How does internet development affect energy-saving and emission reduction? Evidence from China

Agent-based Internet of Things: State-of-the-art and research challenges

Context Analogy-based Software development Effort Estimation (ASEE) techniques have gained considerable attention from the software engineering community. However, existing systematic map and review studies on software development effort prediction have not investigated in depth several issues of ASEE techniques, to the exception of comparisons with other types of estimation techniques. Objective The objective of this research is twofold: (1) to classify ASEE studies which primary goal is to propose new or modified ASEE techniques according to five criteria: research approach, contribution type, techniques used in combination with ASEE methods, and ASEE steps, as well as identifying publication channels and trends and (2) to analyze these studies from five perspectives: estimation accuracy, accuracy comparison, estimation context, impact of the techniques used in combination with ASEE methods, and ASEE tools. Method We performed a systematic mapping of studies for which the primary goal is to develop or to improve ASEE techniques published in the period 1990–2012, and reviewed them based on an automated search of four electronic databases. Results In total, we identified 65 studies published between 1990 and 2012, and classified them based on our predefined classification criteria. The mapping study revealed that most researchers focus on addressing problems related to the first step of an ASEE process, that is, feature and case subset selection. The results of our detailed analysis show that ASEE methods outperform the eight techniques with which they were compared, and tend to yield acceptable results especially when combining ASEE techniques with Fuzzy Logic (FL) or Genetic Algorithms (GA). Conclusion Based on the findings of this study, the use of other techniques such FL and GA in combination with an ASEE method is promising to generate more accurate estimates. However, the use of ASEE techniques by practitioners is still limited: developing more ASEE tools may facilitate the application of these techniques and then lead to increasing the use of ASEE techniques in industry.

http://romisatriawahono.net/lecture/rm/survey/software%20engineering/Software%20Cost%20Effort%20Estimation/Idri%20-%20Analogy%20based%20Software%20Effort%20Estimation%20-%202014.pdf

Analogy-based software development effort estimation: A systematic mapping and review

Conducting reputation management is very important for Internet of vehicles. However, most of the existing work evaluate the effectiveness of their schemes with settled attacking behaviors in their simulation, which cannot represent the real scenarios. In this paper, we propose to consider dynamical and diversity attacking strategies in the simulation of reputation management scheme evaluation. To that end, we apply evolutionary game theory to model the evolution process of malicious users’ attacking strategies, and discuss the methodology of the evaluation simulations. We further apply our evaluation method to a reputation management scheme with multiple utility functions, and discuss the evaluation results. The results indicate that our evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme.

Evaluating Reputation Management Schemes of Internet of Vehicles Based on Evolutionary Game Theory

Diagnostics based on time series are sometimes difficult to perform, particularly when the time series is continuous and non-stationary, i.e. they seldom contain recurring patterns which makes it difficult to identify similarities with other time series. This doctoral thesis presents an artificial intelligence approach to the analysis of continuous non-stationary signals for diagnostic purposes. One way to solve this kind of problem is to break down the series into new forms that are more easily interpreted, and to identify familiar patterns within them. The newly formed series is analysed, using the Case-Based Reasoning paradigm. Known problemsolution pairs are stored in memory and reused for solving problems by classifying new patterns occurring in time series obtained subsequently. Reasoning is conducted on the basis of the knowledge available and a best-guess solution obtained using the available knowledge is presented. The memory need not therefore contain a problem, which has been solved previously and is identical with the problem which is to be solved. This approach to problem solving has been applied to physiological time series as a clinical decision support system. The system provides decision support by classifying patterns of respiratory sinus arrhythmia from heart rate and capnography time series.

