### Abstract

Original language | English |
---|---|

Article number | 8633895 |

Pages (from-to) | 21857-21869 |

Number of pages | 13 |

Journal | IEEE Access |

Volume | 7 |

DOIs | |

Publication status | Published - 4 Feb 2019 |

MoE publication type | Not Eligible |

### Fingerprint

### Keywords

- Approximate range emptiness
- data structure
- information-centric network
- Internet of Things
- space lower bound

### Cite this

*IEEE Access*,

*7*, 21857-21869. [8633895]. https://doi.org/10.1109/ACCESS.2019.2897154

}

*IEEE Access*, vol. 7, 8633895, pp. 21857-21869. https://doi.org/10.1109/ACCESS.2019.2897154

**Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things.** / Wang, Xiujun; Liu, Zhi; Gao, Yan; Zheng, Xiao; Chen, Xianfu; Wu, Celimuge.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things

AU - Wang, Xiujun

AU - Liu, Zhi

AU - Gao, Yan

AU - Zheng, Xiao

AU - Chen, Xianfu

AU - Wu, Celimuge

PY - 2019/2/4

Y1 - 2019/2/4

N2 - The approximate range emptiness problem requires a memory-efficient data structure D to approximately represent a set S of n distinct elements chosen from a large universe U= {0,1,⋯,N-1} and answer an emptiness query of the form “S∩[a;b]=0?” for an interval [a;b] of length L (a,b∈U), with a false positive rate ε. The designed D for this problem can be kept in high-speed memory and quickly determine approximately whether a query interval is empty or not. Thus, it is crucial for facilitating online query processing in the information-centric Internet of Things applications, where the IoT data are continuously generated from a large number of resource-constrained sensors or readers and then are processed in networks. However, the existing works on the approximate range emptiness problem only consider the simple case when the set S is static, rendering them unsuitable for the continuously generated IoT data. In this paper, we study the approximate range emptiness problem over sliding windows in the IoT Data streams, denoted by ε-ARESD-problem, where both insertion and deletion are allowed. We first prove that, given a sliding window size n and an interval length L, the lower bound of memory bits needed in any data structure for ε-ARESD-problem is n log 2 (nL/ε)+Θ(n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. The extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.

AB - The approximate range emptiness problem requires a memory-efficient data structure D to approximately represent a set S of n distinct elements chosen from a large universe U= {0,1,⋯,N-1} and answer an emptiness query of the form “S∩[a;b]=0?” for an interval [a;b] of length L (a,b∈U), with a false positive rate ε. The designed D for this problem can be kept in high-speed memory and quickly determine approximately whether a query interval is empty or not. Thus, it is crucial for facilitating online query processing in the information-centric Internet of Things applications, where the IoT data are continuously generated from a large number of resource-constrained sensors or readers and then are processed in networks. However, the existing works on the approximate range emptiness problem only consider the simple case when the set S is static, rendering them unsuitable for the continuously generated IoT data. In this paper, we study the approximate range emptiness problem over sliding windows in the IoT Data streams, denoted by ε-ARESD-problem, where both insertion and deletion are allowed. We first prove that, given a sliding window size n and an interval length L, the lower bound of memory bits needed in any data structure for ε-ARESD-problem is n log 2 (nL/ε)+Θ(n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. The extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.

KW - Approximate range emptiness

KW - data structure

KW - information-centric network

KW - Internet of Things

KW - space lower bound

UR - http://www.scopus.com/inward/record.url?scp=85062823786&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2019.2897154

DO - 10.1109/ACCESS.2019.2897154

M3 - Article

VL - 7

SP - 21857

EP - 21869

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 8633895

ER -