Peter Herrmann
@ntnu.no
University Professor at the Department of Information Security and Communication Technology
Norwegian University of Science and Technology (NTNU)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Networks and Communications, Computational Theory and Mathematics, Control and Systems Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Magnus Oplenskedal, Peter Herrmann, and Amir Taherkordi
Elsevier BV
Ergys Puka and Peter Herrmann
IEEE
The interaction between vehicles and the external infrastructure usually relies on the availability of cellular networks. Large sparsely populated regions, however, contain often extended areas with missing cellular network connectivity, so-called dead spots. To accelerate the delivery of messages from a vehicle to the infrastructure in such zones, we developed a Context-Aware Message Flooding Protocol that utilizes ephemeral ad-hoc networks between nearby vehicles in a dead spot. This allows us to let a message be delivered by an ad-hoc network peer instead of its creator if the peer leaves the dead spot earlier. In previous work, we introduced an initial version of this protocol and proved formally that it guarantees the fastest possible delivery of messages via the cellular network to the infrastructure, and, at the same time, keeps the number of produced duplicates to a minimum. This version was, however, based upon idealized drivers' behavior since we assumed that a vehicle leaves a dead spot exactly at the point of time previously predicted. To rectify this strict assumption, in this paper we present an updated protocol version named CAMFLOOP. Unlike its predecessor, it considers deviations like speed changes or aberrations from the planned route. Moreover, we report on our tests of CAMFLOOP using the traffic simulator SUMO with scenarios inspired by the Australian Outback, a region with dead spots that may span hundreds of kilometers. The simulation results show that deviations from the planned speeds and routes lead to communication errors only rarely. Further, the protocol provides significant reductions of the delivery time by more than 40% on average in larger dead spots, while usually less than two copies of each message created in a dead spot are delivered to the infrastructure.
Zeeshan Ali Khan and Peter Herrmann
IEEE
Many devices used in the Internet of Things (IoT) have scarce processing resources and restricted energy budgets. Thus, established security mechanisms to protect IoT nodes against malicious attacks cannot be applied. Trust management is a promising technology to circumvent the processing limitations since it makes the use of lightweight but still powerful security mechanisms possible. In earlier work, we proposed a trust-based routing solution that builds the reputation of IoT devices to detect maliciously behaving ones. It has, however, the disadvantage of additional battery draining since it works only with constant idle listening of the communication channel which is quite energy intensive. In this paper, we present a method to alleviate this problem by aligning the proposed security algorithm with the popular IEEE 802.15.4 protocol that offers functionality to reduce active channel listening. In particular, we suggest an adaption to one of the application modes of IEEE 802.15.4 such that we can use our trust-based algorithm with often only slight losses from idle listening. The results of the protocol adaptations are discussed for two different scenarios.
Magnus Oplenskedal, Amir Taherkordi, and Peter Herrmann
IEEE
Accurately detecting the mobile contexts of public transport vehicles and their passengers is a key requirement of intelligent context-aware services in such systems. A prominent example is in-vehicle presence detection which can be used to provide various services such as automated ticketing, dynamic vehicle distribution, and live route optimization. To use such services in practice, in-vehicle presence detection needs to be close to infallible. However, most existing solutions in this field suffer from low spatiotemporal accuracy. To address this challenge, we introduce Ataraxis in this paper-an approach to hardwareless in-vehicle presence detection. In particular, we develop a deep convolutional neural network that can be trained to detect, if a user is inside a public transportation vehicle such as a tram, subway, or bus, from the raw sensor events generated by the sensors in a single ordinary smartphone. We show that this information can be used to infer the in-vehicle presence of users over time when combined with other sources such as the GPS trace of the user and that of the public transport vehicles. Ataraxis has the capability to distinguish between the four user modes: driving a car, riding a bike, walking, and using public transport with an accuracy of 98.69%. This is higher than the accuracy of existing techniques for transport mode detection. We also made experiments on the battery consumption and CPU overhead. The results show that Ataraxis incurs a negligible computational overhead and power consumption on smartphones, even though we base our approach on sensor data collection and a deep learning model.
