We consider the problem of providing vehicular Internet access using roadside 802.11 access points. We build on previous work in this area [18, 8, 5, 11] with an extensive experimental analysis of protocol operation at a level of detail not previously explored. We report on data gathered with four capture devices from nearly 50 experimental runs conducted with vehicles on a rural highway. Our three primary contributions are: (1) We experimentally demonstrate that, on average, current protocols only achieve 50% of the overall throughput possible in this scenario. In particular, even with a streamlined connection setup procedure that does not use DHCP, high packet losses early in a vehicular connection are responsible for the loss of nearly 25% of overall throughput, 15% of the time. (2) We quantify the effects of ten problems caused by the mechanics of existing protocols that are responsible for this throughput loss; and (3) We recommend best practices for using vehicular opportunistic connections. Moreover, we show that overall throughput could be significantly improved if environmental information was made available to the 802.11 MAC and to TCP. The central message in this paper is that wireless conditions in the vicinity of a roadside access point are predictable, and by exploiting this information, vehicular opportunistic access can be greatly improved.
Wireless radios of the future will likely be frequency-agile, that is, supporting opportunistic and adaptive use of the RF spectrum. Such radios must coordinate with each other to build an accurate and consistent map of spectral utilization in their surroundings. We focus on the problem of sharing RF spectrum data among a collection of wireless devices. The inherent requirements of such data and the time-granularity at which it must be collected makes this problem both interesting and technically challenging. We propose GUESS, a novel incremental gossiping approach to coordinated spectral sensing. It (1) reduces protocol overhead by limiting the amount of information exchanged between participating nodes, (2) is resilient to network alterations, due to node movement or node failures, and (3) allows exponentially-fast information convergence. We outline an initial solution incorporating these ideas and also show how our approach reduces network overhead by up to a factor of 2.4 and results in up to 2.7 times faster information convergence than alternative approaches.
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