Towards a Queueing-Based Framework for In-Network Function Computation

We seek to develop network algorithms for function computation in sensor networks. Specifically, we want dynamic joint aggregation, routing, and scheduling algorithms that have analytically provable performance benefits due to in-network computation as compared to simple data forwarding. To this end, we define a class of functions, the Fully-Multiplexible functions, which includes several functions such as parity, MAX, and k th -order statistics. For such functions we exactly characterize the maximum achievable refresh rate of the network in terms of an underlying graph primitive, the min-mincut. In acyclic wireline networks, we show that the maximum refresh rate is achievable by a simple algorithm that is dynamic, distributed, and only dependent on local information. In the case of wireless networks, we provide a MaxWeight-like algorithm with dynamic flow splitting, which is shown to be throughput-optimal

A Novel Method to Calculate Click Through Rate for Sponsored Search

Sponsored search adopts generalized second price (GSP) auction mechanism which works on the concept of pay per click which is most commonly used for the allocation of slots in the searched page. Two main aspects associated with GSP are the bidding amount and the click through rate (CTR). The CTR learning algorithms currently being used works on the basic principle of (#clicks_i/ #impressions_i) under a fixed window of clicks or impressions or time. CTR are prone to fraudulent clicks, resulting in sudden increase of CTR. The current algorithms are unable to find the solutions to stop this, although with the use of machine learning algorithms it can be detected that fraudulent clicks are being generated. In our paper, we have used the concept of relative ranking which works on the basic principle of (#clicks_i /#clicks_t). In this algorithm, both the numerator and the denominator are linked. As #clicks_t is higher than previous algorithms and is linked to the #clicks_i, the small change in the clicks which occurs in the normal scenario have a very small change in the result but in case of fraudulent clicks the number of clicks increases or decreases rapidly which will add up with the normal clicks to increase the denominator, thereby decreasing the CTR.Comment: 10 pages, 1 figur

Effect of Walkability on Users Choice of “Walking” the Last Mile to Transit Stations: A Case of Delhi Metro

There is growing recognition of the importance of Last Mile Connectivity (LMC) to mass transit systems. In the context of Delhi, albeit a shift can be seen in the provisioning of LMC, and despite previous studies indicating that more than 50% of metro rail users walk to and/or from metro stations, yet the seriousness with which pedestrian environment is woven into transit planning is lacking.The paper is based on an empirical study conducted by the author, of approximately 800 samples of metro users surveyed across seven stations of Delhi Metro, representing different station typologies, ridership and locational contexts. The paper focuses on the “walk” choice of users across a variety of factors related to their socio-economic strata, trip characteristics and station context. A parallel study is conducted to audit the pedestrian environment within one kilometre distance around each station. The paper further attempts to investigate whether pedestrian environment affects user choice of opting for “walk” as the last mile choice. It also ranks the performance of the case stations in terms of various attributes of walkability.In conclusion, the paper contends that overall walkability environment offered to transit commuters is crucial in the share of walk trips for last mile commute and the distance commuters are willing to walk. It recommends that walking as LMC choice needs to be promoted through enhanced user experience in absence of which a significant amount of last mile travel will happen through unsustainable mechanised modes