. This model identifies the key variables affecting battery consumption and provides personalized energy-saving strategies. Our approach offers a solution for improving battery performance, contributing to more efficient energy management in both hardware and networking terms while adapting to individual usage patterns. The results demonstrate that our approach can significantly predict the battery to anticipate power demands based on user-specific usage. While challenges remain, such as improving the generalizability of the model across different devices, this approach provides a scalable and adaptive method to improve the energy efficiency of smartphones, which will allow efficient management solutions to be suggested, contributing to better battery and network management to improve user experience and device longevity.