Dynamic Packet Length Control for Cognitive Radio Networks

Mahdi, Ali H.; Kalil, Mohamed A.; Mitschele‐Thiel, Andreas

doi:10.1109/vtcfall.2013.6692169

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…It also included fragmentation and aggregation processes for optimal sizing of packet lengths. In Mahdi et al, 10 "adaptive particle swarm optimization" (ADPSO) is presented for packet length and energy consumption optimization. It incorporated case-based reasoning for reducing the computation time.…”

Section: Literature Surveymentioning

confidence: 99%

“…BER increases, when SNR increases. For binary phase-shift keying (BPSK), 10 BER can be represented as in Equation (4).…”

Section: Communication Channel Modelmentioning

confidence: 99%

“…in which E comm is the total energy consumed per packet, neglecting packet retransmissions due to packet error rate; E T is the energy consumed by the transmitter node per packet; E R is the energy consumed by the receiver node per packet; E T,circ is the energy needed per bit by transmitter electronics and signal processing; E R,circ is the energy needed per bit by receiver electronics and signal processing; P T denotes the output transmit power represents the amount of energy spent on the RF amplifier and u represents the packet size. Packet size is expressed in Equation (10).…”

Section: Energy Efficiency Model Of the Networkmentioning

confidence: 99%

“…9 The shorter packets increase the performance under varying channel conditions, whereas the longer packets are useful in transmitting a large amount of data in a short time but at the expense of packet loss with increased interference. 10 As a result, the packet sizing approach should be effective, boosting link quality for multi-hop networks while excluding both energy efficiency and overhead.…”

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: Literature Surveymentioning

confidence: 99%

“…BER increases, when SNR increases. For binary phase-shift keying (BPSK), 10 BER can be represented as in Equation (4).…”

Section: Communication Channel Modelmentioning

confidence: 99%

Section: Energy Efficiency Model Of the Networkmentioning

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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“…Dynamic packet length adaptation has been investigated in 802.11-based wireless systems [17], [18], [19], [20] or cognitive radio networks [21]. Lettierri et al study the effect of variable packet length on several metrics, and implement a dynamic adaptation scheme on custom Linux OS [17].…”

Section: Related Workmentioning

confidence: 99%

Exploiting Error Estimating Codes for Packet Length Adaptation in Low-Power Wireless Networks

Dong

Zhang

2015

IEEE Trans. on Mobile Comput.

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We propose Plena, a packet length adaptation scheme exploiting recently proposed Error Estimating Codes (EEC) for lowpower wireless networks. We carefully consider two low-power MAC protocols in modeling the energy consumption of different packet lengths. Based on the modeling and analysis, we give closed form solutions for the optimal packet length in terms of energy efficiency. EEC allows for instant estimation of the bit error rate (BER) in a partially correct packet, making it possible for predicting packet reception ratios (PRRs) at different packet lengths without the need of direct measurement in an iterative process. Fast convergence of Plena makes it suitable for mobile environments in which the channel characteristics are frequently changing. We evaluate Plena's computation overhead based on the commonly used TelosB nodes. Results show that the computation overhead of Plena is acceptably small. We also perform extensive simulations and testbed experiments to study the performance of Plena under different configurations. Results show that Plena achieves better energy efficiency than existing works.

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The use of different strategies of search space reduction in mitigation of optimization selection problem

Queiroz¹,

Silva²

2015

2015 Latin American Computing Conference (CLEI)

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Dynamic Packet Length Control for Cognitive Radio Networks

Cited by 5 publications

References 13 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

Exploiting Error Estimating Codes for Packet Length Adaptation in Low-Power Wireless Networks

The use of different strategies of search space reduction in mitigation of optimization selection problem

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