Ismael Amezcua Valdovinos

Proxy servers are widely used in many contexts since they can enhance performance, security, and access control for production networks. The Constrained Application Protocol (CoAP), the de facto standard of Internet of Things (IoT) communications, specifies mechanisms and semantics to applications based on RESTful approach similar to HTTP. The CoAP standard defines an additional operation mode for proxying request, which is supported by several programming languages and platforms. However, there is no current implementation of a CoAP proxy for embedded systems, namely for devices supporting the Contiki-NG operating system. This paper discusses the design, implementation, and evaluation of a forward CoAP Proxy server for 6LoWPAN embedded systems with cache capabilities. We perform simulation and experimental evaluations with topologies involving up to three hops from the proxy to study performance in terms of response times and the number of exchanged packets. In simulation environments results show a 48.03%, 85.39%, and 134.21% in response time reduction at one, two, and three hops away. In experimental environments, results show that the use of our embedded CoAP Proxy server reduces response times in 7.46%, 30.67%, and 37.43% when requests are targeted to servers at one, two, and three hops away from the proxy respectively. In both scenarios, a reduction in the number of exchanged packets of 71.42%, 125%, and 166.66% for requests at each hop is achieved.

Disabled pedestrians are among the most vulnerable groups in road traffic. Using technology to assist this vulnerable group could be instrumental in reducing the mobility challenges they face daily. On the one hand, the automotive industry is focusing its efforts on car automation. On the other hand, in recent years, assistive technology has been promoted as a tool for consolidating the functional independence of people with disabilities. However, the success of these technologies depends on how well they help self-driving cars interact with disabled pedestrians. This paper proposes an architecture to facilitate interaction between disabled pedestrians and self-driving cars based on deep learning and 802.11p wireless technology. Through the application of assistive technology, we can locate the pedestrian with a disability within the road traffic ecosystem, and we define a set of functionalities for the identification of hand gestures of people with disabilities. These functions enable pedestrians with disabilities to express their intentions, improving their confidence and safety level in tasks within the road ecosystem, such as crossing the street.

The auto industry is accelerating, and self-driving cars are becoming a reality. However, the acceptance of such cars will depend on their social and environmental integration into a road traffic ecosystem comprising vehicles, motorcycles, bicycles, and pedestrians. One of the most vulnerable groups within the road ecosystem is pedestrians. Assistive technology focuses on ensuring functional independence for people with disabilities. However, little effort has been devoted to exploring possible interaction mechanisms between pedestrians with disabilities and self-driving cars. This paper analyzes how self-driving cars and disabled pedestrians should interact in a traffic ecosystem supported by wearable devices for pedestrians to feel safer and more comfortable. We define the concept of an Assistive Self-driving Car (ASC). We describe a set of procedures to identify people with disabilities using an IEEE 802.11p-based device and a group of messages to express the intentions of disabled pedestrians to self-driving cars. This interaction provides disabled pedestrians with increased safety and confidence in performing tasks such as crossing the street. Finally, we discuss strategies for alerting disabled pedestrians to potential hazards within the road ecosystem.

El uso de las aplicaciones del Internet de las Cosas (IoT) está beneficiando a un número cada vez mayor de productores y organizaciones agrícolas en sus procesos de producción, mejorando su eficacia a largo plazo y contribuyendo al desarrollo sostenible, pues se estima que para 2023 estarán cerca de 12 millones de sensores agrícolas en uso globalmente. Por ello, los agricultores de diferentes sectores agrícolas, incluyendo el hortícola ornamental, están apostando por la pronta adopción del IoT para mejorar la eficiencia en su trabajo diario; aun cuando, en países subdesarrollados, los problemas para adquirir, acceder y utilizar las nuevas Tecnologías de Información y Comunicación (TIC) han dificultado esta tarea. En este artículo se describe el diseño arquitectural de una plataforma IoT, utilizada para la monitorización ambiental en viveros de plantas de ornato, permitiendo el almacenamiento y visualización dinámica de los datos sensados en la nube. La metodología en espiral para el desarrollo de prototipos fundamenta el diseño arquitectural presentado. Diseño que proporciona una solución innovadora al integrar un nodo portable que utiliza tecnología WiFi y una red de sensores estática basada en el estándar 6LoWPAN para el sensado, recolección y visualización de datos. Como resultados, este diseño contribuirá a establecer prácticas de agricultura inteligente para mejorar la calidad, productividad, rentabilidad y sustentabilidad en la producción de plantas ornamentales, así como a ahorrar tiempo y dinero en las empresas y productores de este sector al disminuir el esfuerzo laboral y riesgo de errores humanos en la recopilación de datos.

