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An agent-based approach for energy-efficient sensor networks in logistics

Accepted version
Peer-reviewed

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Authors

Hernández, MP 
Kaupe, V 
Kurrek, P 

Abstract

As part of the fourth industrial revolution, logistics processes are augmented with connected information systems to improve their reliability and sustainability. Above all, customers can analyse process data obtained from the networked logistics operations to reduce costs and increase margins. The logistics of managing liquid goods is particularly challenging due to the strict transport temperature requirements involving monitoring via sensors attached to containers. However, these sensors transmit much redundant information that, at times, does not provide additional value to the customer, while consuming the limited energy stored in the sensor batteries. This paper aims to explore and study alternative approaches for location tracking and state monitoring in the context of liquid goods logistics. This problem is addressed by using a combination of data-driven sensing and agent-based modelling techniques. The simulation results show that the longest life span of batteries is achieved when most sensors are put into sleep mode yielding an increase of ×21.7 and ×3.7 for two typical routing scenarios. However, to allow for situations in which high quality sensor data is required to make decisions, agents need to be made aware of the life cycle phase of individual containers. Key contributions include (1) an agent-based approach for modelling the dynamics of liquid goods logistics to enable monitoring and detect inefficiencies, (2) the development and analysis of three sensor usage strategies for reducing the energy consumption, and (3) an evaluation of the trade-offs between energy consumption and location tracking precision for timely decision making in resource constrained monitoring systems.

Description

Keywords

4605 Data Management and Data Science, 46 Information and Computing Sciences, 7 Affordable and Clean Energy

Journal Title

Engineering Applications of Artificial Intelligence

Conference Name

Journal ISSN

0952-1976

Volume Title

Publisher

Elsevier BV