A Reliable Data Loss Aware Algorithm for Fog-IoT Networks

Abkenar, Forough Shirin; Jamalipour, Abbas

doi:10.1109/tvt.2020.2981970

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…The size of tasks follows a uniform distributed value between 60 and 80 KB. All TNs have the same maximum transmission power, which is equal to 23 dBm [45]. The rotary UAV-FNs are considered, where 0 = 55 J/m and ℎ = 170 W [34].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The network is reliable if and only if the UAVs are available to the TNs. According to the definitions provided in [45,46], the reliability is defined with respect to each TN as the the probability that each UAV is operational to process the assigned task to it, and the probability that the communication link between each TN and UAV is operational during the communication period. We define as the failure rate of which follows a Poisson process.…”

Section: Reliability Modelmentioning

confidence: 99%

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Efficient Task Allocation Protocol for a Hybrid-Hierarchical Spatial-Aerial-Terrestrial Edge-Centric IoT Architecture

Jamalipour

Abkenar

2022

IEICE Trans. Commun.

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In this paper, we propose a novel Hybrid-Hierarchical spatial-aerial-Terrestrial Edge-Centric (H 2 TEC) for the space-air integrated Internet of Things (IoT) networks. (H 2 TEC) comprises unmanned aerial vehicles (UAVs) that act as mobile fog nodes to provide the required services for terminal nodes (TNs) in cooperation with the satellites. TNs in (H 2 TEC) offload their generated tasks to the UAVs for further processing. Due to the limited energy budget of TNs, a novel task allocation protocol, named TOP, is proposed to minimize the energy consumption of TNs while guaranteeing the outage probability and network reliability for which the transmission rate of TNs is optimized. TOP also takes advantage of the energy harvesting by which the low earth orbit satellites transfer energy to the UAVs when the remaining energy of the UAVs is below a predefined threshold. To this end, the harvested power of the UAVs is optimized alongside the corresponding harvesting time so that the UAVs can improve the network throughput via processing more bits. Numerical results reveal that TOP outperforms the baseline method in critical situations that more power is required to process the task. It is also found that even in such situations, the energy harvesting mechanism provided in the TOP yields a more efficient network throughput.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Reliability Modelmentioning

confidence: 99%

Efficient Task Allocation Protocol for a Hybrid-Hierarchical Spatial-Aerial-Terrestrial Edge-Centric IoT Architecture

Jamalipour

Abkenar

2022

IEICE Trans. Commun.

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“…We consider a system in which M = 100 TNs are uniformly distributed in a region with a radius of r = 2000 m. Each TN (i, c) generates a task in each time slot, where the task's size follows a uniform distributed value of U [60, 80] KB. All TNs have the same maximum transmission power, which is equal to 23 dBm [31]. The rotary UAV-FNs are considered with a coverage radius of R = 200 m, where E 0 = 55 J/m and P h = 170 W [45].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In the considered system model, the outage probability happens when the transmission rate of a TN exceeds the channel capacity between the TN and the corresponding UAV-FN beyond which the UAV-FN cannot receive the tasks. Accordingly, considering R (i,c) as the transmission rate of TN (i, c) , the outage probability is defined as P out = P r(C (i,c)j < R (i,c) ) [31], [32]. We assume that the fading coefficient of all channels in a cluster is the same, i.e., ℏ (i,c)j = ℏ, ∀ TN (i, c) ∈ cluster c. By employing the methods and assumptions provided in [33], we have…”

Section: B Outage Probability Modelmentioning

confidence: 99%

ENERGENT: An energy-efficient UAV-assisted fog-IoT framework for disaster management

et al. 2022

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In this paper, we propose ENergy-efficient disastER manaGmENT (ENERGENT) as a novel framework for disaster management in the unmanned aerial vehicle (UAV)-assisted Fog-Internet of things (IoT) networks. ENERGENT optimizes the energy consumption of the terminal nodes (TNs), as well as the UAVs, using three proposed algorithms. The first algorithm optimally adjusts the 3D placement of the UAVs such that these nodes consume the minimum energy to reach the desired cluster of the TNs. Besides, the transmit power and the transmission rate of the TNs are set in a way that their energy consumption is minimized and the outage probability requirements are met in the network. In the second algorithm, we propose an optimal task offloading scheme where tasks are offloaded to the UAVs in order to meet the network delay constraints. Finally, the third algorithm takes advantage of wireless power transfer to transfer energy to the TNs when their remaining energy degrades a predefined threshold. This scheme guarantees a minimum throughput for all TNs within a cluster by which the total network throughput is maximized. Simulation results reveal that ENERGENT outperforms the existing methods in terms of optimized network energy consumption, delay, and throughput.

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“…Therefore, this architecture also can provide end users with flexible, low latency, and real-time services [31]. Based on these major architectures, there are also further enhancements in IoT networks improving energy efficiency [32,33] and data reliability [34].…”

Section: Edge Computingmentioning

confidence: 99%