Internet of Things (IoT) technologies, when adequately integrated, cater for logistics optimisation and operations' environmental impact monitoring, both key aspects for today's EU ports management. This article presents Obelisk, a scalable and multi-tenant cloud-based IoT integration platform used in the EU H2020 PortForward project. The landscape of IoT protocols being particularly fragmented, the first role of Obelisk is to provide uniform access to data originating from a myriad of devices and protocols. Interoperability is achieved through adapters that provide flexibility and evolvability in protocol and format mapping. Additionally, due to ports operating in a hub model with various interacting actors, a second role of Obelisk is to secure access to data. This is achieved through encryption and isolation for data transport and processing, respectively, while user access control is ensured through authentication and authorisation standards. Finally, as ports IoTisation will further evolve, a third need for Obelisk is to scale with the data volumes it must ingest and process. Platform scalability is achieved by means of a reactive micro-services based design. Those three essential characteristics are detailed in this article with a specific focus on how to achieve IoT data platform scalability. By means of an air quality monitoring use-case deployed in the city of Antwerp, the scalability of the platform is evaluated. The evaluation shows that the proposed reactive micro-service based design allows for horizontal scaling of the platform as well as for logarithmic time complexity of its service time.

While the Internet of Things continues to grow, the LoRaWAN standard is generating special interest due to its open-source nature, ultralow-power consumption and long-range connectivity. Recent works have explored the challenges of implementing LoRaWAN, with scalability being considered one of the major bottlenecks imposed by its Aloha-based medium access control (MAC) layer. Despite much on-going research on LoRaWAN scheduling aimed at alleviating this concern, experimental approaches are rarely found in the literature. In this work, we describe the steps taken and the technical issues overcome to move from a low-overhead synchronization and scheduling concept to its real-world implementation on top of LoRaWAN Class A. Accordingly, an end-to-end architecture was designed and deployed on top of STM32L0 MCUs, which communicate with a central entity responsible for providing synchronization metrics and allocating transmission slots on demand. The clock drift of devices was measured in a temperature-controlled chamber, which served as a basis to define slot lengths in the network. As a result, an operational end-to-end system was implemented and evaluated for different setup scenarios, with 10-ms accuracy being achieved. Our experimental results show significant improvements in packet delivery ratios with respect to Aloha-based setups, especially under high network loads (up to 29% for SF12), thereby demonstrating the feasibility of the presented approach.

The wireless Internet of Things (IoT) landscape is quite diverse. For instance, Low-Power Wide-Area Network (LPWAN) technologies offer low data rate communication over long distance, whereas Wireless Personal Area Network (WPAN) technologies can reach higher data rates, but with a reduced range. For simple IoT applications, communication requirements can be fulfilled by a single technology. However, the requirements of more demanding IoT use cases can vary over time and with the type of data being exchanged. This is pushing the design towards multimodal approaches, where different wireless IoT technologies are combined and the most appropriate one is used as per the need. This paper considers the combination of Narrow Band IoT (NB-IoT) and Bluetooth Low Energy (BLE) as communication options for an IoT device that is running a Lightweight Machine to Machine/Constrained Application Protocol (LwM2M/CoAP) protocol stack. It analyses the challenges incurred by different protocol stack options, such as different transfer modes (IP versus non-IP), the use of Static Context Header Compression (SCHC) techniques, and Datagram Transport Layer Security (DTLS) security modes, and discusses the impact of handover between both communication technologies. A suitable end-to-end architecture for the targeted multimodal communication is presented. Using a prototype implementation of this architecture, an in-depth assessment of handover and its resulting latency is performed.

As the Internet of Things (IoT) continues to expand, the need to combine communication technologies to cope with the limitations of one another and to support more diverse requirements will proceed to increase. Consequently, we started to see IoT devices being equipped with multiple radio technologies to connect to different networks over time. However, the detection of the available radio technologies in an energy-efficient way for devices with limited battery capacity and processing power has not yet been investigated. As this is not a straightforward task, a novel approach in such heterogeneous networks is required. This article analyzes different low-power wide-area network technologies and how they can be integrated in such a heterogeneous system. Our contributions are threefold. First, an optimal protocol stack for a constrained device with access to multiple communication technologies is put forward to hide the underlying complexity for the application layer. Next, the architecture to hide the complexity of a heterogeneous network is presented. Finally, it is demonstrated how devices with limited processing power and battery capacity can have access to higher bandwidth networks combined with longer range networks and on top are able to save energy compared to their homogeneous counterparts, by measuring the impact of the novel vertical handover algorithm.

Multiple Radio Access Technology (multi-RAT) communication with Low Power Wide Area Networks (LPWAN) significantly increases the flexibility of Internet of Things (IoT) applications. Location-based services that build upon such a multimodal communication architecture are able to switch to an optimal localization method depending on the constraints of the active wireless technology. Furthermore, the resulting location estimate can aid location-based handover mechanisms to reduce the energy consumption of a multi-RAT IoT device. In this research, we present our design of a multimodal localization framework and illustrate the benefit of such a framework with two IoT use case examples. For the first use case, valuable artwork is tracked during transportation to a museum. In the second use case, we monitor the usage and location of large construction tools. Finally, we propose how our localization framework can be improved to deal with implementation challenges and to reduce location estimation errors.

Due to the limited bandwidth of Low-Power Wide-Area Networks (LPWAN), the application layer is currently often tied straight above the link layer, limiting the evolution of sensor networks distributed over a large area. Consequently, the highly efficient Static Context Header Compression (SCHC) standard was introduced, where devices can compress the IPv6 and upper layer protocols down to a single byte. This approach, however, assumes that every compression context is distributed before deployment, again limiting the evolution of such networks. Therefore, this paper presents two context registration mechanisms leveraging on the SCHC adaptation layer. This is done by analyzing current registration solutions in order to find limitations and optimizations with regard to very constrained networks. Both solutions and the current State-of-The-Art (SoTA) are evaluated in a Lightweight Machine to Machine (LwM2M) environment. In such situation, both developed solutions decrease the energy consumption already after 25 transmissions, compared with the current SoTA. Furthermore, simulations show that Long Range (LoRa) devices still have a 80% chance to successfully complete the registration flow in a network with a 50% Packet Error Ratio. Briefly, the work presented in this paper delivers bootstrapping tools to constrained, SCHC-enabled networks while still being able to reduce energy consumption.