A Pre-Processed Cross Link Detection Protocol for geographic routing in mobile ad hoc and sensor networks under realistic environments with obstacles

Sun, Yan; Jiang, Qiangfeng; Singhal, Mukesh

doi:10.1016/j.jpdc.2010.12.013

Cited by 6 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, none of the above works systematically combines obstacle detection and routing design to improve the performance of CRAHNs. In [34], Sun et al proposed a pre-processed cross link detection protocol, for geographic routing in mobile ad hoc network, which extracts an almost planar subgraph from a realistic network graph, instead of a unit disk graph, for face routing and makes the greedy-face-greedy geographic routing work correctly in realistic environments with obstacles. Wu et al [35] presented a voronoi-trajectory based hybrid routing for ad hoc networks from an obstacle perspective.…”

Section: Obstacle Aware Data Transmission In Wireless Networkmentioning

confidence: 99%

See 1 more Smart Citation

OODT: Obstacle Aware Opportunistic Data Transmission for Cognitive Radio Ad Hoc Networks

Zhong

Yuan

et al. 2020

IEEE Trans. Commun.

View full text Add to dashboard Cite

In recent years, a large number of smart devices will be connected in Internet of Things (IoT) using an ad hoc network, which needs more frequency spectra. The cognitive radio (CR) technology can improve spectrum utilization in an opportunistic communication manner for IoT, forming a promising paradigm known as cognitive radio ad hoc networks, CRAHNs. However, dynamic spectrum availability and mobile devices/persons make it difficult to develop an efficient data transmission scheme for CRAHNs under an obstacle environment. Opportunistic routing can leverage the broadcast nature of wireless channels to enhance network performance. Inspired by this, in this paper, we propose an Obstacle aware Opportunistic Data Transmission scheme (OODT) in CRAHNs from a computational geometry perspective, considering energy efficiency and social features. In the proposed scheme, we exploit a new routing metric, which is based on an obstacle avoiding algorithm using a polygon boundary 1-searcher technology, and an auction model for selecting forwarding candidates. In addition, we prove that the candidate selection problem is NP-hard and propose a heuristic algorithm for candidate selection. The simulation results show that the proposed scheme can achieve better performance than existing schemes.

show abstract

Section: Obstacle Aware Data Transmission In Wireless Networkmentioning

confidence: 99%

“…How can we guarantee data transmission efficiency for opportunistic transmission scheme with considering the existence of obstacles in CRAHNs? In fact, there have been several obstacle-aware routing protocols proposed recently [34][35][36][37]. However, they only addressed traditional routing design with obstacles.…”

Section: Introductionmentioning

confidence: 99%

OODT: Obstacle Aware Opportunistic Data Transmission for Cognitive Radio Ad Hoc Networks

Zhong

Yuan

et al. 2020

IEEE Trans. Commun.

View full text Add to dashboard Cite

show abstract

“…Wireless sensor networks are widely used for data acquisition since wireless is low cost and more flexible than wired solutions [2]. Extensive research has been performed on efficiency [3][4] and mobility [5][6][7]. Since it is impossible for the system designer to envision all possible contexts beforehand, decision making control systems largely rely on data mining and machine learning techniques such as an artificial neural network (ANN), a support vector machine (SVM), a selforganizing map (SOM), a hidden Markov model (HMM), and frequent pattern mining (FPM) [8].…”

Section: Introductionmentioning

confidence: 99%

Sequential Behavior Pattern Discovery with Frequent Episode Mining and Wireless Sensor Network

Lü

et al. 2017

IEEE Commun. Mag.

View full text Add to dashboard Cite

By recognizing patterns in occupants' daily activities, building systems are able to optimize and personalize services. Established technologies are available for data collection and pattern mining, but they all share the drawback that the methodology used for data collection tends to be ill suited for pattern recognition. For this research, we developed a bespoke wireless sensor network (WSN) and combined it with a compact data format for frequent episode mining (FEM) to overcome this obstacle. The proposed framework has been evaluated with both synthetic data from a smart home simulator and with real data from a self-organizing WSN in a student's home. We are able to demonstrate that the framework is capable of discovering sequential patterns in heterogeneous sensor data. With corresponding scenarios, patterns in daily activities can be deduced. The framework is self-contained, scalable and energy efficient and is thus applicable in multiple building system settings.

show abstract

“…However it is difficult to obtain the static connected plain graph in wireless network [4]. The Geographic Perimeter Stateless Routing(GPSR) [5], [6], [7], [8], [9], [17], [18], [19], [20] makes extensive use of geography to achieve scalability in wireless routing protocols. In the absence of location errors, geographic routing that uses a combination of greedy forwarding and face routing has been found to be really efficient.…”

Section: Introductionmentioning

confidence: 99%

Improvised tree selection algorithm in Greedy Distributed Spanning Tree Routing

Das

Misra

2014

2014 Recent Advances in Engineering and Computational Sciences (RAECS)

View full text Add to dashboard Cite

The Geographic Perimeter Stateless Routing (GPSR)use location information to forward packets greedily. Nodes need to keep only this local information, hence called as stateless. When Greedy Forwarding is fails, the algorithm recovers by switching to face routing, which is based on the right-hand rule in planarized node graph, to route around the void. The Gabriel Graph(GG) and the Relative Neighbourhood Graph (RNG) are the two graphs used for planarization but may lead to graph partitioning. Alternatively, instead of switching to face routing and planarizition , the existing Greedy Distributed Spanning Tree Routing(GDSTR) scheme enables nodes to continue routing by traversing on a spanning tree until it reaches a point where greedy forwarding can take place. GDSTR sometimes incorporates two spanning trees although it uses only one. So, in this work, we proposed new tree selection algorithm in GDSTR which improves the performance in terms of path and hop stretch with the competitive schemes.

show abstract

A Pre-Processed Cross Link Detection Protocol for geographic routing in mobile ad hoc and sensor networks under realistic environments with obstacles

Cited by 6 publications

References 25 publications

OODT: Obstacle Aware Opportunistic Data Transmission for Cognitive Radio Ad Hoc Networks

OODT: Obstacle Aware Opportunistic Data Transmission for Cognitive Radio Ad Hoc Networks

Sequential Behavior Pattern Discovery with Frequent Episode Mining and Wireless Sensor Network

Improvised tree selection algorithm in Greedy Distributed Spanning Tree Routing

Contact Info

Product

Resources

About