In recent years, the Internet of Things (IoT) has made great progress. The interconnection between IoT and the Internet enables real-time information processing and transaction implementation through heterogeneous intelligent devices. But the security, the privacy, and the reliability of IoT are key challenges that limit its development. The features of the blockchain, such as decentralization, consensus mechanism, data encryption, and smart contracts, are suitable for building distributed IoT systems to prevent potential attacks and to reduce transaction costs. As a decentralized and transparent database platform, blockchain has the potential to raise the performance of IoT security to a higher level. This article systematically analyzes state of the art of IoT security based on the blockchain, paying special attention to the security features, issues, technologies, approaches, and related scenarios in blockchain-embedded IoT. The integration and interoperation of blockchain and IoT is an important and foreseeable development in the computational communication system.

This paper explores technological trajectory transition in the perspective of innovation ecosystem and their effect on innovation performance of latecomers in market. A structural equation model is developed and tested with data collected from 366 firms in China. In specific, this paper categories technological trajectory transition creative accumulative technological trajectory transition (CCT) and creative disruptive technological trajectory transition (CDT). The results indicate that firms' organizational learning ability positively affect their technological trajectory transition and innovation performance. Firms' network relationship strength negatively affects their technological trajectory transition and positively affect their innovation performance. Governments' environmental concerns positively affect firms' technological trajectory transition and their innovation performance, whereas firms' environmental concerns do not. CCT does not positively affect their innovation performance. In contrast, CDT positively affects their innovation performance.

This paper identifies three stages in the radical technological innovation process, namely formation process in niches, breaking out of niches and entering regimes, and new regime formation. It then adopts Multi-level Perspective (MLP) to explore the formation process, operating mechanism, breakthrough path, and impact factors of radical technological innovation. A three-phase model, which includes formation of radical innovation, breakout of radical innovation, and new regimes construction, is proposed to analyze radical technological innovation. The model is adopted in a case study to analyze the leapfrogging development of technologies in China’s mobile communication industry. This paper enriches technological innovation theory and provides supports for policy making and guidance for industries/enterprises practices regarding technological innovation in emerging economies.

The paper explores the relationship among technological innovation, technological trajectory transition, and firms’ innovation performance. Technological innovation is studied from the perspectives of innovation novelty and innovation openness. Technological trajectory transition is categorized into creative cumulative technological trajectory transition and creative disruptive technological trajectory transition. A structural equation model is developed and tested with data collected by surveying 366 Chinese firms. The results indicate that both innovation novelty and innovation openness positively affects creative cumulative technological trajectory transition as well as creative disruptive technological trajectory transition. Innovation openness and creative disruptive technological trajectory transition both positively affect firms’ innovation performance. However, neither innovation novelty nor creative cumulative technological trajectory transition positively affects firms’ innovation performance. Implications for managers and directions for future studies are discussed.

With the transformation of Internet of Things to Web of Things (WoT), a variety of applications are required to deal with huge volumes of real-time and heterogeneous data. However, in most applications, due to the weak semantics of data itself and the loose combination with specific scenarios, it is sometimes difficult to depict the spatiotemporal feature of the scenario in an application only through the data. In this paper, a spatiotemporal semantic scenario meta-model-based configurable platform is proposed for the development of WoT applications to address this issue, based on the data configuration, event stream configuration, and service encapsulation, the entire WoT scenario can be depicted with an abstract data model and related rules, and the business process can be changed by redefining corresponding rules when requirements change. A case study is given to verify the feasibility of our platform. The result shows that the platform can provide background support for WoT applications in a promising way.

The Mobile Internet Information Technology (MIIT) has been widely accepted as one of the most promising technologies in the next decades, having various applications and different value positions. However, few published studies explore and examine the effects of MIIT on community management. Based on the Dramaturgical Theory, this article uses a case study method to get an insightful understanding of MIIT. This article found that the MIIT was used by grid organizations to realize technological innovation and change organizational routines and structures, but eventually it was shaped by them, so this new technology was only able to embed itself into the public service model as a secondary or complementary role.

