Low-Latency Service Data Aggregation Using Policy Obligations

The Internet of Things, large scale sensor networks or even in social media, are now well established and their use is growing daily. Usage scenarios in these fields highlight the requirement to process, procure, and provide information with almost zero latency. This work is introducing new concepts for enabling fast communication by limiting information flow through filtering concepts combined with data processing techniques adopted from complex event processing. Specifically we introduce a novel mediation services architecture using filter policies to reduce latency. The filter policies define when and what data services need to provide to the mediator and thus save on bandwidth. The filter policies describe temporal conditions between two events removing the need to keep a complete history while still allowing temporal reasoning. Promising experimental results highlight the advantages to be gained from the approach

Low-Latency Service Data Aggregation using Policy Obligations

Abstract-The Internet of Things, large scale sensor networks or even in social media, are now well established and their use is growing daily. Usage scenarios in these fields highlight the requirement to process, procure, and provide information with almost zero latency. This work is introducing new concepts for enabling fast communication by limiting information flow through filtering concepts combined with data processing techniques adopted from complex event processing. Specifically we introduce a novel mediation services architecture using filter policies to reduce latency. The filter policies define when and what data services need to provide to the mediator and thus save on bandwidth. The filter policies describe temporal conditions between two events removing the need to keep a complete history while still allowing temporal reasoning. Promising experimental results highlight the advantages to be gained from the approach