Future Internet, Volume 5, Issue 4 (December 2013) – 10 articles , Pages 460-630

Most direction-of-arrival (DOA) estimation approaches for conformal array suffer from high computational complexity, which cause high energy loss for the direction finding system. Thus, a low energy consumption DOA estimation algorithm for conformal array antenna is proposed in this paper. The arbitrary baseline direction finding algorithm is extended to estimate DOA for a conformal array in ultra-wideband. The rotation comparison method is adopted to solve the ambiguity of direction finding. The virtual baseline approach is used to construct the virtual elements. Theoretically, the virtual elements can be extended in the space flexibility. Four elements (both actual and virtual elements) can be used to obtain a group of solutions. The space angle estimation can be obtained by using sub-array divided technique and matrix inversion method. The stability of the proposed algorithm can be guaranteed by averaging the angles obtained by different sub-arrays. Finally, the simulation results verify the effectiveness of the proposed method with high DOA estimation accuracy and relatively low computational complexity. Full article

Abstract: The existing I/O workload generators and trace capturing tools are not adaptable to generating nor capturing the I/O requests of Android apps. The Android platform needs proper tools to capture and replay real world workload in the Android platform to verify the result of benchmark tools. This paper introduces Android Storage Performance Analysis Tool, AndroStep, which is specifically designed for characterizing and analyzing the behavior of the I/O subsystem in Android based devices. The AndroStep consists of Mobibench (workload generator), MOST (Mobile Storage Analyzer), and Mobigen (workload replayer). Mobibench is an Android app that generates a filesystem as well as SQLite database operations. Mobibench can also vary the number of concurrent threads to examining the filesystem scalability to support concurrency, e.g., metadata updates, journal file creation/deletion. MOST captures the trace and extracts key filesystem access characteristics such as access pattern with respect to file types, ratio between random vs. sequential access, ratio between buffered vs. synchronous I/O, fraction of metadata accesses, etc. MOST implements reverse mapping feature (finding an inode for a given block) and retrospective reverse mapping (finding an inode for a deleted file). Mobigen is a trace capturing and replaying tool that is specifically designed to perform the user experiment without actual human intervention. Mobigen records the system calls generated from the user behavior and sanitizes the trace for replayable form. Mobigen can replay this trace on different Android platforms or with different I/O stack configurations. As an example of using AndroStep, we analyzed the performances of twelve Android smartphones and the SQLite performances on five different filesystems. AndroStep makes otherwise time consuming I/O stack analysis extremely versatile. AndroStep makes a significant contribution in terms of shedding light on internal behavior of the Android I/O stack. Full article

The progressive consolidation of information technologies on a large scale has been facilitating and progressively increasing the production, collection, and diffusion of geographic data, as well as facilitating the integration of a large amount of external information into geographic information systems (GIS). Traditional GIS is transforming into a consolidated information infrastructure. This consolidated infrastructure is affecting more and more aspects of internet computing and services. Most popular systems (such as social networks, GPS, and decision support systems) involve complex GIS and significant amounts of information. As a web service, GIS is affected by exactly the same problems that affect the web as a whole. Therefore, next generation GIS solutions have to address further methodological and data engineering challenges in order to accommodate new applications’ extended requirements (in terms of scale, interoperability, and complexity). The conceptual and semantic modeling of GIS, as well as the integration of semantics into current GIS, provide highly expressive environments that are capable of meeting the needs and requirements of a wide range of applications. [...] Full article

The purpose of this special issue is to explore social inequalities in the digital environment. The motivation for this issue is derived from the disproportionate focus on technological and economic aspects of the Information Society to the detriment of sociological and cultural aspects. The research presented here falls along three dimensions of inequality. Two papers explore the ways that race orders interaction online. A second pair of papers explores the experiences of technology users with physical and mental disabilities. A final paper looks at gender, and the higher rates of intimate partner violence experienced by women online. Taken as a whole, these five papers highlight some of the ways that the digital environment can reproduce or mitigate inequalities that have been molded and routinized in the physical environment. [...] Full article

