In wireless sensor networks, data transmission reliability is a fundamental challenge due to several physical constraints such as interference, power consumption, and environmental effects. In current wireless sensor implementations, a single bit error requires retransmitting the entire frame. This incurs extra processing overhead and power consumption, especially for large frames. Frame fragmentation into small blocks with individual error detection codes can reduce the unnecessary retransmission of the correctly received blocks. The optimal block size, however, varies based on the wireless channel conditions. In this paper, we propose an interference-aware frame fragmentation scheme called iFrag. iFrag effectively addresses the challenges associated with dynamic partitioning of blocks. We show through analytical and experimental results that iFrag achieves up to 3× improvement in throughput when the channel condition is noisy, while reduces the delay to 12% compared to other static fragmentation approach. On average, it shows 13% gain in throughput across all channel conditions used in our experiments. This significant improvement is due to dynamic nature of iFrag that minimizes the retransmission overhead by selecting the appropriate number of blocks in each data frame.

The widespread use of GPS-enabled smartphones along with the popularity of micro-blogging and social networking applications, e.g., Twitter and Facebook, has resulted in the generation of huge streams of geo-tagged textual data. Many applications require real-time processing of these streams. For example, location-based e-coupon and ad-targeting systems enable advertisers to register millions of ads to millions of users. The number of users is typically very high and they are continuously moving, and the ads change frequently as well. Hence sending the right ad to the matching users is very challenging. Existing streaming systems are either centralized or are not spatial-keyword aware, and cannot efficiently support the processing of rapidly arriving spatial-keyword data streams. This paper presents Tornado, a distributed spatial-keyword stream processing system. Tornado features routing units to fairly distribute the workload, and furthermore, co-locate the data objects and the corresponding queries at the same processing units. The routing units use the Augmented-Grid, a novel structure that is equipped with an efficient search algorithm for distributing the data objects and queries. Tornado uses evaluators to process the data objects against the queries. The routing units minimize the redundant communication by not sending data updates for processing when these updates do not match any query. By applying dynamically evaluated cost formulae that continuously represent the processing overhead at each evaluator, Tornado is adaptive to changes in the workload. Extensive experimental evaluation using spatio-textual range queries over real Twitter data indicates that Tornado outperforms the nonspatio-textually aware approaches by up to two orders of magnitude in terms of the overall system throughput.

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