Mobile ad hoc relaying for upward vertical handoff in hybrid WLAN/cellular systems

Khadivi, Pejman; Todd, T.D.; Samavi, Shadrokh; Saidi, Hossein; Zhao, Dongmei

doi:10.1016/j.adhoc.2007.01.005

Cited by 18 publications

(5 citation statements)

References 33 publications

(45 reference statements)

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“…After receiving all control packets by the central controller node, it will create a view of the communication pattern of the network nodes, and it will be able to create the adjacency graph of the active nodes in the network. Based on the received location information, the central SDN controller can calculate the durability of the communication between each pair of neighboring nodes [ 33 ]: The above equation shows that v i represents the speed of node i and φ i determines its direction of motion. Also, r represents the radio communication range of the node, and d represents the distance between nodes i and j, which is estimated by sampling the received signal strength indicator (RSSI).…”

Section: Proposed Modelmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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A distributed SDN-based intrusion detection system for IoT using optimized forests

Luo

2023

PLoS ONE

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Along with the expansion of Internet of Things (IoT), the importance of security and intrusion detection in this network also increases, and the need for new and architecture-specific intrusion detection systems (IDS) is felt. In this article, a distributed intrusion detection system based on a software defined networking (SDN) is presented. In this method, the network structure is divided into a set of sub-networks using the SDN architecture, and intrusion detection is performed in each sub-network using a controller node. In order to detect intrusion in each sub-network, a decision tree optimized by black hole optimization (BHO) algorithm is used. Thus, the decision tree deployed in each sub-network is pruned by BHO, and the split points in its decision nodes are also determined in such a way that the accuracy of each tree in detecting sub-network attacks is maximized. The performance of the proposed method is evaluated in a simulated environment and its performance in detecting attacks using the NSLKDD and NSW-NB15 databases is examined. The results show that the proposed method can identify attacks in the NSLKDD and NSW-NB15 databases with an accuracy of 99.2% and 97.2%, respectively, which indicates an increase compared to previous methods.

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Section: Proposed Modelmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

A distributed SDN-based intrusion detection system for IoT using optimized forests

Luo

2023

PLoS ONE

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“…This node is called the central node Ct. After receiving all control packets of the topology construction by the central node, a view of the communication pattern of the network nodes will be created by the central node and this node will be able to create the graph of network active nodes. The central node, by using the positional information received from the active nodes, calculates the stability of the connection between both active nodes, such as i and j, as follows 29 : …”

Section: Proposed Secure Routing Algorithm In the Iot (Sraiot)mentioning

confidence: 99%

Secure routing in the Internet of Things (IoT) with intrusion detection capability based on software-defined networking (SDN) and Machine Learning techniques

Rui,

Pan,

Shu

2023

Sci Rep

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Routing and security are the two main prerequisites for ensuring the correct operation of wireless networks. The importance of these cases doubles in wide networks such as IoT. This paper presents an algorithm to improve Secure Routing in IoT called SRAIOT. This algorithm uses a hierarchical structure to determine the connections between network components and data transfer routing. In SRAIOT, the network structure is managed hierarchically and through SDN. For this purpose, the IoT network is first divided into a set of subnets using the SDN solution, communication control and authentication are managed using the controller nodes of each subnet. The communication between two objects (located in different subnets) will be possible if their identity is confirmed through the controller nodes related to them. On the other hand, in order to identify the sources of attacks and network security threats, the controller nodes in each subnet monitor the network traffic pattern using an ensemble learning model and identify possible attacks in their subnet. The performance of SRAIOT was tested in the simulation, and the results were compared with previous methods. The results of these tests show that SRAIOT improves network performance regarding routing and detecting attacks.

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“…In a camera sensor network, we can consider the compression of captured flame images [33]. We can also investigate on routing algorithms for fire detection using camera sensor networks [34]. One could also use the proposed color features of this paper for image segmentation applications [35].…”

Section: Future Workmentioning

confidence: 99%

Saliency Based Fire Detection Using Texture and Color Features

Jamali

Karimi

Samavi

2020

2020 28th Iranian Conference on Electrical Engineering (ICEE)

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Due to industry deployment and extension of urban areas, early warning systems have an essential role in giving emergency. Fire is an event that can rapidly spread and cause injury, death, and damage. Early detection of fire could significantly reduce these injuries. Video-based fire detection is a low cost and fast method in comparison with conventional fire detectors. Most available fire detection methods have a high falsepositive rate and low accuracy. In this paper, we increase accuracy by using spatial and temporal features. Captured video sequences are divided into Spatio-temporal blocks. Then a saliency map and combination of color and texture features are used for detecting fire regions. We use the HSV color model as a spatial feature and LBP-TOP for temporal processing of fire texture. Fire detection tests on publicly available datasets have shown the accuracy and robustness of the algorithm.

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Mobile ad hoc relaying for upward vertical handoff in hybrid WLAN/cellular systems

Cited by 18 publications

References 33 publications

A distributed SDN-based intrusion detection system for IoT using optimized forests

A distributed SDN-based intrusion detection system for IoT using optimized forests

Secure routing in the Internet of Things (IoT) with intrusion detection capability based on software-defined networking (SDN) and Machine Learning techniques

Saliency Based Fire Detection Using Texture and Color Features

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