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Publications (10 of 62) Show all publications
Perez-Ramirez, D. F., Pérez-Penichet, C., Tsiftes, N., Voigt, T., Kostic, D. & Boman, M. (2023). DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks. In: Association for Computing Machinery (Ed.), IPSN '23: Proceedings of the 22nd International Conference on Information Processing in Sensor Networks: . Paper presented at IPSN '23: The 22nd International Conference on Information Processing in Sensor Networks (pp. 163). New York, NY, United States
Open this publication in new window or tab >>DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks
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2023 (English)In: IPSN '23: Proceedings of the 22nd International Conference on Information Processing in Sensor Networks / [ed] Association for Computing Machinery, New York, NY, United States, 2023, p. 163-Conference paper, Published paper (Refereed)
Abstract [en]

Novel backscatter communication techniques enable battery-free sensor tags to interoperate with unmodified standard IoT devices, extending a sensor network’s capabilities in a scalable manner. Without requiring additional dedicated infrastructure, the battery-free tags harvest energy from the environment, while the IoT devices provide them with the unmodulated carrier they need to communicate. A schedule coordinates the provision of carriers for the communications of battery-free devices with IoT nodes. Optimal carrier scheduling is an NP-hard problem that limits the scalability of network deployments. Thus, existing solutions waste energy and other valuable resources by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning scheduler that leverages graph neural networks to efficiently provide near-optimal carrier scheduling. We train our scheduler with optimal schedules of relatively small networks obtained from a constraint optimization solver, achieving a performance within 3% of the optimum. Without the need to retrain, our scheduler generalizes to networks 6 × larger in the number of nodes and 10 × larger in the number of tags than those used for training. DeepGANTT breaks the scalability limitations of the optimal scheduler and reduces carrier utilization by up to compared to the state-of-the-art heuristic. As a consequence, our scheduler efficiently reduces energy and spectrum utilization in backscatter networks.

Place, publisher, year, edition, pages
New York, NY, United States: , 2023
Keywords
scheduling, machine learning, wireless backscatter communications, combinatorial optimization
National Category
Communication Systems Computer Sciences Information Systems
Identifiers
urn:nbn:se:ri:diva-64865 (URN)10.1145/3583120.3586957 (DOI)979-8-4007-0118-4 (ISBN)
Conference
IPSN '23: The 22nd International Conference on Information Processing in Sensor Networks
Projects
SSF Instant Cloud ElasticityHorizon 2020 AI@Edge
Funder
Swedish Foundation for Strategic ResearchEU, Horizon 2020, 101015922Swedish Research Council, 2017-045989
Note

This work was financially supported by the Swedish Foundationfor Strategic Research (SSF), by the European Union’s Horizon 2020AI@EDGE project (Grant 101015922), and by the Swedish ResearchCouncil (Grant 2017-045989). 

Available from: 2023-05-23 Created: 2023-05-23 Last updated: 2023-06-08Bibliographically approved
Poncelet, C., Sagonas, K. & Tsiftes, N. (2022). So Many Fuzzers, So Little Time: Experience from Evaluating Fuzzers on the Contiki-NG Network (Hay)Stack. In: : . Paper presented at 37th IEEE/ACM International Conference on Automated Software Engineering.
Open this publication in new window or tab >>So Many Fuzzers, So Little Time: Experience from Evaluating Fuzzers on the Contiki-NG Network (Hay)Stack
2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Fuzz testing (“fuzzing”) is a widely-used and effective dynamic technique to discover crashes and security vulnerabilities in software, supported by numerous tools, which keep improving in terms of their detection capabilities and speed of execution. In this paper, we report our findings from using state-of-the-art mutation-based and hybrid fuzzers (AFL, Angora, Honggfuzz, Intriguer, MOpt-AFL, QSym, and SymCC) on a non-trivial code base, that of Contiki-NG, to expose and fix serious vulnerabilities in various layers of its network stack, during a period of more than three years. As a by-product, we provide a Git-based platform which allowed us to create and apply a new, quite challenging, open-source bug suite for evaluating fuzzers on real-world software vulnerabilities. Using this bug suite, we present an impartial and extensive evaluation of the effectiveness of these fuzzers, and measure the impact that sanitizers have on it. Finally, we offer our experiences and opinions on how fuzzing tools should be used and evaluated in the future.

