Recent progress of factors influencing information technology adoption in local government context

Information Technology (IT) adoption is increasingly being studied in many different contexts, both in public and private sectors. However, there are not many review papers published on IT adoption specifically in a local government context. Local governments have unique characteristics in terms of the organization’s structure, the power of authority, norms and culture. Hence, the primary aim of this study was to review recent literature from the year 2013 to 2017 on IT adoption at the organizational level in a local government context. We strategized our review methods through utilizing relevant keyword search in Scopus, Web of Science, Emerald and Springer Link databases which include journals, proceedings, books and book chapters. The search identified 715 publications during the initial stage using the snowballing technique. Thereafter, 22 relevant publications were filtered out during the quality assessment stage. Within the context of local government, this review presented the analyses of IT adoption research progress, the research domains, research methodology and the factors influencing IT adoption. This study identified 37 factors of IT adoption in local government context which have been categorized into four main dimensions which are Technological, Organizational, Individual and Environmental (T-O-I-E). Surprisingly, policy and regulations, top management support, relative advantage, cost, governance, personnel skills and citizen demand emerged as among the most influential factors for IT adoption in the context of local governments. The results from this study will help other researchers to understand the current stage of IT adoption in local government context in terms of research domains, research methodology, and the factors influencing IT adoption

A Priority Based Enterprise Architecture Implementation Assessment Model: An Analytic Hierarchy Process (AHP) Approach

Despite of many Enterprise Architecture (EA) frameworks and methodologies available, in reality EA implementation is a challenging process. In order to assure a progressive EA implementation, assessment and monitoring mechanism is required. The existing EA assessment approaches are mostly based on checklist or maturity model and designed to assess post EA implementation. Less EA assessment is found to cater on the pre and during EA implementation process. This indicates that the lack of systematic assessment mechanism, especially for pre and during EA implementation phase. Hence, based on the gap identified, this study proposes a priority based assessment model for pre and during EA implementation process. This integrated model of Balanced Scorecard (BSC) and Analytic Hierarchy Process (AHP) is designed to assess the priority and capability of the organization in implementing EA. The assessment criteria were formulated from findings of an exploratory study. Six main criteria and 27 sub-criteria have been identified as the Critical Success Factors (CSFs) in EA implementation. Based on these CSFs, a Priority based EA Implementation Assessment Model (PEAIAM) has been formulated and presented in this paper

CloudIDS: Cloud Intrusion Detection Model Inspired by Dendritic Cell Mechanism

Cloud Computing Security is a new era of computer technology and opens a new research area and creates a lot of opportunity of exploration. One of the new implementation in Cloud is Intrusion Detection System (IDS).There are problems with existing IDS approach in Cloud environment. Implementing traditional IDS need a lot of self-maintenance and did not scale with the customer security requirements. In addition, maintenance of traditional IDS in Cloud Computing system requires expertise and consumes more time where not each Cloud user has. A decentralized traditional IDS approach where being deployed in current Cloud Computing infrastructure will make the IDS management become complicated. Each user's IDS will not be the same in term of type and configurations and each user may have outdated signatures. Inter VM's communication also become a big concern when we implementing Cloud Computing system where communication between Clouds are not monitored and controlled by the traditional IDS. A specific IDS model for Cloud computing is required to solve these problems. In this paper, we develop a prototype of Cloud IDS inspired by Dendritic Cell mechanism. Experiment result proved that Cloud IDS was able to detect any attempt to attack the Cloud environment. The experiments show that the Cloud IDS model based on Dendritic Cell algorithm able to identify and detect novel threat that targeting Cloud environment

Inter-service provider charging protocol: A solution to address range anxiety of electric vehicle owners

Range anxiety describes the drivers' stress regarding the available battery range while driving an electric vehicle. Considering this issue as a barrier against general acceptance of electric vehicles, several researches has been reviewed. The results show that there is no direct communication among current networks of charging stations which causes isolation in these networks. Thus, the users are not able to use cross-network facilities which leads to range anxiety. To overcome, a protocol is suggested to be used in development of RESTful web services to provide direct communication among the networks of charging stations