The Industrial Internet of Things (IIoT) is considered a key enabler for Industry 4.0. Modern wireless industrial protocols such as the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) deliver high reliability to fulfill the requirements in IIoT by following strict schedules computed in a Scheduling Function (SF) to avoid collisions and to provide determinism. The standard does not define how such schedules are built. The SF plays an essential role in 6TiSCH networks since it dictates when and where the nodes are communicating according to the application requirements, thus directly influencing the reliability of the network. Moreover, typical industrial environments consist of heavy machinery and complementary wireless communication systems that can create interference. Hence, we propose a distributed SF, namely the Channel Ranking Scheduling Function (CRSF), for IIoT networks supporting IPv6 over the IEEE 802.15.4e TSCH mode. CRSF computes the number of cells required for each node using a buffer-based bandwidth allocation mechanism with a Kalman filtering technique to avoid sudden allocation/deallocation of cells. CRSF also ranks channel quality using Exponential Weighted Moving Averages (EWMAs) based on the Received Signal Strength Indicator (RSSI), Background Noise (BN) level measurements, and the Packet Delivery Rate (PDR) metrics to select the best available channel to communicate. We compare the performance of CRSF with Orchestra and the Minimal Scheduling Function (MSF), in scenarios resembling industrial environmental characteristics. Performance is evaluated in terms of PDR, end-to-end latency, Radio Duty Cycle (RDC), and the elapsed time of first packet arrival. Results show that CRSF achieves high PDR and low RDC across all scenarios with periodic and burst traffic patterns at the cost of increased end-to-end latency. Moreover, CRSF delivers the first packet earlier than Orchestra and MSF in all scenarios. We conclude that CRSF is a viable option for IIoT networks with a large number of nodes and interference. The main contributions of our paper are threefold: (i) a bandwidth allocation mechanism that uses Kalman filtering techniques to effectively calculate the number of cells required for a given time, (ii) a channel ranking mechanism that combines metrics such as the PDR, RSSI, and BN to select channels with the best performance, and (iii) a new Key Performance Indicator (KPI) that measures the elapsed time from network formation until the first packet reception at the root.

While there have been extensive studies of denial of service (DoS) attacks and DDoS attack mitigation, such attacks remain challenging to mitigate. For example, Low-Rate DDoS (LR-DDoS) attacks are known to be difficult to detect, particularly in a software-defined network (SDN). Hence, in this paper we present a flexible modular architecture that allows the identification and mitigation of LR-DDoS attacks in SDN settings. Specifically, we train the intrusion detection system (IDS) in our architecture using six machine learning (ML) models (i.e., J48, Random Tree, REP Tree, Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM)) and evaluate their performance using the Canadian Institute of Cybersecurity (CIC) DoS dataset. The findings from the evaluation demonstrate that our approach achieves a detection rate of 95%, despite the difficulty in detecting LR-DoS attacks. We also remark that in our deployment, we use the open network operating system (ONOS) controller running on Mininet virtual machine in order for our simulated environment to be as close to real-world production networks as possible. In our testing topology, the intrusion prevention detection system mitigates all attacks previously detected by the IDS system. This demonstrates the utility of our architecture in identifying and mitigating LR-DDoS attacks.

This work presents a performance evaluation of two of the most popular end-to-end third generation technologies. The technologies analyzed were EVDO Rev. A and HSDPA. Although these technologies are present around the world, most of the work based on these technologies is only simulation, being most of the real-case scenario investigations, internal carrier studies. We used a real-case scenario using TCP and UDP. We measured throughput, jitter and packet loss/discard as QoS performance parameters. We performed tests using both technologies concurrently with two identical computers at downlink and uplink at 0 km/h, 60 km/h and 90 km/h. These computers send/receive data to a static server host located in California. In general, we noticed that HSDPA and EVDO lose performance when speed increases. The most affected performance parameter was the throughput being most of the average values under 50 percent of the nominal throughput value. At downlink, HSDPA was the most speed increase sensitive technology and at uplink EVDO was the most speed sensitive. HSDPA at downlink goes from 32.9 percent of the nominal throughput value to 8.57 percent of nominal throughput value at 90 km/h. EVDO in the uplink goes from 17.11 percent of nominal throughput value to 6.79 percent of the nominal throughput value at 90 km/h.