Keywords
Software security, security testing, fuzz testing, coverage-guided fuzzing, hybrid fuzzing, IoT, Contiki-NG
National Category
Software Engineering Computer Sciences
Identifiers
urn:nbn:se:ri:diva-61138 (URN)10.1145/3551349.3556946 (DOI)
Conference
37th IEEE/ACM International Conference on Automated Software Engineering
Funder
Swedish Foundation for Strategic Research, RIT17-0038
Available from: 2022-11-10 Created: 2022-11-10 Last updated: 2023-05-26Bibliographically approved
Oikonomou, G., Duquennoy, S., Elsts, A., Eriksson, J., Tanaka, Y. & Tsiftes, N. (2022). The Contiki-NG open source operating system for next generation IoT devices. SoftwareX, 18, Article ID 101089.
Open this publication in new window or tab >>The Contiki-NG open source operating system for next generation IoT devices
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2022 (English)In: SoftwareX, E-ISSN 2352-7110, Vol. 18, article id 101089Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Elsevier B.V., 2022
Keywords
Contiki-NG, Internet of Things, Resource-Constrained Devices, Embedded systems, Open systems, Titanium compounds, 6LoWPAN, Contiki, Cross-platform, Embedded device, It focus, Low-power communication, Open source operating systems, Open-source, Resourceconstrained devices
National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-59217 (URN)10.1016/j.softx.2022.101089 (DOI)2-s2.0-85129561163 (Scopus ID)
Note

 Funding details: European Commission, EC; Funding details: Stiftelsen för Strategisk Forskning, SSF; Funding details: VINNOVA; Funding text 1: This work has been partially supported by VINNOVA and the Swedish Foundation for Strategic Research through the aSSIsT project.; Funding text 2: Since its open source release in 2006, the original Contiki OS! has been used by numerous research projects funded by a host of organisations, for example: (i) The European Commission (EC) under Horizon Europe, H2020! ( H2020! ), as well as by previous framework programmes, (ii) Various national research funding bodies, such as the UK’s EPSRC! ( EPSRC! ) or the Swedish Knowledge Foundation.

Available from: 2022-06-10 Created: 2022-06-10 Last updated: 2023-05-26Bibliographically approved
Kanwar, J., Finne, N., Tsiftes, N., Eriksson, J., Voigt, T., He, Z., . . . Saguna, S. (2021). JamSense: Interference and Jamming Classification for Low-power Wireless Networks. In: 2021 13th IFIP Wireless and Mobile Networking Conference (WMNC): . Paper presented at 2021 13th IFIP Wireless and Mobile Networking Conference (WMNC) (pp. 9-16).
Open this publication in new window or tab >>JamSense: Interference and Jamming Classification for Low-power Wireless Networks
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2021 (English)In: 2021 13th IFIP Wireless and Mobile Networking Conference (WMNC), 2021, p. 9-16Conference paper, Published paper (Refereed)
Abstract [en]

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.

Keywords
Bluetooth, Energy resources, Wireless networks, Packet loss, Interference, Tools, Hardware
National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-57434 (URN)10.23919/WMNC53478.2021.9619007 (DOI)
Conference
2021 13th IFIP Wireless and Mobile Networking Conference (WMNC)
Available from: 2021-12-29 Created: 2021-12-29 Last updated: 2023-06-08Bibliographically approved
Köckemann, U., Alirezaie, M., Renoux, J., Tsiftes, N., Uddin Ahmed, M., Morberg, D., . . . Loutfi, A. (2020). Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes. Sensors, 20(3), Article ID 879.
Open this publication in new window or tab >>Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes
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2020 (English)In: Sensors, E-ISSN 1424-8220, Vol. 20, no 3, article id 879Article in journal (Refereed) Published
Abstract [en]

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods.While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning.It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
smart home data sets; data collection software; prototype installation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-43882 (URN)10.3390/s20030879 (DOI)2-s2.0-85079189175 (Scopus ID)
Funder
Knowledge Foundation
Available from: 2020-02-13 Created: 2020-02-13 Last updated: 2023-05-26Bibliographically approved
Gonzalo Peces, C., Eriksson, J. & Tsiftes, N. (2019). Sleepy Devices Versus Radio Duty Cycling: The Case of Lightweight M2M. IEEE Internet of Things Journal, 6(2), 2550-2562
Open this publication in new window or tab >>Sleepy Devices Versus Radio Duty Cycling: The Case of Lightweight M2M
2019 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 2, p. 2550-2562Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
802.15.4, Internet of Things, LwM2M, TSCH
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-39258 (URN)10.1109/JIOT.2018.2871721 (DOI)2-s2.0-85054219063 (Scopus ID)
Funder
Knowledge FoundationEU, Horizon 2020, 646184
Available from: 2019-06-28 Created: 2019-06-28 Last updated: 2023-05-26Bibliographically approved
Köckemann, U., Tsiftes, N. & Loutfi, A. (2018). Integrating Constraint-based Planning with LwM2M for IoT Network Scheduling. In: : . Paper presented at Workshop on AI for Internet of Things (AI4IoT), Stockholm, July 15, 2018.
Open this publication in new window or tab >>Integrating Constraint-based Planning with LwM2M for IoT Network Scheduling
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes the design and implementationof a network scheduler prototype for IoT networks within the e-healthcare domain. The network scheduler combines a constraint-based task planner with the Lightweight Machine-to-Machine (LwM2M) protocol to be able to reconfigure IoT networks at run-time based on recognized activities and changes in the environment. To support such network scheduling, we implement a LwM2M application layer for the IoT devices that provides sensor data, network stack information, and a set of controllable parameters that affect the communication performance and the energy consumption.

