Carlos E. Otero

A systematic approach for resource allocation in software projects

The completion of reliable software products within their expected time frame represents a major problem for companies that develop software applications. Today, the software industry continues to struggle with delivering products in a timely manner. A major cause for delays is the training time required for engineers and other personnel to acquire the necessary skills to complete software tasks. Therefore, it is important to develop systematic personnel assignment processes that consider complete skill sets of candidates to provide solutions that reduce training time. This paper presents a novel methodology to assign resources to tasks when optimum skill sets are not available. The methodology takes into account existing capabilities of candidates, required levels of expertise, and priorities of required skills for the task. A sample case is used to show the model capabilities, and the results are compared with the current resource assignment approach.

Multiresponse Optimization of Stochastic WSN Deployment Using Response Surface Methodology and Desirability Functions

Due to reliance on stochastic deployment, delivery of large-scale WSN presents a major problem in the application of wireless sensor networks (WSN) technology. When deployed in a stochastic manner, the WSN has the utmost challenge of guaranteeing acceptable operational efficiency upon deployment. The research presented in this paper evaluates application of the response surface methodology (RSM) and desirability functions for analysis and optimization of stochastic WSN deployments based on multiple efficiency metrics. Through case studies, the approach is proven successful in modeling individual efficiency metrics, and in providing a way for analyzing deployments, based on numerous efficiency metrics. Additionally, the approach may be used to quantify the effects of optimizing partial efficiency metrics on the overall deployment efficiency.

A fuzzy expert system architecture for capability assessments in skill-based environments

The fast pace at which new technologies and techniques are being developed to improve the design and development of products increases the demand for specialized individual skills in the workforce. As a result of higher demands, candidates with exact required skills to work tasks are usually unavailable. Due to the lack of proper methods to assess personnel capabilities, decision makers are forced to assign resources to tasks based on shallow assessments. To tackle this issue, this research presents a layered expert architecture where subcomponents can be customized to specific industrial settings. A fuzzy logic scheme is described to model personnel capabilities as imprecise parameters, and to consider complete skill sets of resources when evaluating their levels of expertise in a skill. The proposed approach leads to thorough capability assessments, as well as an increased number of capable candidates. A case study is presented to show the implementation of the solution approach.

Research Directions for Engineering Big Data Analytics Software

Many software startups and research and development efforts are actively trying to harness the power of big data and create software with the potential to improve almost every aspect of human life. As these efforts continue to increase, full consideration needs to be given to the engineering aspects of big data software. Since these systems exist to make predictions on complex and continuous massive datasets, they pose unique problems during specification, design, and verification of software that needs to be delivered on time and within budget. But, given the nature of big data software, can this be done? Does big data software engineering really work? This article explores the details of big data software, discusses the main problems encountered when engineering big data software, and proposes avenues for future research.

A Wireless Sensor Networks' Analytics System for Predicting Performance in On-Demand Deployments

The need for advanced tools that provide efficient design of on-demand deployment of wireless sensor networks (WSN) is critical for meeting our nations demand for increased intelligence, reconnaissance, and surveillance. For practical applications, WSN deployments can be time consuming and error prone since they have the utmost challenge of guaranteeing connectivity and proper area coverage upon deployment. This creates an unmet demand for decision-support systems that help manage this complex process. This paper presents research to develop a system for predicting optimal deployments of WSN. Specifically, it presents results of image processing algorithms for terrain classification, results of modeling WSN signal propagation under different terrain conditions, results of optimization and visualization techniques for high-dimensional deployments, and system architecture for efficient integration and future deployment. Results show a feasible approach that can be used to automatically determine areas of high signal obstruction-which is essential to estimate obstruction parameters in simulations-and mapping of accurate WSN path-loss models to enhance the overall decision-making process during predeployment of large-scale WSN.

