Low-power wide area networking technology offers long-range communication, which enables new types of services. Several solutions exist; LoRaWAN is arguably the most adopted. It promises ubiquitous connectivity in outdoor IoT applications, while keeping network structures and management simple. This technology has received a lot of attention in recent months from network operators and solution providers. However, the technology has limitations that need to be clearly understood to avoid inflated expectations and disillusionment. This article provides an impartial and fair overview of the capabilities and limitations of LoRaWAN. We discuss those in the context of use cases, and list open research and development questions.

The Internet of Things (IoT) is revolutionizing technology in a wide variety of areas, from smart healthcare to smart transportation. Due to the increasing trend in the number of IoT devices and their different levels of energy requirements, one of the significant concerns in IoT implementations is powering up the IoT devices with conventional limited lifetime batteries. One efficient solution to prolong the lifespan of these implementations is to integrate energy harvesting technologies into IoT systems. However, due to the characteristics of the energy harvesting technologies and the different energy requirements of the IoT systems, this integration is a challenging issue. Since Medium Access Control (MAC) layer operations are the most energy-consuming processes in wireless communications, they have undergone different modifications and enhancements in the literature to address this issue. Despite the essential role of the MAC layer to efficiently optimize the energy consumption in IoT systems, there is a gap in the literature to systematically understand the possible MAC layer improvements allowing energy harvesting integration. In this survey paper, we provide a unified framework for different wireless technologies to measure their energy consumption from a MAC operation-based perspective, returning the essential information to select the suitable energy harvesters for different communication technologies within IoT systems. Our analyses show that only 23% of the presented protocols in the literature fulfill Energy Neutral Operation (ENO) condition. Moreover, 48% of them are based on the hybrid approaches, which shows its capability to be adapted to energy harvesting. We expect this survey paper to lead researchers in academia and industry to understand the current state-of-the-art of energy harvesting MAC protocols for IoT and improve the early adoption of these protocols in IoT systems.

Elderly people with physical or cognitive disabilities such as visual impairment lack independence in their everyday activities, such as shopping in retail stores. Ambient assisted living (AAL) technologies can help. The authors of this article propose the use of RFID technologies to enable an AAL system in a retail store. This article presents an RFID-enabled intelligent system that can detect user-object interactions via a supervised machine learning algorithm. The authors' proposal detects RFID signals from different objects and classifies them as interacted or static objects through an automatic training system, achieving a detection accuracy over 86 percent.

IoT-based environments may infer anomalies based on the data processed from their heterogeneous sensors. Within the technologies evolving the IoT concept, nowadays the Radio Frequency Identification (RFID) technology is a de facto standard in areas like retail or logistics. For instance, most retailers attach RFID-labels to their items to avoid stock-out in the inventory or speed up cash processes. Besides identification, RFID provides further RF data which can be used for information management like anomaly detection (i.e. a shoplifting in a RFID loss prevention system). This manuscript presents two IoT scenarios to detect anomalies using multivariate outlier detection methods, uniquely using RFID data. This research empirically evaluates the authors' proposed methods by reproducing a RFID-enabled store, and the two proposed scenarios. The evaluation achieved a False Positive Rate around 0.1% and a True Positive Rate around 87%.