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ACTOR: Adaptive Control of Transmission Power in RPL
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5590-0784
School of Engineering (ISEP/IPP), Politécnico do Porto, 4249-015 Porto, Portugal.ORCID iD: 0000-0002-6139-6542
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7755-4795
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2024 (English)In: Sensors, E-ISSN 1424-8220, Vol. 24, no 7, article id 2330Article in journal (Refereed) Published
Abstract [en]

RPL-Routing Protocol for Low-Power and Lossy Networks (usually pronounced "ripple")-is the de facto standard for IoT networks. However, it neglects to exploit IoT devices' full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent-child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.

Place, publisher, year, edition, pages
MDPI , 2024. Vol. 24, no 7, article id 2330
Keywords [en]
wireless sensor networks, Routing Protocol for Low-Power Lossy Networks (RPL), radio resource management, transmission power control, multi-armed bandit, reinforcement learning, Upper Confidence Bound (UCB), performance evaluation, simulation, testbed, IPv6, 6LoWPAN, IEEE 802.15.4
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-66493DOI: 10.3390/s24072330ISI: 001201045700001PubMedID: 38610541Scopus ID: 2-s2.0-85190249617OAI: oai:DiVA.org:mdh-66493DiVA, id: diva2:1854126
Available from: 2024-04-24 Created: 2024-04-24 Last updated: 2024-11-14Bibliographically approved
In thesis
1. Reliable Low-Power Wireless Networks in Dynamic Environments
Open this publication in new window or tab >>Reliable Low-Power Wireless Networks in Dynamic Environments
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The widespread adoption of the Internet of Things (IoT) necessitates robust support for emerging applications that exhibit increased dynamism characterized by device mobility, fluctuating radio signals in the environment, and varying traffic loads. Enhancing reliability against these challenges is best achieved through leveraging diversity in frequency, temporal and spatial domains. This Thesis focuses on low-power IoT networks that are running complex protocols such as Routing Protocol for Low-power and Lossy Networks (RPL) and Time Slotted Channel Hopping (TSCH). Improving reliability of these protocols to mitigate dynamism can be based on either distributed or centralized schemes depending on the trade-offs that are explored throughout the Thesis. The Thesis introduces two frameworks for mitigating device mobility: SDMob and Forte. SDMob integrates real-time tracking algorithms, such as the particle filter, within RPL by offloading computation to a centralized controller, enabling timely handovers through efficient route updates. The Forte framework extends recent autonomous TSCH schedulers by incorporating a centralized scheduler that intervenes only when necessary, striking a balance between autonomy and centralized control. Additionally, centralized controllers can also improve the "stretched" routes of RPL through route projection, as addressed by the RPL-RP protocol.  However, due to the communication overhead associated with centralized solutions, the Thesis also explores on-device solutions. The proposed ACTOR framework integrates transmission power control within RPL networks, enabling them to adapt to environments with evolving RF and density.

Overall, this Thesis provides practical solutions for enhancing the performance of commercial off-the-shelf low-power IoT boards and networks, specifically targeting the routing and MAC layers. Through extensive real-world experiments, simulations, and modeling, the Thesis provides compelling evidence that by adapting key controllable parameters -such as frequency, transmission power, and routing— enables IoT networks to achieve resilience in the face of environmental and operational variability.

Abstract [sv]

Den utbredda användningen av Internet of Things (IoT) kräver robust stöd för framväxande applikationer som uppvisar ökad dynamik, såsom enhetsmobilitet, varierande radiofrekvensmiljö och varierande trafikbelastningar. Att förbättra tillförlitligheten vid dessa utmaningar uppnås bäst genom att utnyttja mångfald i frekvens-, tids- och rumsliga domäner. Denna avhandling presenterar metoder för lågenergi-IoT-nätverk som använder komplexa underliggande protokoll som Routing Protocol for Low-power and Lossy Networks (RPL) och Time Slotted Channel Hopping (TSCH). Att förbättra tillförlitligheten hos dessa protokoll kan baseras på antingen distribuerade eller centraliserade system beroende på avvägningarna.

Avhandlingens bidrag som förbättrar enhetsmobilitet inkluderar SDMob och Forte-ramverket. Det förstnämnda presenterar realtids spårningsalgoritmer, såsom partikelfilter, genom att avlasta algoritmer till en centraliserad kontroller, som utnyttjar dess beräkningskapacitet för att optimera överlämning genom ruttuppdateringar. Forte-ramverket är en utvidgning av de senaste autonoma TSCH-schemaläggarna genom att integrera en centraliserad schemaläggare som ingriper endast när det är nödvändigt. Centraliserade kontroller kan förbättra de "utsträckta" rutterna för RPL genom ruttprojektion, vilket introduceras i RPL-RP-bidraget. Å andra sidan, på grund av kommunikationsoverheaden av centraliserade lösningar, utforskar avhandlingen också enhetsbaserade system som det föreslagna ACTOR-ramverket som integrerar transmissionsstyrning i RPL-nätverk för att fungera i miljöer med utvecklande RF och densitet.

Sammanfattningsvis ger denna avhandling praktiska lösningar för kommersiella lågenergikort/nätverk, specifikt i routing- och MAC-lagren, för att förbättra deras prestanda i mötet med dynamik. Genom omfattande verkliga experiment, simuleringar och modellering, ger avhandlingen övertygande bevis på att genom att anpassa en mängd kontrollerbara parametrar såsom frekvens, transmissionsstyrka och rutter, kan IoT-nätverk uppnå motståndskraft trots miljömässig och operativ variabilitet.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2024
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 422
National Category
Computer Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-69142 (URN)978-91-7485-690-3 (ISBN)
Public defence
2025-01-14, Beta, Mälardalens universitet, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2024-11-21 Created: 2024-11-14 Last updated: 2024-12-24Bibliographically approved

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Rabet, IliarFotouhi, HosseinVahabi, MaryamBjörkman, Mats

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