Minimizing End-to-End Delay in Wireless Networks Using a Coordinated EDF Schedule

2010 ◽  
Author(s):  
Praveen Jayachandran ◽  
Matthew Andrews
Keyword(s):  
Wireless Networks ◽  
End To End Delay ◽  
End To End

scholarly journals Minimizing end-to-end delay in multi-hop wireless networks with optimized transmission scheduling

2019 ◽  
Vol 89 ◽  
pp. 236-248 ◽  
Author(s):  
Antonio Capone ◽  
Yuan Li ◽  
Michał Pióro ◽  
Di Yuan
Keyword(s):  
Wireless Networks ◽  
Transmission Scheduling ◽  
End To End Delay ◽  
End To End

scholarly journals Real-Time Communications in Large-Scale Wireless Networks

2008 ◽  
Vol 2008 ◽  
pp. 1-16 ◽  
Author(s):  
Liang Song ◽  
Dimitrios Hatzinakos
Keyword(s):  
Wireless Networks ◽  
Real Time ◽  
Large Scale ◽  
Visual Sensor ◽  
Broadband Internet ◽  
Multihop Wireless ◽  
Data Packets ◽  
End To End Delay ◽  
End To End ◽  
Qos Performance

There is an emerging need for realizing real-time quality of service (QoS) over multihop wireless communications in large-scale wireless networks. The applications can include wireless mesh infrastructure for broadband Internet access supporting multimedia services, visual sensor networks for surveillance, and disaster-relief networks. However, a number of challenges still exist as revealed by recent works, where the dataflow QoS performance such as throughput and end-to-end delay can degrade fast with the number of wireless hops. We propose to use large-scale cognitive networking methods to resolve the wireless multihop challenges. By the cognitive-networking concept, data packets travel along opportunistically available paths in the network with opportunistically available spectrum in every hop. Reliable end-to-end communications can be achieved for real-time services, where we show that (1) dataflow throughput can be independent of any number of wireless hops, (2) end-to-end delay and delay variance increase linearly with the number of wireless hops, and (3) delay variance decreases to zero with higher network density. These results are supported by analysis, simulations, and experiments.

scholarly journals Enhanced Resource Allocation Algorithm for Heterogeneous Wireless Networks

2020 ◽  
Vol 24 (6) ◽  
pp. 763-773
Author(s):  
Topside E. Mathonsi ◽  
◽  
Tshimangadzo Mavin Tshilongamulenzhe ◽  
Bongisizwe Erasmus Buthelezi
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Packet Loss ◽  
Bandwidth Allocation ◽  
Heterogeneous Wireless Networks ◽  
Allocation Algorithm ◽  
End To End Delay ◽  
Radio Access ◽  
End To End ◽  
Bandwidth Allocation Algorithm

In heterogeneous wireless networks, service providers typically employ multiple radio access technologies to satisfy the requirements of quality of service (QoS) and improve the system performance. However, many challenges remain when using modern cellular mobile communications radio access technologies (e.g., wireless local area network, long-term evolution, and fifth generation), such as inefficient allocation and management of wireless network resources in heterogeneous wireless networks (HWNs). This problem is caused by the sharing of available resources by several users, random distribution of wireless channels, scarcity of wireless spectral resources, and dynamic behavior of generated traffic. Previously, resource allocation schemes have been proposed for HWNs. However, these schemes focus on resource allocation and management, whereas traffic class is not considered. Hence, these existing schemes significantly increase the end-to-end delay and packet loss, resulting in poor user QoS and network throughput in HWNs. Therefore, this study attempts to solve the identified problem by designing an enhanced resource allocation (ERA) algorithm to address the inefficient allocation of available resources vs. QoS challenges. Computer simulation was performed to evaluate the performance of the proposed ERA algorithm by comparing it with a joint power bandwidth allocation algorithm and a dynamic bandwidth allocation algorithm. On average, the proposed ERA algorithm demonstrates a 98.2% bandwidth allocation, 0.75 s end-to-end delay, 1.1% packet loss, and 98.9% improved throughput performance at a time interval of 100 s.

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