Energy optimization using swarm intelligence for IoT-Authorized underwater wireless sensor networks

Gavali, Research Scholar Ashwini B; Kadam, Research Scholar Megha V; Patil, Research Scholar Sarita

doi:10.1016/j.micpro.2022.104597

Cited by 12 publications

(6 citation statements)

References 60 publications

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“…We compare the outcomes of our proposed RAPCOL with four existing models: (i) IDV-HOP [14], (ii) NDV-Hop [19], (iii) Modified DV-Hop [21], and (iv) Improved DV-Hop [22]. We evaluate the performance of our proposed RAPCOL by varying the parameters of beacon node ratio, sensor node density, transmission range, sensing field, and residual energy.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The range-free approaches in WSNs provide a less precise location, but this category gains more attention due to its low cost, higher lifetime, and design simplicity. However, sensor networks for high-risk environments, irrespective of the localization approaches, are resources constrained in terms of power, size, memory, bandwidth, and cost of sensor nodes [13][14][15]. Hence, less energy consumption and more network lifetime are the most desirable aspects of WSNs.…”

Section: Introductionmentioning

confidence: 99%

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Proceedings of the 22nd ACM SIGPLAN International Workshop on Erlang

2023

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Industrial applications and automation controls are closely connected with heterogeneous Wireless Sensor Network (WSN); thus, Industrial Internet-of Thing (IIoT) enhances the productivity, scalability, and flexibility of the operations. However, in many cases such as tracking a device or service localization becomes very crucial to state back the operations at times. Existing literature show a various localization solutions, but they face the drawbacks of high energy consumption, and non-coherence in heterogeneity. Besides, in a WSN-based IIoT, non-cooperativeness exists among nodes due to various system and environmental parameters, which make the system resource-exhaustive. In this paper, we introduce a novel localization algorithm that addresses the above-mentioned problems. We call our proposed algorithm RAnge-free Power efficient COoperative Localization (RAPCOL). Specifically, the use of co-operative beacon nodes to broadcast the information to Base Station and low energy consumption highlights the novelty of the work. RAPCOL uses weight metrics for selecting optimal cooperative beacon nodes to prolong the network lifetime. We also introduce an Improvised Particle Swarm Optimization (IPSO) that credits in the contribution in RAPCOL. We run a overall nine sets of experiments to analyze the localization accuracy, effect of beacon nodes, sensor nodes, network connectivity, and sensing field, error frequency, residual energy, time, and network lifetime. A comparative study with the existing localization models shows that RAPCOL is 30% better than the existing models in terms of accuracy and resource consumption. We observe stable performance of RAPCOL with a differentiated effect of beacon nodes and sensor nodes. We also observe that our proposed IPSO 20% better in fast convergence to the optimal solution. Though RAPCOL localization time is 12% higher than other existing protocols, RAPCOL's accuracy and energy saving mechanism make it efficient for IIoTs.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Proceedings of the 22nd ACM SIGPLAN International Workshop on Erlang

2023

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“…Gavali et al [67] proposed a protocol involving the use of an effective fitness functionbased particle swarm optimization (EFF-PSO) algorithm to strategically select the optimal forwarder node responsible for UWSN data delivery. In the protocol, the intended source node identifies forwarding relay nodes based on location information.…”

Section: Uwsnsmentioning

confidence: 99%

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

Nkenyereye,

Ndibanje

2024

Electronics

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The term “Internet of Underwater Things (IoUT)” refers to a network of intelligent interconnected underwater devices designed to monitor various underwater activities. The IoUT allows for a network of autonomous underwater vehicles (AUVs) to communicate with each other, sense their surroundings, collect data, and transmit them to control centers on the surface at typical Internet speeds. These data serve as a valuable resource for various tasks, including conducting crash surveys, discovering shipwrecks, detecting early signs of tsunamis, monitoring animal health, obtaining real-time aquatic information, and conducting archaeological expeditions. This paper introduces an additional set of alternative simulation tools for underwater networks. We categorize these tools into open-source and licensed simulator options and recommend that students consider using open-source simulators for monitoring underwater networks. There has not been widespread deployment or extensive research on underwater 5G-based networks. However, simulation tools provide some general insights into the challenges and potential issues associated with evaluating such networks, based on the characteristics of underwater communication and 5G, by surveying 5G-based underwater networks and 5G key aspects addressed by the research community in underwater network systems. Through an extensive review of the literature, we discuss the architecture of both Internet of Underwater application-assisted AUVs and Internet of Underwater Things communications in the 5G-based system.

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“…Gavali and Patil 34 defined that the enhanced optimization techniques in UWAC IoT enabled WSN to improve EE. In this work, particle swarm optimizers (PSO) were introduced to enhance the EE in the UWAC IoT‐enabled WSN system.…”

Section: Related Workmentioning

confidence: 99%

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

Kapileswar,

Phani Kumar

2023

Int J Communication

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SummaryNowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO‐OFDM) provide better communication performance that can be applied to the fast‐growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high‐speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC‐IoT‐enabled MIMO‐OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi‐directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively.

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Energy optimization using swarm intelligence for IoT-Authorized underwater wireless sensor networks

Cited by 12 publications

References 60 publications

Proceedings of the 22nd ACM SIGPLAN International Workshop on Erlang

Proceedings of the 22nd ACM SIGPLAN International Workshop on Erlang

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

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