Privata 5G nätverk – antingen som en del av ett publikt eller som ett helt eget mobilnät erbjuder möjliga lösningar på ett antal av de utmaningar som finns kring WiFi och trådlösa uppkopplingar i logistik och produktion. Tekniken börjar bli väletablerad och antalet leverantörer ökar snabbt samtidigt som priser för infrastrukturen sjunker i pris. Förutom att femte generationens mobilnät löser problem kring mobilitet och uppkoppling så finns ett antal intressanta funktioner såsom positionering och standardiserad edge computing för lokala digitala tjänster. Dessa funktioner bör dock ses som en del av en framtida uppgradering snarare än något som finns tillgängligt idag (dock inte så långt bort in i framtiden). Alla de besökta sågverken hade liknande utmaningar kring trådlös uppkoppling och i vissa fall har även 4G-baserade lösningar börjat användas – med gott resultat.

Tyvärr fick vi inte konkreta priser från de operatörer och leverantörer som dialog fördes med men via vissa leverantörer kan man ändå få en viss indikation (se t ex AWS erbjudande där det för ett privat nät med två radioaccespunkter ligger på ca 140 KSEK / månad totalt).

Contiki-NG (Next Generation) is an open source, cross-platform operating system for severely constrained wireless embedded devices. It focuses on dependable (reliable and secure) low-power communications and standardised protocols, such as 6LoWPAN, IPv6, 6TiSCH, RPL, and CoAP. Its primary aims are to (i) facilitate rapid prototyping and evaluation of Internet of Things research ideas, (ii) reduce time-to-market for Internet of Things applications, and (iii) provide an easy-to-use platform for teaching embedded systems-related courses in higher education. Contiki-NG started as a fork of the Contiki OS and retains many of its original features. In this paper, we discuss the motivation behind the creation of Contiki-NG, present the most recent version (v4.7), and highlight the impact of Contiki-NG through specific examples. © 2022 The Authors

This paper presents a low-resolution infrared thermal dataset of people and thermal objects, such as a working laptop, in indoor environments. The dataset was collected by a far infrared thermal camera (32x24 pixels), which can capture the position and shape information of thermal objects without privacy issues that enable trustworthy computer vision applications. The dataset consists of 1770 thermal images with high-quality annotation collected from an indoor room with around 15°C. We implemented a privacy-preserving human detection method and trained a multiple object detection (MOD) model based on the dataset. The human detection method reaches 90.3% accuracy. On the other hand, the MOD model achieved 56.8% mean average precision (mAP). Researchers can implement interesting applications based on our dataset, for example, privacy-preserving people counting systems, occupancy estimation systems for smart buildings, and social distance detectors. 

Low-power wireless networks transmit at low output power and are hence susceptible to cross-technology interference. The latter may cause packet loss which may waste scarce energy resources by requiring the retransmission of packets. Jamming attacks are even more harmful than cross-technology interference in that they may totally prevent packet reception and hence disturb or even disrupt applications. Therefore, it is important to recognize such jamming attacks. In this paper, we present JamSense. JamSense extends SpeckSense, a system that is able to detect multiple sources of interference, with the ability to classify jamming attacks. As SpeckSense, JamSense runs on resource-constrained nodes. Our experimental evaluation on real hardware shows that JamSense is able to identify jamming attacks with high accuracy while not classifying Bluetooth or WiFi interference as jamming attacks.

Wireless low-power, multi-hop networks are exposed to numerous attacks also due to their resource-constraints. While there has been a lot of work on intrusion detection systems for such networks, most of these studies have considered only a few topologies, scenarios and attacks. One of the reasons for this shortcoming is the lack of sufficient data traces that are required to train many machine learning algorithms. In contrast to other wireless networks, multi-hop networks do not contain one entity that can capture all the traffic which makes it more difficult to acquire such traces. In this paper we present Multi-Trace. Multi-Trace extends the Cooja simulator with multi-level tracing facilities that enable data logging at different levels while maintaining a global time. We discuss the opportunities that traces generated by Multi-Trace enable for researchers interested in input for their machine learning algorithms. We present experiments that show the efficiency with which Multi-Trace generates traces. We expect Multi-Trace to be a useful tool for the research community.

