Achieving optimal data storage position in wireless sensor networks

Abstract

a b s t r a c tData storage in wireless sensor networks (WSNs) involves producers (such as sensor nodes) storing in storage positions a large amount of data which they have collected and consumers (e.g., base stations, users, and sensor nodes) then retrieving that data. When addressing this issue, previous work failed to utilize data rates and locations of multiple producers and consumers to determine optimal data storage positions to be communication cost-effective in a mesh network topology. In this paper, we first formalize the data storage problem into a one-to-one (one producer and one consumer) model and a many-tomany (m producers and n consumers) model with the goal of minimizing the total energy cost. Based on above models, we propose optimal data storage (ODS) algorithms that can produce global optimal data storage position in linear, grid, and mesh network topologies. To reduce the computation of ODS in the mesh network topology, we present a near-optimal data storage (NDS) algorithm, which is an approximation algorithm and can obtain a local optimal position. Both ODS and NDS are locality-aware and are able to adjust the storage position adaptively to minimize energy consumption. Simulation results show that NDS not only provides substantial cost benefit over centralized data storage (CDS) and geographic hash table (GHT), but performs as well as ODS in over 75% cases.

A Virtual-Ring-Based Data Storage and Retrieval Scheme in Wireless Sensor Networks

“…Yu et al [3] proposed an adaptive data storage and retrieval scheme, where the frequencies of event and query were considered. However, the scheme only chooses one node as the storage node, thus the hotspot problem still exists.…”

“…The first one is based on the transmission trail [4][5][6][7], the second one is based on the deterministic storage node/nodes [3,[8][9][10][11][12][13], and the last one is the combination of the former two approaches [14][15][16].…”

“…On the contrary, the data replicas of event should be stored near the consumers. Recently, Yu et al [3] has proposed an in-network storage and retrieval scheme, in which the frequencies of event and query are taken into consideration when selecting the optimal storage node. However, there is only one storage node in the scheme, which leads unbalanced load distribution and can shrink the lifetime of the sensor networks.…”

“…Secondly, we propose a novel in-network data storage and retrieval scheme based on the virtual ring model for wireless sensor networks, where the frequencies of event and query are considered. Thirdly, we have done a lot of experiments to evaluate the performance of our scheme and compare it with the scheme presented in [3], which also considers the event and query frequencies. Simulation results show that VRS outperforms the existing work in load balance, delay of data storage and retrieval, and the lifetime of the sensor networks.…”

“…Recently, a lot of in-network data storage and retrieval schemes [3][4][5][6][7][8][9][10][11][12][13] have been proposed. In most of the existing schemes, the frequencies of event and query are less considered, while in fact they play an important role on the efficiency of the schemes.…”