A Quality-Based Requirement Prioritization Framework Using Binary Inputs

Despite the clear need to prioritize requirements in software projects, finding a practical method for requirements prioritization has proven difficult. Existing requirements prioritization methods that provide the most consistent results are also the most complex, and therefore the most difficult to implement. More informal methods save time and are easier to apply, but may not be suitable for practical scenarios because they lack the structure and consistency required to properly analyze requirements. This paper proposes a novel and practical approach for prioritizing requirements in software projects. The proposed approach attempts to quantify the quality of requirements to provide a measurement that is representative of all quality criteria identified for a specific software project. The derived quality measurement can be easily computed to serve as the main metric for requirements prioritization.

A multi-hop, multi-segment architecture for perimeter security over extended geographical regions using wireless sensor networks

This paper presents a novel wireless sensor network architecture designed for perimeter security monitoring over extended geographical regions. The architecture relies on customized protocols and deployment techniques to disseminate perimeter event detection and tracking data to the sink, which maybe located miles away from the perimeter of interest. Through consideration of application specific characteristics, the proposed architecture reduces the number of deployed nodes, which results in reduced network complexity and cost without sacrificing the mission success.

A support vector machine for terrain classification in on-demand deployments of wireless sensor networks

Terrain characteristics can significantly alter the quality of the results provided by the deployment methodology of large-scale wireless sensor networks. For example, transmissions between nodes that are heavily obstructed will require additional transmission power to establish connection between nodes. In some cases, heavily obstructed areas may prevent nodes from establishing a connection at all. Therefore, terrain analysis and classification of specific deployment areas should be incorporated in the methodology process for evaluation and optimization of the performance of wireless sensor networks upon deployment. Although there exists radio frequency (RF) models capable of modeling obstructions, such as vegetation, foliage, etc., automatic assignment of parameter values for these models may be troublesome, specifically in highly irregular deployments terrains, where proximity of poor and optimal conditions for signal propagation may be adjacent to each other. In these situations, parameter estimation for modeling terrain obstruction may result in overly optimistic or pessimistic results, causing characterizations or predictions that deviate from the true performance of the WSN once deployed. This paper presents the results of employing a support vector machine for automatic terrain classification. The approach can be used to automatically determine areas of high obstruction, which is essential to estimate obstruction parameters in simulations and enhancing the overall decision-making process during pre-deployment of large-scale and irregular deployment terrains.

Software requirement prioritization using fuzzy multi-attribute decision making

Although many approaches have been proposed to prioritize requirements in software projects, almost none has been widely adopted. This is mostly due to their complexity, time commitment, lack of consistency, or implementation difficulties. This paper proposes a novel approach to do so that is practical, easy to implement and can show a reasonable level of consistency. In addition, it takes in consideration the imprecise nature of requirements and quality attributes by modeling the latter as fuzzy variables. The problem of prioritizing requirements is formulated as a fuzzy multi-attribute decision problem in which the expected value operator is used to rank the alternatives listed in the problem formulation. This approach can be easily extended to include other quality attributes as well as customized to fit the needs of most software projects.

Empirical Path Loss Models for Wireless Sensor Network Deployments in Short and Tall Natural Grass Environments

Extensive research has not been done on propagation modeling for natural short- and tall-grassy environments for the purpose of wireless sensor deployment. This study is essential for efficiently deploying wireless sensors in different applications such as tracking the grazing habits of cows on the grass or monitoring sporting activities. This study proposes empirical path loss models for wireless sensor deployments in grassy environments. The proposed models are compared with the theoretical models to demonstrate their inaccuracy in predicting the path loss between sensor nodes deployed in natural grassy environments. The results show that the theoretical model values deviate from the proposed model values by 12%-42%. In addition, the results of the proposed models are compared with those of the experimental results obtained from similar natural grassy terrains at different locations resulting in similar outcomes. Finally, the results of the proposed models are compared with those of the previous studies and other terrain models such as those in dense tree environments. These comparisons show that there is a significant difference in path loss and empirical model parameters. The proposed models as well as the measured data can be used for efficient planning and future deployments of wireless sensor networks in similar grass terrains.