Peter Herrmann, Ergys Puka, and Tor Rune Skoglund
IEEE
Personal Information Displays (PID) at bus stops help making the usage of public transport more attractive. If no electric grid is nearby, however, the installation of PIDs is very expensive due to the high wiring costs. To resolve this issue, the partners of the R&D project IoT-STOP develop a novel PID system that will be independent from the access to power lines. The system uses e-papers as displays that can be accessed using a cellular network. To prevent long, energy-intensive idle listening, the network receiver operates only when the passenger information, in particular, the Expected Times of Arrival (ETA) of the buses, is updated. Between two updates, the receiver is switched off such that adjustments after sudden events are not possible. Therefore, the update periods have to be carefully selected. In this paper, we introduce a predictor that estimates time intervals between updates. Our method is based on linear regression using samples of previous bus rides to forecast arrival times. Its predictions are applied by an algorithm to detect areas during the journey of a bus at which its ETA at a later stop changes with a certain probability. The forecasted times for passing such areas are then selected to update the PID at this stop. In addition, we present a number of tests of the predictor carried out at some bus stops in Bergen, Norway. The results show that the proposed method indeed predicts sensible update times of the PID systems.
Ergys Puka, Peter Herrmann, and Amir Taherkordi
IEEE
An often overlooked practical problem in Intelligent Transportation Systems (ITS) is the presence of areas without cellular network connectivity, so-called dead spots, which aggravates the communication between vehicles and the external infrastructure. In our previous work, we suggested to mitigate this problem by using a hybrid data dissemination protocol that combines cellular network communication with ad-hoc networks between vehicles. If the vehicles in such ad-hoc networks are in a dead spot but have a good estimation about the time they will leave it again, messages can be forwarded to the vehicle that is supposed to regain cellular network coverage first. Since this vehicle may transmit the stored messages immediately after having left the dead spot, the delivery time is improved. In this paper, we first analyze the behavior of the aforementioned data dissemination protocol in larger dead spots in which a message may be carried by several vehicles before being delivered via the cellular network. The analysis reveals that messages are not always delivered in the fastest possible time. To address this concern, a new protocol variant named context-aware message flooding protocol is introduced. This protocol, indeed, guarantees the fastest possible forwarding of messages to their recipients. This is achieved at the cost of delivering duplicates that, however, are only produced when the delivery of a message is sped up.
Ronal Bejarano, Roope Paakkonen, Jan Olaf Blech, Ian Peake, Peter Herrmann, and Valeriy Vyatkin
IEEE
Remote monitoring and control of factory equipment promises a more streamlined and therefore less expensive system operation and maintenance. The geographical distance between a factory and its control center, however, may influence the Quality of Service parameters of the network connections which might stymie the overall control process. To get a better understanding of these potential issues and their impact, we conducted a series of measurements over varying distances for the remote control, operation and simulation of Automated Guided Vehicles (AGVs) that are often used in modern factory environments. To achieve these tests, we defined three communication patterns reflecting local and remote connections as well as the usage of cloud-based services. Applying these patterns, we connected the Factory of the Future at the Aalto University in Finland with the VxLab at the RMIT University in Australia and the Microsoft Azure cloud in the Netherlands. This allowed us to measure important Quality of Service networking parameters for the communication over short, medium, and very long distances. In this paper, we present first empirical results and discuss their impact on the remote control of AGVs.
Magnus Oplenskedal, Amir Taherkordi, and Peter Herrmann
ACM
A key feature of modern public transportation systems is the accurate detection of the mobile context of transport vehicles and their passengers. A prominent example is automatic in-vehicle presence detection which allows, e.g., intelligent auto-ticketing of passengers. Most existing solutions, in this field, are based on either using active RFID or Bluetooth Low Energy (BLE) technology, or mobile sensor data analysis. Such techniques suffer from low spatiotemporal accuracy in in-vehicle presence detection. In this paper, we address this issue by proposing a deep learning model and the design of an associated generic distributed framework. Our approach, called DeepMatch, utilizes the smartphone of a passenger to analyze and match the event streams of its own sensors and the event streams of the counterpart sensors in an in-vehicle reference unit. This is achieved through a new learning model architecture using Stacked Convolutional Autoencoders for feature extraction and dimensionality reduction, as well as a dense neural network for stream matching. In this distributed framework, feature extraction and dimensionality reduction is offloaded to the smartphone, while matching is performed in a server, e.g., in the Cloud. In this way, the number of sensor events to be transmitted for matching on the server side will be minimized. We evaluated DeepMatch based on a large dataset taken in real vehicles. The evaluation results show that the statistical accuracy of our approach is 0.978 for in-vehicle presence detection which, as we will argue, is sufficient to be used in, e.g., auto-ticketing systems.
