Eventual consistency is a more natural model than strong consistency for a distributed system, since it is closer to the underlying physical reality. Therefore, we propose that it is important to find a programming model that is both congenial to developers and supports eventual consistency. In particular, we consider that a crucial test for such a model is that it should support edge computation in a both natural and secure way. We present a preliminary work report with an initial solution, called Lasp, which resembles a concurrent functional language while naturally supporting an eventually consistent coordination-free distribution model.
Declarative approaches such as Tiny AGgregation, @cite_4 have been proposed for data collection and aggregation across sensor networks. However, these approaches have presented abstractions that are not for general programming as they are specific to the details of aggregation in sensor networks. Finally, declarative approaches @cite_10 have also been proposed for computation in large-scale peer-to-peer systems where clients own their own data. However, in an effort to make their language Turing complete, they relied on the programmer explicitly encoding the details around how statements would be evaluated, to ensure termination.
@cite_10 @cite_4
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The widespread dissemination of small-scale sensor nodes has sparked interest in a powerful new database abstraction for sensor networks: Clients “program” the sensors through queries in a high-level declarative language permitting the system to perform the low-level optimizations necessary for energy-efficient query processing. In this paper we consider multi-query optimization for aggregate queries on sensor networks. We develop a set of distributed algorithms for processing multiple queries that incur minimum communication while observing the computational limitations of the sensor nodes. Our algorithms support incremental changes to the set of active queries and allow for local repairs to routes in response to node failures. A thorough experimental analysis shows that our approach results in significant energy savings, compared to previous work. Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets. Declarative queries are proving to be an attractive paradigm for ineracting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques. In-network data aggregation is essential for wireless sensor networks where energy resources are limited. In a previously proposed data dissemination scheme (directed diffusion with opportunistic aggregation), data is opportunistically aggregated at intermediate nodes on a low-latency tree. In this paper, we explore and evaluate greedy aggregation, a novel approach that adjusts aggregation points to increase the amount of path sharing, reducing energy consumption. Our preliminary results suggest that, under investigated scenarios, greedy aggregation can achieve up to 45 energy savings over opportunistic aggregation in high-density networks without adversely impacting latency or robustness.