T-AAD: Lightweight traffic auto-adaptations for low-power MAC protocols

Papadopoulos, Georgios Z.; Beaudaux, Julien; Gallais, Antoine; Noël, Thomas

doi:10.1109/medhocnet.2014.6849108

Cited by 16 publications

(6 citation statements)

References 17 publications

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“…• Preamble sampling: The transmitter sends a long preamble, and the receiver has to turn its radio on periodically to detect this preamble. Then, the receiver has to stay awake in order to receive the data packet, which follows the preamble [40]. Contiki-MAC [41] represents a widely used preamble sampling protocol, which exploits several optimizations for reducing the energy consumption.…”

Section: A What Should We Measure?mentioning

confidence: 99%

A Tutorial on Performance Evaluation and Validation Methodology for Low-Power and Lossy Networks

Kritsis

Papadopoulos

Gallais

et al. 2018

IEEE Commun. Surv. Tutorials

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Envisioned communication densities in Internet of Things (IoT) applications are increasing continuously. Because these wireless devices are often battery powered, we need specific energy efficient (low-power) solutions. Moreover, these smart objects use low-cost hardware with possibly weak links, leading to a lossy network. Once deployed, these Low-power Lossy Networks (LLNs) are intended to collect the expected measurements, handle transient faults and topology changes, etc. Consequently, validation and verification during the protocol development are a matter of prime importance. A large range of theoretical or practical tools are available for performance evaluation. A theoretical analysis may demonstrate that the performance guarantees are respected, while simulations or experiments aim on estimating the behaviour of a set of protocols within real-world scenarios. In this article, we review the various parameters that should be taken into account during such a performance evaluation. Our primary purpose is to provide a tutorial that specifies guidelines for conducting performance evaluation campaigns of network protocols in LLNs. We detail the general approach adopted in order to evaluate the performance of layer 2 and 3 protocols in LLNs. Furthermore, we also specify the methodology that should be adopted during the performance evaluation, while reviewing the numerous models and tools that are available to the research community.

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Section: A What Should We Measure?mentioning

confidence: 99%

A Tutorial on Performance Evaluation and Validation Methodology for Low-Power and Lossy Networks

Kritsis

Papadopoulos

Gallais

et al. 2018

IEEE Commun. Surv. Tutorials

Self Cite

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“…Clearly, exchanging information between nodes brings in overhead. TDMA scheduling schemes for energy‐limited WSNs should be simply designed to avoid overhead and computational complexity [18].…”

Section: Related Workmentioning

confidence: 99%

TDMA scheduling schemes targeting high channel utilization for energy‐harvesting wireless sensor networks

et al. 2021

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As a contention-free channel access protocol, Time Division Multiple Access (TDMA) is widely applied in Energy Harvesting Wireless Sensor Networks (EH-WSNs) due to ease of implementation. TDMA scheduling in EH-WSNs faces the problem of low channel utilization since time slots assigned to some nodes may not be used by them due to shortage of energy or data. It is important to design TDMA scheduling schemes that enhance channel utilization. In this paper, the Fixed Frame Size Scheme (FFSS) and the Adaptive Frame Size Scheme (AFSS) are proposed to improve channel utilization of EH-WSNs. The FFSS aims at the optimal TDMA slot assignment for the TDMA scheduling with fixed frame size, and the AFSS considers the TDMA with variable frame size and targets both the optimal frame size and the optimal slot assignment. The optimization problems maximizing channel utilization are formulated for the FFSS and the AFSS, respectively, which take the upcoming energy and data into account. The optimization problems are transformed into assignment-like problems and solved by the Hungarian-based algorithm in polynomial time. Simulation results indicate that the proposed FFSS and AFSS can considerably improve channel utilization in the EH-WSNs compared with the existing TDMA scheduling schemes. INTRODUCTIONBeing main components of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) have been widely deployed. The drawback of inconvenient replacing battery in the traditional WSNs is remedied by energy-harvesting technique, which brings in various Energy-Harvesting WSNs (EH-WSNs) [1]. Nodes in the EH-WSNs can gain sustainable operation by scavenging energy from ambient energy sources [2-4], including radio frequency, sun light, wind, etc. Channel access or medium access schemes in the EH-WSNs can be classified as contention-free and contention-based [5]. The representatives of contention-free and contentionbased schemes are Time Division Multiple Access (TDMA) and Carrier Sensing Multiple Access/Colission Avoidance (CSMA/CA), respectively. In addition to easy implementation, TDMA has advantages of collision-free, energy-efficient, andThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Considering the topology and traffic dynamics in a wireless network due to mobility, mobile nodes have to often perform neighbourhood discovery and handover procedures, since link (mobile to any static node) fluctuation and disconnection frequently occur [10]. To tackle the previously stated issues, we decided to integrate our proposed mechanism to the link layer, jointly with the LPL-based family of MAC protocols [6], [11]. Under LPL protocols nodes sample the medium at regular intervals to detect a carrier for incoming packets.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Inspired by the rendezvous time used in T-AAD [11], we here introduce the Contention Avoidance Algorithm (CAA) to mitigate the channel contention and hidden terminal problems. To this aim, we propose to incorporate the queue length information in every MobIQ's data packet.…”

Section: B Contention Avoidance Algorithm (Caa)mentioning

confidence: 99%

“…where indicates the total number of packets that a a mobile node is expected to transmit (i.e., queue length) obtained from overhearing the data packet, and are the time intervals for a successful exchange of data and ACK packets, and finally, in order to ensure that the calculated period is at least as long as the total burst transmission (including MAC retransmissions), a margin of error (i.e., ) is added. We consider to cope with the potential packet retransmissions due to external interference, which can be determined according to the occurring network conditions [11].…”

Section: B Contention Avoidance Algorithm (Caa)mentioning

confidence: 99%

See 1 more Smart Citation

A Mobility-Supporting MAC Scheme for Bursty Traffic in IoT and WSNs

Papadopoulos¹,

Kotsiou²,

Gallais³

et al. 2016

2016 IEEE Global Communications Conference (GLOBECOM)

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International audienceRecent boom of mobile applications has become an essential class of mobile Internet of Things (IoT), whereby large amounts of sensed data are collected and shared by mobile sensing devices for observing phenomena such as traffic or the environmental. Currently, most of the proposed Medium Access Control (MAC) protocols mainly focus on static networks. However, mobile sensor nodes may pose many communication challenges during the design and development of a MAC protocol. These difficulties first require an efficient connection establishment between a mobile and static node, and then an efficient data packet transmissions. In this study, we propose MobIQ, an advanced mobility-handling MAC scheme for low-power MAC protocols, which achieves for efficient neighbour(hood) discovery and low-delay communication. Our thorough performance evaluation, conducted on top of Contiki OS, shows that MobIQ outperforms state-of-the-art solutions such as MoX-MAC, MOBINET and ME-ContikiMAC, in terms of significantly reducing delay, contention to the medium and energy consumption

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T-AAD: Lightweight traffic auto-adaptations for low-power MAC protocols

Cited by 16 publications

References 17 publications

A Tutorial on Performance Evaluation and Validation Methodology for Low-Power and Lossy Networks

A Tutorial on Performance Evaluation and Validation Methodology for Low-Power and Lossy Networks

TDMA scheduling schemes targeting high channel utilization for energy‐harvesting wireless sensor networks

A Mobility-Supporting MAC Scheme for Bursty Traffic in IoT and WSNs

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