Jon Arild Ekberg Meyer, Ergys Puka, and Peter Herrmann
Springer International Publishing
Ergys Puka and Peter Herrmann
IEEE
The cellular network coverage in sparsely populated and mountainous areas is often patchy. That can be a significant impediment for services based on connections between vehicles and their environment. In this paper, we present a method to reduce the waiting time occurring when a vehicle intends to send a message via a cellular network but is currently in a dead spot without sufficient coverage. We use a hybrid network approach combining cellular network access with ad-hoc networks between vehicles that are nearby. In particular, we introduce a data dissemination protocol that allows the vehicles connected through an ad-hoc network to find out which one will most likely leave the dead spot first. Messages can then be sent to this vehicle that forwards them as soon as it regains cellular network access. Further, we developed an initial implementation of this protocol using the technology WiFi Direct that is realized on many mobile phones. Implementation details of the prototype as well as analysis results regarding data transmission time limits of fast driving vehicles are discussed in the article as well.
Magnus Oplenskedal, Amirhosein Taherkordi, and Peter Herrmann
IEEE
Recent developments in the field of indoor RealTime Locating Systems (RTLS) using mobile devices stimulate decision support for users. For instance, smartphone-based navigation in shops can enable location-aware recommendations of certain products to customers. An impeding factor to realize such systems is that they need the exact position of products. Existing product localization solutions, however, are based on tagging or manual location registering which tend to be quite costly and laborious. In this paper, we propose an automated product localization approach solving this problem. Our system infers the location of products based on the results of accumulating two sets of customer data, i.e., the locations at which the customers stop for picking up items as well as the list of the items, they purchase. These two data sets are accumulated for a large number of users, making it possible to build correct mappings between the products and their positions. We introduce a basic version of our localization algorithm and two extensions. One helps to improve calculating the position of relocated products while the other one fosters a faster localization using a smaller number of user data sets. We discuss the results of various simulation runs which give evidence that our system has a good potential to work in practice
Zeeshan Ali Khan and Peter Herrmann
Hindawi Limited
Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.
Magnus Karsten Oplenskedal, Peter Herrmann, Jan Olaf Blech, and Amir Taherkordi
IEEE
Modern Intelligent Transportation Systems (ITS) operate highly automatically. Therefore, they have to be able to handle a large variety of situations each demanding a particular system behavior. That aggravates the development of control software that has to guarantee safe and expedient operation in all possible situations. To support a suitable reconfiguration of the controllers to changing environments, the use of self-adaptation seems to be a highly promising approach. In this paper, we propose to combine model-based engineering of control software with simulation. That allows us to create and test controller software in parallel with the physical systems, it shall operate. Moreover, this approach makes it possible to safely confront a transport system with situations that, otherwise, could only be reproduced taking a significant risk. In particular, we introduce a framework for the creation of control software using simulators together with a development structure. The suggested design process is illustrated with a mobile robot example.
Ergys Puka, Peter Herrmann, Tomas Levin, and Christian B. Skjetne
IEEE
The quality of vehicle-to-vehicle and vehicle-to-infrastructure communication is a highly relevant aspect for the successful adoption of Intelligent Transportation Systems. In this paper, we present our plans to collect connectivity-related data on the Norwegian road network on a big scale. For that, a fleet of transporters will be equipped with Android devices sensing relevant connectivity data like the signal strength of cellular networks or the round trip delay of messages sent from a device to a remote server and back via these networks. The retrieved data are then stored together with the GPS data of the location where the measurements were taken. Data collected at the same place during various tours are aggregated, and the resulting data set can be used for various kinds of analysis and prediction. We introduce the architecture of our Android-application. Moreover, we discuss the results of some first experiments taken during various tours in a sparsely populated area around Trondheim. In particular, we found out some interesting correlations between the signal strength and the round trip delay which are quite different depending on the communication protocols used.
Anna Zamansky, Maria Spichkova, Guillermo Rodriguez-Navas, Peter Herrmann, and Jan Olaf Blech
SCITEPRESS - Science and Technology Publications
The use of lightweight formal methods (LFM) for the development of industrial applications has become a major trend. Although the term "lightweight formal methods" has been used for over ten years now, there seems to be no common agreement on what "lightweight" actually means, and different communities apply the term in all kinds of ways. In this paper, we explore the recent trends in the use of LFM, and establish our opinion that cost-effectiveness is the driving force to deploy LFM. Further, we propose a simple framework that should help to classify different LFM approaches and to estimate which of them are most cost-effective for a certain software engineering project. We demonstrate our framework using some examples.
Zeeshan Ali Khan, Johanna Ullrich, Artemios G. Voyiatzis, and Peter Herrmann
ACM
Local-area networks comprising the Internet of Things (IoT) consist mainly of devices that have limited processing capabilities and face energy constraints. This has an implication on developing security mechanisms, as they require significant computing resources. In this paper, we design a trust-based routing solution with IoT devices in mind. Specifically, we propose a trust-based approach for managing the reputation of every node of an IoT network. The approach is based on the emerging Routing Protocol for Low power and Lossy networks (RPL). The proposed solution is simulated for its routing resilience and compared with two other variants of RPL.
