Localization of Wireless Sensor Network Based on Genetic Algorithm

This paper proposes a novel localization approach based on genetic algorithm for Wireless Sensor Networks. In this method, we use a new way to approximate the distance between unknown node and anchor node when anchor node is out of an unknown node’s communicate radius. In addition, we use self-adapting genetic algorithm into localization to ensure it could produce the result as similar as its real position in any environment. Our simulate experiment on various network topologies shows surprisingly good results. These demonstrate that the approach could help unknown nodes obtain high accuracy position whether in open space or the environment with obstruction even the unconnected well environment. In comparison, we find that previous anchor node free localization approach cannot work well in the unidealization environment

Accurate range-free localization for anisotropic wireless sensor networks

Journal ArticlePosition information plays a pivotal role in wireless sensor network (WSN) applications and protocol/ algorithm design. In recent years, range-free localization algorithms have drawn much research attention due to their low cost and applicability to large-scale WSNs. However, the application of range-free localization algorithms is restricted because of their dramatic accuracy degradation in practical anisotropic WSNs, which is mainly caused by large error of distance estimation. Distance estimation in the existing range-free algorithms usually relies on a unified per hop length (PHL) metric between nodes. But the PHL between different nodes might be greatly different in anisotropic WSNs, resulting in large error in distance estimation. We find that, although the PHL between different nodes might be greatly different, it exhibits significant locality; that is, nearby nodes share a similar PHL to anchors that know their positions in advance. Based on the locality of the PHL, a novel distance estimation approach is proposed in this article. Theoretical analyses show that the error of distance estimation in the proposed approach is only one-fourth of that in the state-of-the-art pattern-driven scheme (PDS). An anchor selection algorithm is also devised to further improve localization accuracy by mitigating the negative effects from the anchors that are poorly distributed in geometry. By combining the locality-based distance estimation and the anchor selection, a range-free localization algorithm named Selective Multilateration (SM) is proposed. Simulation results demonstrate that SM achieves localization accuracy higher than 0.3r, where r is the communication radius of nodes. Compared to the state-of-the-art solution, SM improves the distance estimation accuracy by up to 57% and improves localization accuracy by up to 52% consequently.This work is partially supported by the National Science Foundation of China (61103203, 61173169, 61332004, and 61420106009), the Hong Kong RGC General Research Fund (PolyU 5106/11E), the International Science & Technology Cooperation Program of China (2013DFB10070), and the EU FP7 QUICK project (PIRSES-GA-2013-612652)

무선 센서 네트워크에서 노드 연결 밀도 완화를 통한 에너지 효율적인 위치 추정 알고리즘

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 김성철.로봇이나 센서들이 소형화되고, 센싱 기술과 통신 기술이 발전하면서 low-cost, low-power의 특징을 가진 센서들이 많이 개발되었다. 최근에 이러한 센서 노드들의 다양한 용도에 대해 연구가 활발해지고 있다. 병원에서 환자`나 의사들의 이동을 보거나, 숲에서 일어난 화재의 위치를 파악하거나 군용 로봇의 위치를 파악하는 등 센서 노드에 있어서 어떤 목적으로 쓰이더라도 위치를 추정하는 것은 반드시 포함되어야 하는 기술이 되었다. 본 논문에서는 무선 센서 네트워크 환경에서 센서 노드를 추정함에 있어서 에너지 효율을 높이기 위한 두 가지 알고리즘을 제시한다. 다차원 척도법은 데이터를 종류에 따라 수치화하였을 때, 비슷한 유형의 데이터들을 상대적인 거리로 표현함으로써 데이터들의 성격을 시각화하는 기법이다. 여기서 데이터를 수치화하는 방법으로 노드 사이의 거리를 대입하면 노드 간의 상대적인 거리를 통해 추정 위치를 시각화할 수 있다. 노드의 밀도가 높을 때, 다차원 척도법을 그대로 사용할 경우, 연산량 증가로 인해 측위가 느려지고, CPU 사용도 많아지게 된다. 본 논문에서는 분산 측위 환경에서 이웃 노드(커버리지 안에 1 홉으로 통신이 가능한 노드)의 개수에 따라 송신 전력을 조절하는 알고리즘을 제안한다. 중심 노드만을 고려하여 이웃 노드의 개수에 따라 송신 전력을 이산적으로 조절하는 방법과 가장 바깥쪽 이웃 노드의 Connectivity를 고려하여 전력을 조절하는 방법을 제시한다. 전체 노드 개수에 따른 정확도와 측위에 사용되는 에너지를 시뮬레이션을 통해 비교 및 검증한다.제 1 장 서 론 1 제 2 장 배경이론 및 문제의 정의 4 제 1 절 Received Signal Strength Indicator (RSSI) 4 제 2 절 멀티홉 통신(Multihop Communication) 5 제 3 절 중앙처리 측위과 분산처리 측위 6 제 3 장 위치 추정 알고리즘 9 제 1 절 삼변 측량법과 최소 제곱법 9 제 2 절 Classical Multidimensional Scaling (CMDS) 12 제 3 절 Distributed-Weighted Multidimensional Scaling(DW-MDS) 15 제 4 장 Energy-Efficient Multidimensional Scaling 19 제 1 절 Discretely Power-Controlled Multidimensional Scaling (DPC-MDS) 19 제 2 절 Edge node Removed Multidimensional Scaling (ER-MDS) 22 제 5 장 시뮬레이션 결과 25 제 1 절 거리 오차를 통한 알고리즘 성능 분석 27 제 2 절 DW-MDS와 제안한 알고리즘의 에너지 소모 비교 31 제 3 절 기준 이웃 노드 개수에 따른 알고리즘 성능 변화 35 제 4 절 제안한 알고리즘과 DW-MDS의 복잡도 비교 39 제 6 장 결론 41Maste

Approximate convex decomposition based localization in wireless sensor networks

Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. An important research direction for localization is to develop schemes using connectivity information only. These schemes primary apply hop counts to distance estimation. Not surprisingly, they work well only when the network topology has a convex shape. In this paper, we develop a new Localization protocol based on Approximate Convex Decomposition (ACDL). It can calculate the node virtual locations for a large-scale sensor network with arbitrary shapes. The basic idea is to decompose the network into convex subregions. It is not straight-forward, however. We first examine the influential factors on the localization accuracy when the network is concave such as the sharpness of concave angle and the depth of the concave valley. We show that after decomposition, the depth of the concave valley becomes irrelevant. We thus define concavity according to the angle at a concave point, which can reflect the localization error. We then propose ACDL protocol for network localization. It consists of four main steps. First, convex and concave nodes are recognized and network boundaries are segmented. As the sensor network is discrete, we show that it is acceptable to approximately identify the concave nodes to control the localization error. Second, an approximate convex decomposition is conducted. Our convex decomposition requires only local information and we show that it has low message overhead. Third, for each convex subsection of the network, an improved Multi-Dimensional Scaling (MDS) algorithm is proposed to compute a relative location map. Fourth, a fast and low complexity merging algorithm is developed to construct the global location map. Our simulation on several representative networks demonstrated that ACDL has localization error that is 60%-90% smaller as compared with the typical MDS-MAP algorithm and 20%-30% - maller as compared to a recent state-of-the-art localization algorithm CATL.Department of ComputingRefereed conference pape