Advances in technology call for a parallel evolution in the software. New techniques are needed to support this dynamism, to track and guide its evolution process. This applies especially in the field of embedded systems, and certainly in Wireless Sensor Networks (WSNs), where hardware platforms and software environments change very quickly. Commonly, operating systems play a key role in the development process of any application. The most used operating system in WSNs is TinyOS, currently at its TinyOS 2.1.2 version. The evolution from TinyOS 1.x and TinyOS 2.x made the applications developed on TinyOS 1.x obsolete. In other words, these applications are not compatible out-of-the-box with TinyOS 2.x and require a porting action. In this paper, we discuss on the porting of embedded system (i.e., Wireless Sensor Networks) applications in response to operating systems’ evolution. In particular, using a model-based approach, we report the porting we did of Agilla, a Mobile-Agent Middleware (MAMW) for WSNs, on TinyOS 2.x, which we refer to as Agilla 2. We also provide a comparative analysis about the characteristics of Agilla 2 versus Agilla. The proposed Agilla 2 is compatible with TinyOS 2.x, has full capabilities and provides new features, as shown by the maintainability and performance measurement presented in this paper. An additional valuable result is the architectural modeling of Agilla and Agilla 2, missing before, which extends its documentation and improves its maintainability.

Wireless sensor networks (WSNs) have become very popular in recent years due to their wide applications’ spectrum. One of the main issues arising while conceiving with WSNs functionality is the opportunity of developing a proper localisation algorithm that has to be tailored basing on hw / sw nodes limitations. This study proposes a localisation algorithm for WSNs based on received signal strength measurements whose results, although comparable with existing localisation algorithms, proves here to be of practical implementation on devices with very low computational capabilities as well as efficient in real‐time usages. Extensive simulations and indoor/outdoor experimental measurements have been performed to verify the effectiveness of the proposed algorithm, taking also into account implementation issues on commercial sensor nodes.