A wildland fire is an uncontrolled fire that occurs mainly in forest areas, although it can also invade urban or agricultural areas. Among the main causes of wildfires, human factors, either intentional or accidental, are the most usual ones. The number and impact of forest fires are expected to grow as a consequence of the global warming. In order to fight against these disasters, it is necessary to adopt a comprehensive, multifaceted approach that enables a continuous situational awareness and instant responsiveness. This paper describes a hierarchical wireless sensor network aimed atearly fire detection in risky areas, integrated with the fire fighting command centres, geographical information systems, and fire simulators. This configuration has been successfully tested in two fire simulations involving all the key players in fire fighting operations: fire brigades, communication systems, and aerial, coordination, and land means.

The emergence and spread of Internet of Things (IoT) technologies along with the edge computing paradigm has led to an increase in the computational load on sensor end-devices. These devices are now expected to provide high-level information instead of just raw sensor measurements. Therefore, the processing tasks must share the processor time with the communication tasks, and both of them may have strict timing constraints. In this work, we present an empirical study, from the edge computing perspective, of the process management carried out by an IoT Operating System (OS), showing the cross-influence between the processing and communication tasks in end-devices. We have conducted multiple tests in two real scenarios with a specific OS and a set of wireless protocols. In these tests, we have varied the processing and communication tasks timing parameters, as well as their assigned priority levels. The results obtained from these tests demonstrate that there is a close relationship between the characteristics of the processing tasks and the communication performance, especially when the processing computational load is high. In addition, these results also show that the computational load is not the only factor responsible for the communication performance degradation, as the relationship between the processing tasks and the communication protocols timing parameters also plays a role. These conclusions should be taken into account for future OSs and protocol developments.

The wireless Internet of Things (IoT) family grows without interruption. Every day more applications and wireless devices are available to interconnect and help solve multiple problems in areas such as health, critical infrastructure, industry, etc. Many of the tasks to be performed by the IoT network require time synchronization for their correct operation, either to use the spectrum more efficiently, to add data from different sensors, or to carry out coordinated communications. Each of these applications has different requirements regarding time synchronization. This means that the decision of which strategy to follow to synchronize an IoT end device becomes a task that requires important prior analysis and usually, if developers are experts in the topic, ends with the implementation of an ad hoc solution. In this article, we present a methodology to choose an adequate time-synchronization strategy for any wireless IoT application. We also present a tool that executes the methodology, guiding the IoT application developer through some input forms. This combination of methodology and tool abstracts developers from the complexities of time-synchronization strategies, allowing them to choose the correct strategy regardless of their level of knowledge in wireless IoT time synchronization. As a result, the methodology offers a set of time-synchronization strategies that are adjusted to the needs of developers and applications.

Road traffic accidents are one of the main causes of death and disability worldwide. Workers responsible for maintaining and repairing roadways are especially prone to suffer these events, given their exceptional exposure to traffic. Since these actuations usually coexist with regular traffic, an errant driver can easily intrude the work area and provoke a collision. Some authors have proposed mechanisms aimed at detecting breaches in the work zone perimeter and alerting workers, which are collectively called intrusion alarm systems. However, they have several limitations and have not yet fulfilled the necessities of these scenarios. In this paper, we propose a new intrusion alarm system based on a Wireless Sensor Network (WSN). Our system is comprised of two main elements: vehicle detectors that form a virtual barrier and detect perimeter breaches by means of an ultrasonic beam and individual warning devices that transmit alerts to the workers. All these elements have a wireless communication interface and form a network that covers the whole work area. This network is in charge of transmitting and routing the alarms and coordinates the behavior of the system. We have tested our solution under real conditions with satisfactory results.

Cognitive wireless sensor network (CWSN) is a new paradigm, integrating cognitive features in traditional wireless sensor networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in cognitive wireless sensor networks is an important problem since these kinds of networks manage critical applications and data. The specific constraints of WSN make the problem even more critical, and effective solutions have not yet been implemented. Primary user emulation (PUE) attack is the most studied specific attack deriving from new cognitive features. This work discusses a new approach, based on anomaly behavior detection and collaboration, to detect the primary user emulation attack in CWSN scenarios. Two non-parametric algorithms, suitable for low-resource networks like CWSNs, have been used in this work: the cumulative sum and data clustering algorithms. The comparison is based on some characteristics such as detection delay, learning time, scalability, resources, and scenario dependency. The algorithms have been tested using a cognitive simulator that provides important results in this area. Both algorithms have shown to be valid in order to detect PUE attacks, reaching a detection rate of 99% and less than 1% of false positives using collaboration

The increase in the number of mobile and Internet of Things (IoT) devices, along with the demands of new applications and services, represents an important challenge in terms of spectral coexistence. As a result, these devices are now expected to make an efficient and dynamic use of the spectrum, and to provide processed information instead of simple raw sensor measurements. These communication and processing requirements have direct implications on the architecture of the systems. In this work, we present MIGOU, a wireless experimental platform that has been designed to address these challenges from the perspective of resource-constrained devices, such as wireless sensor nodes or IoT end-devices. At the radio level, the platform can operate both as a software-defined radio and as a traditional highly integrated radio transceiver, which demands less node resources. For the processing tasks, it relies on a system-on-a-chip that integrates an ARM Cortex-M3 processor, and a flash-based FPGA fabric, where high-speed processing tasks can be offloaded. The power consumption of the platform has been measured in the different modes of operation. In addition, these hardware features and power measurements have been compared with those of other representative platforms. The results obtained confirm that a state-of-the-art tradeoff between hardware flexibility and energy efficiency has been achieved. These characteristics will allow for the development of appropriate solutions to current end-devices’ challenges and to test them in real scenarios.