In application-level high bandwidth multicast (HBM), physical links can be shared by multiple long-lived unicast flows. We identify several data transfer patterns which can cause suboptimal bandwidth usage of narrow links and which have not been clearly identified in previous solutions for application-level HBM.We propose a distributed solution to avoid these problematic patterns, with which end systems are coordinated and each is responsible to forward a bounded amount of data. Consequently, the outgoing traffic of each end system is balanced and limited. It avoids congestion due to merging unicast flows, which increases the utilization of the narrow links. Receivers that are close by topologically request their data in a disjoint and coordinated fashion, which leads to much reduced duplicated data at the narrow links. Simulation results show that our solution can achieve higher throughputs at the receivers, which is due to more efficient utilization of the narrow links' bandwidth, than meshbased or multiple-tree approaches.

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Achieving IP handoff with a short latency and minimal packet loss is essential for mobile devices that roam across IP subnets. Many existing solutions require changes to be made to the network or transport layer, and they tend to suffer from long handoff latency in either soft or hard handoff scenario, or both; and some are difficult to deploy in practice. We propose a new protocol, called the adaptive multipath protocol, to achieve efficient IP handoff. Based on link-layer signal strength measurements, two different schemes are used to handle soft and hard handoff respectively. Seamless IP handoff is achieved by using multiple transport layer connections on top of persistent link-layer connectivity during soft handoff. To achieve low handoff latency during hard handoff, a set of distributed sessions repositories (SRs), which are independent of the end hosts, are employed. Simulation results clearly support our claims. In particular, the latency for hard handoff is found to be as low as 50% of that of Fast handoff.

Abstract-Wireless sensor networks (WSNs) should handle multiple sensing tasks for various applications. How to improve the quality of the data acquired in such resource constrained environment is a challenging issue. In this paper, we propose a sensor-channel co-allocation model for scheduling the sensing tasks. The proposed model considers the capability, coupling and load balancing constraints for sensing data acquisition, and can guarantee transmission of sensed data in real-time while avoiding data incompleteness in an efficient way. A spatiotemporal metric called sensing-span is proposed to evaluate the tasks' execution cost of achieving desired data quality. We extend computation task scheduling algorithms to support sensor-channel co-allocation problem and a heuristic called Minimum Service Capability Fragment (MSCF) is introduced for task scheduling to minimize the waste of reserved channel capacity. Simulation results show that MSCF can improve the performance of data acquisition in WSNs as compared with other heuristics, when scheduling a large number of concurrent data acquisition tasks.