Performance Evaluation of Hierarchical Slotted ALOHA for IoT Applications

Author(s):  
Takafumi Oku ◽  
Tomotaka Kimura ◽  
Jun Cheng
Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1478 ◽  
Author(s):  
Alireza Hassani ◽  
Alexey Medvedev ◽  
Pari Delir Haghighi ◽  
Sea Ling ◽  
Arkady Zaslavsky ◽  
...  

As IoT grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for IoT intelligence, efficiency, effectiveness, performance, and sustainability. As the standardisation efforts for IoT are fast progressing, efforts in standardising context management platforms led by the European Telecommunications Standards Institute (ETSI) are gaining more attention from both academic and industrial research organizations. These standardisation endeavours will enable intelligent interactions between ‘things’, where things could be devices, software components, web-services, or sensing/actuating systems. Therefore, having a generic platform to describe and query context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange, reuse and share context between each other. CDQL consists of two main parts, namely: context definition model, which is designed to describe situations and high-level context; and Context Query Language (CQL), which is a powerful and flexible query language to express contextual information requirements without considering details of the underlying data structures. An important feature of the proposed query language is its ability to query entities in IoT environments based on their situation in a fully dynamic manner where users can define situations and context entities as part of the query. We exemplify the usage of CDQL on three different smart city use cases to highlight how CDQL can be utilised to deliver contextual information to IoT applications. Performance evaluation has demonstrated scalability and efficiency of CDQL in handling a fairly large number of concurrent context queries.


2019 ◽  
Vol 52 (24) ◽  
pp. 312-317 ◽  
Author(s):  
Harith Kharrufa ◽  
Naveed Salman ◽  
Ma Lei ◽  
A.H. Kemp

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