Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

GRIDKIT: Pluggable overlay networks for Grid computing

A `second generation' approach to the provision of Grid middleware is now emerging which is built on service-oriented architecture and web services standards and technologies. However, advanced Grid applications have significant demands that are not addressed by present-day web services platforms. As one prime example, current platforms do not support the rich diversity of communication `interaction types' that are demanded by advanced applications (e.g. publish-subscribe, media streaming, peer-to-peer interaction). In the paper we describe the Gridkit middleware which augments the basic service-oriented architecture to address this particular deficiency. We particularly focus on the communications infrastructure support required to support multiple interaction types in a unified, principled and extensible manner-which we present in terms of the novel concept of pluggable overlay networks

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Security and Privacy Issues in Wireless Mesh Networks: A Survey

This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio

Na neizrazitoj logici zasnovano upravljanje frekvencijom za ODMRP u mobilnim ad hoc mrežama

On Demand Multicast Routing Protocol (ODMRP) is a popular solution designed for ad hoc networks with mobile hosts. Its efficiency, simplicity, and robustness to mobility render it one of the most widely used multicast routing protocols in Mobile Ad hoc NETworks (MANET). In ODMRP, there is no input rate control for upper layer traffic. So, it’s possible that high dense traffic flow causes congestion in networks. In this work, an enhancement to ODMRP is proposed referred to as fuzzy logic based Rate Control ODMRP (FRC-ODMRP). FRC-ODMRP attempts to adapt the arrival rate from upper layers to the state in the network by using feedback information from receivers of the multicast group. Accordingly, source comes up with a decision whether to increase or decrease its transmission rate based on information collected from the receivers. In this research, delay and packet delivery ratio reconsidered as indicators of congestion in addition to number of received packets. Simulation results demonstrate that FRC-ODMRP achieves significant performance improvements in comparison to conventional ODMRP and QoS-ODMRP. Indeed, it efficiently handles simultaneous traffic flows such that no one could dominate available bandwidth of networks.On Demand Multicast Routing Protocol (ODMRP) popularno je rješenje namijenjeno ad hoc mrežama s mobilnim domaćinima. Efikasnost, jednostavnost i robusnost u smislu mobilnosti učini su ovu metodu jednom od najraširenijih multicast protokola u ad hoc mobilnim mrežam (eng. MANET). Kod ODMRP-a nema upravljanja ulaznom frekvencijom za promet višeg sloja. Zbog toga je moguće da gusti promet uzrokuje zagušenje u mrežama. U ovome je radu predstavljeno poboljšanje ODMRP-a nazvano ODMRP zasnovan na fuzzy logici (FRC-ODRMP). FRC-ODRMP pokušava prilagoditi dolazne signale iz viših slojeva stanju u mreži koristeći povratnu informaciju od primatelja iz multicast grupe. Prilikom istraživanja dodatno je uzet omjer kašnjenja i dostavljenih paketa kao pokazatelj zagušenosti mreže uz broj dostavljenih paketa. Simulacijski rezultati pokazuju kako FRC-ODMRP značajno poboljšava performanse u odnosu na konvencionalni ODMRP i Qos-ODMRP. Dodatno, simultani promet efikasno je upravljan tako da nitko ne može dominirati dostupnom propusnošću mreže

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