A Straight Skeleton Based Connectivity Restoration Strategy in the Presence of Obstacles for WSNs

Wang, Xiaoding; Xu, Li; Zhou, Shuming

doi:10.3390/s17102299

Cited by 6 publications

(9 citation statements)

References 30 publications

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“…Wang et al [19] utilize the star topology and the center of mass for connectivity restoration. In [20], a straight skeleton based strategy is designed. Chen et al [21] consider the case that there are obstacles on the deployment area and propose an algorithm for obstacle avoidance.…”

Section: State-of-the-artmentioning

confidence: 99%

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Learning-Based IoT Data Aggregation for Disaster Scenarios

et al. 2020

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Industrial Internet of Everything (IIoE), as the deep integration of industry 6.0, the Internet of Things (IoT) and 6G mobile communication technology, pave the way for intelligent industry, enabling industrial optimization and automation. To ensure the high quality of services (QoS) in IIoE, tremendous real-time information generated by the pervasive smart things needs to be aggregated and processed quickly and reliably. However, a large-scale disaster could damage the entire communication network and cut off data aggregation such that Qos is compromised. In this paper, an Intelligent NIB based Data Aggregation Strategy, named (IDAS), is proposed for after disaster scenarios in IIoE. Specifically, IDAS first applies both iterative cubature kalman filter and radial basis function neural network to predict the data collection rates of survived infrastructures. Then, an energy efficient task distribution mechanism is design. Next, a deep reinforcement learning method is developed for the car-carrying NIB route design to perform corresponding task. Eventually, all data are aggregated toward the rescue headquarter by NIB deployment based on Fermat tree constructions. The theoretical analysis and simulations indicate that IDAS is not only energy efficient for after disaster scenarios but requires the least NIB consumption while compared with contemporary strategies.

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Section: State-of-the-artmentioning

confidence: 99%

“…IDAS is first compared with DQNMDC [15], LEEF [2], DRLDC [13], and CISIL [1] in aggregation ratio and energy cost. And the SNIB consumption comparison is conducted between IDAS and baselines OACRQST [21], CRPA [23] and OASS [20]. Fig.…”

Section: Performance Evaluation a Simulation Setupmentioning

confidence: 99%

Learning-Based IoT Data Aggregation for Disaster Scenarios

et al. 2020

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“…Senturk and Akkaya [18] presents an connectivity restoration algorithm in disjoint MSNs, which considers the realistic terrains when avoiding obstacles. Wang et al [23] presents a novel connectivity restoration strategy -proposed-Obstacle-Avoid connectivity restoration strategy, based on Straight Skeletons (OASS), which employs both the polygon based representative selection with the presence of obstacles and the straight skeleton based SMT establishment.…”

Section: Related Workmentioning

confidence: 99%

Obstacle-Avoiding Connectivity Restoration Based on Quadrilateral Steiner Tree in Disjoint Wireless Sensor Networks

Chen

2019

IEEE Access

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In the harsh environment, wireless sensor networks can suffer from a significant damage that causes many nodes/links to fail simultaneously and the network to get splitted into multiple disjoint partitions. In such case, linking the separated partitions by placing the least number of relay nodes and obstacle-avoiding to re-establish a strongly connected network topology is necessary to maintain the functional operations of wireless sensor network. However, the problem of finding the minimum count and the position of relay nodes is NP-hard hence heuristics methods are preferred. In this paper, we present a novel Obstacle-Avoiding Connectivity Restoration based on Quadrilateral Steiner Tree (OACRQST) algorithm to address this problem. First, the appropriate quadrilaterals are selected to connect the separated partitions and the Steiner nodes of these quadrilaterals are found. Then the disjoint islands are connected with the triangle Steiner tree or minimum spanning tree method, which are not connected by these selected quadrilaterals. Finally, relay nodes are placed to the appropriate position according to the edges of Steiner tree to restore network connectivity. However, if the found position is in the area of the obstacle, the relay node will be placed at the position of the bordering that nearest to the found position. If the network still is not connected, the relay nodes will be placed at the appropriate position according to the bordering of the obstacle starting from the node placed at the position of the bordering nearest to the found position exploring the left rule. Extensive simulation experiments demonstrate the beneficial aspects of the resulting topology with respect to number of relaying nodes, degree of connectivity and fault resilience.INDEX TERMS Wireless sensor network, connectivity recovery, quadrilateral Steiner tree.

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“…The RNs are then placed towards the CoM to accomplish the connectivity of the entire network. The OAAS (obstacle avoidance connectivity restoration strategy based on Straight Skeleton) connectivity restoration technique, while considering the existence of obstacles for WSNs, works in three phases [9]. During the first phase, the modified minimum spanning tree (MST) construction algorithm, namely, Ad-Prim, is applied for the selection of representatives in order to establish the intercomponent connection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Calculate ( reng ( )) (5) if ( reng ( )) < threshold then (6) //Send energy alert message to neighboring nodes (Nbr j ) (7) energy alert (Nbr j ) (8) For each neighboring node Nbr j of n i (9) //Neighboring node will increase its transmission range (10) recovery offered (Nbr j ) (11) end if (12) if reng ( ) < threshold then (13) Send energy defficiency message to slave keeper (14) Send activity time message to master keeper (15) Send activity granted message from master keeper have sufficient energy, then the total number of iterations (best score) required to execute the algorithm is (n × E). If the number of slave keepers is s, then the total executions required will be {(n × E) × s}.…”

Section: Complexitymentioning

confidence: 99%

Intelligent On‐Demand Connectivity Restoration for Wireless Sensor Networks

Mahmood

Khan

Hassan

et al. 2018

Wireless Communications and Mobile Computing

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Wireless sensor networks are envisioned to play a very important role in the Internet of Things in near future and therefore the challenges associated with wireless sensor networks have attracted researchers from all around the globe. A common issue which is well studied is how to restore network connectivity in case of failure of single or multiple nodes. Energy being a scarce resource in sensor networks drives all the proposed solutions to connectivity restoration to be energy efficient. In this paper we introduce an intelligent on-demand connectivity restoration technique for wireless sensor networks to address the connectivity restoration problem, where nodes utilize their transmission range to ensure the connectivity and the replacement of failed nodes with their redundant nodes. The proposed technique helps us to keep track of system topology and can respond to node failures effectively. Thus our system can better handle the issue of node failure by introducing less overhead on sensor node, more efficient energy utilization, better coverage, and connectivity without moving the sensor nodes.

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A Straight Skeleton Based Connectivity Restoration Strategy in the Presence of Obstacles for WSNs

Cited by 6 publications

References 30 publications

Learning-Based IoT Data Aggregation for Disaster Scenarios

Learning-Based IoT Data Aggregation for Disaster Scenarios

Obstacle-Avoiding Connectivity Restoration Based on Quadrilateral Steiner Tree in Disjoint Wireless Sensor Networks

Intelligent On‐Demand Connectivity Restoration for Wireless Sensor Networks

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