5th International ICST Conference on Body Area Networks

Research Article

Context-Aware Body Area Networks (CABAN) for Interactive Smart Environments: Interference Characterization

  • @INPROCEEDINGS{10.1145/2221924.2221929,
        author={Sean Heaney and Emi Garcia-Palacios and William Scanlon},
        title={Context-Aware Body Area Networks (CABAN) for Interactive Smart Environments: Interference Characterization},
        proceedings={5th International ICST Conference on Body Area Networks},
        publisher={ACM},
        proceedings_a={BODYNETS},
        year={2012},
        month={6},
        keywords={Body Area Networks Interactive Smart Environments Context Awareness Interference.},
        doi={10.1145/2221924.2221929}
    }
    
  • Sean Heaney
    Emi Garcia-Palacios
    William Scanlon
    Year: 2012
    Context-Aware Body Area Networks (CABAN) for Interactive Smart Environments: Interference Characterization
    BODYNETS
    ACM
    DOI: 10.1145/2221924.2221929
Sean Heaney1,*, Emi Garcia-Palacios2, William Scanlon2
  • 1: The Queen's University Belfast
  • 2: The Queen‘s University Belfast
*Contact email: sheaney14@qub.ac.uk

Abstract

Body Area Networks are unique in that the large-scale mobility of users allows the network itself to travel across a diverse range of operating domains or even to enter new and unknown environments. This network mobility is unlike node mobility in that sensed changes in inter-network interference level may be used to identify opportunities for intelligent inter-networking, for example, by merging or splitting from other networks, thus providing an extra degree of freedom. This paper introduces the concept of context-aware bodynets for interactive environments using inter-network interference sensing. New ideas are explored at both the physical and link layers with an investigation based on a „smart‟ office environment. A series of carefully controlled measurements of the mesh interconnectivity both within and between an ambulatory body area network and a stationary desk-based network were performed using 2.45 GHz nodes. Received signal strength and carrier to interference ratio time series for selected node to node links are presented. The results provide an insight into the potential interference between the mobile and static networks and highlight the possibility for automatic identification of network merging and splitting opportunities.