Σχεδίαση και ανάπτυξη δικτύου ZIGBEE-MESH για παρακολούθηση περιβαλλοντικών συνθηκών σε πλοίο
Design and development of ZigBee Mesh network for environmental monitoring on a ship
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Keywords
ZigBee ; Δίκτυο Πλέγματος (Mesh) ; Ασύρματα δίκτυα αισθητήρων ; Ναυτιλία ; Έξυπνο πλοίο ; Smart ship ; IEEE 802.15.4 ; Προγνωστική συντήρηση ; IoT ; MQTTAbstract
This thesis focuses on explaining how a ship can receive live data from various factors and areas of interest, enabling it to interpret and safeguard against events and situations through the design and development of a wireless network based on ZigBee technology with a Mesh topology. Traditionally, the monitoring of parameters such as temperature, humidity, the presence of hazardous gases, and mechanical vibrations in the engine room or cargo holds, relies on wired systems. These are characterized by high installation costs, maintenance complexity, and difficulty in expansion. The objective of this research is to propose a reliable, wireless, and energy-efficient alternative solution.
Initially, all factors of interest are analyzed, as well as the difficulties that arise regarding information propagation. An analysis of the ZigBee Mesh system is conducted, demonstrating how it is capable, at a relatively low economic cost, of overcoming these problems. It provides the operator with user-friendly features, allowing them to act solely as an information collector and nothing more. Through the IEEE 802.15.4 standard and the AODV routing protocol, the network acquires the capability of dynamic discovery of alternative communication routes and self-healing in the event that a physical obstacle disrupts the line of sight between the nodes.
Although there are several transceivers on the market that may be capable of this application, this thesis relied on the low-power AT86RF230 transceiver, distributing the roles of the Coordinator, Routers, and End Devices alongside the sensors of each area.
From the evaluation of the design, it is concluded that the ZigBee network offers excellent autonomy for the remote battery-operated sensors, a drastic reduction in implementation costs, and a high success rate.
Finally, proposals for the future utilization of the system are suggested, such as the integration of Machine Learning algorithms for the predictive maintenance of mechanical equipment, and the utilization of energy harvesting techniques, paving the way for the transition into the new era of "Smart Ships".