scholarly journals An Efficient Network Discovery Storage based Resilient Packet-Forwarding Scheme for the Mitigation of Black Hole and Wormhole Attacks in 6lowpan Sensor Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 1543-1547
Keyword(s):  
Black Hole ◽  
Sensor Networks ◽  
Network Formation ◽  
Dynamic Network ◽  
Sensor Nodes ◽  
Packet Forwarding ◽  
Wireless Devices ◽  
Network Layer ◽  
Network Discovery ◽  
Dynamic Network Formation

In wireless networks 6LOWPAN with the low power wireless devices has limited processing capabilities. In this network, the malicious node attacks at the network layer due to its nature of self configuration and dynamic network formation. It increased number of packet dropping attacks in network layer like Black Hole attacks and Worm Hole Attacks may cause the undesired operations in the time of routing the packet transfer. It degrades the performance of the legitimate users in the network. This work proposes an Efficient Network Discovery Storage Based Resilient Packet-Forwarding Scheme for the mitigation of malicious black hole and wormhole attacked nodes in 6Lowpan Sensor Networks. It also cooperates to manage the storage and prevent packet drop of sensor nodes present in the 6lowpan network.

scholarly journals Dynamic network formation with incomplete information

2015 ◽  
Vol 59 (2) ◽  
pp. 301-331 ◽  
Author(s):  
Yangbo Song ◽  
Mihaela van der Schaar
Keyword(s):  
Incomplete Information ◽  
Network Formation ◽  
Dynamic Network ◽  
Dynamic Network Formation

scholarly journals End-To-End Delay Optimization in Wireless Sensor Network (WSN)

2012 ◽  
pp. 247-251
Author(s):  
Shweta K. Kanhere ◽  
Mahesh Goudar ◽  
Vijay M. Wadhai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Relay Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Packet Forwarding ◽  
Central System ◽  
Neighboring Node ◽  
Packet Delivery ◽  
End To End

In this paper, we are interested in optimizing the delay of event-driven wireless sensor networks, for which events does not occur frequently. In such systems, most of the energy is consumed when the radios are on, waiting for an arrival to occur. Sleep-wake scheduling is an effective mechanism to prolong the lifetime of this energy constrained wireless sensor networks by optimization of the delay in the network but this scheme could result in substantial delays because a transmitting node needs to wait for its next-hop relay node to wake up. An attempt has been made to reduce these delays by developing new method of packet forwarding schemes, where each nod opportunistically forwards a packet to the its neighboring node that wakes up among multiple candidate nodes. In this paper, the focus is to study how to optimize the packet forwarding schemes by optimization of the expected packet-delivery delays from the sensor nodes to the sink. Based on optimized delay scheme result, we then provide a solution to the central system about how to optimally control the system parameters of the sleep-wake scheduling protocol and the packet forwarding protocol to maximize the network lifetime, subject to a constraint on the expected end-to-end packet delivery delay. Our numerical results indicate that the proposed solution can outperform prior heuristic solutions in the literature, especially under the practical scenarios where there are obstructions, e.g., a lake or a mountain, in the area of wireless sensor networks.

A Novel Nature Instilled Moving Sink Architecture for Data Gathering in Wireless Sensor Networks

2020 ◽  
Vol 11 (1) ◽  
pp. 36-48
Author(s):  
Amiya Bhusan Bagjadab ◽  
Sushree Bibhuprada B. Priyadarshini
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Gathering ◽  
Mobile Sink ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Packet Forwarding ◽  
Battery Power ◽  
Communication Distance

Wireless sensor networks are commonly used to monitor certain regions and to collect data for several application domains. Generally, in wireless sensor networks, data are routed in a multi-hop fashion towards a static sink. In this scenario, the nodes closer to the sink become heavily involved in packet forwarding, and their battery power is exhausted rapidly. This article proposes that a special node (i.e., mobile sink) will move in the specified region and collect the data from the sensors and transmit it to the base station such that the communication distance of the sensors will be reduced. The aim is to provide a track for the sink such that it covers maximum sensor nodes. Here, the authors compared two tracks theoretically and in the future will try to simulate the two tracks for the sink movement so as to identify the better one.

scholarly journals A Note on Connectivity and Stability in Dynamic Network Formation

Games ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 49
Author(s):  
Tackseung Jun ◽  
Jeong-Yoo Kim
Keyword(s):  
Network Formation ◽  
Dynamic Network ◽  
Static Model ◽  
Formation Problem ◽  
Dynamic Network Formation

We consider the dynamic network formation problem under the requirement that the whole network be connected and remain connected after q nodes are destroyed. We propose the concept of dynamic Cq-stability and characterize dynamic Cq-stable networks for any q≥0. Comparison with the outcome in the static model is also discussed.

Self-healing and reprocessable bromo butylrubber based on combined ionic cluster formation and hydrogen bonding

2020 ◽  
Vol 11 (6) ◽  
pp. 1188-1197 ◽  
Author(s):  
Sebastian Stein ◽  
Anton Mordvinkin ◽  
Brigitte Voit ◽  
Hartmut Komber ◽  
Kay Saalwächter ◽  
...  
Keyword(s):  
Hydrogen Bonding ◽  
Network Formation ◽  
Cluster Formation ◽  
Dynamic Network ◽  
Self Healing ◽  
Competing Interactions ◽  
Ionic Cluster ◽  
Dynamic Network Formation

The properties of modified bromobutyl rubber are strongly influenced by competing interactions via hydrogen bridges and ionic cluster formation. Dynamic network formation enables self-healing and reprocessability of the material.

scholarly journals Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

PLoS ONE ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. e0158185 ◽  
Author(s):  
Gabriele Lohmann ◽  
Johannes Stelzer ◽  
Verena Zuber ◽  
Tilo Buschmann ◽  
Daniel Margulies ◽  
...  
Keyword(s):  
Human Brain ◽  
Network Formation ◽  
Dynamic Network ◽  
New Method ◽  
Fmri Data ◽  
Edge Density ◽  
Dynamic Network Formation

scholarly journals Monitoring Passive Wireless Devices

2021 ◽  
Author(s):  
Zohar Naor
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Data Gathering ◽  
Geographic Area ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Devices ◽  
Iot Devices ◽  
Passive Rfid

Abstract This study suggests using a user-initiated detecting and data gathering from power-limited and even passive wireless devices, such as passive RFID tags, wireless sensor networks (WSNs), and Internet of Things (IoT) devices, that either power limitation or poor cellular coverage prevents them from communicating directly with wireless networks. While previous studies focused on sensors that continuously transmit their data, the focus of this study is on passive devices. The key idea is that instead of receiving the data transmitted by the sensor nodes, an external device (a reader), such as an unnamed aerial vehicle (UAV), or a smartphone is used to detect IoT devices and read the data stored in the sensor nodes, and then to deliver it to the cloud, in which it is stored and processed. While previous studies on UAV-aided data collection from WSNs focused on the UAV path planning, the focus of this study is on the rate at which the passive sensor nodes should be polled. That is, to find the minimal monitoring rate that still guarantees accurate and reliable data collection. The proposed scheme enables us to deploy wireless sensor networks over a large geographic area (e.g., for agricultural applications), in which the cellular coverage is very poor if any. Furthermore, the usage of initiated data collection can enable the deployment of passive WSNs. Thus, can significantly reduce both the operational cost, as well as the deployment cost, of the WSN.

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