There is a growing interest in applications that utilize continuous sensing of individual activity or context, via sensors embedded or associated with personal mobile devices (e.g., smartphones). Reducing the energy overheads of sensor data acquisition and processing is essential to ensure the successful continuous operation of such applications, especially on battery-limited mobile devices. To achieve this goal, this paper presents a framework, called ACQUA, for 'acquisition-cost' aware continuous query processing. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the one or more smartphones, with a pull-based asynchronous model, where a smartphone retrieves appropriate blocks of relevant sensor data from individual sensors, as an integral part of the query evaluation process. We describe algorithms that dynamically optimize the sequence (for complex stream queries with conjunctive and disjunctive predicates) in which such sensor data streams are retrieved by the query evaluation component, based on a combination of a) the communication cost & selectivity properties of individual sensor streams, and b) the occurrence of the stream predicates in multiple concurrently executing queries. We also show how a transformation of a group of stream queries into a disjunctive normal form provides us with significantly greater degrees of freedom in choosing this sequence, in which individual sensor streams are retrieved and evaluated. While the algorithms can apply to a broad category of sensor-based applications, we specifically demon- strate their application to a scenario where multiple stream processing queries execute on a single smartphone, with the sensors transferring their data over an appropriate PAN technology, such as Bluetooth or IEEE 802.11. Extensive simulation experiments indicate that ACQUA's intelligent batch-oriented data acquisition process can result in as much as 80% reduction in the energy overhead of continuous query processing, without any loss in the fidelity of the processing logic.

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In this paper, we reduce the energy overheads of continuous mobile sensing for context-aware applications that are interested in collective context or events. We propose a cloudbased query management and optimization framework, called CloQue, which can support concurrent queries, executing over thousands of individual smartphones. CloQue exploits correlation across context of different users to reduce energy overheads via two key innovations: i) dynamically reordering the order of predicate processing to preferentially select predicates with not just lower sensing cost and higher selectivity, but that maximally reduce the uncertainty about other context predicates; and ii) intelligently propagating the query evaluation results to dynamically update the uncertainty of other correlated, but yetto-be evaluated, context predicates. An evaluation, using real cellphone traces from a real world dataset shows significant energy savings (between 30 to 50% compared with traditional short-circuit systems) with little loss in accuracy (5% at most).

Abstract-Many emerging context-aware mobile applications involve the execution of continuous queries over sensor data streams generated by a variety of on-board sensors on multiple personal mobile devices (aka smartphones). To reduce the energyoverheads of such large-scale, continuous mobile sensing and query processing, this paper introduces CQP, a collaborative query processing framework that exploits the overlap (in both the sensor sources and the query predicates) across multiple smartphones. The framework automatically identifies the shareable parts of multiple executing queries, and then reduces the overheads of repetitive execution and data transmissions, by having a set of 'leader' mobile nodes execute and disseminate these shareable partial results. To further reduce energy, CQP utilizes lower-energy short-range wireless links (such as Bluetooth) to disseminate such results directly among proximate smartphones. We describe algorithms to support our server-assisted distributed query sharing and optimization strategy. Simulation experiments indicate that this approach can result in 60% reduction in the energy overhead of continuous query processing; when 'leader' selection is dynamically rotated to equitably share the burden, we observe an increase of up to 65% in operational lifetime.

In this study, the first and second harvesting Pisang Awak in the field were sprayed with different dosage of paclobutrazol in order to analyze the mechanism of its induction of flowering. The results showed that there was no significant difference in the total number of newly extracted leaves between treatments. The first harvesting was 3.0 g/plant, and the second one was 2.0 g/plant. The pumping speed of leaves is the fastest and the accumulation of leaves number is the earliest. The 3.0 g/plant treatment of first harvesting Pisang Awak had the earliest bud extraction stage, and the bud extraction rate reached 62.5% at 170 days after treatment, about 40 days earlier than control group. Within the range of 1.0~5.0 g/plant, PP333 promoted carbohydrate synthesis in leaves of Pisang Awak, significantly reduced the accumulation of nitrogen, significantly increased the carbon-nitrogen ratio (C/N), and significantly reduced the contents of GA3 and IAA in the leaves. The results showed that the exogenous polyazole could accelerate the pumping speed of leaves, promote flowering and early bud extraction by regulating the distribution of carbon and nitrogen nutrients and the content of endogenous hormones, thus providing technical guidance for the management of early bud extraction culture of Pisang Awak.