Time resolved bit error rate analysis of a fast switching tunable laser for use in optically switched networks

We investigate the use of different direct detection modulation formats in a wavelength switched optical network. We find the minimum time it takes a tunable sampled grating distributed Bragg reflector laser to recover after switching from one wavelength channel to another for different modulation formats. The recovery time is investigated utilizing a field programmable gate array which operates as a time resolved bit error rate detector. The detector offers 93 ps resolution operating at 10.7 Gb/s and allows for all the data received to contribute to the measurement, allowing low bit error rates to be measured at high speed. The recovery times for 10.7 Gb/s non-return-to-zero on–off keyed modulation, 10.7 Gb/s differentially phase shift keyed signal and 21.4 Gb/s differentially quadrature phase shift keyed formats can be as low as 4 ns, 7 ns and 40 ns, respectively. The time resolved phase noise associated with laser settling is simultaneously measured for 21.4 Gb/s differentially quadrature phase shift keyed data and it shows that the phase noise coupled with frequency error is the primary limitation on transmitting immediately after a laser switching event.

Understanding supply chain disruption risk with the aid of social networks and information flows analysis

Supply Chain Risk Management (SCRM) has become a popular area of research and study in recent years. This can be highlighted by the number of peer reviewed articles that have appeared in academic literature. This coupled with the realisation by companies that SCRM strategies are required to mitigate the risks that they face, makes for challenging research questions in the field of risk management. The challenge that companies face today is not only to identify the types of risks that they face, but also to assess the indicators of risk that face them. This will allow them to mitigate that risk before any disruption to the supply chain occurs. The use of social network theory can aid in the identification of disruption risk. This thesis proposes the combination of social networks, behavioural risk indicators and information management, to uniquely identify disruption risk. The propositions that were developed from the literature review and exploratory case study in the aerospace OEM, in this thesis are:- By improving information flows, through the use of social networks, we can identify supply chain disruption risk. - The management of information to identify supply chain disruption risk can be explored using push and pull concepts. The propositions were further explored through four focus group sessions, two within the OEM and two within an academic setting. The literature review conducted by the researcher did not find any studies that have evaluated supply chain disruption risk management in terms of social network analysis or information management studies. The evaluation of SCRM using these methods is thought to be a unique way of understanding the issues in SCRM that practitioners face today in the aerospace industry.

Survival data analysis using neural networks

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Feed-forward neural networks and topographic mappings for exploratory data analysis

A recent novel approach to the visualisation and analysis of datasets, and one which is particularly applicable to those of a high dimension, is discussed in the context of real applications. A feed-forward neural network is utilised to effect a topographic, structure-preserving, dimension-reducing transformation of the data, with an additional facility to incorporate different degrees of associated subjective information. The properties of this transformation are illustrated on synthetic and real datasets, including the 1992 UK Research Assessment Exercise for funding in higher education. The method is compared and contrasted to established techniques for feature extraction, and related to topographic mappings, the Sammon projection and the statistical field of multidimensional scaling.

Performance modelling and analysis of multiple coexisting IEEE 802.15.4 wireless sensor networks

With the features of low-power and flexible networking capabilities IEEE 802.15.4 has been widely regarded as one strong candidate of communication technologies for wireless sensor networks (WSNs). It is expected that with an increasing number of deployments of 802.15.4 based WSNs, multiple WSNs could coexist with full or partial overlap in residential or enterprise areas. As WSNs are usually deployed without coordination, the communication could meet significant degradation with the 802.15.4 channel access scheme, which has a large impact on system performance. In this thesis we are motivated to investigate the effectiveness of 802.15.4 networks supporting WSN applications with various environments, especially when hidden terminals are presented due to the uncoordinated coexistence problem. Both analytical models and system level simulators are developed to analyse the performance of the random access scheme specified by IEEE 802.15.4 medium access control (MAC) standard for several network scenarios. The first part of the thesis investigates the effectiveness of single 802.15.4 network supporting WSN applications. A Markov chain based analytic model is applied to model the MAC behaviour of IEEE 802.15.4 standard and a discrete event simulator is also developed to analyse the performance and verify the proposed analytical model. It is observed that 802.15.4 networks could sufficiently support most WSN applications with its various functionalities. After the investigation of single network, the uncoordinated coexistence problem of multiple 802.15.4 networks deployed with communication range fully or partially overlapped are investigated in the next part of the thesis. Both nonsleep and sleep modes are investigated with different channel conditions by analytic and simulation methods to obtain the comprehensive performance evaluation. It is found that the uncoordinated coexistence problem can significantly degrade the performance of 802.15.4 networks, which is unlikely to satisfy the QoS requirements for many WSN applications. The proposed analytic model is validated by simulations which could be used to obtain the optimal parameter setting before WSNs deployments to eliminate the interference risks.

