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 RESEARCH |
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Pervasive and Mobile Computing
 According to EMC report in 2004, there are about 1.5 billion cell phone users worldwide - more than three times the number of personal computers - and the number is expected to exceed 4 billion users by 2010. The main goal of mobile computing is anytime anywhere access and, thus, liberating people from relying on a computing or communication device at a fixed location.
Mobile devices however have strict resource limitations as compared to traditional personal computers. This includes battery lifetime, memory storage, and processing speed. To combat the current limitations of mobile computing, one possibility is to introduce new technologies for long lifetime batteries, fast and abundant memory, and fast processors. Significant research efforts are also spent in designing resource-aware algorithms and protocols for software running on these devices so as to consume minimal battery lifetime and memory.
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IP Network Planning
All-IP converged networks should be planned carefully to accommodate new services with high and stable quality level. The ultimate objective of network planning is to lay the foundation for making profit out of network operation. This is achieved by granting a given network sufficiently high but not too high performance quality. Network planning examines the tradeoff between performance quality and resulting costs. Its task is to select the scenario with the optimal tradeoff, i.e. the one with the best quality for minimum cost. In summary, the objectives of network planning are: 1) economical network ownership and 2) guaranteed quality of service. With these objectives, a general network design problem can be formulated to determine the optimal values of variables such as topology, routing table and scheme, link capacities, bandwidth allocation and domain design. The solution of this general problem is complex and not yet feasible due to the interdependencies among the different design variables. As a result, most research in this area is focused on solving subproblems. In the literature, four basic design subproblems are defined: the flow assignment, the capacity assignment, the capacity and flow assignments, and the topology, capacity and flow assignments. Flow assignment determines the optimal routes over which information is transferred among the communicating nodes; capacity assignment determines the link capacities required for high quality transmission at minimum cost, and finally topology assignment determines node locations and link selection.
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Quality of Service for Realtime Services
With the evolution of the internet protocol as the ubiquitous infrastructure, various categories of services are demanded. One category provides internet services where customers are willing to pay a certain price to make their service reliable and fast enough. Within this category, a couple of service classes can be provided in decreasing quality such as gold, silver, and bronze. A second category can provide timely service with low delay and low jitter (inter-packet delay variation). Examples of applications belonging to this service category are telephony and videoconferencing. For such a service, customers are willing to pay a higher price to achieve premium communications quality. Finally, the best-effort service is granted to customers who are not willing to pay any additional price for improved quality.
IP networks were originally designed for best effort service that grants no guarantees to timeliness and reliability of delivery. Traffic is processed as soon as possible regardless of its type and performance criteria. However, in order to be widely acceptable as the universal infrastructure for all types of services, IP networks have to support traffic generated by any of the new evolving applications. This places a huge burden on IP networks, which have to handle various services with different traffic characteristics and quality of service (QoS) demands. Therefore, mechanisms are still needed that provide QoS and importantly differentiated QoS, where traffic classes with different service requirements are treated appropriately.
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Network Coding
 Network coding has emerged recently as a promising research area in the field of networking systems. Network coding moves away from the classical approach of networking in which intermediate nodes send packets identical to what they receive, instead with network coding intermediate nodes send packets that are linear combination of packets they receive. When packets don't have the same length the shorter ones are padded with zeros. Note that linear combination is different than concatenation, a linear combination of a set of packets of maximum length L result in an encoded packet of size L.
Network coding can be used to improve throughput. This can be illustrated by the famous butterfly example shown in the adjacent figure.
Without network coding nodes R1 and R2 can receive only A or B, however as shown in the example above when using network coding R1 and R2 can receive both A and B. Network coding provide robustness and adaptability, it can be incorporated into wireless networks, ad hoc networks, peer to peer networks and mobile networks.
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Peer-to-peer Networking
The vast majority of video applications being delivered today over wireless networks emanate from dedicated infrastructure servers. However, it is very costly to meet the steadily increasing demand for high data rate video applications, both in terms of bandwidth costs and server hardware costs. As a result, much of the video on demand will likely be streamed via peer-to-peer architectures, in which the consumers of the video content are also the suppliers of the content. In peer-to-peer architectures, the peers contribute to the system their storage and network bandwidth resources. Moreover, the peers additionally contribute their power capabilities in wireless ad hoc environments in order to relay video data between nodes in the network. However, since videos are typically large in size and require high share of the network capacity for delivery, many peers may be unwilling to cache them in whole to serve others, the fact that makes providing scalable and high quality video services in a peer-to-peer environment challenging.
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