Assessing the Capability and Priority of Enterprise Architecture Implementation in Malaysian Public Sector

Enterprise Architecture (EA) is an integrated approach of information systems, processes, organisation and people in aligning business and information technology together. However, there is a discrepancy in public sector EA implementation whereby the developing countries are still grappling with issues in the implementation while those developed countries are already harvesting the EA benefits and value. Hence, this study aims to investigate the capability and priority of public sector of the developing countries in implementing the EA by proposing an assessment model. The assessment model is based on Balanced Scorecard (BSC) and Analytic Hierarchy Process (AHP) approach. There are 27 EAI capability and priority criteria identified and grouped into six categories according to BSC perspectives namely Internal Process, Learning and Growth, Authority Support, Cost, Technology and Talent Management. Followed by AHP pairwise comparison in calculating the rank of each criterion which is presented via three case studies from Malaysian Public Sector agencies

Software Effort Estimation Accuracy Prediction of Machine Learning Techniques: A Systematic Performance Evaluation

Software effort estimation accuracy is a key factor in effective planning, controlling and to deliver a successful software project within budget and schedule. The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation (SEE). The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and the other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in the software development. In this paper, the performance of the machine learning ensemble technique is investigated with the solo technique based on two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment criteria, extracting data and drawing results. We have evaluated a state-of-the-art accuracy performance of 28 selected studies (14 ensemble, 14 solo) using Mean Magnitude of Relative Error (MMRE) and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques.Comment: Pages: 27 Figures: 15 Tables:

Implicit thinking knowledge injection framework for Agile requirements engineering

Agile has become commonly used as a software development methodology and its success depends on face-to-face communication of software developers and the faster software product delivery. Implicit thinking knowledge has considered as a very significant for organization self-learning. The main goal of paying attention to managing the implicit thinking knowledge is to retrieve valuable information of how the software is developed. However, requirements documentation is a challenging task for Agile software engineers. The current Agile requirements documentation does not incorporate the implicit thinking knowledge with the values it intends to achieve in the software project. This research addresses this issue and introduce a framework assists to inject the implicit thinking knowledge in Agile requirements engineering. An experiment used a survey questionnaire and case study of real project implemented for the framework evaluation. The results show that the framework enables software engineers to share and document their implicit thinking knowledge during Agile requirements documentation

RSA authentication mechanisms in control grid computing environment using Gridsim toolkit

There are security concerns when our sensitive data is placed in the third party infrastructure such as in the Grid Computing environment. As such, it is difficult to be assured that our data is in the safe hands.Thus, authentication has become the most critical factor pertaining to this.There are several approaches has been discussed in the grid computing environment on the safeguard, scalable and efficient authentication that are either Virtual Organization centric or Resource centric.Most of the grid computing uses public key infrastructure (PKI) to secure the identification, but the vulnerability are still cannot be avoid. In order to satisfy the security need of grid computing environment, we design an alternative authentication mechanism using RSA algorithm to ensure the user identification, and carry out the experiment in the Gridsim toolkit simulator

Cloud denial of service detection by dendritic cell mechanism

The term cloud computing is not new anymore in computing technology. This form of computing technology previously considered only as marketing term, but today cloud computing not only provides innovative improvements in resource utilization but it also creates a new opportunities in data protection mechanisms where the advancement of intrusion detection technologies are blooming rapidly. From the perspective of security, cloud computing also introduces concerns about data protection and intrusion detection mechanism especially cloud computing are exposed to Denial of Service (DoS) attacks. This paper aims to provide DoS detection mechanism for cloud computing environment. As a result, we provide an experiment to examine the capability of the proposed system. The result shows that the proposed system was able to detect DoS attacks that conducted during the experiment with 94.4% detection rate. We conclude the paper with a discussion on the results, then we include together with a graphical summary of the experiment's result

Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities