Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

Alobaidy, Haider A. H.; Mandeep, J. S.; Behjati, Mehran; Nordin, Rosdiadee; Abdullah, Nor Fadzilah

doi:10.1109/access.2022.3151967

Cited by 51 publications

(25 citation statements)

References 184 publications

(431 reference statements)

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“…Multi-UAV cooperation, which is an essential part of this project, could lead to a wide range of uses, including precise geographic localization [9], search and rescue missions [10], intelligent transportation systems [11], target detection [12], disaster tracking and monitoring [13], volcano monitoring [14], delivering medical supplies to otherwise inaccessible regions [15], border patrol missions [16], prevention and control of forest fires [17], UAV control by the brain [18], acting as relays for Internet distribution [19], among others Drones are also extensively employed in the military [20]; for example, the US Navy's LOCUST project hires a swarm of autonomous drones to carry out coordinated military operations [21]. Numerous additional applications are being developed in academia and industry in addition to these marketed ones [22]. Surveying and mapping are two examples of this type of work [23].…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

2022

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Flying Ad-Hoc Network (FANET) is a set of Unmanned Aerial Vehicles (UAVs) interconnected wirelessly. FANETs self-organize and provide low-cost, adaptable, and simple-to-implement flying nodes, enabling them to complete complicated tasks more quickly and collectively. The high mobility of nodes and the highly dynamic topology pose challenges to communication design, particularly when creating a routing protocol for UAV networks; this has inspired researchers to contribute and develop this technology. Hierarchical routing technique known as clustering is necessary to offer scalability, survivability, and distribute payload among UAVs to maintain the performance. This study has proposed a comprehensive survey of the cluster-based routing protocols (CBRPs) in terms of their strengths, weaknesses, specific applications, method, number of nodes, and future improvements for serving FANETs. Moreover, 21 CBRPs based FANETs were reviewed in terms of their topology, challenges, scalability, characteristics, clustering strategy, outstanding features, cluster head (CH) selection, routing metrics, and performance measures.In addition, open issues that need to be addressed in future studies in the field of routing protocols for UAV networks were also debated. INDEX TERMSClustering algorithm, flying ad hoc network, routing protocol, scalability, unmanned aerial vehicle.

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Section: Introductionmentioning

confidence: 99%

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

2022

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“…Based on ODAC connectivity dynamics, logical connections are established by sharing beacon messages between sensor nodes. Equation (3) clarifies the basic dynamic acyclic connectivity model [ 39 , 40 ]. According to the model of acyclic graph connectivity principles, the sensor nodes in the WSN are able to configure with associativity rules.…”

Section: Methodsmentioning

confidence: 99%

Interleaved Honeypot-Framing Model with Secure MAC Policies for Wireless Sensor Networks

Rajasoundaran

Maheswar

Muthuramalingam

et al. 2022

Sensors

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The Wireless Medium Access Control (WMAC) protocol functions by handling various data frames in order to forward them to neighbor sensor nodes. Under this circumstance, WMAC policies need secure data communication rules and intrusion detection procedures to safeguard the data from attackers. The existing secure Medium Access Control (MAC) policies provide expected and predictable practices against channel attackers. These security policies can be easily breached by any intelligent attacks or malicious actions. The proposed Wireless Interleaved Honeypot-Framing Model (WIHFM) newly implements distributed honeypot-based security mechanisms in each sensor node to act reactively against various attackers. The proposed WIHFM creates an optimal Wireless Sensor Network (WSN) channel model, Wireless Interleaved Honeypot Frames (WIHFs), secure hash-based random frame-interleaving principles, node-centric honeypot engines, and channel-covering techniques. Compared to various existing MAC security policies, the proposed model transforms unpredictable IHFs into legitimate frame sequences against channel attackers. Additionally, introducing WIHFs is a new-fangled approach for distributed WSNs. The successful development of the proposed WIHFM ensures resilient security standards and neighbor-based intrusion alert procedures for protecting MAC frames. Particularly, the proposed wireless honeypot methodology creates a novel idea of using honeypot frame traps against open wireless channel attacks. The development of a novel wireless honeypot traps deals with various challenges such as distributed honeypot management principles (node-centric honeypot, secretly interleaved-framing principles, and interleaving/de-interleaving procedures), dynamic network backbone management principles (On Demand Acyclic Connectivity model), and distributed attack isolation policies. This effort provides an effective wireless attack-trapping solution in dynamic WSNs. The simulation results show the advantage of the proposed WIHFM over the existing techniques such as Secure Zebra MAC (SZ-MAC), Blockchain-Assisted Secure-Routing Mechanism (BASR), and the Trust-Based Node Evaluation (TBNE) procedure. The experimental section confirms the proposed model attains a 10% to 14% superior performance compared to the existing techniques.

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“…Meanwhile, ref. [ 25 ] developed an empirical channel model for the UAV-to-BS scenario based on path loss and shadowing effects in the LTE network. The results showed that the path loss exponents decrease by increasing the flight height.…”

Section: Related Workmentioning

confidence: 99%

Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach

Behjati

Zulkifley

Alobaidy

et al. 2022

Sensors

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The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively.

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Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

Cited by 51 publications

References 184 publications

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

Interleaved Honeypot-Framing Model with Secure MAC Policies for Wireless Sensor Networks

Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach

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