The applications of wireless sensor networks comprise a wide variety of scenarios. In most of them, the network is composed of a significant number of nodes deployed in an extensive area in which not all nodes are directly connected. Then, the data exchange is supported by multihop communications. Routing protocols are in charge of discovering and maintaining the routes in the network. However, the appropriateness of a particular routing protocol mainly depends on the capabilities of the nodes and on the application requirements. This paper presents a review of the main routing protocols proposed for wireless sensor networks. Additionally, the paper includes the efforts carried out by Spanish universities on developing optimization techniques in the area of routing protocols for wireless sensor networks.

At present, the boom in unmanned aerial vehicles (UAV) has been increasing in recent years, placing them in an important way in the commercial market. The use of UAV in the daily tasks of industry, commerce or as entertainment for children and adults becomes more recurrent. Each of the UAVs has a specific task, depending on the technologies that are provided, in addition to their basic functions with which they were manufactured. However, in most cases the security of these UAVs is not usually taken into account since some of them are inexpensive and do not have a robust security system that protects the data they send or receive for their operation, that can cause its communication system or the operating system that controls its basic functions of flight, landing, among others, to be compromised. These computer attacks could cause physical or moral harm to people around the same operator of the UAV because they could steal information related to the locations they have visited, or intercept images or videos taken by the UAV. This paper shows the exploitation of GPS vulnerability in the commercial drone of the company 3D Robotics, this vulnerability can cause a malicious user to have control of their autonomy, and carry out illicit activities, such as overflying in spaces not allowed as an airport and private areas. The exploitation of this vulnerability is important to make known that the UAVs should have a more robust security system and also give importance to the security of GPS since the only one that has security is the military GPS.

The exponential growth of devices connected to the network has resulted in the development of new IoT applications and on-line services. However, these advances are limited by the rigidity of the current network infrastructure, in which the administrator has to implement high-level network policies adapting and configuring protocols manually and usually through a command line interface (CLI). At this point, Software-Defined Networking (SDN) appears as a viable alternative network architecture that allows for programming the network and opening the possibility of creating new services and more efficient applications to cover the actual requirements. In this paper, we describe this new technology and analyze its opportunities in the development of IoT applications. Similarly, we present the first applications and projects based on this technology. Finally, we discuss the issues and challenges in its implementation.

In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico’s 2017 Earthquake is presented, and the data extracted during and after the event are reported.

Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one.

The innovations proposed by the cell phone market have grown steadily in recent years, along with the increasing complexity of the hardware, operating systems, and applications available in this market. These changes bring new challenges related to usability that need to be considered during the development process of these applications since the new forms of user-application interactions increasingly require adapting the behavior of smartphone users. In this situation, usability is an important issue that depends on factors such as the Users, their characteristics and abilities, the Task which the users intend to achieve and also the application usage Context. This work presents a systematic literature review with the objective of identifying the heuristics and usability metrics used in the literature and/or industry. Based on the review results, this work presents another contribution with a proposal of a set of usability heuristics focused in mobile applications on smartphones, considering the User, Task and Context as usability factors and Cognitive Load as an important attribute of usability. The components of this set are detailed in a model intended to be used in empirical validations allowing to dynamically incorporate improvements to the proposal.