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Wireless Sensor Networks (WSN)

Wireless Sensor Networks (WSN) have recently been the focus of a significant amount of attention and effort of the research community. The main motivation has been to address the challenges posed by the WSN paradigm, i.e., limited node power, processing, and communication capabilities, dense network deployment, multi-hop communications, and heterogeneous application-specific requirements. The vast majority of these studies applies to conventional WSN applications which need reliable and efficient communication of scalar event features and sensor data such as temperature, pressure, humidity.

With the availability of low-cost small-scale imaging sensors, CMOS cameras, microphones, which may ubiquitously capture multimedia content from the field, Wireless Multimedia Sensor Networks (WMSN) have been proposed and drawn the immediate attention of the research community. WMSN applications, e.g., multimedia surveillance networks, target tracking, environmental monitoring, and traffic management systems, require effective harvesting and communication of event features in the form of multimedia such as audio, image, and video. To this end, additional challenges for energy-efficient multimedia processing and communication in WMSN, i.e., heterogeneous multimedia reliability definitions, tight QoS expectations, and high bandwidth demands, must be addressed as well.

Our research focus on a wireless community Sensor network, that we call CommSensum, based on Open Linked Data, and that is able to capture data from many sensors deployed by different parties. The applications are quite wide, but we mainly address Smart Cities. We are currently working in the following research aspects:

  • Our reserach group coordinates the H2020 project CAPTOR (2016-2019) that runs three big pilots on tropospheric ozone monitoring in Austria, Italy and Spain, driven by grassroots activists and local communities. The CAPTOR monitoring network will use DIY nodes created by our team (the so called "captor" nodes).
  • CommSensum is a community sensor network based on Open Linked Data. This platform forms part of National project PLASMA (2013-2016) and it currently is under development. Please, click here to read more about CommSensum project or connect to the platform via http://commsensum.pc.ac.upc.edu/ ).
  • In project COHWAN (Cooperative and Opportunistic Wireless Heterogeneous Access Networks, 2011-2014) and in project MWMSN (Multi-tier Wireless Multimedia Sensor Networks, Nov 2011-May 2012), we worked in Wireless Multimedia Sensor Networks (WMSN). Among the current research activities, we are building a WMSN Lab to experiment with multimedia applications in sensor architectures. Frame manipulation is required to minimize the amount of data to be sent. Visual Computing techniques such as background subtraction, object recognition, etc, may play an interesting role in this applications. We are also defining and evaluating network protocols for WMSN taking into account traffic characteristics. Some aspects that we have researched related to WMSN are:
    • Coverage of monitoring areas using FoV (Field of Views).
    • Traffic characterization in WMSN using in-network processing techniques.
  • In project COHWAN (Cooperative and Opportunistic Wireless Heterogeneous Access Networks, 2011-2014) , we also have a Workgroup devoted to investigate the performance of several protocol stacks in WSN. For example,It is not clear whether is more appropriate to use IEEE802.15.4/6LowPAN or Low-Power WiFi architectures. Moreover, together with these stacks exist the possibility to have on top UDP/CoAP or TCP/HTTP.
  • We also research in several aspects related to WSN. For example, we have studied:
    • MAC protocols addressed to applications that have real-time traffic.
    • Other researchs have been focused on looking to find energy-latency trade-off points in Duty-Cycle MAC protocols.
    • We also have researched how to provide contextual privacy and data compression at the same time that load balancing the flows of traffic in WSN.