Group size is a key factor in collaborative software development and many other cybernetic applications where task assignments are important. While methods exist to estimate its value for proprietary projects, little is known about how group size affects distributed and decentralized cybernetic applications and in particular open source software (OSS) development. This paper presents a novel approach in which we frame developers' collective resolution of OSS change tasks as a social information foraging problem. This new perspective enables us to predict the optimal group size and quantify group size's effect on individual performance. We test the theory with data mined from two projects: 1) Firefox and 2) Mylyn. This paper not only uncovers the mismatch of optimal and actual group sizes, but also reveals the association of optimality with improved productivity. In addition, the social-level productivity gain is observed as project evolves. We show this paper's impact by extending the frontiers of knowledge in two areas: 1) social coding and 2) recommendation systems.

The advances in mobile technologies enable us to consume or even provide services through powerful mobile devices anytime and anywhere. Services running on mobile devices within limited range can be composed to coordinate together through wireless communication technologies and perform complex tasks. However, the mobility of users and devices in mobile environment imposes high risk on the execution of the tasks. This paper targets reducing this risk by constructing a dependable service composition after considering the mobility of both service requesters and providers. It first proposes a risk model and clarifies the risk of mobile service composition; and then proposes a service composition approach by modifying the simulated annealing algorithm. Our objective is to form a service composition by selecting mobile services under the mobility model and to ensure the service composition have the best quality of service and the lowest risk. The experimental results demonstrate that our approach can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Developers often spend valuable time navigating and seeking relevant code in software maintenance. Currently, there is a lack of theoretical foundations to guide tool design and evaluation to best shape the code base to developers. This paper contributes a unified code navigation theory in light of the optimal food-foraging principles. We further develop a novel framework for automatically assessing the foraging mechanisms in the context of program investigation. We use the framework to examine to what extent the clustering of software entities affects code foraging. Our quantitative analysis of long-lived open-source projects suggests that clustering enriches the software environment and improves foraging efficiency. Our qualitative inquiry reveals concrete insights into real developer's behavior. Our research opens the avenue toward building a new set of ecologically valid code navigation tools.

The Internet of Things (IoT) contains a large number of different devices and heterogeneous networks, which make it difficult to satisfy different quality of service (QoS) requirements and achieve rapid services composition and deployment. In addition, some services in service-oriented IoT are required to be reconfigurable and composable for QoS-aware services. This paper proposed a three-layer QoS scheduling model for service-oriented IoT. At application layer, the QoS schedule scheme explores optimal QoS-aware services composition by using the knowledge of each component service. At network layer, the model aims at dealing with scheduling of heterogeneous networks environment; at sensing layer, it deals with the information acquisition and resource allocation scheduling for different services. The proposed QoS-aware scheduling for service-oriented IoT architecture is able to optimize the scheduling performance of IoT network and minimize the resource costs.

Abstract Modern Internet routers have to handle a large number of packet classification rules, which requires classification schemes to be scalable both in time and space. In this paper, we present a scalable packet classification algorithm that is developed by combining two new concepts to the well‐known bit vector (BV) scheme. We propose a range search method based on a cache‐aware tree (CATree) which makes full use of processor's cache line to reduce the number of dynamic random access memory (DRAM) accesses. Theoretically, the number of DRAM accesses of CATree is about log(m+1) times lower than that of the widely used binary search algorithm, where m is the number of keys in a single cache line. Based on our computational results on a set of 1024 keys, the CATree algorithm is 36% faster than binary search algorithm and the performance is better when applied to a larger set of keys. In addition, we develop a rule re‐arrangement algorithm to reduce the bitmap space of BV. With this re‐arrangement, the rules for the same action may be assigned an identical priority. This reduces the number of priorities as well as the memory space of the bitmap. Furthermore, this also reduces the number of memory accesses and hence, increases the CPU cache utilization. With CATree and rule re‐arrangement, the cache‐aware bit vector with rule re‐arrangement algorithm achieves better performance in comparison with the regular BV scheme, both in space and time. In our experiments, the proposed algorithm reduces the bitmap memory space of a practical set of firewall rules by two orders of magnitude and is 91% faster than the regular BV.

