148 research outputs found
Cross-layer framework and optimization for efficient use of the energy budget of IoT Nodes
Both physical and MAC-layer need to be jointly optimized to maximize the
autonomy of IoT devices. Therefore, a cross-layer design is imperative to
effectively realize Low Power Wide Area networks (LPWANs). In the present
paper, a cross-layer assessment framework including power modeling is proposed.
Through this simulation framework, the energy consumption of IoT devices,
currently deployed in LoRaWAN networks, is evaluated. We demonstrate that a
cross-layer approach significantly improves energy efficiency and overall
throughput. Two major contributions are made. First, an open-source LPWAN
assessment framework has been conceived. It allows testing and evaluating
hypotheses and schemes. Secondly, as a representative case, the LoRaWAN
protocol is assessed. The findings indicate how a cross-layer approach can
optimize LPWANs in terms of energy efficiency and throughput. For instance, it
is shown that the use of larger payloads can reduce up to three times the
energy consumption on quasi-static channels yet may bring an energy penalty
under adverse dynamic conditions
Cooperative Deep Reinforcement Learning for Multiple-Group NB-IoT Networks Optimization
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resources allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, the problem is to determine, in an online fashion at each
Transmission Time Interval (TTI), the configurations that maximizes the
long-term average number of IoT devices that are able to both access and
deliver data. Given the complexity of optimal algorithms, a Cooperative
Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is
developed, whereby each DQN agent independently control a configuration
variable for each group. The DQN agents are cooperatively trained in the same
environment based on feedback regarding transmission outcomes. CMA-DQN is seen
to considerably outperform conventional heuristic approaches based on load
estimation.Comment: Submitted for conference publicatio
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild
Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising
technology for massive Machine Type Communication (mMTC). Given that its
deployment is rapidly progressing worldwide, measurement campaigns and
performance analyses are needed to better understand the system and move toward
its enhancement. With this aim, this paper presents a large scale measurement
campaign and empirical analysis of NB-IoT on operational networks, and
discloses valuable insights in terms of deployment strategies and radio
coverage performance. The reported results also serve as examples showing the
potential usage of the collected dataset, which we make open-source along with
a lightweight data visualization platform.Comment: Accepted for publication in IEEE Communications Magazine (Internet of
Things and Sensor Networks Series
Evaluation, Modeling and Optimization of Coverage Enhancement Methods of NB-IoT
Narrowband Internet of Things (NB-IoT) is a new Low Power Wide Area Network
(LPWAN) technology released by 3GPP. The primary goals of NB-IoT are improved
coverage, massive capacity, low cost, and long battery life. In order to
improve coverage, NB-IoT has promising solutions, such as increasing
transmission repetitions, decreasing bandwidth, and adapting the Modulation and
Coding Scheme (MCS). In this paper, we present an implementation of coverage
enhancement features of NB-IoT in NS-3, an end-to-end network simulator. The
resource allocation and link adaptation in NS-3 are modified to comply with the
new features of NB-IoT. Using the developed simulation framework, the influence
of the new features on network reliability and latency is evaluated.
Furthermore, an optimal hybrid link adaptation strategy based on all three
features is proposed. To achieve this, we formulate an optimization problem
that has an objective function based on latency, and constraint based on the
Signal to Noise Ratio (SNR). Then, we propose several algorithms to minimize
latency and compare them with respect to accuracy and speed. The best hybrid
solution is chosen and implemented in the NS-3 simulator by which the latency
formulation is verified. The numerical results show that the proposed
optimization algorithm for hybrid link adaptation is eight times faster than
the exhaustive search approach and yields similar latency
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