AI for dynamic packet size optimization of batteryless IoT nodes: a case study for wireless body area sensor networks

Tabrizi, Hamed Osouli; Al‐Turjman, Fadi

doi:10.1007/s00521-020-04813-x

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…The proposed model is evaluated for different test conditions to see its versatility under changing conditions and compared with conventional optimization algorithms. The parameter values 11 used in the calculations for the single‐hop and the 2‐hop systems are given in Table 2. In multi‐hop, the scenario has 100 static nodes uniformly scattered in a 10 m × 10 m footprint.…”

Section: Resultsmentioning

confidence: 99%

“…When the packet size expands, the higher PER is the primary cause of increased energy usage. We prefer to use the new way, namely the S 2 SO approach, to further minimize the PER than the method 11 in any dimensions.…”

Section: Literature Surveymentioning

confidence: 99%

“…Optimization is done in the constrained network. These constraints are designed by developing the energy efficiency model and power consumption model 11 for the network. The packet length is optimized by splitting or joining the data packets.…”

Section: Proposed System Modelmentioning

confidence: 99%

“…A packet size optimization algorithm 11 is utilized for networks without batteries to consider the quantity of harvested energy at each node. Hence, packet size is dynamically optimized for data transmission in each round.…”

Section: Introductionmentioning

confidence: 99%

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Sling‐shot spider optimization algorithm based packet length control in wireless sensor network and Internet of Things‐based networks

Pavalarajan

2022

Int J Communication

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Wireless sensor network (WSN) is a well-developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error-free data and run an energyefficient network. In this paper, we propose a sling-shot spider optimization (S 2 SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)-based networks. The S 2 SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1-hop, 2-hop, and multi-hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks.The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio-inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Proposed System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sling‐shot spider optimization algorithm based packet length control in wireless sensor network and Internet of Things‐based networks

Pavalarajan

2022

Int J Communication

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“…Wireless sensor network makes a large number of sensor nodes with information detection ability, data processing ability and wireless communication ability form a wireless network through self‐organization. Through the cooperative work between nodes, it realizes the detection, collection and processing of target information in the network coverage area, and transmits the information to the monitoring terminal through wireless multi‐hop mode 1–4 …”

Section: Introductionmentioning

confidence: 99%

Research and application of node fuzzy identification and localization in wireless sensor networks

2021

Int J Communication

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In recent years, sensor technology has attracted the attention of countries all over the world because of its potential huge application value, and it has played an important role in people's daily life and social production activities.This paper mainly studies the location algorithm based on ranging in the location algorithm and analyzes the advantages and disadvantages of the algorithm. In order to optimize the positioning of wireless sensor networks, a fuzzy controller is designed to fuzzify the input information of sensor nodes. Using Matlab network simulation tool, the congestion control algorithm proposed in this paper was simulated and compared with several typical congestion control protocols in wireless sensor networks. Experimental results show that the algorithm can improve network throughput, reduce network packet loss, and have a significant impact on network performance. The network throughput of the congestion control algorithm based on fuzzy logs is stable at 180 packets per second. Compared with CODA, the congestion control algorithm based on fuzzy logarithm increases the packet loss rate by 55-66%, and the SenTCP algorithm increases by 37-40%. It can be seen that congestion control algorithms based on fuzzy logic can play an important role in wireless sensor networks.

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Fast reinforcement learning algorithms for joint adaptive source coding and transmission control in IoT devices with renewable energy storage

Namjoonia

Sheikhi

Hakami

2022

Neural Comput & Applic

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AI for dynamic packet size optimization of batteryless IoT nodes: a case study for wireless body area sensor networks

Cited by 9 publications

References 14 publications

Sling‐shot spider optimization algorithm based packet length control in wireless sensor network and Internet of Things‐based networks

Sling‐shot spider optimization algorithm based packet length control in wireless sensor network and Internet of Things‐based networks

Research and application of node fuzzy identification and localization in wireless sensor networks

Fast reinforcement learning algorithms for joint adaptive source coding and transmission control in IoT devices with renewable energy storage

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