92,298 research outputs found
Real-time Bidding for Online Advertising: Measurement and Analysis
The real-time bidding (RTB), aka programmatic buying, has recently become the
fastest growing area in online advertising. Instead of bulking buying and
inventory-centric buying, RTB mimics stock exchanges and utilises computer
algorithms to automatically buy and sell ads in real-time; It uses per
impression context and targets the ads to specific people based on data about
them, and hence dramatically increases the effectiveness of display
advertising. In this paper, we provide an empirical analysis and measurement of
a production ad exchange. Using the data sampled from both demand and supply
side, we aim to provide first-hand insights into the emerging new impression
selling infrastructure and its bidding behaviours, and help identifying
research and design issues in such systems. From our study, we observed that
periodic patterns occur in various statistics including impressions, clicks,
bids, and conversion rates (both post-view and post-click), which suggest
time-dependent models would be appropriate for capturing the repeated patterns
in RTB. We also found that despite the claimed second price auction, the first
price payment in fact is accounted for 55.4% of total cost due to the
arrangement of the soft floor price. As such, we argue that the setting of soft
floor price in the current RTB systems puts advertisers in a less favourable
position. Furthermore, our analysis on the conversation rates shows that the
current bidding strategy is far less optimal, indicating the significant needs
for optimisation algorithms incorporating the facts such as the temporal
behaviours, the frequency and recency of the ad displays, which have not been
well considered in the past.Comment: Accepted by ADKDD '13 worksho
Location Privacy in Spatial Crowdsourcing
Spatial crowdsourcing (SC) is a new platform that engages individuals in
collecting and analyzing environmental, social and other spatiotemporal
information. With SC, requesters outsource their spatiotemporal tasks to a set
of workers, who will perform the tasks by physically traveling to the tasks'
locations. This chapter identifies privacy threats toward both workers and
requesters during the two main phases of spatial crowdsourcing, tasking and
reporting. Tasking is the process of identifying which tasks should be assigned
to which workers. This process is handled by a spatial crowdsourcing server
(SC-server). The latter phase is reporting, in which workers travel to the
tasks' locations, complete the tasks and upload their reports to the SC-server.
The challenge is to enable effective and efficient tasking as well as reporting
in SC without disclosing the actual locations of workers (at least until they
agree to perform a task) and the tasks themselves (at least to workers who are
not assigned to those tasks). This chapter aims to provide an overview of the
state-of-the-art in protecting users' location privacy in spatial
crowdsourcing. We provide a comparative study of a diverse set of solutions in
terms of task publishing modes (push vs. pull), problem focuses (tasking and
reporting), threats (server, requester and worker), and underlying technical
approaches (from pseudonymity, cloaking, and perturbation to exchange-based and
encryption-based techniques). The strengths and drawbacks of the techniques are
highlighted, leading to a discussion of open problems and future work
Towards Optimal Distributed Node Scheduling in a Multihop Wireless Network through Local Voting
In a multihop wireless network, it is crucial but challenging to schedule
transmissions in an efficient and fair manner. In this paper, a novel
distributed node scheduling algorithm, called Local Voting, is proposed. This
algorithm tries to semi-equalize the load (defined as the ratio of the queue
length over the number of allocated slots) through slot reallocation based on
local information exchange. The algorithm stems from the finding that the
shortest delivery time or delay is obtained when the load is semi-equalized
throughout the network. In addition, we prove that, with Local Voting, the
network system converges asymptotically towards the optimal scheduling.
Moreover, through extensive simulations, the performance of Local Voting is
further investigated in comparison with several representative scheduling
algorithms from the literature. Simulation results show that the proposed
algorithm achieves better performance than the other distributed algorithms in
terms of average delay, maximum delay, and fairness. Despite being distributed,
the performance of Local Voting is also found to be very close to a centralized
algorithm that is deemed to have the optimal performance
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