Distributed Spectrum Monitoring Systems

Today's spectrum measurements are mainly performed by governmental agencies which drive around using expensive specialized hardware. Each country creates and maintains its own national frequency allocation plan which describes how the EM spectrum shall be used. Despite, the EM spectrum is well-organized in terms of frequency allocation, its actual usage in different geographical places and times is not well-known at all. The idea of distributed spectrum monitoring has recently gained attention to capture the real-time usage of the wireless spectrum at large geographical scale.

Although the frequency allocation of electromagnetic (EM) spectrum is well-organized, there is little knowledge of its actual usage in different geographical places and times. While traditional methods to monitor the spectrum usage rely on expensive and specialized hardware of governmental agencies, an attractive emerging alternative consists on building a networked and distributed infrastructure using spectrum analyzers connected over the public Internet. However, despite this strict allocation scheme of the spectrum, the actual usage of the spectrum at different geographical places and times is often unknown. This problem is due to the fact that today’s spectrum measurements are primarily performed by governmental agencies which drive around using expensive and bulky specialized hardware. This monitoring approach does not scale well and is not able to cover the pervasive deployment of wireless networks as well as the increasing range of spectrum frequencies being used. Recent suggestions to enable wide-scale and real-time spectrum monitoring have therefore been to build a networked and distributed infrastructure using remote spectrum analyzers, or leverage the masses and to crowdsource the measurement stations by trading-off radio device sensitivity with cooperation. Our work [1] is aligned with these ideas of exploiting the crowd for scalable monitoring of the spectrum’s actual usage at different frequencies, locations and times. However, in contrast to previous works, we envision a monitoring system that builds upon low-cost hardware and we study the design challenges at system level such that a large number of nodes can be distributed to collaboratively enlarge the system’s overall coverage. The collaboration would allow individual sensing nodes working together in a coordinated fashion towards a common goal of monitoring the spectrum over the defined area of coverage. For this, the individual nodes would report their spectrum sensing results to a fusion center that stores and assembles the collected data.

A detail map of the actual EM spectrum usage created by crowdsourcing would democratize the access and knowledge of the radio spectrum leading to the emergence of new desirable services for a wide range of different applications [2]. Cognitive radio could use the data as a baseline to optimizing their efforts to dynamically accessing the spectrum. The spectrum sensed might also be used to identify regions with high/low levels of electro smog, which it could be an incentive for users to participate in this crowdsourcing system.

 

References

  1. Damian Pfammatter, Domenico Giustiniano, Vincent Lenders (April 2015) 
    A Software-defined Sensor Architecture for Large-scale Wideband Spectrum Monitoring (Paper) [PDF Download PDF in new window]
    In: ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2015), 13-16 April 2015, Seattle, USA
  2. Roberto Calvo Palomino, Damian Pfammatter, Domenico Giustiniano, Vincent Lenders (April 2015) 
    Demonstration Abstract: A Low-cost Sensor Platform for Large-scale Wideband Spectrum Monitoring (Demo) [PDF Download PDF in new window]
    In: ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2015), 13-16 April 2015, Seattle, USA

Resources

Git Repository: https://git.networks.imdea.org/electrosense/rtl-spec