The growth of the Internet of Things (IoTs) and the number of connected devices is driven by emerging applications and business models. One common aim is to provide systems able to synchronize these devices, handle the big amount of daily generated data and meet business demands. This paper proposes a cost-effective cloud-based architecture using an event-driven backbone to process many applications’ data in real-time, called REDA. It supports the Amazon Web Service (AWS) IoT core, and it opens the door as a free software-based implementation. Measured data from several wireless sensor nodes are transmitted to the cloud running application through the lightweight publisher/subscriber messaging transport protocol, MQTT. The real-time stream processing platform, Apache Kafka, is used as a message broker to receive data from the producer and forward it to the correspondent consumer. Micro-services design patterns, as an event consumer, are implemented with Java spring and managed with Apache Maven to avoid the monolithic applications’ problem. The Apache Kafka cluster co-located with Zookeeper is deployed over three availability zones and optimized for high throughput and low latency. To guarantee no message loss and to simulate the system performances, different load tests are carried out. The proposed architecture is reliable in stress cases and can handle records goes to 8000 messages in a second with low latency in a cheap hosted and configured architecture.

LoRa technology, renowned for its low-power, long-range capabilities in IoT applications, faces challenges in real-world scenarios, including fading channels, interference, and environmental obstacles. This paper aims to study the reliability of LoRa in Non-Line-of-Sight (NLoS) conditions and in noisy and mobile environments for Industrial IoT (IIoT) applications. Experimental measurements consider factors like vegetation and infrastructure, introducing mobility to replicate NLoS conditions. Utilizing an open-source LoRa Physical Layer (PHY) Software-Defined Radio (SDR) prototype developed with GNU Radio, we assess communication reliability through metrics such as Block Error Rate (BLER), Signal-to-Noise-Interference-plus-Noise Ratio (SINR), and data rate. The study reveals the estimated overall reliability of the LoRa signal at 90.23%, emphasizing specific configuration details. This work contributes to the broader field of LoRa communication, encompassing hardware, software, protocols, and management, enhancing our understanding of LoRa’s dependability in challenging IIoT environments.

Wireless Sensor Networks (WSNs) are prone to highly constrained resources, as a result ensuring the proper functioning of the network is a requirement. Therefore, an effective WSN management system has to be integrated for the network efficiency. Our objective is to model, design, and propose a homogeneous WSN hybrid architecture. This work features a dedicated power utilization optimization strategy specifically for WSNs application. It is entitled Hybrid Energy-Efficient Power manager Scheduling (HEEPS). The pillars of this strategy are based on the one hand on time-out Dynamic Power Management (DPM) Intertask and on the other hand on Dynamic Voltage and Frequency Scaling (DVFS). All tasks are scheduled under Global Earliest Deadline First (GEDF) with new scheduling tests to overcome the Dhall effect. To minimize the energy consumption, the HEEPS predicts, defines and models the behavior adapted to each sensor node, as well as the associated energy management mechanism. HEEPS’s performance evaluation and analysis are performed using the STORM simulator. A comparison to the results obtained with the various state of the art approaches is presented. Results show that the power manager proposed effectively schedules tasks to use dynamically the available energy estimated gain up to 50%.

Energy efficiency presents a significant challenge to the reliability of Internet of Things (IoT) services. Wireless Sensor Networks (WSNs) present as an elementary technology of IoT, which has limited resources. Appropriate energy management techniques can perform increasing energy efficiency under variable workload conditions. Therefore, this paper aims to experimentally implement a hybrid energy management solution, combining Dynamic Voltage and Frequency Scaling (DVFS) and Duty-Cycling. The DVFS technique is implemented as an effective power management scheme to optimize the operating conditions during data processing. Moreover, the duty-cycling method is applied to reduce the energy consumption of the transceiver. Hardware optimization is performed by selecting the low-power microcontroller, MSP430, using experimental estimation and characterization. Another contribution is evaluating the energy-saving design by defining the normalized power as a metric to measure the consumed power of the proposed model per throughput. Extensive simulations and real-world implementations indicate that normalized power can be significantly reduced while sustaining performance levels in high-data IoT use cases.

As a real boost to competitiveness and productivity for sectors such as health, agriculture, or industry 4.0, the IoT market is transforming business models and revolutionizing practices. A major challenge is to develop applications with high computing potential with limited resources. To satisfy both high-level performance and low-power requirements, a deep characterization method for microcontrollers is proposed since selecting the appropriate device is a central pillar of smart energy policy. The high-level analysis investigates the potential of various low-power microcontrollers with a benchmark utilizing a periodic duty cycle model of typical WSN applications. Afterwards, deep characterization is carried-out in two steps. The preselectionphase is based on estimation whose results will serve as input for the second measurement phase. A highly accurate four-wiremeasurement involving guarding and shielding of all interfaces and the measurement cave is used for all measurements. To make the results comparable, a normalized power, defined as a measure of power per throughput, is introduced. The measurement characterizes the active power consumption depending on the supply voltage and frequency as well as sleep power, data logging power and peripherals power.

In most of the real-life application scenarios, where Wireless Sensor Networks (WSNs) are deployed, the energy is a limited and valuable resource. Many energy-based strategies were proposed in the literature to reduce the power consumption of sensor nodes and thus enhance the network lifetime. When those algorithms are developed and tested with some well-known network simulators like NS3, OPNET, the energy estimation aspect is not a key criterion, because these simulators have their own specific energy models. By implementing these algorithms in a real sensor node, the energy consumption can be measured and compared to the results provided by other previously implemented approaches. This paper proposes a method for estimating the energy consumption of a low power wireless node panStamp NRG 2.0. The proposed model calculates the consumed energy of the system, at run time for different operation cycles. Besides, since the biggest amount of energy is consumed during the communication, this paper aims to characterize the power consumption in the transmission state. A proper metric of the energy consumed per byte transmission pattern of the packets is provided. The results can be used to derive not only the energy consumption for other protocols but also the energy needed to transmit one byte as well as the payload size are given.