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IPv6 over LoRaWAN™
(2016)
Although short-range wireless communication explicitly targets local and regional applications, range continues to be a highly important issue. The range directly depends on the so-called link budget, which can be increased by the choice of modulation and coding schemes. The recent transceiver generation in particular comes with extensive and flexible support for software-defined radio (SDR). The SX127× family from Semtech Corp. is a member of this device class and promises significant benefits for range, robust performance, and battery lifetime compared to competing technologies. This contribution gives a short overview of the technologies to support Long Range (LoRa™) and the corresponding Layer 2 protocol (LoRaWAN™). It particularly describes the possibility to combine the Internet Protocol, i.e. IPv6, into LoRaWAN™, so that it can be directly integrated into a full-fledged Internet of Things (IoT). The proposed solution, which we name 6LoRaWAN, has been implemented and tested; results of the experiments are also shown in this paper.
Wireless communication systems more and more become part of our daily live. Especially with the Internet of Things (IoT) the overall connectivity increases rapidly since everyday objects become part of the global network. For this purpose several new wireless protocols have arisen, whereas 6LoWPAN (IPv6 over Low power Wireless Personal Area Networks) can be seen as one of the most important protocols within this sector. Originally designed on top of the IEEE802.15.4 standard it is a subject to various adaptions that will allow to use 6LoWPAN over different technologies; e.g. DECT Ultra Low Energy (ULE). Although this high connectivity offers a lot of new possibilities, there are several requirements and pitfalls coming along with such new systems. With an increasing number of connected devices the interoperability between different providers is one of the biggest challenges, which makes it necessary to verify the functionality and stability of the devices and the network. Therefore testing becomes one of the key components that decides on success or failure of such a system. Although there are several protocol implementations commonly available; e.g., for IoT based systems, there is still a lack of according tools and environments as well as for functional and conformance testing. This article describes the architecture and functioning of the proposed test framework based on Testing and Test Control Notation Version 3 (TTCN-3) for 6LoWPAN over ULE networks.
In the last decade, IPv6 over Low power Wireless Personal Area Networks (IEEE802.15.4), also known as 6LoWPAN, has well evolved as a primary contender for short range wireless communications and holds the promise of an Internet of Things, which is completely based on the Internet Protocol. The authors' team has developed a 6LoWPAN protocol stack in C language, the stack without the necessity to use a specific design environment or operating system. It is highly flexible, modular, and portable and can be enhanced by several interesting modules, like a Wake-On-Radio-(WOR) MAC layer or a TLS1.2 based security sublayer. The stack is made available as open source at https://github.com/hso-esk/emb6. It was extensively tested on the Automated Physical Testbed (APTB) for Wireless Systems, which is available in the authors' lab and allows a flexible setup and full control of arbitrary topologies. The results of the measurements demonstrate a very good stability and short-term with long-term performance also under dynamic conditions.
Die zunehmende Anzahl von Transistoren mit immer kleineren Strukturgrößen führt zu einer zunehmenden Leistungsaufnahme in modernen Prozessoren. Das gilt insbesondere für High-End Prozessoren, die mit einer hohen Taktfrequenz betrieben werden. Die aufgenommene Leistung wird in Wärme umgewandelt, die in einer Temperaturerhöhung der Prozessoren resultiert. Hohe Betriebstemperaturen verursachen u.a. eine verringerte Rechenleistung, eine kürzere Lebensdauer des Prozessors und höhere Leckströme. Aus diesen Gründen wird aktives, dynamisches thermisches Management immer wichtiger. Dieser Beitrag stellt eine Erweiterung zu dem Standard- Linux-Scheduler in der Kernel-Version 3.0 für eingebettete Systeme vor: einen PID-Regler, der unter Angabe einer Solltemperatur eine dynamische Frequenz- und Spannungsskalierung durchführt. Die Experimente auf dem Freescale LMX6 Quadcore-Prozessor zeigen, dass der PID-Regler die Betriebstemperatur des Prozessors an die Solltemperatur regeln kann. Er ist die Grundlage für eine in Zukunft zu entwickelnde prädiktive Regelung.
The integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements.
A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time‑variant Multi‑objective Particle Swarm Optimization Algorithm (AT‑MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time‑variant weights for the velocity of the particle swarm optimization and the non‑dominated sorting and mutation schemes from NSGA‑III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA‑II), and the Pareto Envelope‑based Selection Algorithm with region‑based selection (PESA‑II), and NSGA‑III. The proposed AT‑MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT‑MOPSO achieved 52% energy efficiency compared to NSGA‑III.
To show how this algorithm can be applied to a real‑world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT‑MOPSO can be used with existing Blockchain systems and the benefits it provides.
The EREMI project is a 2-year project funded under the ERASMUS+ framework programme and its team has developed and will validate an advanced higher education program, including life-long learning, on the interdisciplinary topic of resource efficiency in manufacturing industries and the overall system optimization of low or not digitized physical infrastructure. All of these will be achieved by applying IoT technologies towards efficient industrial systems, and by utilizing a high-level educated human capital on these economically, politically, and technically crucial and highly relevant topics for the rapidly developing industries and economies of intensively economically and industrially transforming countries - Bulgaria, North Macedonia, and Romania. Efficiency will be attained by utilizing the experience and expertise of the involved German partner organisation.
In recent years, the topic of embedded machine learning has become very popular in AI research. With the help of various compression techniques such as pruning, quantization and others compression techniques, it became possible to run neural networks on embedded devices. These techniques have opened up a whole new application area for machine learning. They range from smart products such as voice assistants to smart sensors that are needed in robotics. Despite the achievements in embedded machine learning, efficient algorithms for training neural networks in constrained domains are still lacking. Training on embedded devices will open up further fields of applications. Efficient training algorithms would enable federated learning on embedded devices, in which the data remains where it was collected, or retraining of neural networks in different domains. In this paper, we summarize techniques that make training on embedded devices possible. We first describe the need and requirements for such algorithms. Then we examine existing techniques that address training in resource-constrained environments as well as techniques that are also suitable for training on embedded devices, such as incremental learning. At the end, we also discuss which problems and open questions still need to be solved in these areas.
Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.
Digital transformation strengthens the interconnection of companies in order to develop optimized and better customized, cross-company business models. These models require secure, reliable, and traceable evidence and monitoring of contractually agreed information to gain trust between stakeholders. Blockchain technology using smart contracts allows the industry to establish trust and automate cross-company business processes without the risk of losing data control. A typical cross-company industry use case is equipment maintenance. Machine manufacturers and service providers offer maintenance for their machines and tools in order to achieve high availability at low costs. The aim of this chapter is to demonstrate how maintenance use cases are attempted by utilizing hyperledger fabric for building a chain of trust by hardened evidence logging of the maintenance process to achieve legal certainty. Contracts are digitized into smart contracts automating business that increase the security and mitigate the error-proneness of the business processes.