The use of integrated information systems for healthcare has been started more than a decade ago. In recent years, rapid advances in information integration methods have spurred tremendous growth in the use of integrated information systems in healthcare delivery. Various techniques have been used for probing such integrated systems. These techniques include service-oriented architecture (SOA), EAI, workflow management, grid computing, and others. Many applications require a combination of these techniques, which gives rise to the emergence of enterprise systems in healthcare. Development of the techniques originated from different disciplines has the potential to significantly improve the performance of enterprise systems in healthcare. This editorial paper briefly introduces the enterprise systems in the perspective of healthcare informatics.

This paper strives to review the contribution of systems theory to enterprise architecture and integration, make a summary of methods or tools at systems level and probe into some crucial concepts and thinking of systems theory applied in the enterprise integration activities. As the summary to this paper, some new prospects in enterprise architecture and integration are presented to tackle the increasing complexity and new requirements on modern enterprises.

Business process management systems traditionally focused on supporting the modeling and automation of business processes, with the objective of enabling fast and cost-effective process execution. As more and more processes become automated, customers become increasingly interested in managing process execution. This paper presents a set of concepts and a methodology toward business process intelligence (BPI) using dynamic process performance evaluation, including measurement models based on activity-based management (ABM) and a dynamic enterprise process performance evaluation methodology. The proposed measurement models support the analysis of six process flows within a manufacturing enterprise includingactivityflow,informationflow,resourceflow,costflow,cashflow, andprofitflow, which are crucial for enterprise managers to control the process execution quality and detect problems and areas for improvements. The proposed process performance evaluation methodology usestime,quality,service,cost,speed,efficiency, andimportanceas seven evaluation criteria. A prototype system supporting dynamic enterprise process modeling, analysis of six process flows, and process performance prediction has been implemented to validate the proposed methodology.

Abstract:Artificial intelligence (AI) has been applied to the telecommunications industry for more than a decade. The purpose of this paper is to examine the application of AI in the telecommunications industry sector. Our research finds that AI's first main application in telecommunications is in the network management area. Expert systems and machine learning are the two AI techniques that have been widely used in telecommunications, while machine learning and distributed artificial intelligence are the two AI techniques which are most promising for the future. The research also finds that different AI techniques have their unique applications in the telecommunications industry.

Abstract: In this paper, the effect of superoxide dismutase (SOD) in treating certain types of oral diseases is evaluated with a fuzzy relation composition method and a fuzzy integral method. Evaluation results demonstrate that SOD is effective in treating oral diseases. It is found also that SOD and SOD‐containing complex enzyme are proven treatments for certain types of oral diseases. The effects of some applications are compared and discussed.

Abstract: A production scheduling problem for hot rolling seamless steel tube at Tianjin Pipe Corporation of China is studied. Considering the complexity of the problem and the acceptable time for solving it, a rule-based heuristic approach is proposed and implemented. The proposed approach is a bottleneck scheduling method and considers simultaneously all production processes in three production units and ‘optimizes’ them as a whole. Additionally, the running result shows, on average, that a 3% increase in throughput and a 5% reduction in late deliveries have been achieved since the system implementation.

Abstract: Due to the rapid growth of the IT industry as well as the high failure rate of software development projects, it is highly desirable to develop a qualitative approach for dealing with uncertainties in the software development process. In order to manage risk and uncertainty, a software project uncertainty management approach based on a generalized stochastic Petri net is proposed. It consists of two main components: model building based on a Petri net and a Markov process algorithm. The technique is presented with an example. Finally, the proposed model is demonstrated with a real‐world case.