Keywords
LwM2M, Internet of Things, network scheduling, e-healthcare
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-33949 (URN)
Conference
Workshop on AI for Internet of Things (AI4IoT), Stockholm, July 15, 2018
Funder
Knowledge Foundation
Available from: 2018-06-25 Created: 2018-06-25 Last updated: 2023-05-26Bibliographically approved
Ahmed, M. U., Fotouhi, H., Köckemann, U., Lindén, M., Tomasic, I., Tsiftes, N. & Voigt, T. (2018). Run-Time Assurance for the E-care@home System. In: Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 225): . Paper presented at International Conference on IoT Technologies for HealthCare HealthyIoT 2017: Internet of Things (IoT) Technologies for HealthCare. 24 October 2017 through 25 October 2017 (pp. 107-110).
Open this publication in new window or tab >>Run-Time Assurance for the E-care@home System
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2018 (English)In: Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 225), 2018, p. 107-110Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the design and implementation of the software for a run-time assurance infrastructure in the E-care@home system. An experimental evaluation is conducted to verify that the run-time assurance infrastructure is functioning correctly, and to enable detecting performance degradation in experimental IoT network deployments within the context of E-care@home. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Keywords
Health care, Care homes, Design and implementations, Experimental evaluation, IOT networks, Performance degradation, Runtimes, Internet of things
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-33463 (URN)10.1007/978-3-319-76213-5_15 (DOI)2-s2.0-85042521264 (Scopus ID)9783319762128 (ISBN)
Conference
International Conference on IoT Technologies for HealthCare HealthyIoT 2017: Internet of Things (IoT) Technologies for HealthCare. 24 October 2017 through 25 October 2017
Note

 Funding details: 2015–2019, Knowledge Foundation; .

Available from: 2018-03-09 Created: 2018-03-09 Last updated: 2023-06-08Bibliographically approved
Eriksson, J., Finne, N., Tsiftes, N., Duquennoy, S. & Voigt, T. (2018). Scaling RPL to Dense and Large Networks with Constrained Memory. In: Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks: . Paper presented at EWSN ’18 Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks Madrid, Spain — February 14 - 16, 2018 (pp. 126-134).
Open this publication in new window or tab >>Scaling RPL to Dense and Large Networks with Constrained Memory
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2018 (English)In: Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks, 2018, p. 126-134Conference paper, Published paper (Refereed)
Abstract [en]

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.

Keywords
RPL, Scalability, Wireless Networking
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-36441 (URN)
Conference
EWSN ’18 Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks Madrid, Spain — February 14 - 16, 2018
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2023-06-08Bibliographically approved
Tsiftes, N. & Voigt, T. (2018). Velox VM: A safe execution environment for resource-constrained IoT applications. Journal of Network and Computer Applications, 118, 61-73
Open this publication in new window or tab >>Velox VM: A safe execution environment for resource-constrained IoT applications
2018 (English)In: Journal of Network and Computer Applications, ISSN 1084-8045, E-ISSN 1095-8592, Vol. 118, p. 61-73Article in journal (Refereed) Published
Abstract [en]

We present Velox, a virtual machine architecture that provides a safe execution environment for applications in resource-constrained IoT devices. Our goal with this architecture is to support developers in writing and deploying safe IoT applications, in a manner similar to smartphones with application stores. To this end, we provide a resource and security policy framework that enables fine-grained control of the execution environment of IoT applications. This framework allows device owners to configure, e.g., the amount of bandwidth, energy, and memory that each IoT application can use. Velox's features also include support for high-level programming languages, a compact bytecode format, and preemptive multi-threading.

In the context of IoT devices, there are typically severe energy, memory, and processing constraints that make the design and implementation of a virtual machine with such features challenging. We elaborate on how Velox is implemented in a resource-efficient manner, and describe our port of Velox to the Contiki OS. Our experimental evaluation shows that we can control the resource usage of applications with a low overhead. We further show that, for typical I/O-driven IoT applications, the CPU and energy overhead of executing Velox bytecode is as low as 1–5% compared to corresponding applications compiled to machine code. Lastly, we demonstrate how application policies can be used to mitigate the possibility of exploiting vulnerable applications.

Keywords
Internet of things, Embedded systems, Virtual machine, Resource management, Policy enforcement, High-level programming
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-33948 (URN)10.1016/j.jnca.2018.06.001 (DOI)2-s2.0-85048323589 (Scopus ID)
Funder
VINNOVAKnowledge Foundation
Available from: 2018-06-25 Created: 2018-06-25 Last updated: 2023-06-08Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3139-2564

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