This paper analyzes the packet reception ratio (PRR) in unmanned aerial vehicle (UAV)-to-UAV communications (U2U) in a realistic urban environment considering 3D blockage effects by buildings and interference from other UAVs. Buildings are generated based on real-world data, leading to mixed and non-homogeneous building profiles beyond the assumptions of existing mathematical models. To this end, we use stochastic simulations to analyze the PRR dynamics and the trade-off in the line-of-sight (LoS) conditions for the communication and interference links under realistic blockage effects. Our findings have important implications for U2U deployment in future smart cities. In particular, the simulation results reveal the importance of incorporating real-world building data and performing a joint analysis on the number of UAVs and their operating height range for different sub-regions with varying building profiles.

This paper analyzes the impacts of realistic blockage effects by buildings for the packet reception ratio (PRR) in unmanned aerial vehicle (UAV)-to-UAV communications (U2U). The key idea is to generate buildings based on real-world data on an urban landscape, i.e., more mixed and inhomogeneous building profiles than the existing mathematical models, to simulate the PRR stochastically. This key idea enables us to capture the impact of such inhomogeneous blockage profiles on line-of-sight conditions in a communication link between target UAVs and interference links from other UAVs, providing a key trade-off in the PRR. Our simulation results highlight this trade-off as the number of UAVs and UAV heights change. The results also illustrate how the inhomogeneous building profiles affect the PRR in each sub-region of the urban landscape under study. Our findings have important implications for U2U in real-world urban areas by using realistic building models and performing a joint analysis on the number of UAVs and their operating height across different sub-regions within the urban area of interest.

Agent-based modeling and simulation (ABMS) is a powerful analysis tool that has led to significant contributions in the field of innovation diffusion. In this article, we examine the potential and pitfalls of extending adoption models used in agent-based diffusion via machine learning (ML) and soft computing (SC) techniques. More specifically, we 1) classify features related to agents’ decision-making and social interactions that are generally not considered in current adoption models; 2) present, along with illustrative examples, an assessment of the potential of hybrid ABMS involving ML and SC to incorporate and model these features; and 3) identify essential considerations for the implementation and applicability of such adoption models. To support future efforts in developing computational systems based on these hybrid ABMS, the article also highlights research areas to further investigate at the intersection of ABMS, ML, and SC.

Within any incremental development paradigm, there exists a tension between the desire to deliver value to the customer early and the desire to reduce cost by avoiding architectural refactoring and rework in subsequent releases. What is lacking is an analytical framework that quantifies opportunities and risks of choosing one or the other of these strategies or a blend of the two. This article demonstrates the use of design structure and domain mapping matrices for analyzing architectural dependencies and proposes an optimization-based decision-making technique to support effective release planning. The optimization models recommend the order in which architectural elements and features should be implemented across different releases so as to: (a) minimize rework cost; (b) maximize early value delivery; or (c) optimize an integrated measure of cost and value. These analytic models can be applied earlier in the life cycle and, hence, provide timely information about the progress and changes that occur at each iteration.

While many large touchscreen devices have a split keyboard option to enable two-thumb typing, there are significant differences in the design which affect users’ text entry rate. A mixed-integer programming model is developed to optimize key-to-thumb assignment with the objective of minimizing the expected time to type a character. Computer simulations are conducted to determine the optimal key dimension under different values of the Fitts’ law’s slope coefficient, typing error rate, and alternate-hand advantage phenomenon. The results show that text entry rate and the optimal key-to-thumb assignment depend on key dimensions, user’s speed-accuracy profile, and the level of alternate-hand advantage. The optimal keyboard is proposed. To validate the analytical findings, an empirical study is conducted with eighteen users and six different keyboards in terms of key dimensions and typing zone. Empirical results report between 7% and 18% improvement in the text entry rate over the other split keyboards tested.