Winnowing ontologies based on application use

The requirements of specific applications and services are often over estimated when ontologies are reused or built. This sometimes results in many ontologies being too large for their intended purposes. It is not uncommon that when applications and services are deployed over an ontology, only a few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could be helpful to winnow the ontology, i.e., simplify or shrink the ontology to smaller, more fit for purpose size. Some approaches to handle this problem have already been suggested in the literature. However, none of that work showed how ontology-based applications can be used in the ontology-resizing process, or how they might be affected by it. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services,and investigates the possibility of relying on this usage information to winnow that ontology

Identification as a deterrent for security enhancement in cognitive radio networks

Cognitive Radio Networks (CRNs) are prone to emerging coexistence security threats such as Primary User Emulation Attack (PUEA). Specifically, a malicious CRN may mimic licensees’ (Primary Users (PUs)) signal characteristics to force another CRN to vacate its channels thinking that PUs have returned. While existing schemes are promising to some extent on detecting PUEAs, they are not able to prevent the attacks. In this article, we propose a PUEA Deterrent (PUED) algorithm that can provide PUEAs' commission details: offender CRNs and attacks’ time and bandwidth. There are many similarities between PUED and Closed-Circuit Television (CCTV) in terms of: deterrence strategy, reason for use, surveillance characteristics, surveillance outcome, and operation site. According to the criminology literature, robust CCTV systems have shown a significant reduction in visible offences (e.g. vehicle theft), reducing crime rates by 80%. Similarly, PUED will contribute the same effectiveness in deterring PUEAs. Furthermore, providing PUEAs’ details will prevent the network’s cognitive engine from considering the attacks as real PUs, consequently avoiding devising unreliable spectrum models for the attacked channels. Extensive simulations show the effectiveness of the PUED algorithm in terms of improving CRNs’ performance

Virtual Machines Embedding for Cloud PON AWGR and Server Based Data Centres

In this study, we investigate the embedding of various cloud applications in PON AWGR and Server Based Data Centres

Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one

Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part

Semantics, sensors, and the social web: The live social semantics experiments

The Live Social Semantics is an innovative application that encourages and guides social networking between researchers at conferences and similar events. The application integrates data and technologies from the Semantic Web, online social networks, and a face-to-face contact sensing platform. It helps researchers to find like-minded and influential researchers, to identify and meet people in their community of practice, and to capture and later retrace their real-world networking activities at conferences. The application was successfully deployed at two international conferences, attracting more than 300 users in total. This paper describes this application, and discusses and evaluates the results of its two deployment

Social dynamics in conferences: analyses of data from the Live Social Semantics application

Popularity and spread of online social networking in recent years has given a great momentum to the study of dynamics and patterns of social interactions. However, these studies have often been confined to the online world, neglecting its interdependencies with the offline world. This is mainly due to the lack of real data that spans across this divide. The Live Social Semantics application is a novel platform that dissolves this divide, by collecting and integrating data about people from (a) their online social networks and tagging activities from popular social networking sites, (b) their publications and co-authorship networks from semantic repositories, and (c) their real-world face-to-face contacts with other attendees collected via a network of wearable active sensors. This paper investigates the data collected by this application during its deployment at three major conferences, where it was used by more than 400 people. Our analyses show the robustness of the patterns of contacts at various conferences, and the influence of various personal properties (e.g. seniority, conference attendance) on social networking patterns

Influence and interaction of temperature, H2S and pH on concrete sewer pipe corrosion

Concrete sewer pipes are known to suffer from a process of hydrogen sulfide gas induced sulfuric acid corrosion. This leads to premature pipe degradation, performance failure and collapses which in turn may lead to property and health damage. The above work reports on a field study undertaken in working sewer manholes where the parameters of effluent temperature and pH as well as ambient temperature and concentration of hydrogen sulfide were continuously measured over a period of two months. Early results suggest that effluent pH has no direct effect on hydrogen sulfide build up; on average the effluent temperature is 3.5°C greater than the ambient temperature inside the manhole and also it was observed that hydrogen sulfate concentration increases with increasing temperature

A new processing approach for reducing computational complexity in cloud-RAN mobile networks

Cloud computing is considered as one of the key drivers for the next generation of mobile networks (e.g. 5G). This is combined with the dramatic expansion in mobile networks, involving millions (or even billions) of subscribers with a greater number of current and future mobile applications (e.g. IoT). Cloud Radio Access Network (C-RAN) architecture has been proposed as a novel concept to gain the benefits of cloud computing as an efficient computing resource, to meet the requirements of future cellular networks. However, the computational complexity of obtaining the channel state information in the full-centralized C-RAN increases as the size of the network is scaled up, as a result of enlargement in channel information matrices. To tackle this problem of complexity and latency, MapReduce framework and fast matrix algorithms are proposed. This paper presents two levels of complexity reduction in the process of estimating the channel information in cellular networks. The results illustrate that complexity can be minimized from O(N3) to O((N/k)3), where N is the total number of RRHs and k is the number of RRHs per group, by dividing the processing of RRHs into parallel groups and harnessing the MapReduce parallel algorithm in order to process them. The second approach reduces the computation complexity from O((N/k)3) to O((N/k)2:807) using the algorithms of fast matrix inversion. The reduction in complexity and latency leads to a significant improvement in both the estimation time and in the scalability of C-RAN networks

Semantic metrics

In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can be boiled down to one fundamental operation: computing the similarity and?or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a variety of research disciplines, and enrich them with semantics based on standard Description Logic constructs. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level and we speculate on the usability of our ideas through potential areas