Simulation and analysis of adaptive routing and flow control in wide area communication networks

This thesis presents the development of new simulation and analytic models for the performance analysis of wide area communication networks. The models are used to analyse adaptive routing and flow control in fully connected circuit switched and sparsely connected packet switched networks. In particular the performance of routing algorithms derived from the L(_R-I) linear learning automata model are assessed for both types of network. A novel architecture using the INMOS Transputer is constructed for simulation of both circuit and packet switched networks in a loosely coupled multi- microprocessor environment. The network topology is mapped onto an identically configured array of processing centres to overcome the processing bottleneck of conventional Von Neumann architecture machines. Previous analytic work in circuit switched work is extended to include both asymmetrical networks and adaptive routing policies. In the analysis of packet switched networks analytic models of adaptive routing and flow control are integrated to produce a powerful, integrated environment for performance analysis The work concludes that routing algorithms based on linear learning automata have significant potential in both fully connected circuit switched networks and sparsely connected packet switched networks

Designing and optimization of VOIP PBX infrastructure

In the recent decade, communication has stirred from the old wired medium such as public switched telephone network (PSTN) to the Internet. Present, Voice over Internet Protocol (VoIP) Technology used for communication on internet by means of packet switching technique. Several years ago, an internet protocol (IP) based organism was launched, which is known as Private Branch Exchange "PBX", as a substitute of common PSTN systems. For free communication, probably you must have to be pleased with starting of domestic calls. Although, fairly in few cases, VoIP services can considerably condense our periodical phone bills. For instance, if someone makes frequent global phone calls, VoIP talk service is the actual savings treat which cannot achieve by using regular switched phone. VoIP talk services strength help to trim down your phone bills if you deal with a lot of long-distance (international) and as well as domestic phone calls. However, with the VoIP success, threats and challenges also stay behind. In this dissertation, by penetration testing one will know that how to find network vulnerabilities how to attack them to exploit the network for unhealthy activities and also will know about some security techniques to secure a network. And the results will be achieved by penetration testing will indicate of proven of artefact and would be helpful to enhance the level of network security to build a more secure network in future

Improved learning automata applied to routing in multi-service networks

Multi-service communications networks are generally designed, provisioned and configured, based on source-destination user demands expected to occur over a recurring time period. However due to network users' actions being non-deterministic, actual user demands will vary from those expected, potentially causing some network resources to be under- provisioned, with others possibly over-provisioned. As actual user demands vary over the recurring time period from those expected, so the status of the various shared network resources may also vary. This high degree of uncertainty necessitates using adaptive resource allocation mechanisms to share the finite network resources more efficiently so that more of actual user demands may be accommodated onto the network. The overhead for these adaptive resource allocation mechanisms must be low in order to scale for use in large networks carrying many source-destination user demands. This thesis examines the use of stochastic learning automata for the adaptive routing problem (these being adaptive, distributed and simple in implementation and operation) and seeks to improve their weakness of slow convergence whilst maintaining their strength of subsequent near optimal performance. Firstly, current reinforcement algorithms (the part causing the automaton to learn) are examined for applicability, and contrary to the literature the discretised schemes are found in general to be unsuitable. Two algorithms are chosen (one with fast convergence, the other with good subsequent performance) and are improved through automatically adapting the learning rates and automatically switching between the two algorithms. Both novel methods use local entropy of action probabilities for determining convergence state. However when the convergence speed and blocking probability is compared to a bandwidth-based dynamic link-state shortest-path algorithm, the latter is found to be superior. A novel re-application of learning automata to the routing problem is therefore proposed: using link utilisation levels instead of call acceptance or packet delay. Learning automata now return a lower blocking probability than the dynamic shortest-path based scheme under realistic loading levels, but still suffer from a significant number of convergence iterations. Therefore the final improvement is to combine both learning automata and shortest-path concepts to form a hybrid algorithm. The resulting blocking probability of this novel routing algorithm is superior to either algorithm, even when using trend user demands

Using natural means to reduce surface transport noise during propagation outdoors

This paper reviews ways of reducing surface transport noise by natural means. The noise abatement solutions of interest can be easily (visually) incorporated in the landscape or help with greening the (sub)urban environment. They include vegetated surfaces (applied to faces or tops of noise walls and on building façades and roofs ), caged piles of stones (gabions), vegetation belts (tree belts, shrub zones and hedges), earth berms and various ways of exploiting ground-surface-related effects. The ideas presented in this overview have been tested in the laboratory and/or numerically evaluated in order to assess or enhance the noise abatement they could provide. Some in-situ experiments are discussed as well. When well-designed, such natural devices have the potential to abate surface transport noise, possibly by complementing and sometimes improving common (non-green) noise reducing devices or measures. Their applicability strongly depends on the available space reserved for the noise abatement and the receiver position

SYSTEM MODEL FOR AUTONOMOUS ROAD FREIGHT TRANSPORTATION

Emerging info-communication and vehicle technologies (especially vehicle automation) facilitate evolvement of autonomous road freight transportation. The entire transport system and its operation undergo a major change. New service concepts are growing and the existing ones are being transformed. The changing is particularly significant in city logistics. However, there are debates about the ways of automation of processes targeting improvement of capacity utilisation and decrease of expenditures. The main research questions of the paper are therefore: what properties of the future autonomous freight transportation are presumed; what system structure is to be constructed and how the system is to be operated? After introducing the basic notions and reviews of the current systems and developments, the shifting from traditional freight transportation to autonomous system is investigated by several aspects. A system- and process-oriented analytical modelling method has been applied. The main system constituents and their connections are modelled. Finally, we elaborate the operational model of road freight transportation, which is applicable principally in metropolitan areas. In conclusion, the presented results appoint the research and innovation trends towards the automation of freight transportation

Learning algorithms for the control of routing in integrated service communication networks

There is a high degree of uncertainty regarding the nature of traffic on future integrated service networks. This uncertainty motivates the use of adaptive resource allocation policies that can take advantage of the statistical fluctuations in the traffic demands. The adaptive control mechanisms must be 'lightweight', in terms of their overheads, and scale to potentially large networks with many traffic flows. Adaptive routing is one form of adaptive resource allocation, and this thesis considers the application of Stochastic Learning Automata (SLA) for distributed, lightweight adaptive routing in future integrated service communication networks. The thesis begins with a broad critical review of the use of Artificial Intelligence (AI) techniques applied to the control of communication networks. Detailed simulation models of integrated service networks are then constructed, and learning automata based routing is compared with traditional techniques on large scale networks. Learning automata are examined for the 'Quality-of-Service' (QoS) routing problem in realistic network topologies, where flows may be routed in the network subject to multiple QoS metrics, such as bandwidth and delay. It is found that learning automata based routing gives considerable blocking probability improvements over shortest path routing, despite only using local connectivity information and a simple probabilistic updating strategy. Furthermore, automata are considered for routing in more complex environments spanning issues such as multi-rate traffic, trunk reservation, routing over multiple domains, routing in high bandwidth-delay product networks and the use of learning automata as a background learning process. Automata are also examined for routing of both 'real-time' and 'non-real-time' traffics in an integrated traffic environment, where the non-real-time traffic has access to the bandwidth 'left over' by the real-time traffic. It is found that adopting learning automata for the routing of the real-time traffic may improve the performance to both real and non-real-time traffics under certain conditions. In addition, it is found that one set of learning automata may route both traffic types satisfactorily. Automata are considered for the routing of multicast connections in receiver-oriented, dynamic environments, where receivers may join and leave the multicast sessions dynamically. Automata are shown to be able to minimise the average delay or the total cost of the resulting trees using the appropriate feedback from the environment. Automata provide a distributed solution to the dynamic multicast problem, requiring purely local connectivity information and a simple updating strategy. Finally, automata are considered for the routing of multicast connections that require QoS guarantees, again in receiver-oriented dynamic environments. It is found that the distributed application of learning automata leads to considerably lower blocking probabilities than a shortest path tree approach, due to a combination of load balancing and minimum cost behaviour

A public communication system

Report: 55, [6] p., digital fileA study of the use of the new communications technology by government to enhance citizen participation and increase program effectiveness.Government of Ontario, Committee on Government Productivity; Institute of Urban Studie

How informal ties matter: encroachment on road reservations along the Kumasi–Accra highway in Ghana

A failure of urban planning in many developing countries is evidenced by encroachment on road reservations. Urban planning literature suggests that such encroachment is largely explained by poverty and urban growth. But how do encroachers find space in the road reservations? This paper examines encroachment along the Anloga Junction to Ejisu section of the Kumasi–Accra highway in Ghana. It argues that formal rules are not effective in governing the road reservations: informal rules rooted in social networks of reciprocity matter more. The research involved interviews with encroachers, senior officials from government institutions and traditional authorities. It emerged that encroachers invoked mainly ethnic and political party ties with public officials to secure space in the road reservations. This occurred in an environment of non-enforcement of relevant laws, weak formal collaboration among public institutions, and inadequate political commitment. There is a need for effective application of the principles and methods of multi-stakeholder governance, linking improved legal regulation with informal processes, to achieve better outcomes