A Cross-Layer Judgment Scheme for Solving Retransmission Problem in IEEE 802.15.4-Based Wireless Body Sensor Networks

Tseng, Hsueh-Wen; Chuang, Yue-Ru

doi:10.1109/jsen.2013.2260146

Cited by 9 publications

(16 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 2.4 GHz radio maps 4 bits of information onto 32 chip PN sequence, followed by offset orthogonal phase shift (OQPSK). The topology of IEEE 802.15.4 networks has two architectures: star and peer to peer topologies in which WBAN topologies seem to be mostly presented by a star topology [1], [5]. In star topology, when a sensor wants to forward its data to a coordinator, it can choose two operational modes: beacon-enabled mode and non-beacon-enable mode.…”

Section: Related Workmentioning

confidence: 99%

“…They may be data collision, hidden terminal problem, and fading [5]. In the case of the fading channel, the receiver cannot recognize the received packet because the received signal strength is smaller than a certain threshold.…”

Section: Related Workmentioning

confidence: 99%

“…Then, the relay nodes encode the packet in the same way as the source. The IF step: after the coordinator uses ED and ACN parameters in [5] to recognize the reasons causing the failed packet from the source. If the coordinator cannot decode the source's packet, it sends a NACK packet to inform the fading channel (F) and to request to help.…”

Section: Adaptive Relay Transmission Schemementioning

confidence: 99%

“…Thus, the retransmission from the source may reduce the probability of successful retransmission. One possible solution is to suspend the retransmission of the failed packet from the sender until the next period [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Adaptive Relay Transmission Scheme for Reliable Data Forwarding in Wireless Body Area Networks

Nguyen

Kim

2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThe new generation of telemedicine systems enables healthcare service providers to monitor patients not only in the hospital but also when they are at home. In order to efficiently exploit these systems, human information collected from end devices must be sent to the medical center through reliable data transmission. In this paper, we propose an adaptive relay transmission scheme to improve the reliability of data transmission for wireless body area networks. In our proposal, relay nodes that have successfully decoded a packet from the source node are selected as relay nodes in which the best relay with the highest channel gain is selected to forward the failed packet instead of the source node. The scheme addresses both the data collision problem and the inefficient relay selection in relay transmission. Our experimental results show that the proposed scheme provides a better performance than previous works in terms of the packet delivery ratio and end-to-end delay. key words: wireless body area networks, reliable data transmission, relay transmission

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Adaptive Relay Transmission Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Relay Transmission Scheme for Reliable Data Forwarding in Wireless Body Area Networks

Nguyen

Kim

2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Further, in order to deal with limited memory, energy, and processing power of sensor nodes, this standard supports appropriate physical and MAC layers. A WBAN is typically based on three types of topologies including star, peer-to-peer, and mesh [4]. Star topologies are suitable for limited range and small networks and rely on a coordinator to coordinate sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Backoff Mechanism for IEEE 802.15.4 Beacon‐Enabled Wireless Body Area Networks

Henna

Sarwar

2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Carrier sense multiple access mechanism with collision avoidance (CSMA/CA) in IEEE 802.15.4-based wireless body area networks (WBANs) may impair the transmission reliability of emergency traffic under high traffic loads, which may result in loss of high valued medical information. Majority of the recent proposals recommend an early retransmission of failed frame while ignoring the history of past failed transmissions. More importantly, these proposals do not consider the number of failed transmissions experienced by each sensor node, thereby affecting the reliability of retransmissions. In this paper, we propose a dynamic retransmission adaptive intelligent MAC (RAI-MAC) scheme. In our proposed scheme retransmission class of each sensor node is decided by the coordinator according to the number of failed transmissions of each node as observed by the coordinator during the last superframe. Based on the retransmission class received from the coordinator, each node adjusts its next backoff value. The proposed scheme increases the probability of successful frame retransmissions without incurring extra overhead. The simulation results prove that the proposed scheme based on its adaptive retransmission mechanism achieves higher average throughput and average end-to-end delay, while not compromising on energy efficiency as compared to the IEEE 802.15.4 and Block Acknowledgment (Block Ack). Moreover, our scheme appears more stable in terms of average throughput, end-to-end delay, and energy efficiency under different values of beacon order (BO) and superframe order (SO).

show abstract

Optimal interference mitigation with deep learningbased channel access in wireless body area networks

Periyamuthaiah,

Vembu

2024

Int J Communication

View full text Add to dashboard Cite

SummaryWireless body area networks (WBANs) are essential for medical applications, especially in remote health monitoring, as they transmit crucial and time‐sensitive data collected by nodes positioned around or within the body. However, the coexistence of WBANs with wireless channels can degrade performance due to interference. This study introduces OIM‐DLCAM, an optimal interference mitigation scheme for WBANs, which utilizes a deep learning‐based channel access method. OIM‐DLCAM addresses interference through the multiobjective Hungarian optimization (MOHO) algorithm, considering design constraints such as node transmission power, packet delivery ratio, and interference range. Additionally, it employs a deep probabilistic neural network‐based channel access method (DPNN‐CAM) to effectively mitigate interference by making decisions regarding contention window size, frame length, and buffer size. The proposed OIM‐DLCAM scheme ensures fairness between users while enhancing system performance. Simulation results from both static and dynamic sensor node scenarios demonstrate its effectiveness under various conditions, showcasing its potential to improve WBAN performance in medical applications. The simulations reveal that OIM‐DLCAM outperforms existing state‐of‐the‐art schemes across various scenarios, with efficiency gains of up to 86.187%, 72.452%, and 47.954% for WBAN node density, mobility, and packet arrival rate, respectively. Moreover, it significantly reduces the average end‐to‐end delay and packet drop rate while improving throughput and packet delivery ratio compared with existing schemes. Additionally, comparisons with industry standards, such as the IEEE 802.15.4e norm, validate the suitability of OIM‐DLCAM for cofounded WBANs.

show abstract

A Cross-Layer Judgment Scheme for Solving Retransmission Problem in IEEE 802.15.4-Based Wireless Body Sensor Networks

Cited by 9 publications

References 14 publications

An Adaptive Relay Transmission Scheme for Reliable Data Forwarding in Wireless Body Area Networks

An Adaptive Relay Transmission Scheme for Reliable Data Forwarding in Wireless Body Area Networks

An Adaptive Backoff Mechanism for IEEE 802.15.4 Beacon‐Enabled Wireless Body Area Networks

Optimal interference mitigation with deep learningbased channel access in wireless body area networks

Contact Info

Product

Resources

About