Result: Application of the Java Message Service in mobile monitoring environments

Abstract: Distributed systems and sensor networks in particular are in need of efficient asynchronous communication, message security and integrity, and scalability. These points are especially important in mobile environments where mobile remote sensors are connected to a control center only via intermittent communication. We present a general approach for dealing with the issues that arise in such scenarios. This approach is applied to provide flexible and efficient cargo monitoring on trains. The Java Message Service (JMS) presents a flexible transport layer for asynchronous communication that enables transparent store-and-forward queuing for entities that need to be connected to each other. Previously JMS was primarily used in always-connected high-bandwidth enterprise communication systems. We present the advantages of using JMS in a mobile, bandwidth-limited, and intermittently connected monitoring environment and provide a working implementation called the Transportation Security SensorNet (TSSN) that makes use of an implementation of JMS called ActiveMQ. This solution is employed here to enable monitoring of cargo in motion along trusted corridors. Results obtained from experiments and a field trial show that using JMS provides not just a practical alternative to often custom binary communication layers, but a better and more flexible approach, by providing transparency. Applications on both communication ends only need to implement JMS connectors while the remaining functionality is provided by the JMS implementation. Another benefit arises from the exchangeability of JMS implementations. In utilizing JMS we demonstrate a new, flexible and scalable approach to cope with challenges inherent in intermittent and low-bandwidth communication in mobile monitoring environments. [Copyright &y& Elsevier]

Copyright of Journal of Network & Computer Applications is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)