Towards a Secure and Scalable Maritime Monitoring System Using Blockchain and Low-Cost IoT Technology

Sensors (Basel). 2022 Jun 29;22(13):4895. doi: 10.3390/s22134895.

Abstract

Maritime Domain Awareness (MDA) is a strategic field of study that seeks to provide a coastal country with an effective monitoring of its maritime resources and its Exclusive Economic Zone (EEZ). In this scope, a Maritime Monitoring System (MMS) aims to leverage active surveillance of military and non-military activities at sea using sensing devices such as radars, optronics, automatic Identification Systems (AISs), and IoT, among others. However, deploying a nation-scale MMS imposes great challenges regarding the scalability and cybersecurity of this heterogeneous system. Aiming to address these challenges, this work explores the use of blockchain to leverage MMS cybersecurity and to ensure the integrity, authenticity, and availability of relevant navigation data. We propose a prototype built on a permissioned blockchain solution using HyperLedger Fabric-a robust, modular, and efficient open-source blockchain platform. We evaluate this solution's performance through a practical experiment where the prototype receives sensing data from a Software-Defined-Radio (SDR)-based low-cost AIS receiver built with a Raspberry Pi. In order to reduce scalability attrition, we developed a dockerized blockchain client easily deployed on a large scale. Furthermore, we determined, through extensive experimentation, the client optimal hardware configuration, also aiming to reduce implementation and maintenance costs. The performance results provide a quantitative analysis of the blockchain technology overhead and its impact in terms of Quality of Service (QoS), demonstrating the feasibility and effectiveness of our solution in the scope of an MMS using AIS data.

Keywords: Docker; automatic identification system; hyperledger fabric; maritime monitoring system; permissioned blockchain.

MeSH terms

  • Blockchain*
  • Computer Security
  • Humans
  • Monitoring, Physiologic
  • Software
  • Technology

Grants and funding

This work was supported by FCT through the LASIGE Research Unit, ref. UIDB/00408/2020 and ref. UIDP/00408/2020, and by the Admiral Wandenkolk Instruction Center (CIAW), Brazilian Navy.