231 research outputs found
Dimmer: Self-Adaptive Network-Wide Flooding with Reinforcement Learning
The last decade saw an emergence of Synchronous Transmissions (ST) as an
effective communication paradigm in low-power wireless networks. Numerous ST
protocols provide high reliability and energy efficiency in normal wireless
conditions, for a large variety of traffic requirements. Recently, with the
EWSN dependability competitions, the community pushed ST to harsher and
highly-interfered environments, improving upon classical ST protocols through
the use of custom rules, hand-tailored parameters, and additional
retransmissions. The results are sophisticated protocols, that require prior
expert knowledge and extensive testing, often tuned for a specific deployment
and envisioned scenario. In this paper, we explore how ST protocols can benefit
from self-adaptivity; a self-adaptive ST protocol selects itself its best
parameters to (1) tackle external environment dynamics and (2) adapt to its
topology over time. We introduce Dimmer as a self-adaptive ST protocol. Dimmer
builds on LWB and uses Reinforcement Learning to tune its parameters and match
the current properties of the wireless medium. By learning how to behave from
an unlabeled dataset, Dimmer adapts to different interference types and
patterns, and is able to tackle previously unseen interference. With Dimmer, we
explore how to efficiently design AI-based systems for constrained devices, and
outline the benefits and downfalls of AI-based low-power networking. We
evaluate our protocol on two deployments of resource-constrained nodes
achieving 95.8% reliability against strong, unknown WiFi interference. Our
results outperform baselines such as non-adaptive ST protocols (27%) and PID
controllers, and show a performance close to hand-crafted and more
sophisticated solutions, such as Crystal (99%)
Reinforcement-based data transmission in temporally-correlated fading channels: Partial CSIT scenario
Reinforcement algorithms refer to the schemes where the results of the
previous trials and a reward-punishment rule are used for parameter setting in
the next steps. In this paper, we use the concept of reinforcement algorithms
to develop different data transmission models in wireless networks. Considering
temporally-correlated fading channels, the results are presented for the cases
with partial channel state information at the transmitter (CSIT). As
demonstrated, the implementation of reinforcement algorithms improves the
performance of communication setups remarkably, with the same feedback
load/complexity as in the state-of-the-art schemes.Comment: Accepted for publication in ISWCS 201
Minimizing Age of Collection for Multiple Access in Wireless Industrial Internet of Things
This paper investigates the information freshness of Industrial Internet of
Things (IIoT) systems, where each IoT device makes a partial observation of a
common target and transmits the information update to a central receiver to
recover the complete observation. We consider the age of collection (AoC)
performance as a measure of information freshness. Unlike the conventional age
of information (AoI) metric, the instantaneous AoC decreases only when all
cooperative packets for a common observation are successfully received. Hence,
effectively allocating wireless time-frequency resources among IoT devices to
achieve a low average AoC at the central receiver is paramount. Three multiple
access schemes are considered in this paper: time-division multiple access
(TDMA) without retransmission, TDMA with retransmission, and frequency-division
multiple access (FDMA). First, our theoretical analysis indicates that TDMA
with retransmission outperforms the other two schemes in terms of average AoC.
Subsequently, we implement information update systems based on the three
schemes on software-defined radios. Experimental results demonstrate that
considering the medium access control (MAC) overhead in practice, FDMA achieves
a lower average AoC than TDMA with or without retransmission in the high
signal-to-noise ratio (SNR) regime. In contrast, TDMA with retransmission
provides a stable and relatively low average AoC over a wide SNR range, which
is favorable for IIoT applications. Overall, we present a
theoretical-plus-experimental investigation of AoC in IIoT information update
systems
Self-Learning Power Control in Wireless Sensor Networks
Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay
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Intelligent and bandwidth-efficient medium access control protocols for IEEE 802.11p-based Vehicular Ad hoc Networks
Vehicle-to-Vehicle (V2V) technology aims to enable safer and more sophisticated transportation via the spontaneous formation of Vehicular Ad hoc Networks (VANETs). This type of wireless networks allows the exchange of kinematic and other data among vehicles, for the primary purpose of safer and more efficient driving, as well as efficient traffic management and other third-party services. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs.
This thesis focuses on optimising channel access strategies, which is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. To start with, the default channel access control method for V2V is evaluated hardware via modifying the appropriate wireless interface Linux driver to enable finer on-the-fly control of IEEE 802.11p access control layer parameters. More complex channel sharing scenarios are evaluated via simulations and findings on the behaviour of the access control mechanism are presented. A complete channel sharing efficiency assessment is conducted, including throughput, fairness and latency measurements. A new IEEE 802.11p-compatible Q-Learning-based access control approach that improves upon the studied protocol is presented. The stations feature algorithms that “learn” how to act optimally in VANETs in order to maximise their achieved packet delivery and minimise bandwidth wastage. The feasibility of Q-Learning to be used as the base of selflearning protocols for IEEE 802.11p-based V2V communication access control in dense environments is investigated in terms of parameter tuning, necessary time of exploration, achieving latency requirements, scaling, multi-hop and accommodation of simultaneous applications. Additionally, the novel Collection Contention Estimation (CCE) mechanism for Q-Learning-based access control is presented. By embedding it on the Q-Learning agents, faster convergence, higher throughput, better service separation and short-term fairness are achieved in simulated network deployments.
The acquired new insights on the network performance of the proposed algorithms can provide precise guidelines for efficient designs of practical, reliable, fair and ultra-low latency V2V communication systems for dense topologies. These results can potentially have an impact across a range of related areas, including various types of wireless networks and resource allocation for these, network protocol and transceiver design as well as QLearning applicability and considerations for correct use
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