Wireless sensor networks are powerful, distributed, self-organizing systems used for event and environmental monitoring. In-network query processors like TinyDB offer a user friendly SQL-like application development. Due to the sensor nodes' resource limitations, monolithic approaches often support only a restricted number of operators. For this reason, complex processing is typically outsourced to the base station. Nevertheless, previous work has shown that complete or partial innetwork processing can be more efficient than the base station approach. In this paper, we introduce AnduIN, a system for developing, deploying, and running complex innetwork processing tasks. In particular, we present the query planning and execution strategies used in AnduIN, a system combining sensor-local in-network processing and a data stream engine. Query planning employs a multi-dimensional cost model taking energy consumption into account and decides autonomously which query parts will be processed within the sensor network and which parts will be processed at the central instance.
Abstract. Distributed hash tables are designed to provide reliable distributed data management, but present challenges for networks in which nodes have varying characteristics such as battery or computing power. Assuming that nodes are aware of their resource availability and relative network positions, this paper presents a novel distributed hash table protocol which uses nodes' resource levels to remove load from weak nodes, whose overuse may cause delays or failure, while using nodes' positions to reduce cross-network traffic, which may cause unwanted network load and delays. This protocol provides nodes with links that are physically near with high resource availability, and simultaneously provides scalability and an O(log(N )) routing complexity with N network nodes. Theoretical analysis and simulated evaluation show significant decreases in the routing and maintenance overhead for weak nodes, the physical distances that lookups traverse, and unwanted node failures, as well as an increase node lifetime.
Data is increasingly distributed across networks of mobile nodes such as wireless sensor networks, distributed smartphone applications, or ad hoc recovery networks in disaster scenarios, but must still be reliably collected, stored, and retrieved. While such networks run in either ad hoc mode or use existing infrastructure, all of them must deal with node heterogeneity. Wireless nodes invariably have differing levels of power availability, and often varying connectivity and computing power. While many distributed hash tables (DHTs) have been designed for mobile ad hoc or heterogeneous networks, they do not consider differences in node strength, or resource availability, for an arbitrary number of resource availability levels. In this paper, we present a scalable, location aware, hierarchical DHT that utilizes nodes' varying resource availability levels to increase and prolong the mobile network's data storage and retrieval capabilities. Furthermore, we compare this DHT to other location aware flat and hierarchical approaches, examining their structures' suitability for nodes with varying resource availability.
Large-scale distributed hash tables (DHT) are typically implemented without respect to node location or characteristics, thus producing physically long routes and squandering network resources. Some systems have integrated round trip times through proximity-aware identifier selection (PIS), proximity-aware route selection (PRS), and proximity-aware neighbor selection (PNS). While PRS and PNS tend to optimize existing systems, PIS deterministically selects node identifiers based on physical node location, leading to a loss of scalability and robustness. The trade off between the scalability and robustness gained from a DHT's randomness and the better allocation of network resources that comes with a location-aware, deterministically structured DHT make it difficult to design a system that is both robust and scalable and resource conserving. We present initial ideas for the construction of a small-world DHT which mitigates this trade off by retaining scalability and robustness while effectively integrating round trip times and additional node quality with the help of Vivaldi network coordinates.
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