Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications

The EmVoc framework for empirically evaluating modeling language vocabulary qualities: a pilot evaluation study

Conceptual modeling is central for a variety of activities surrounding the planning, design, development and maintenance of software-intensive systems. To allow development of conceptual models that are understood in the same way by different people, conceptual modeling languages are developed which contain concepts and rules that dictate how correct models can be built according to the language. A key component of a modeling language is a set of concepts that modelers must use to describe world phenomena. Once the concepts are chosen, a visual and/or linguistic vocabulary is adopted for representing the concepts. Both the choices of concepts and the vocabulary used to represent them, however, may affect the quality of the language under consideration. Some choices may match the intentions of the language or may allow for a shared understanding of the concepts better than other choices. We present EmVoc, a framework for empirically measuring the appropriateness of conceptual modeling language vocabularies based on observing how language user sample classify domain content under concepts of the language. The framework is based on a set of abstract empirical constructs that show how such empirical observations can be analyzed in order to detect vocabulary issues of various kinds. The constructs can be operationalized into concrete measures based on the specific data collection instrument or interests of the study. As a first evaluation of our framework we apply it to compare an existing language with an artificial one that is manufactured to exhibit specific issues. We then test if the metrics indeed detect these issues and only. In this paper, we present the complete experimental design and report on the results of a pilot administration. The complete study will be reported in a future version of this paper