Software projects are required to be tracked during their execution for controlling them. According to state-space approach, the tracking technique consists of software project state transition equation and software project status measurement equation. A key factor in tracking software projects is to represent the project with uncertainty involved in the parameters. Traditional and hybrid software project tracking technique is designed with state space approach and simulated using discrete event simulation in plan-space and execution-space. The uncertainty considered here is epistemological and is modeled as a normal distribution using an approximation method. The initial state of the project in execution-space also has an uncertainty associated with it. The project tracking technique consists of project state transition equation and project status measurement equation, in plan-space and is formulated with Monte Carlo method in execution-space. The project status is derived using project measurement equation as a function of project state. The project state is derived using project state transition equation. The software product is developed iteratively and incrementally. With Monte Carlo simulation runs, simulation result shows the uncertainty propagation iteration-wise, both individually and totally; and the effect of uncertainty on project status is shown by showing project status in execution-space and plan-space. Besides, the project completion somewhere during the last iteration is shown with simulation.
Recently, researchers and practitioners in wireless sensor networks (WSNs) are focusing on energy-oriented communication and computing considering next-generation smaller and tiny wireless devices. The tiny sensor-enabled devices will be used for the purpose of sensing, computing, and wireless communication. The hundreds/thousands of WSNs sensors are used to monitor specific activities and report events via wireless communication. The tiny sensor-enabled devices are powered by smaller batteries to work independently in distributed environments resulting in limited maximum lifetime of the network constituted by these devices. Considering the non-uniform distribution of sensor-enabled devices in the next-generation mobility centric WSNs environments, energy consumption is imbalanced among the different sensors in the overall network environments. Toward this end, in this paper, a cluster-oriented routing protocol termed as prediction-oriented distributed clustering (PODC) mechanism is proposed for WSNs focusing on non-uniform sensor distribution in the network. A network model is presented, while categorizing PODC mechanism in two activities including setting cluster of nodes and the activity in the steady state. Further cluster set up activity is described while categorizing in four subcategories. The proposed protocol is compared with individual sensor energy awareness and distributed networking mode of clustering (EADC) and scheduled sensor activity-based individual sensor energy awareness and distributed networking mode of clustering (SA-ADC). The metrics including the overall lifetime of the network and nodes individual energy consumption in realistic next-generation WSNs environments are considered in the experimental evaluation. The results attest the reduced energy consumption centric benefits of the proposed framework PODC as compared to the literature. Therefore, the framework will be more applicable for the smart product development in the next-generation WSNs environments.
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