Sharing large data collections between mobile peers

New directions in the provision of end-user computing experiences mean that we need to determine the best way to share data between small mobile computing devices. Partitioning large structures so that they can be shared efficiently provides a basis for data-intensive applications on such platforms. In conjunction with such an approach, dictionary-based compression techniques provide additional benefits and help to prolong battery life

Managing and Improving Upon Bandwidth Challenges in Computer Network

Managing the bandwidth of a computer network is always faced with great challenges. This research was necessitated by the urgent need to manage the University network currently experiencing congestion in both the local LA� and on the internet backhaul with a view to improving network performance and reduce the huge recurrent on the WA� link. However, there exists various ways that have been deployed towards solving these problems. In this paper we examined existing bandwidth management, effect of limited bandwidth on the network performance and profound solutions of techniques that enhanced or improved the bandwidth efficiency. Also, included in this research work are the studies of the effect of limited bandwidth on work load, type of protocol used and the effect of network congestion on the quality of service of a Wide Area �etwork (WA�). By comparison, from the modeling of the effect of work load and limited bandwidth on the throughput of a wide area network based on experimental simulation and real time simulation scenarios, some observations were made and recommendation of solutions were given from the analyzed results

Statistical multiplexing of video sources for packet switching networks

Communication networks are fast evolving towards truly integrated networks handling all types of traffic. They employ integrated switching technologies for voice. video and data. Statistical or asynchronous time division multiplexing of full motion video sources is an initial step towards packetized video networks. The main goal is to utilize the common communication channel efficiently, without loosing quality at the receiver. This work discusses the concept of using statistical multiplexing for packet video communications. The topology of a single internal packet network to support ISDN services has been adopted. Simulations have been carried out to demonstrate the statistical smoothing effect of packetized video in the networks having high speed links. Results indicate that the channel rate per source decreased in an exponential manner as the number of sources increased. An expression for the average usage time t of the channel has been derived in terms of channel rate per source and the number of sources multiplexed. Also the average usage time of the channel is higher for buffered data than that of the multiplexed data. The high speed communication links in the internal network are lightly loaded, which indicates that these links can accommodate more data

Fast-Convergent Learning-aided Control in Energy Harvesting Networks

In this paper, we present a novel learning-aided energy management scheme ($\mathtt{LEM}$) for multihop energy harvesting networks. Different from prior works on this problem, our algorithm explicitly incorporates information learning into system control via a step called \emph{perturbed dual learning}. $\mathtt{LEM}$ does not require any statistical information of the system dynamics for implementation, and efficiently resolves the challenging energy outage problem. We show that $\mathtt{LEM}$ achieves the near-optimal $[O(\epsilon), O(\log(1/\epsilon)^2)]$ utility-delay tradeoff with an $O(1/\epsilon^{1-c/2})$ energy buffers ($c\in(0,1)$). More interestingly, $\mathtt{LEM}$ possesses a \emph{convergence time} of $O(1/\epsilon^{1-c/2} +1/\epsilon^c)$, which is much faster than the $\Theta(1/\epsilon)$ time of pure queue-based techniques or the $\Theta(1/\epsilon^2)$ time of approaches that rely purely on learning the system statistics. This fast convergence property makes $\mathtt{LEM}$ more adaptive and efficient in resource allocation in dynamic environments. The design and analysis of $\mathtt{LEM}$ demonstrate how system control algorithms can be augmented by learning and what the benefits are. The methodology and algorithm can also be applied to similar problems, e.g., processing networks, where nodes require nonzero amount of contents to support their actions