Optimized repair of a partitioned network topology

Lalouani, Wassila; Younis, Mohamed; Badache, Nadjib

doi:10.1016/j.comnet.2017.02.003

Cited by 17 publications

(11 citation statements)

References 38 publications

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“…In the last phase, minimum numbers of RNs are deployed with the help of Straight Skeleton Based SMT (Steiner Minimal Tree) Construction Algorithm (SSIC). A novel technique, namely, BIND (Boundary-aware optimized Interconnection of Disjoint segments) proposed in [10], overcomes the shortcomings of SMT (Steiner Minimum Tree) based solution to find the least number of relay nodes to restore the connectivity of a partitioned network. The Connectivity Restoration with Assured Fault Tolerance (CRAFT) algorithm proposed in [11] attempts to form a Backbone Polygon (BP) around the center of the damaged network area where no partition lies inside.…”

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%

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

Intelligent On‐Demand Connectivity Restoration for Wireless Sensor Networks

Mahmood

Khan

Hassan

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…The failures can also create a network partition where parts of the deployed network get entirely disconnected from the base station (sink) node. Thus network connectivity to avoid such partitioning can be assured by integrating node deployment mechanisms along with recovery mechanisms (Stojmenovic et al, 2011;Mahmood et al, 2018;Lee et al, 2015;Jha et al, 2019;Senturk et al, 2014;Ranga et al, 2016b;Lalouani et al, 2017;Bayrakdar, 2020d).…”

Section: Introductionmentioning

confidence: 99%

“…It is the most popular and efficient mechanism where existing and additional nodes are deployed to re-establish connection among nodes (Jha et al, 2019;Joshi & Younis, 2016;Ranga et al, 2016b;Chouikhi et al, 2017). Finding an optimal relay count required to restore links is an optimization problem and requires complete information about the number of partitions and locations of nodes in them (Younis et al, 2014;Shriwastav & Ghose, 2018;Senturk et al, 2014;Chouikhi et al, 2017;Alfadhly et al, 2011;Cheng et al, 2017;Nikolov & Haas, 2016;Devi & Manickam, 2014;Zahid et al, 2018;Liu et al, 2018;Lee et al, 2016;Kumar & Amgoth, 2019;Lalouani et al, 2017;Ranga et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

“…In (Lalouani et al, 2017) Boundary-aware optimized Interconnection of Disjoint segments (BIND) algorithm uses the shorter length topology in the two-dimensional plane to restore the network. The subset of nodes is selected from the boundaries of disjoint segments and then interconnected using the additional Steiner pairs to form interconnected topology between the segments.…”

Section: Introductionmentioning

confidence: 99%

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Distributed partition detection and recovery using UAV in wireless sensor and actor networks

Zear¹,

Ranga

2021

KJS publishes peer-review articles in Mathematics, Computer Sci

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Wireless Sensor and Actor Networks (WSANs) have been extensively employed in various domains ranging from elementary data collection to real-time control and monitoring for critical applications. Network connectivity is a vital robustness measure for overall network performance. Different network functions such as routing, scheduling, and QoS provisioning depends on network connectivity. The failure of articulation points in the network disassociates the network into disjoint segments. We proposed Distributed Partition Detection and Recovery using Unmanned Aerial Vehicle (UAV) (DPDRU) algorithm, as an optimal solution to recover the partitioned network. It consists of three steps: Initialization, Operational and Detection, and Recovery. In the Initialization phase sink node collects all the information about the network. In the Operational and Detection phase, network nodes communicate regularly by exchanging HEARTBEATS, detects failure if some nodes do not get a message from the neighbor node and send failure reports, and sink node identifies network partition. In the recovery phase, the sink node sends UAV at the positional coordinates of the failed node and examines network recovery after UAV reaches the desired location. Our approach primarily focuses on reducing message overhead by sending few update messages to sink node and energy consumption by engaging network nodes only for communication. The requirements of the recovery process (physical movement and communication) are fulfilled by UAV. The algorithm is tested according to the following parameters: Detection Time, Recovery Time, message overhead, and distance traveled by UAV. Simulation results validate the efficacy of the proposed algorithm based on these parameters to provide reliable results. The minimum and the maximum number of messages transmitted are 11 for 10 nodes and 24 for 100 nodes respectively. Hence these results demonstrate that the message overhead in our proposed solution is less as compared to other techniques when the number of nodes increases.

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Network partition detection and recovery with the integration of unmanned aerial vehicle

Zear,

Ranga,

Gola

2024

Concurrency and Computation

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SummaryWireless sensor and actor networks (WSANs) consist of nodes associated in an ad hoc manner to perform sensing tasks for information gathering and acting functions on the basis of gathered information. Connectivity is an essential requirement of large‐scale wireless networks, and WSANs are supposed to stay connected. The nodes in hostile environments are prone to failures such as battery depletion, physical damage, or hardware malfunction. The failure of some nodes, like cut vertex nodes, can partition the network into multiple network segments. Most of the solutions for network partition recovery proposed in the literature depend on the assumption that the network is obstacle‐free. However, an obstacle‐free environment is not possible in real‐life situations. In the last few decades, UAVs or drones have been engaged in various applications such as industrial inspections, remote sensing, agriculture, military, disaster relief, and so forth, UAVs can be employed to strengthen the connections in wireless networks by coordinating with ground nodes since they can render services in rough areas where ground nodes cannot provide services. Thus, our research is based on using UAVs as relay nodes to reconnect the disjoint partitions. This paper proposes two algorithms: Drone assisted partition recovery algorithm (DAPRA) and drone assisted detection and partition recovery algorithm (DADPRA). In both algorithms, partitions are detected by the sink node. In DAPRA sink node determines the failed cut‐vertex node and sends UAV to the location of the failed cut‐vertex node. In DADPRA algorithm, UAV identifies the failed cut‐vertex node and reconnects the disjoint network segments. DAPRA and DADPRA are analyzed according to the state‐of‐the‐art parameters, that is, recovery and detection time, UAV's travel distance, and the total messages transmitted. The proposed algorithms are compared with similar Distributed Partition Detection and Recovery using UAV (DPDRU) approach. The simulation results show the proposed algorithms detect network partitioning in less time as compared to DPDRU approach.

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Optimized repair of a partitioned network topology

Cited by 17 publications

References 38 publications

Intelligent On‐Demand Connectivity Restoration for Wireless Sensor Networks

Intelligent On‐Demand Connectivity Restoration for Wireless Sensor Networks

Distributed partition detection and recovery using UAV in wireless sensor and actor networks

Network partition detection and recovery with the integration of unmanned aerial vehicle

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