IEEE Access special section editorial: Mission-Critical Sensors and Sensor Networks (MC-SSN)

Qilian Liang, Tariq S. Durrani, Jinhwan Koh, Jing Liang, Yonghui Li, Xin Wang

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Abstract

Mission-critical sensors and sensor networks (MC-SSN) have been applied to missions such as battlefield, border patrol, search and rescue, critical structure monitoring, and surveillance. To support critical missions, sensors and sensor networks need to be flexible and interactive and continuously work despite limited bandwidth, intermittent connectivity, and with a large number of devices on the network. Sometimes, humans will be the elements within mission-critical sensors and sensor networks that are most vulnerable to deception, and humans will be handicapped when they are concerned about the received information from the network is not trustworthy, even if their concern is misplaced. In MCSSN, the advantages of linking multiple electronic support measures and electronic attack assets to achieve improved capabilities across a networked mission-critical force have yet to be quantified. Algorithms are sought for fused and/or coherent cross-platform radio frequency (RF) sensing. The MC-SSN algorithms should be capable of utilizing RF returns from multiple aspects in time-coordinated sensors and sensor networks. Such adaptation, management, and reorganization of information sources, devices, and networks must be accomplished almost autonomously in order to avoid imposing additional burdens on the humans, and without much reliance on support and maintenance services. Moreover, humans, under extreme cognitive and physical stress, will be strongly challenged by the massive complexity of the MC-SSN and the information it will provide and carry. Advances in technologies that capitalize on the benefits of the MC-SSN have to assist humans in making effective use of this massive, complex, confusing, and potentially deceptive ocean of information while taking into account the ever-changing mission. New approaches and low-complexity algorithms are expected to enable MC-SSN to automatically manage and effect risk and uncertainty in a highly deceptive, mixed cooperative/adversarial, information-centric environment.
Original languageEnglish
Pages (from-to)49457-49466
Number of pages10
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2 Apr 2021

Keywords

  • General Engineering
  • General Materials Science
  • General Computer Science

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