Three-tier neural model for service provisioning over collaborative flying ad hoc networks

Sharma, Vishal; Kumar, Rajesh

doi:10.1007/s00521-016-2584-1

Cited by 10 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This will cause a significant conflict for UAVs in critical missions. The security solutions such as IDS, cryptographic algorithms, and security policies will help to secure this network 177,178 . The various cyber‐attacks in the UAV‐Fog system and its countermeasures are highlighted in Table 6.…”

Section: Collaboration Of Uavs For a Hetnet With Fog Computingmentioning

confidence: 99%

See 1 more Smart Citation

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Gupta

2022

Int J Communication

View full text Add to dashboard Cite

Summary A drone or unmanned aerial vehicles (UAVs) is becoming a trending area for researchers worldwide. UAV's contribution is increasing in day‐to‐day life, whether it is in a military zone, disaster management, healthcare sector, smart cities, Internet of Things (IoT), urban air mobility, and many more. In contrast, UAV's limited computational capability and low‐energy sources pose significant challenges for real‐time data processing, storage, networking, and security that are critical in emergencies such as floods, earthquakes, and cyclones. UAVs are rapidly used to satisfy user requirements as well as services. As the demand for UAVs aided heterogeneous wireless networks increases in critical emergencies, fog computing serves several benefits to fulfill users' demands in terms of low latency, support, data storage, mobility, availability, scalability, and so on. This study aims to present a comprehensive study with their technical aspects for understanding fog computing, security issues, privacy concerns, and risks, along with its solutions. This paper suggests the collaboration of UAV‐Fog architecture based on the four‐tier network consisting of smart things, local UAVs, UAV‐Fog, and cloud server, to control UAV's data and also described some of the security issues faced by this cloud infrastructure. Further, this research article also sheds new light on some scenarios of UAV‐Fog for such deployments, applications, opportunities, challenges, and their major security threats and their countermeasures. Afterward, we design taxonomy of the collaboration of UAV‐Fog with their respective approaches.

show abstract

Section: Collaboration Of Uavs For a Hetnet With Fog Computingmentioning

confidence: 99%

“…The security solutions such as IDS, cryptographic algorithms, and security policies will help to secure this network. 177,178 The various cyber-attacks in the UAV-Fog system and its countermeasures are highlighted in Table 6. To ensure security in the UAV-Fog model, there are some important countermeasures are as:…”

Section: Cyber-attacksmentioning

confidence: 99%

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Gupta

2022

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In such type of threats and vulnerabilities, additional security and countermeasures are needed to handle vulnerability exploitations. The security solutions like Intrusion Detection Systems (IDS) [16], cryptographic mechanisms, and security policies can help to protect IoD [29].…”

Section: Network Platforms Servicesmentioning

confidence: 99%

Internet of Drones (IoD): Threats, Vulnerability, and Security Perspectives

Choudhary¹,

Sharma²,

Gupta³

et al. 2018

ReBICTE

View full text Add to dashboard Cite

The development of the Internet of Drones (IoD) becomes vital because of a proliferation of dronebasedcivilian or military applications. The IoD based technological revolution upgrades the currentInternet environment into a more pervasive and ubiquitous world. IoD is capable of enhancing thestate-of-the-art for drones while leveraging services from the existing cellular networks. Irrespectiveto a vast domain and range of applications, IoD is vulnerable to malicious attacks over open-air radiospace. Due to increasing threats and attacks, there has been a lot of attention on deploying securitymeasures for IoD networks. In this paper, critical threats and vulnerabilities of IoD are presented.Moreover, taxonomy is created to classify attacks based on the threats and vulnerabilities associatedwith the networking of drone and their incorporation in the existing cellular setups. In addition, thisarticle summarizes the challenges and research directions to be followed for the security of IoD.

show abstract

“…However, the survey of research institutes indicated that the utilization rate of licensed spectrum is low 1,2 . Cognitive radio (CR) technology can make secondary users (SUs) without authorization to actively search for the idle spectrum resources in the radio environment 3,4 . Rapidly and effectively distributing the idle spectrum resources after spectrum sensing without interfering with the primary users (PUs) is a problem that must be solved by spectrum sharing technology.…”

Section: Introductionmentioning

confidence: 99%

Combination weighted clustering algorithms in cognitive radio networks

Wang

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

The topology of the distributed cognitive radio network is volatile as influenced by the behavior of primary users, and this condition leads to the large communication overhead and low utilization of spectrum resources. A combination weighted clustering algorithm is proposed in the study to reduce the communication overhead of the distributed cognitive network and maintain the stability of the network structure. First, a clustering algorithm considering the available channel, geographic location, and experienced data (used for collecting the behavior of secondary users (SUs) in the network and the evaluation on it) of SUs is put forward through analyzing the characteristics of the idle channels in cognitive network. Three factors, namely, average channel capacity, stability, and channel quality, are converted into quantifiable values. The cluster head is determined on the basis of the three factors. Then, the cluster members and gateway nodes are determined using the weighting formula and the location information of the cluster head. Results show that the proposed clustering algorithm can generate 15% more clusters than other algorithms and reduce 40% of network communication overhead when the transmission distance between cognitive users and the channel number change. Thus, the stability of the cluster structure is maintained and the communication overhead is decreased. This study provides references for the construction of the stable distributed cognitive network.

show abstract

Three-tier neural model for service provisioning over collaborative flying ad hoc networks

Cited by 10 publications

References 24 publications

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Internet of Drones (IoD): Threats, Vulnerability, and Security Perspectives

Combination weighted clustering algorithms in cognitive radio networks

Contact Info

Product

Resources

About