Peter Herrmann and Jan Olaf Blech
IEEE
Modern Cyber-Physical Systems are often driven bya plethora of controllers that are connected with each other andtheir environment. To guarantee a safe and robust execution ofthe systems, their control units have to strictly fulfill certainproperties which calls for the use of formal analysis methods inthe software development process. We present the combinationof the model-based engineering technique Reactive Blocks andthe spatiotemporal analysis tool BeSpaceD facilitating the formalverification of controller software.
Zeeshan Ali Khan and Peter Herrmann
IEEE
Many Internet of Things (IoT) networks comprise tiny devices with limited processing power and tight energy restrictions. These limitations make it difficult to use established security mechanisms protecting the devices against malicious attacks. This holds particularly for Intrusion Detection Systems (IDS) that help to detect various net-based attacks but often demand significant network and computing resources. In this article, we design and evaluate some IDS mechanisms for IoT Networks that are suited to small devices. They use a trust management mechanism that allows devices to manage reputation information about their neighbors. This mechanism makes it possible to single out maliciously behaving units in a processing and energy-friendly way. The approach is explained in the context of the healthcare domain.
Alexander Svae, Amir Taherkordi, Peter Herrmann, and Jan Olaf Blech
ACM
Autonomous systems have become more and more important in today's transport sector. They often operate in dynamic environments in which unpredictable events may occur at any time. These events may affect the safe operation of vehicles, calling for highly efficient control software technologies to reason about and react on their appearance. A crucial efficiency parameter is timeliness as vehicles often operate under high speed. The contribution of this paper is the presentation and analysis of design aspects of dynamic control software in the context of an autonomous train experiment. This is achieved through a self-adaptation software framework intended for autonomous trains and built on a demonstrator using Lego Mindstorms. The main mission of the framework is to collect context information, reason about it, and adapt the train behavior accordingly. The adaptation framework is implemented using the development tool Reactive Blocks and tested on the demonstrator. The evaluation results provide useful insights into the performance of the framework, particularly about the time needed to reason about the context and to carry out reconfigurations.
Amir Taherkordi, Peter Herrmann, Jan Olaf Blech, and Álvaro Férnandez
Springer International Publishing
Magnus Karsten Oplenskedal, Peter Herrmann, and Jan Olaf Blech
IEEE
In this paper, we report on our experiences with using a model-based development framework for both, the development and the simulation of robot control software. Our work can be seen as a step towards facilitating the development and maintenance of robot control software. The integrated simulation supports a development process, where individual components can be easily tested and validated without the need to have a full robot system working.
Simon Hordvik, Kristoffer Øseth, Henrik Heggelund Svendsen, Jan Olaf Blech, and Peter Herrmann
Springer International Publishing
Peter Herrmann and Jan Olaf Blech
Springer International Publishing
Peter Herrmann, Alexander Svae, Henrik Heggelund Svendsen, and Jan Olaf Blech
SCITEPRESS - Science and and Technology Publications
The model-based engineering technique Reactive Blocks supports the development of reactive systems by UML-based graphic modeling of control and data flows, model checker supported analysis, and automated code generation. Moreover, it facilitates the cooperation of teams of engineers by enabling the definition of formally precise behavioral interfaces that make the separation of the modelling process into various work packages easy. In this paper, we illustrate the use of Reactive Blocks for a joint student project that realized the monitoring and control of Lego Mindstormsbased trains in Norway through a control center in Australia. In particular, we explain how the Reactive Blocks interfaces and the applied communication protocols were used to split the project into work packages separately handled by the students involved.
Simon Hordvik, Kristoffer Øseth, Jan Olaf Blech, and Peter Herrmann
SCITEPRESS - Science and and Technology Publications
We present a method to engineer the control software of transport systems and analyze their safety using the
Reactive Blocks framework. The development benefits from the model-based approach and makes the analysis
of the systems at design time possible. The software is analyzed for freedom of collisions and other spatiotemporal
properties by combining test runs of already existing devices to find out their physical constraints with
the analysis of simulation runs using the verification tool BeSpaceD. This allows us to discover potential safety
hazards already during the development of the control software. In particular, we introduce a methodology for
the engineering and safety analysis of transportation systems and elaborate its practical usability by means of
a demonstrator based on Lego Mindstorms.