ABSTRACT
Industrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge computing and machine learning. IIoT provides some of the essential building blocks to drive manufacturing systems to the next level of productivity, efficiency, and safety. Hardware failures and faults in IIoT are critical challenges to be faced. These anomalies can cause accidents and financial loss, affect productivity, and mobilize staff by producing false alarms. In this context, this article proposes a framework called Detection and Alert State for Industrial Internet of Things Faults (DASIF). The DASIF framework applies edge computing to execute highly precise and low latency machine learning models to detect industrial IoT faults and autonomously enforce an adaptive communication policy, triggering a state of alert in case of fault detection. The state of alert is a pre-stage countermeasure where the network increases communication reliability by using data replication combined with multiple-path communication. When the system is under alert, it can process a fine-grained inspection of the data for efficient decison-making. DASIF performance was obtained considering a simulation of the IIoT network and a real petrochemical dataset.
ABSTRACT
This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs)-Drones as data collectors to the Internet of Things (IoT). We have proposed a model for only one aircraft, as well as for multiple ones. A clustering technique that extends the scope of the number of IoT devices (e.g., sensors) visited by UAVs is also addressed. The flight plan generated from the model focuses on preventing breakdowns due to a lack of battery charge to maximize the number of nodes visited. In addition to the drone autonomous flight planning, a data storage limitation aspect is also considered. We have presented the energy consumption of drones based on the aerodynamic characteristics of the type of aircraft. Simulations show the algorithm's behavior in generating routes, and the model is evaluated using a reliability metric.
ABSTRACT
Internet of Things (IoT) is evolving to multi-application scenarios in smart cities, which demand specific traffic patterns and requirements. Multi-applications share resources from a single multi-hop wireless networks, where smart devices collaborate to send collected data over a Low-Power and Lossy Networks (LLNs). Routing Protocol for LLNs (RPL) emerged as a routing protocol to be used in IoT scenarios where the devices have limited resources. Instances are RPL mechanisms that play a key role in order to support the IoT scenarios with multiple applications, but it is not standardized yet. Although there are related works proposing multiple instances in RPL on the same IoT network, those works still have limitations to support multiple applications. For instance, there is a lack of flexibility and dynamism in management of multiple instances and service differentiation for applications. In this context, the goal of this work is to develop a solution called DYNAmic multiple RPL instanceS for multiple ioT applicatIons (DYNASTI), which provides more dynamism and flexibility by managing multiple instances of RPL. As a result of this, the traffic performance of multiple applications is enhanced through the routing, taking into consideration the distinct requirements of the applications. In addition, DYNASTI enables the support of sporadic applications as well as the coexistence between regular and sporadic applications. DYNASTI achieved results that demonstrate a significant improvement in reducing the number of control messages, which resulted in increased packet received, decreased end-to-end delay, reduced energy consumption, and an improvement in service differentiation to multiple applications.
