Location Aware Peak Value Queries in sensor networks

Abstract

In the applications of wireless sensor networks, the peak values, such as largest sensed values and their locations, are very useful for detecting abnormal events happened in the monitored region. Although the results returned by the traditional top-queries provide largest sensed values, they ignore the spatial-correlation of the sensed data so that the locations of the returned values are very close to each other and only tell a small area being abnormal or few number of abnormal events happening. Due to this reason, the Location Aware Peak Value Query, denoted by LAP-( , ) query, is proposed in this paper. For any given and , the LAP-( , ) query returns largest sensed values and their locations, and the distance between the any two locations is larger than . The problem of processing LAP-( , ) query is proved to be NP-hard, and two distributed approximation algorithms are proposed to solve this problem. One is a distributed greedy algorithm with ratio bound 5.8. The other one is a region partition based algorithm with ratio bound 3. The theoretical analysis and experimental results show that the proposed algorithms have high performance in terms of accuracy and energy consumption.

PCTopk: Privacy-and Correctness-Preserving Functional Top-k Query on Un-trusted Data Storage in Two-Tiered Sensor Networks

“…The hashed data point, as the example demonstrated in Fig. 3, 3 , and d i,4 are the given leaf nodes, we have evidence h 4 , as the root node…”

“…3, the leaf nodes (blue nodes) are data points, the inner nodes (gray nodes) are signified as hashed data points, and the root node (red node) denotes the verifiable information, HM AC. The data characterizes the order relation in each layer; that is, the 3 , and d i,4 in layer 1 is with the descending order from d i,4 to d i,1 , and moreover, the second dimension of d i,1 , d i,2 , d i, 3 , and d i,4 is with the ascending order from d i,4 to d i,1 . Similarly, the order relations are still reserved in other layers.…”

“…Recently, many researchers [13] [5] [15] [3] have explored various top-k query schemes in WSNs. Among all those schemes, they strived to propose energy-efficient processing approaches for top-k query such that the list of k highest (or lowest) sensor readings are retrieved.…”

Critical data points retrieving method for big sensory data in wireless sensor networks

“…The authors proposed to use samples of past sensor readings, which can reduce energy significantly. The work in [18] The relationship between the errors of δ-approximate critical points and t values, the extremum and inflection points are also critical points.…”

“…Under such assumptions, a great number of query processing techniques on discrete sensory data have been proposed, including the curve query processing algorithms [13], aggregation query processing algorithms [14][15][16][17], top-k query processing algorithms [18][19][20], skyline [21,22], and quantilen [23,24] query processing algorithms. Although all the above algorithms are efficient for processing the discrete sensory data, they cannot meet the complicated query requirements given by users and do not support to retrieve the critical data points in current WSNs since only discrete data were considered.…”