Adaptive polling interval scheduling to support real-time service in Bluetooth networks

2006 ◽  
Vol 1 (3/4) ◽  
pp. 204 ◽  
Author(s):  
ode Ma ◽  
Shin Yeh Low
Keyword(s):  
Real Time ◽  
Interval Scheduling ◽  
Time Service ◽  
Adaptive Polling ◽  
Polling Interval

Sub-nanosecond one-way real-time time service system based on UTC

GPS Solutions ◽  
2021 ◽  
Vol 25 (2) ◽  
Author(s):  
Meifang Wu ◽  
Baoqi Sun ◽  
Yuanxin Wang ◽  
Zhe Zhang ◽  
Hang Su ◽  
...  
Keyword(s):  
Real Time ◽  
Service System ◽  
Time Service

Real-Time Service Migration for Voice over IP Services

2008 ◽  
Author(s):  
Tan Miaoqing ◽  
Andres Arjona ◽  
Antti Ylä-Jääski
Keyword(s):  
Real Time ◽  
Voice Over Ip ◽  
Time Service ◽  
Service Migration

Multichannel Service Delivery Architecture

2015 ◽  
pp. 589-601
Author(s):  
Randall E. Duran ◽  
Anh Duc Do
Keyword(s):  
Real Time ◽  
Service Oriented Architecture ◽  
Mobile Banking ◽  
Time Service ◽  
System Architectures ◽  
Banking Services ◽  
Wide Range ◽  
Service Oriented ◽  
Multichannel Service ◽  
Service Channels

System architectures that deliver real-time services to customers must be flexible, scalable, and support a wide range of communication channels. This chapter presents an architecture that was designed to support multiple delivery channels and was successfully used to implement mobile banking services. The considerations behind the design and the approach used to deliver SMS-based mobile services using service-oriented architecture principles are reviewed and some of the practical challenges that were encountered with the implementation are explored. The ability for this solution architecture to support other real-time service channels is also examined.

An Overview of Memory Provisioning in Real-Time Wireless Sensor Networks

2013 ◽  
Vol 5 (4) ◽  
pp. 73-91
Author(s):  
Jacqueline Stewart ◽  
Thomas McCabe ◽  
Robert Stewart ◽  
Sean Kennedy
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Service Delivery ◽  
Real Time ◽  
Service Providers ◽  
Wireless Sensor ◽  
Analysis Tool ◽  
Time Service ◽  
Main Challenge ◽  
Stack Analysis

Wireless Sensor Networks and the smart applications designed to operate upon them have enjoyed a rapid increase in popularity over the last decade. The main challenge currently is the provision of real-time service delivery for wireless sensor networks to cater for new applications with guaranteed Quality of Service (QoS) requirements. However each application has a different service requirement. In order to deliver real-time services the dimensioning of such networks is important to service providers in order to meet these service requirements. If packets cannot be stored due to insufficient memory they are lost. Lost packets result in the resending of the packets and hence an increase in delay in delivery of the application traffic. It is this memory provisioning of these wireless sensor networks that is the focus of the work presented in this paper. More specifically the relationship between the application design, implementation and memory resources required to run the service are explored using a stack analysis tool. This stack analysis tool enables the stack footprint to be measured. Results of memory usage for two different WSN applications are presented. Recommendations based on this study for efficient memory provisioning and ultimately real-time service delivery are given.

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