This paper describes a noise-aware dominance operator for evolutionary algorithms to solve the multiobjective optimization problems (MOPs) that contain noise in their objective functions. This operator takes objective value samples of given two individuals (or solution candidates), estimates the impacts of noise on the samples and determines whether it is confident enough to judge which one is superior/inferior between the two individuals. Since the proposed operator assumes no noise distributions a priori, it is well applicable to various MOPs whose objective functions follow unknown noise distributions. Experimental results show that it operates reliably in noisy MOPs and outperforms existing noise-aware dominance operators.

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Abstract. Developing applications for wireless sensor networks (WSN) is a complicated process because of the wide variety of WSN applications and lowlevel implementation details. Model-Driven Engineering offers an effective solution to WSN application developers by hiding the details of lower layers and raising the level of abstraction. However, balancing between abstraction level and unambiguity is challenging issue. This paper presents Baobab, a metamodeling framework for designing WSN applications and generating the corresponding code, to overcome the conflict between abstraction and reusability versus unambiguity. Baobab allows users to define functional and non-functional aspects of a system separately as software models, validate them and generate code automatically.

Mechanical tests, for example, tensile and hardness tests, are usually used to evaluate the properties of rubber materials. In this work, mechanical properties of selected rubber materials, that is, natural rubber (NR), styrene butadiene rubber (SBR), nitrile butadiene rubber (NBR), and ethylene propylene diene monomer (EPDM), were evaluated using a near infrared (NIR) spectroscopy technique. Here, NR/NBR and NR/EPDM blends were first prepared. All of the samples were then scanned using a FT-NIR spectrometer and fitted with an integration sphere working in a diffused reflectance mode. The spectra were correlated with hardness and tensile properties. Partial least square (PLS) calibration models were built from the spectra datasets with preprocessing techniques, that is, smoothing and second derivative. This indicated that reasonably accurate models, that is, with a coefficient of determination [R2] of the validation greater than 0.9, could be achieved for the hardness and tensile properties of rubber materials. This study demonstrated that FT-NIR analysis can be applied to determine hardness and tensile values in rubbers and rubber blends effectively.

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Wireless sensor applications (WSNs) are often required to simultaneously satisfy conflicting operational objectives (e.g., latency and power consumption). Based on an observation that various biological systems have developed the mechanisms to overcome this issue, this paper proposes a biologically-inspired adaptation mechanism, called MON-SOON. MONSOON is designed to support data collection applications, event detection applications and hybrid applications. Each application is implemented as a decentralized group of software agents, analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data and/or detect an event (a significant change in sensor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding environment conditions and adaptively invoking biologicallyinspired behaviors such as pheromone emission, reproduction and migration. Each agent has its own behavior policy, as a gene, which defines how to invoke its behaviors. MONSOON allows agents to evolve their behavior policies (genes) and adapt their operations to given objectives. Simulation results show that MONSOON allows agents (WSN applications) to simultaneously satisfy conflicting objectives by adapting to dynamics of physical operational environments and network environments (e.g., sensor readings and node/link failures) through evolution.

This paper proposes and evaluates a model-driven performance engineering framework for wireless sensor networks (WSNs). The proposed framework, called Moppet, is designed for application developers to rapidly implement WSN applications and estimate their performance. It leverages the notion of feature modeling so that it allows developers to graphically and intuitively specify features (e.g., functionalities and configuration policies) in their applications. It also validates a set of constraints among features and generates application code. Moppet also uses event calculus in order to estimate a WSN application's performance without generating its code nor running it on simulators and real networks. Currently, it can estimate power consumption and lifetime of each sensor node. Experimental results show that, in a small-scale WSN of 16 iMote nodes, Moppet's average performance estimation error is 8%. In a large-scale simulated WSN of 400 nodes, its average estimation error is 2%. Moppet scales well to the network size with respect to estimation accuracy. Moppet generates lightweight nesC code that can be deployed with TinyOS on resource-limited nodes. The current experimental results show that Moppet is well-applicable to implement biologically-inspired routing protocols such as pheromone-based gradient routing protocols and estimate their performance.