Advancement and Trends in Medical Case-Based Reasoning: An Overview of Systems and System Development

In recent years popularity of private cars is getting urban traffic more and more crowded. As result traffic is becoming one of important problems in big cities in all over the world. Some of the traffic concerns are congestions and accidents which have caused a huge waste of time, property damage and environmental pollution. This research paper presents a novel intelligent traffic administration system, based on Internet of Things, which is featured by low cost, high scalability, high compatibility, easy to upgrade, to replace traditional traffic management system and the proposed system can improve road traffic tremendously. The Internet of Things is based on the Internet, network wireless sensing and detection technologies to realize the intelligent recognition on the tagged traffic object, tracking, monitoring, managing and processed automatically. The paper proposes an architecture that integrates internet of things with agent technology into a single platform where the agent technology handles effective communication and interfaces among a large number of heterogeneous highly distributed, and decentralized devices within the IoT. The architecture introduces the use of an active radio-frequency identification (RFID), wireless sensor technologies, object ad-hoc networking, and Internet-based information systems in which tagged traffic objects can be automatically represented, tracked, and queried over a network. This research presents an overview of a framework distributed traffic simulation model within NetLogo, an agent-based environment, for IoT traffic monitoring system using mobile agent technology.

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Intelligent Traffic Information System Based on Integration of Internet of Things and Agent Technology

Accurate software cost estimation is a vital task that affects the firm's software investment decisions before committing required resources to that project or bidding for a contract. This study proposes an improved Case-Based Reasoning (CBR) approach integrated with multi-agent technology to retrieve similar projects from multi-organizational distributed datasets. The study explores the possibility of building a software cost estimation model by collecting software cost data from distributed predefined project cost databases. The model applying CBR method to find similar projects in historical data derived from measured software projects developed by different organizations.

Software Cost Estimation Model Based on Integration of Multi-agent and Case-Based Reasoning

Due to diversity, heterogeneity and complexity of the existing healthcare structure, providing suitable healthcare services is a complicated process. This work describes the conceptual design of an e-healthcare system, which implements integration strategies and suitable technologies that will handle the interoperability problem among its essential components. The proposed solution combines intelligent agent technology and case based reasoning for highly distributed applications in healthcare environment. Intelligent agents play a critical role in providing correct information for diagnostic, treatment, etc. They work on behalf of human agents taking care of routine tasks, thus increasing speed and reliability of the information exchanges. CBR is used to generate advices to a certain e-healthcare problems by analyzing solutions given to previously solved problems and to build intelligent systems for disease diagnostics and prognosis. Preliminary experimental simulation based on Agent Development Framework (JADE) demonstrated the feasibility of this model.

Framework architecture for improving healthcare information systems using agent technology

The botnet problem of launching Distributed Denial of Service (DDoS) attacks on other networks mainly arises from the rapid growth in the number of insecure Internet of Things (IoT) devices distributed across these networks. The focus of this work is to defend such an IoT network and its associated resources from attacks, and to prevent such networks from becoming a part of botnet launching DDoS attacks on other networks and resources. To achieve these objectives, this research emphases designing of a botnet prevention model for Internet of Things using emerging technologies such as Blockchain, Smart Contract, and Software Defined Networking (SDN). Blockchain is decentralized structure which fits with the decentralized nature of IoT. For securing IoT network, the proposed solution presented in this research based on building above the IoT network a Blockchain network and on top of it to use Smart Contracts that embedded the SDN rules.

Framework Architecture for Securing IoT Using Blockchain, Smart Contract and Software Defined Network Technologies

In this study we propose multi-agent distributed electronic negotiation system, where the learning process is facilitated by using the CBR approach in combination with case database and addition of ability to roam through a network allows considering outside options using mobile agent technology. CBR is an AI methodology which combines re-use of the past experiences with case base. Cases similar to the current case at hand are retrieved from case base, revised and used to develop new offers and counter-offers.

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Hasan Al-Sakran

Papers

Intelligent Traffic Information System Based on Integration of Internet of Things and Agent Technology

Software Cost Estimation Model Based on Integration of Multi-agent and Case-Based Reasoning

Framework architecture for improving healthcare information systems using agent technology

Framework Architecture for Securing IoT Using Blockchain, Smart Contract and Software Defined Network Technologies

A Framework for Developing Experience Based e-Negotiation System