Although recent research suggests that the selective nature of new media helps foster issue specialists, little empirical evidence has been documented, mostly due to theoretical and methodological limitations. Extending the concept of issue publics, the present study proposes a method to estimate the degree to which an individual is a specialist- or a generalist-type citizen. Applying the method to the 2008 American National Election Studies data, the study reveals various characteristics of specialists and generalists. The results indicate that specialist-type citizens are positively associated with online news use, but negatively associated with conventional news media, such as television, newspaper, and radio. The implications of the growth of specialists as well as the validity of the proposed method are discussed. Full article

This paper reports on the design of an OPNET simulation platform to test the performance of sending real-time safety video over VANET (Vehicular Adhoc NETwork) using the WiMAX technology. To provide a more realistic environment for streaming real-time video, a video model was created based on the study of video traffic traces captured from a realistic vehicular camera, and different design considerations were taken into account. A practical controller over real-time streaming protocol is implemented to control data traffic congestion for future road safety development. Our driving video model was then integrated with the WiMAX OPNET model along with a mobility model based on real road maps. Using this simulation platform, different mobility cases have been studied and the performance evaluated in terms of end-to-end delay, jitter and visual experience. Full article

Emergency rescues require that first responders provide support to evacuate injured and other civilians who are obstructed by the hazards. In this case, the emergency personnel can take actions strategically in order to rescue people maximally, efficiently and quickly. The paper studies the effectiveness of a random neural network (RNN)-based task assignment algorithm involving optimally matching emergency personnel and injured civilians, so that the emergency personnel can aid trapped people to move towards evacuation exits in real-time. The evaluations are run on a decision support evacuation system using the Distributed Building Evacuation Simulator (DBES) multi-agent platform in various emergency scenarios. The simulation results indicate that the RNN-based task assignment algorithm provides a near-optimal solution to resource allocation problems, which avoids resource wastage and improves the efficiency of the emergency rescue process. Full article

Online collaboration and web-based knowledge sharing have gained momentum as major components of the Web 2.0 movement. Consequently, knowledge embedded in such platforms is no longer static and continuously evolves through experts’ micro-contributions. Traditional Information Retrieval and Social Network Analysis techniques take a document-centric approach to expertise modeling by creating a macro-perspective of knowledge embedded in large corpus of static documents. However, as knowledge in collaboration platforms changes dynamically, the traditional macro-perspective is insufficient for tracking the evolution of knowledge and expertise. Hence, Expertise Profiling is presented with major challenges in the context of dynamic and evolving knowledge. In our previous study, we proposed a comprehensive, domain-independent model for expertise profiling in the context of evolving knowledge. In this paper, we incorporate Language Modeling into our methodology to enhance the accuracy of resulting profiles. Evaluation results indicate a significant improvement in the accuracy of profiles generated by this approach. In addition, we present our profile visualization tool, Profile Explorer, which serves as a paradigm for exploring and analyzing time-dependent expertise profiles in knowledge-bases where content evolves overtime. Profile Explorer facilitates comparative analysis of evolving expertise, independent of the domain and the methodology used in creating profiles. Full article

Due to widespread adoption of mobile communications devices and increasingly high throughput capacity of cellular networks, Third-Generation (3G) and Long Term Evolution (LTE) are becoming ever more popular. Millions of smart phones with 3G capabilities are sold every year and used for mostly browsing the Internet. Hence, mobile operators have been heavily investing in their packet switched networks to meet customer demand and stay ahead in the market. The widespread use of broadband mobile Internet bring along also some IP based threats such as the Denial of Service (DoS) attacks, botnets and malwares. In this paper, we performed DoS and flooding attacks on 3G mobile networks and measured their effect on the most critical elements of a network such as the Radio Network Controller (RNC) and the Serving GPRS Support Node (SGSN) devices. We carried out our experiments on a real mobile network, not just a simulation environment, and hence our findings depict a realistic picture of the vulnerabilities existent in 3G mobile networks. We propose alternative solutions to avoid these vulnerabilities and mitigate the issues raised. Full article

The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces. Full article