Storage of sensed data in wireless sensor networks is essential when the sink node is unavailable due to failure and/or disconnections, but it can also provide efficient access to sensed data to multiple sink nodes. Recent approaches to data storage rely on Geographic Hash Tables for efficient data storage and retrieval. These approaches however do not support different QoS levels for different classes of data as the programmer has no control on the level of redundancy of data (and thus on data dependability). Moreover, they result in a great unbalance in the storage usage in each sensor, even when sensors are uniformly distributed. This may cause serious data losses, waste energy and shorten the overall lifetime of the sensornet. In this paper, we propose a novel protocol, Q-NiGHT, which (1) provides a direct control on the level of QoS in the data dependability, and (2) uses a strategy similar to the rejection method to build a hash function which scatters data approximately with the same distribution as sensors. The benefits of Q-NiGHT are assessed through a detailed simulation experiment, also discussed in the paper. Results show its good performance on different sensors distributions on terms of both protocol costs and load balance between sensors.

In-network storage of data in Wireless Sensor Networks (WSNs) is considered a promising alternative to external storage since it contributes to reduce the communication overhead inside the network. Recent approaches to data storage rely on Geographic Hash Tables (GHT) for efficient data storage and retrieval. These approaches, however, assume that sensors are uniformly distributed in the sensor field, which is seldom true in real applications. Also they do not allow to tune the redundancy level in the storage according to the importance of the data to be stored. To deal with these issues, we propose an approach based on two mechanisms. The first is aimed at estimating the real network distribution. The second exploits a data dispersal method based on the estimated network distribution. Experiments through simulation show that our approach approximates quite closely the real distribution of sensors and that our dispersal protocol sensibly reduces data losses due to unbalanced data load.

Summary. Dependable data storage in wireless sensor networks is becoming increasingly important, due to the lack of reliability of the individual sensors. Recently, data centric storage (DCS) has been proposed to manage in network sensed data. DCS reconsiders ideas and techniques successfully proposed in peer to peer systems within the framework of wireless sensor networks. In particular it assume that data are uniquely named and data storage and retrieval is achieved using names instead of sensor nodes addresses. In this paper, we discuss the limitations of previous approaches, and in particular of Geographic Hash Tables (GHT), and introduce DELiGHT, a protocol which provides fine QoS control by the user and ensures even data distribution, also in non uniform sensor networks. The merits of DELiGHT have been evaluated through simulation in uniform and Gaussian distributed sensor networks. The simulation results show that the protocol provides a better load balancing than the previous proposals and that the QoS is ensured without appreciable overhead.

Heterogeneous wireless sensor networks are made up of different kinds of nodes. Some nodes, the sensors, are used as an interface to the physical environment. Other nodes act instead as servers, providing various services to the sensors. In this paper we define an architecture to enable the sensors to efficiently localize the services, and hence the servers. Our is a two-tier server architecture. The first tier is made up of the actual servers. The second tier is formed by nodes that are basically standard nodes (like the sensors). These nodes know the current position of the servers (they are called server locators). Sensors needing service query the server locators to find the corresponding service. The service locator sends a service position to the sensor. Finally, once got ahold of a server location, a sensor uses the service directly. Our server architecture provides load balancing (of queries to the servers) and is tolerant to server faults. Sensor nodes are endowed with caches to maintain the location of popular services. Experiments demonstrate the effectiveness of using caches at the sensor nodes.

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