Lifetime Analysis of Error Control Schemes on Wireless Sensor Networks in Industrial Environments

Tekin, Nazli; Güngör, Vehbi Çağrı

doi:10.1109/siu.2019.8806298

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The effect of this phenomenon is visible in the erroneous packet transfer in IWSN. To solve this effect, Reference [ 97 ] developed a novel Mixed Integer Programming (MIP) Framework that improves the quality of packet transfer for more battery sustainability in IWSN. The influence on the communication channel of WSN depends on the environment in which they are deployed.…”

Section: State-of-the-artmentioning

confidence: 99%

Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights

Dangana

Ansari

Abbasi

et al. 2021

Sensors

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The Internet of Things (IoT) and its applications in industrial settings are set to bring in the fourth industrial revolution. The industrial environment consisting of high profile manufacturing plants and a variety of equipment is inherently characterized by high reflectiveness, causing significant multi-path components that affect the propagation of wireless communications—a challenge among others that needs to be resolved. This paper provides a detailed insight into Narrow-Band IoT (NB-IoT), Industrial IoT (IIoT), and Wireless Sensor Networks (WSN) within the context of indoor industrial environments. It presents the applications of NB-IoT for industrial settings, such as the challenges associated with these applications. Furthermore, future research directions were put forth in the areas of NB-IoT network management using self-organizing network (SON) technology, edge computing for scalability enhancement, security in NB-IoT generated data, and proposing a suitable propagation model for reliable wireless communications.

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Section: State-of-the-artmentioning

confidence: 99%

Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights

Dangana

Ansari

Abbasi

et al. 2021

Sensors

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“…Moreover, mobile networks are dynamic, and a non-coverage area can cause network links to change, leading to collisions during network planning and packet transmission. Traditional algorithms to solve this problem often have high computational complexity and may not be practical for fast-fading channels [14]. Therefore, there is a need for low-cost algorithm designs for resource allocation in UAV-aided networks.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

Nguyen,

Chien

et al. 2023

IJEETC

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Unmanned aerial vehicles (UAVs) are increasingly used in various applications, including infrastructure inspection, traffic monitoring, remote sensing, mapping, and rescue. However, many applications have required UAVs to function autonomously, without human intervention to improve system performance. In this study, we propose a new approach to environmental monitoring using a group of UAVs equipped with sensors under the support of reinforcement learning. Regarding the communication system model, we assume that UAVs can cooperate with each other to learn and share information about the environment, and then relocate to an optimal position while managing connectivity and coverage. After that, we exploit reinforcement learning with a deep deterministic policy gradient (DDPG) algorithm to optimize environmental monitoring with the proposed algorithm. Specifically, the proposed algorithm aims to simulate an environmental monitoring system using UAVs with basic parameters. We further apply the proposed algorithm to evaluate network performance under different parameter settings. Numerical results validate the effectiveness of the proposed learning-based framework in monitoring and sensing data.

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“…To reduce the number of retransmission for lossy channels, Error Correcting Codes are commonly used [5].…”

Section: Introductionmentioning

confidence: 99%

An efﬁcient forward error correction code for wireless sensor networks

Alabady

2021

IJ-ICT

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<p>The main requirements in the design of wireless sensor network applications are to minimize energy consumption and maximize battery lifetime. Power is primarily consumed during wireless transmission and reception. Automatic repeat request (ARQ) and forward error correction (FEC) are the two basic methods to recover erroneous packets. As energy conservation is a major issue of concern in wireless sensor networks, repeat transmission because the error in the data received is not an option, and FEC would be preferred over ARQ. FEC is applied in situations where retransmissions are relatively costly or impossible. A successful data transmission means a higher energy saving and a long-life network. This paper presents a novel linear block forward error correction code for wireless sensor network applications called Low Complexity Parity Check (LCPC). The LCPC code offers lower encoding and decoding complexity than other types of codes. To validate the performance of the LCPC code, the proposed coding scheme was investigated at different values of data transmission with different types of modulations over Additive white Gaussian noise (AWGN) and Rayleigh fading channels. The simulation results show that the proposed code outperforms the renowned LDPC (8, 4), (255,175), and (576, 288) codes.</p>

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Lifetime Analysis of Error Control Schemes on Wireless Sensor Networks in Industrial Environments

Cited by 3 publications

References 7 publications

Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights

Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights

Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

An efﬁcient forward error correction code for wireless sensor networks

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