The Analysis and Optimization of Survivability of MPLS Networks

The problem of MPLS networks survivability analysis is considered in this paper. The survivability indexes are defined which take into account the specificity of MPLS networks and the algorithm of its estimation is elaborated. The problem of MPLS network structure optimization under the constraints on the survivability indexes is considered and the algorithm of its solution is suggested. The experimental investigations were carried out and their results are presented.

Indirect ties in knowledge networks:a social network analysis with ordered weighted averaging operators

This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes.

The value of indirect ties in citation networks:SNA analysis with OWA operator weights

This paper seeks to advance the theory and practice of the dynamics of complex networks in relation to direct and indirect citations. It applies social network analysis (SNA) and the ordered weighted averaging operator (OWA) to study a patent citations network. So far the SNA studies investigating long chains of patents citations have rarely been undertaken and the importance of a node in a network has been associated mostly with its number of direct ties. In this research OWA is used to analyse complex networks, assess the role of indirect ties, and provide guidance to reduce complexity for decision makers and analysts. An empirical example of a set of European patents published in 2000 in the renewable energy industry is provided to show the usefulness of the proposed approach for the preference ranking of patent citations.

Cramer-Rao bound analysis of location estimation in multi-hop wireless sensor networks

Location estimation is important for wireless sensor network (WSN) applications. In this paper we propose a Cramer-Rao Bound (CRB) based analytical approach for two centralized multi-hop localization algorithms to get insights into the error performance and its sensitivity to the distance measurement error, anchor node density and placement. The location estimation performance is compared with four distributed multi-hop localization algorithms by simulation to evaluate the efficiency of the proposed analytical approach. The numerical results demonstrate the complex tradeoff between the centralized and distributed localization algorithms on accuracy, complexity and communication overhead. Based on this analysis, an efficient and scalable performance evaluation tool can be designed for localization algorithms in large scale WSNs, where simulation-based evaluation approaches are impractical. © 2013 IEEE.

Time resolved bit error rate analysis of a fast switching tunable laser for use in optically switched networks

We investigate the use of different direct detection modulation formats in a wavelength switched optical network. We find the minimum time it takes a tunable sampled grating distributed Bragg reflector laser to recover after switching from one wavelength channel to another for different modulation formats. The recovery time is investigated utilizing a field programmable gate array which operates as a time resolved bit error rate detector. The detector offers 93 ps resolution operating at 10.7 Gb/s and allows for all the data received to contribute to the measurement, allowing low bit error rates to be measured at high speed. The recovery times for 10.7 Gb/s non-return-to-zero on–off keyed modulation, 10.7 Gb/s differentially phase shift keyed signal and 21.4 Gb/s differentially quadrature phase shift keyed formats can be as low as 4 ns, 7 ns and 40 ns, respectively. The time resolved phase noise associated with laser settling is simultaneously measured for 21.4 Gb/s differentially quadrature phase shift keyed data and it shows that the phase noise coupled with frequency error is the primary limitation on transmitting immediately after a laser switching event.

Performance analysis of IEEE 802.11 cognitive radio ad hoc networks

IEEE 802.11 standard is the dominant technology for wireless local area networks (WLANs). In the last two decades, the Distributed coordination function (DCF) of IEEE 802.11 standard has become the one of the most important media access control (MAC) protocols for mobile ad hoc networks (MANETs). The DCF protocol can also be combined with cognitive radio, thus the IEEE 802.11 cognitive radio ad hoc networks (CRAHNs) come into being. There were several literatures which focus on the modeling of IEEE 802.11 CRAHNs, however, there is still no thorough and scalable analytical models for IEEE 802.11 CRAHNs whose cognitive node (i.e., secondary user, SU) has spectrum sensing and possible channel silence process before the MAC contention process. This paper develops a unified analytical model for IEEE 802.11 CRAHNs for comprehensive MAC layer queuing analysis. In the proposed model, the SUs are modeled by a hyper generalized 2D Markov chain model with an M/G/1/K model while the primary users (PUs) are modeled by a generalized 2D Markov chain and an M/G/1/K model. The performance evaluation results show that the quality-of-service (QoS) of both the PUs and SUs can be statistically guaranteed with the suitable settings of duration of channel sensing and silence phase in the case of under loading.

Advanced predictive-analysis-based decision support for collaborative logistics networks

Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.