A wide gap exists between the state of the art in developing Wireless Sensor Network (WSN) software and current practices concerning the design, execution, and maintenance of business processes. WSN software is most often developed based on low-level OS abstractions, whereas business process development leverages high-level languages and tools. This state of affairs places WSNs at the fringe of industry. The makeSense system addresses this problem by simplifying the integration of WSNs into business processes. Developers use BPMN models extended with WSN-specific constructs to specify the application behavior across both traditional business process execution environments and the WSN itself, which is to be equipped with application-specific software. We compile these models into a high-level intermediate language-Also directly usable by WSN developers-And then into OS-specific deployment-ready binaries. Key to this process is the notion of meta-Abstraction, which we define to capture fundamental patterns of interaction with and within the WSN. The concrete realization of meta-Abstractions is application-specific; developers tailor the system configuration by selecting concrete abstractions out of the existing codebase or by providing their own. Our evaluation of makeSense shows that i) users perceive our approach as a significant advance over the state of the art, providing evidence of the increased developer productivity when using makeSense; ii) in large-scale simulations, our prototype exhibits an acceptable system overhead and good scaling properties, demonstrating the general applicability of makeSense; and, iii) our prototype-including the complete tool-chain and underlying system support-sustains a real-world deployment where estimates by domain specialists indicate the potential for drastic reductions in the total cost of ownership compared to wired and conventional WSN-based solutions.

Standard protocols for wireless Internet of Things (IoT) communication must be energy-efficient in order to prolong the lifetimes of IoT devices. Two energy-saving strategies for wireless communication are prevalent within the IoT domain: 1) sleepy devices and 2) radio duty cycling. In this paper, we conduct a comprehensive evaluation as to what types of application scenarios benefit the most from either type of energy-saving strategy. We select the lightweight machine to machine (LwM2M) protocol for this purpose because it operates atop the standard constrained application protocol, and has support for sleepy devices through its Queue Mode. We implement the Queue Mode at both the server side and client side, and design enhancements of Queue Mode to further improve the performance. In our experimental evaluation, we compare the performance and characteristics of Queue Mode with that of running LwM2M in a network stack with the standard time-slotted channel hopping as the duty cycling medium access control protocol. By analyzing the results with the support of an empirical model, we find that each energy-saving strategy has different advantages and disadvantages depending on the scenario and traffic pattern. Hence, we also produce guidelines that can help developers to select the appropriate energy-saving strategy based on the application scenario.

The Internet of Things poses new requirements for reliable, bi-directional communication in low-power and lossy networks, but these requirements are hard to fulfill since most existing protocols have been designed for data collection. In this paper, we propose standard-compliant mechanisms that make RPL meet these requirements while still scaling to large networks of IoT devices under significant resource constraints. Our aim is to scale far beyond what can be stored in RAM on the nodes of the network. The only node that needs to have storage for all the routing entries is the RPL root node. Based on experimentation with largescale commercial deployments, we suggest two mechanisms to make RPL scale under resource constraints: (1) end-to-end route registration with DAO and (2) a policy for managing the neighbor table. By employing these mechanisms, we show that the bi-directional packet reception rate of RPL networks increases significantly.

Security has arisen as an important issue for the Internet of Things (IoT). Efficient ways to provide secure communication between devices and sensors is crucial for the IoT devices, which are becoming more and more used and spread in a variety of fields. In this context, Elliptic Curve Cryptography (ECC) is considered as a strong candidate to provide security while being able to be functional in an environment with strong requirements and limitations such as wireless sensor networks (WSN). Furthermore, it is a valid candidate to be used in industry solutions.

In this demo we show a real use case of Elliptic Curve Cryptography for key establishment in combination with symmetric AES encryption. The demo will show the use of a BSD-licensed ECC library for the Contiki OS running on Yanzi Networks Contiki-based nodes that will securely communicate with a Yanzi Gateway.

The Internet of Things (IoT) is the interconnection of everyday physical objects with the Internet and their representation in the digital world. Due to the connectivity of physical objects with the untrusted Internet, security has become an important pillar for the success of IoT-based services. Things in the IoT are resource-constrained devices with limited processing and storage capabilities. Often, these things are battery powered and connected through lossy wireless links. Therefore, lightweight and efficient ways of providing secure communication in the IoT are needed. In this context, Elliptic Curve Cryptography (ECC) is considered as a strong candidate to provide security in the IoT while being able to function in constrained environments. In this paper we present a lightweight implementation and evaluation of ECC for the Contiki OS. For fast, secure and cost-effective mass development of IoT-based services by different vendors, it is important that the IoT protocols are implemented and released as open source and open licensed. To the best of our knowledge our ECC is the first lightweight BSD-licensed ECC for the IoT devices. We show the feasibility of our implementation by a thorough performance analysis using several implementations and optimization algorithms. Moreover, we evaluate it on a real IoT hardware platform.