Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

Aadil, Farhan; Raza, Arif; Khan, Muhammad Fahad; Maqsood, Muazzam; Mehmood, Irfan; Rho, Seungmin

doi:10.3390/s18051413

Cited by 121 publications

(94 citation statements)

References 26 publications

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“…In Reference [101], AI is used to assist the nodes in VANETs and MANETs. In Reference [102], AI is used to form clusters and improve energy efficiency. In Reference [103], a swarm-based approach is applied to routing in a multi-UAV swarm scenario.…”

Section: What Are the Roles Of Artificial Intelligence In Fanets?mentioning

confidence: 99%

“…Some hybrid routing schemes used in MANETs, VANETs, and FANETs are ant colony optimization [119], grey wolf optimization [120,121], moth flame optimization [122,123], and energy aware link based clustering [102].…”

Section: Routing Frameworkmentioning

confidence: 99%

“…Ali et al [102] propose a hybrid routing scheme that enables nodes to form clusters and routing by adjusting the transmission range of nodes in order to reduce energy consumption and computational complexity. The main objectives are to enhance route stability (O.1) and network coverage (O.2) in FANETs.…”

Section: Enhancing Route Stability Using Energy-efficient Techniquementioning

confidence: 99%

“…In addition to high dynamicity (X.1) of network topology, high cost (X.2), and low residual energy (X.3), there are two new challenges: (a) high mobility degree that causes the next geographical locations of nodes to become unpredictable and (b) movement in the three-dimensional space, which differs from the two-dimensional space in MANETs and VANETs [137]. Nevertheless, some routing schemes have been investigated under two-dimensional space in FANETs, including those in References [102,103,112]. To the best of our knowledge, an extensive mobility model that covers the two new challenges are yet to be investigated to support the investigation of routing in FANETs [141]; nevertheless, movement with high mobility degree in a three-dimensional space is the core characteristic of FANETs.…”

Section: Enhancing Mobility Models For the Investigation Of Routing Imentioning

confidence: 99%

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Routing Schemes in FANETs: A Survey

Khan

Yau

Noor

et al. 2019

Sensors

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Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.

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Section: What Are the Roles Of Artificial Intelligence In Fanets?mentioning

confidence: 99%

Section: Routing Frameworkmentioning

confidence: 99%

Section: Enhancing Route Stability Using Energy-efficient Techniquementioning

confidence: 99%

Section: Enhancing Mobility Models For the Investigation Of Routing Imentioning

confidence: 99%

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Routing Schemes in FANETs: A Survey

Khan

Yau

Noor

et al. 2019

Sensors

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“…The prediction performance of our proposed system is measured through multiple metrics [42][43][44][45][46][47][48]. The comparison is mainly drawn based on the difference between the actual value and the predicted one [2].…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

et al. 2018

Self Cite

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Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

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A comparative study on geographic‐based routing algorithms for flying ad‐hoc networks

Agrawal

Kapoor

2021

Concurrency and Computation

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The advancement of the technology and broad availability of low-cost devices (e.g., networking technology, micro-embedded computers, and sensors, etc.) have opened new ways for the expansion of flying ad-hoc networks (FANETs). The unmanned aerial vehicles (UAVs) have the potential to change their trajectory freely in the case of FANETs. This freedom demands efficient routing to make the network connection smooth. However, designing routing protocols for FANETs is a crucial task for highly dynamic nodes like UAVs. The position-based routing demonstrated high efficiency and resilience to handle the high mobility of UAV nodes in FANETs as compared to topology-based routing protocols. Geographic routing protocols utilize the coordinates of the node to greedily proceed toward the destination. This article presents an extensive review of position-based or geographic routing protocols in FANETs. Previous survey papers have performed a comparative analysis of all the protocols on different network conditions, assumptions, tools, and settings, therefore their performance is not comparable. The novelty of this survey is that this article performs the comparative analysis of various geographic routing protocols by evaluating all the geographic routing protocols over a common network scenario with a varying number of nodes. The starting phase of this article covers the basic outline of FANETs. Thereafter, an absolute study of the geographic based routing protocol has been presented by distinguishing the routing protocols on the basis of path and packet forwarding strategy. This article further emphasizes on the strength, weaknesses, quality of service (QoS), and comparative analysis of the various characteristics of the geographic routing protocols. Finally, a performance comparison summary of all the analyzed routing protocols for FANETs has been presented. The open research issues have been discussed at the end of this article.

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Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

Cited by 121 publications

References 26 publications

Routing Schemes in FANETs: A Survey

Routing Schemes in FANETs: A Survey

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

A comparative study on geographic‐based routing algorithms for flying ad‐hoc networks

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