A novel generic distributed intelligent re-configurable mobile network architecture

In this paper we present our vision of the Software Radio (SR) concept. To the author's knowledge this vision of an Intelligent Re-configurable Network encompasses and adds to the main SR concepts presented to date, namely SWSDR, Re-configurable radio systems and networks, and Cognitive radio. The newly proposed architecture and supporting technology provides a flexible way to introduce re-configurability into any network node and protocol layer of a network fixed or mobile, while simultaneously providing a structured framework in which re-configurability can be introduced and hence networks can evolve. This concept has provided a solid foundation and plays a central role in providing a suitable context in which to examine reconfigurabilty issues in the Mobile Terminal and Base Station. We believe that this concept has the potential to help the telecommunications industry in facilitating; the introduction, development and harmonization of reconfigurability within existing and future fixed and mobile networks

Water resources management in a homogenizing world: Averting the Growth and Underinvestment trajectory

Biotic homogenization, a de facto symptom of a global biodiversity crisis, underscores the urgency of reforming water resources management to focus on the health and viability of ecosystems. Global population and economic growth, coupled with inadequate investment in maintenance of ecological systems, threaten to degrade environmental integrity and ecosystem services that support the global socioeconomic system, indicative of a system governed by the Growth and Underinvestment (G&U) archetype. Water resources management is linked to biotic homogenization and degradation of system integrity through alteration of water systems, ecosystem dynamics, and composition of the biota. Consistent with the G&U archetype, water resources planning primarily treats ecological considerations as exogenous constraints rather than integral, dynamic, and responsive parts of the system. It is essential that the ecological considerations be made objectives of water resources development plans to facilitate the analysis of feedbacks and potential trade-offs between socioeconomic gains and ecological losses. We call for expediting a shift to ecosystem-based management of water resources, which requires a better understanding of the dynamics and links between water resources management actions, ecological side-effects, and associated long-term ramifications for sustainability. To address existing knowledge gaps, models that include dynamics and estimated thresholds for regime shifts or ecosystem degradation need to be developed. Policy levers for implementation of ecosystem-based water resources management include shifting away from growth-oriented supply management, better demand management, increased public awareness, and institutional reform that promotes adaptive and transdisciplinary management approaches

Intelligent reconfiguration of large mobile networks using complex organic distributed architecture

This paper presents a possible solution to the intelligent evolution of mobile systems using a Complex Organic Distributed Architecture (CODA), which supports intelligent reconfiguration of all system components. A key feature of this architecture is the deployment of multiple warehouses. The warehouses store data in a variety of ways depending on the type of intelligence required. On Line Analytical Processing (OLAP) software is used to monitor and control data in the system. An effective system of filters and wrappers ensures that data is secure. A system of feedback loops ensures that information travels through the system quickly and effectively

A comparative study of selected classification accuracy in user profiling

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian Networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate

Investigation of Mobile IPv6 and SIP integrated architectures for IMS and VoIP applications

Mobile IPv6 and SIP are protocols designed to support different types of mobility. Mobile IPv6 has been used to support mobility in IP networks and SIP has been used for voice over IP applications. It is the signalling protocol of the IP multimedia subsystem (IMS). In this paper both protocols have been simulated and compared in order to observe their performance for voice over IP (VoIP) applications. In this paper the architectures proposed by researchers in order to combine mobile IPv6 and SIP have also been investigated and compared to analyse their advantages and disadvantages. A network scenario, running mobile IPv6 and SIP for IMS, has also been simulated in order to evaluate the performance offered by the two protocols and to compare them with the results from the simulation of the pure mobile IPv6 and SIP architectures. The comparison shows that the combined scenario offers better performance similar to the one obtained using only mobile IPv6 with route optimization. The scenario simulated was also compared with the integrated architectures for mobile IPv6 and SIP that were investigated

Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate. The obtained simulation results have been evaluated against the existing works of support vector machines (SVMs), decision trees (DTs) and neural networks (NNs)

Implementation of ontology for intelligent hospital ward

We have developed and implemented an ontology for an intelligent hospital ward. Our aim is to address the pervasiveness of computing applications in healthcare environments, which require: sharing of data across the hospital, including data generated by sensors and embedded in such environments, and dealing with semantic heterogeneity that exists across the hospital's data repositories. Our conceptual ontological model that supports such an environment has been implemented using semantic web tools and tested through the application developed with the J2EE technology

The Water Footprint of Data Centers

The internet and associated Information and Communications Technologies (ICT) are diffusing at an astounding pace. As data centers (DCs) proliferate to accommodate this rising demand, their environmental impacts grow too. While the energy efficiency of DCs has been researched extensively, their water footprint (WF) has so far received little to no attention. This article conducts a preliminary WF accounting for cooling and energy consumption in DCs. The WF of DCs is estimated to be between 1047 and 151,061 m3/TJ. Outbound DC data traffic generates a WF of 1–205 liters per gigabyte (roughly equal to the WF of 1 kg of tomatos at the higher end). It is found that, typically, energy consumption constitues by far the greatest share of DC WF, but the level of uncertainty associated with the WF of different energy sources used by DCs makes a comprehensive assessment of DCs’ water use efficiency very challenging. Much better understanding of DC WF is urgently needed if a meaningful evaluation of this rapidly spreading service technology is to be gleaned and response measures are to be put into effect

Opportunistic Downlink Sum-Rate with Multiple Feedback Thresholds

The achievable sum-rate capacity in multiuser systems grows as an increasing function of the number of users when opportunistic scheduling is employed. The scheduling requires the knowledge of channel state information (CSI) which is generated and sent from the users. It has been verified that the sum-rate capacity of a multiuser system with one-bit CSI feedback scheme by which each user whose channel quality is above an optimally selected threshold sends one-bit feedback has the same growth rate as that with the full CSI. However, the one-bit feedback scheme introduces non-negligible capacity loss to the achievable sum-rate. In this paper, a feedback reduction technique with multiple feedback thresholds is presented and analysed. The analysis yields closed-form expressions of the achievable sum-rate and its lower bound. The numerical results show that approximately 84% of the capacity loss can be eliminated in the system employing three optimally selected feedback thresholds. The lower-bound of the achievable sum-rate approaches the full-CSI sum-rate as the number of thresholds increases