Effects of time-inconsistent preferences on information technology infrastructure investments with growth options

Increasing information technology (IT) infrastructure spending and the capability of such projects to provide a platform for a firm to realize value from IT marks their importance. Effective management of IT infrastructure investments includes identification of embedded growth options in the infrastructure, and exercising them in a timely manner. Extant research has recognized that while managers could use real options thinking in IT investment management, managerial bias could affect the timing of option exercise and their realized value. We analyze the effect of time-inconsistent preferences of present-biased managers on the exercise time of real growth options and the realized value using a discrete time option valuation model. The results show that present-biased managers are more likely to exercise options early when the net payoffs are low, the option payoffs have high volatility, and the risk free discount rate is small. In addition, present biased managers are more likely to exercise a growth option early in its life when the project is performing well. We provide implications for practice and IT governance.     

Enhanced dynamic credential generation scheme for protection of user identity in mobile-cloud computing

To improve the resource limitation of mobile devices, mobile users may utilize cloud-computational and storage services. Although the utilization of the cloud services improves the processing and storage capacity of mobile devices, the migration of confidential information on untrusted cloud raises security and privacy issues. Considering the security of mobile-cloud-computing subscribers’ information, a mechanism to authenticate legitimate mobile users in the cloud environment is sought. Usually, the mobile users are authenticated in the cloud environment through digital credential methods, such as password. Once the users’ credential information theft occurs, the adversary can use the hacked information for impersonating the mobile user later on. The alarming situation is that the mobile user is unaware about adversary’s malicious activities. In this paper, a light-weight security scheme is proposed for mobile user in cloud environment to protect the mobile user’s identity with dynamic credentials. The proposed scheme offloads the frequently occurring dynamic credential generation operations on a trusted entity to keep minimum processing burden on the mobile device. To enhance the security and reliability of the scheme, the credential information is updated frequently on the basis of mobile-cloud packets exchange. Furthermore, the proposed scheme is compared with the existing scheme on the basis of performance metrics i.e. turnaround time and energy consumption. The experimental results for the proposed scheme showed significant improvement in turnaround time and energy consumption as compared to the existing scheme.     

Analysis of optimized signal processing algorithms for smart antenna system

By the exponential increase in cellular communication area, the user demands and with their side problems also tend to increase. Several techniques such as cell splitting, frequency reuse, diversity and so on have already been applied to overcome these problems. In all these procedures, smart antenna is one of the best techniques used to boost capacities, enlarge bandwidth, raise the SIR ratios, minimize the effects of fading and improve MIMO communications. An ordinary antenna array which can turn up into a smart antenna array is due to the signal processing algorithms, which consists of angle of arrival (AOA) estimations and Beamforming. In this paper, I will discuss and analyse about the MUSIC algorithm for AOA estimation and least mean squares algorithm for Beamforming.     

Diffusion dynamics of open source software: An agent-based computational economics (ACE) approach

The rising popularity of open source software (OSS) calls for a better understanding of the drivers of its adoption and diffusion. In this research, we propose an integrated framework that simultaneously investigates a broad range of social and economic factors on the diffusion dynamics of OSS using an Agent Based Computational Economics (ACE) approach. We find that interoperability costs, variability of OSS support costs, and duration of PS upgrade cycle are major determinants of OSS diffusion. Furthermore, there are interaction effects between network topology, network density and interoperability costs, which strongly influence the diffusion dynamics of OSS. The proposed model can be used as a building block to further investigate complex competitive dynamics in software markets.     

Estimation of fog utility pricing: a bio-inspired optimisation techniques' perspective

ABSTRACT

Due to the limited data storage capacity available to Internet service providers and large-scale enterprises, the concept of resource sharing arises. The services can be given on lease to enterprises through Service Level Agreements. Being the extension of the cloud computing, fog computing architecture brings the resources near end users. In order to get the services on lease, the enterprises are supposed to pay for the resources or services which are being used by them. In this paper, four nature inspired algorithms are analysed in order to determine the efficient management of services or resources so that the cost of resources can be reduced and the billing can be attained through calculation of the utilised resources. Pigeon inspired optimization, enhanced differential evolution, binary bat algorithm and simple human learning optimization are used to evaluate the energy consumed by the edge nodes or cloudlets that in turn can be used for estimating the bill through the Time of Use pricing variable. We evaluate the aforementioned techniques to analyse their performance regarding the bill calculation on the basis of fog servers usage. Simulation results demonstrate that BAT algorithm gives significantly better results than other three algorithms in terms of resource utilisation and bill reduction.     

A decision support framework for metrics selection in goal-based measurement programs: GQM-DSFMS

Software organizations face challenges in managing and sustaining their measurement programs over time. The complexity of measurement programs increase with exploding number of goals and metrics to collect. At the same time, organizations usually have limited budget and resources for metrics collection. It has been recognized for quite a while that there is the need for prioritizing goals, which then ought to drive the selection of metrics. On the other hand, the dynamic nature of the organizations requires measurement programs to adapt to the changes in the stakeholders, their goals, information needs and priorities. Therefore, it is crucial for organizations to use structured approaches that provide transparency, traceability and guidance in choosing an optimum set of metrics that would address the highest priority information needs considering limited resources. This paper proposes a decision support framework for metrics selection (DSFMS) which is built upon the widely used Goal Question Metric (GQM) approach. The core of the framework includes an iterative goal-based metrics selection process incorporating decision making mechanisms in metrics selection, a pre-defined Attributes/Metrics Repository, and a Traceability Model among GQM elements. We also discuss alternative prioritization and optimization techniques for organizations to tailor the framework according to their needs. The evaluation of the GQM-DSFMS framework was done through a case study in a CMMI Level 3 software company.     

Complementary‐based coalition formation for energy microgrids

In recent years, the notion of electrical energy microgrids (MGs), in which communities share their locally generated power, has gained increasing interest. Typically, the energy generated comes from renewable resources, which means that its availability is variable, ie, sometimes there may be energy surpluses and at other times energy deficits. This energy variability can be ameliorated by trading energy with a connected electricity grid. However, since main electricity grids are subject to faults or other outages, it can be advantageous for energy MGs to form coalitions and share their energy among themselves. In this work, we present our model for the dynamic formation of such MG coalitions. In our model, MGs form coalitions on the basis of complementary weather patterns. Our agent‐based model, which is scalable and affords autonomy among the MGs participating in the coalition (agents can join and depart from coalitions at any time), features methods to reduce overall “discomfort” so that, even when all participating MGs in a coalition experience deficits, they can share energy so that their overall discomfort is reduced. We demonstrate the efficacy of our model by showing empirical studies conducted with real energy production and consumption data.     

Auditory perception based system for age classification and estimation using dynamic frequency sound

Multimedia Tools and Applications - Human age is a crucial factor in social interaction. It determines the way we interact with others. It is also a relevant forensic issue that can provide helpful...     

Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs

We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches.