As the complexity of Internet of Things (IoT) systems increases, a large variety of tools and technologies for IoT management are making their way into both research setups and the market. IoT management solutions must consider the resource restrictions of embedded devices, as well as their heterogeneity and network dynamics. With these in mind, the Internet Engineering Task Force developed several standards targeting the integration and interoperation of heterogeneous devices, such as the Representational State Transfer Configuration Protocol (RESTCONF) or the Constrained Application Protocol (CoAP) Management Interface. Concurrently, the Open Mobile Alliance developed the Lightweight Machine-to-Machine protocol, for IoT device management. This paper provides a comprehensive, up-to-date overview of IoT management technologies, frameworks and protocols. Also, it proposes a taxonomy for IoT devices management. In addition to presenting the various solutions, the paper provides comparative views, standardization timeline, and market analysis. The presented analysis ranges from traditional network management protocols, such as Simple Network Management Protocol, to the newest IoT management and configuration protocols, such as CoAP Management Interface and Lightweight Machine-to-Machine protocols. Moreover, this survey identifies remaining challenges and solutions offered by recent management protocols, not covered by previous surveys.

Dependability and standardization are essential to the adoption of Wireless Sensor Networks (WSN) in industrial applications. Standards such as ZigBee, WirelessHART, ISA100.11a and WIA-PA are, nowadays, at the basis of the main process-automation technologies. However, despite the success of these standards, management of WSNs is still an open topic, which clearly is an obstacle to dependability. Existing diagnostic tools are mostly application- or problem-specific, and do not support standard-based multi-network monitoring. This paper proposes a WSN monitoring architecture for process-automation technologies that addresses the mentioned limitations. Specifically, the architecture has low impact on sensor node resources, uses network metrics already available in industrial standards, and takes advantage of widely used management standards to share the monitoring information. The proposed architecture was validated through prototyping, and the obtained performance results are presented and discussed in the final part of the paper. In addition to proposing a monitoring architecture, the paper provides an in-depth insight into metrics, techniques, management protocols, and standards applicable to industrial WSNs.

In recent years, considerable advances in connecting industrial equipment to the Internet allowed a higher level of operational efficiency, productivity, and automation. Nevertheless, the construction industry still presents challenging requirements, like long-distance wireless communication, interconnection of out-of-coverage areas, constant mobility of machinery, and support for legacy and proprietary systems. All these requirements pose different challenges when compared with traditional smart factory Industry 4.0 solutions. This paper discusses some of the current key questions regarding fleet management in the construction industry, identifies requirements for heavy-duty machinery, and proposes an Industrial Internet of Things (IIoT)-based solution for the integrated monitoring of such machinery. Also, it proposes and presents an open, innovative solution for the integrated management of non-standardized civil construction vehicle technologies. The developed prototype uses the J1939 protocol to collect machinery status and Long-Range Wide Area Network (LoRaWAN) to communicate the monitored data. The solution was assessed at the construction site of the new port of Sines in Portugal. A detailed description of the proposed system is provided, along with information on trials and evaluation results regarding its performance. Additionally, the paper provides insights into open issues and challenges in this field and how our solution can contribute to overcoming them.

The increasing amount of human-related and/or human-originated data in current systems is both an opportunity and a challenge. Nevertheless, despite relying on the processing of large amounts of data, most of the so-called smart systems that we have nowadays merely consider humans as sources of data, not as system beneficiaries or even active “components.” For truly smart systems, we need to create systems that are able to understand human actions and emotions, and take them into account when deciding on the system behavior. Naturally, in order to achieve this, we first have to empower systems with human sensing capabilities, possibly in ways that are as inconspicuous as possible. In this context, in this article we survey existing approaches to unobtrusive monitorization of human beings, namely, of their activity, vital signs, and emotional states. After setting a taxonomy for human sensing, we proceed to present and analyze existing solutions for unobtrusive sensing. Subsequently, we identify and discuss open issues and challenges in this area. Although there are surveys that address some of the concerned fields of research, such as healthcare, human monitorization, or even the use-specific techniques like channel state information or image recognition, as far as we know this is the first comprehensive survey on unobtrusive sensing of human beings.