Asymmetric Directional Multicast for Capillary Machine-to-Machine Using mmWave Communications

Kwon, Jung-Hyok; Kim, Eui-Jik

doi:10.3390/s16040515

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Since unnecessary sector switching in multicast transmissions with directional antennas leads to a long delay, and, hence, to a low throughput, in work [21], asymmetric sectorization for the irregular deployment pattern of multicast group members is optimized by sweeping different sizes of beams to cover all multicast group members with the minimum number of directional transmissions. However, this approach relies on sector antenna models, which provide trivial cut-off solutions [22], thereby leading to non-optimal results.…”

Section: Related Workmentioning

confidence: 99%

“…where |A| is the complexity due to the "while" cycle over all |A| users in the worst case of the unicast transmission (lines [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. This means that each beam j covers only a single user.…”

Section: Complexity Analysismentioning

confidence: 99%

“…Q θ by using(21) or(22);13 if MAX Q < Q θ then 14 MAX Q ← Q θ ; 15 N j ← N θ ; 16 θ j ← θ; ← A \ N j ; 20 T DT N ← T DT N + T DTNj ; 21 end 22 return j, T DT N ; 23 calculate NT (10), EC (12), EF (14), THR (16).…”

mentioning

confidence: 99%

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Efficient Management of Multicast Traffic in Directional mmWave Networks

Chukhno

Pizzi

et al. 2021

IEEE Trans. on Broadcast.

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Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams.

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Section: Related Workmentioning

confidence: 99%

Section: Complexity Analysismentioning

confidence: 99%

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Efficient Management of Multicast Traffic in Directional mmWave Networks

Chukhno

Pizzi

et al. 2021

IEEE Trans. on Broadcast.

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“…t G and r G are the antenna gain of the PTU and the PRU, respectively. We consider a flat-top radiation pattern as the directional antenna model of the PTU [27]; thus, t G is the same as 2 /   , where  is the beamwidth of the directional antenna of the PTU. In contrast, because the PRU receives power from the PTU using its Furthermore, the P-Tx/Rx and D-Tx/Rx of PTU always keep their radio on, while for the PRU, only its D-Tx/Rx is always on.…”

Section: System Modelmentioning

confidence: 99%

Residual Energy Estimation-Based MAC Protocol for Wireless Powered Sensor Networks

Lee

Kwon

Kim

2021

Sensors

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This paper presents a residual energy estimation-based medium access control (REE-MAC) protocol for wireless powered sensor networks (WPSNs) composed of a central coordinator and multiple sensor devices. REE-MAC aims to reduce overhead due to control messages for scheduling the energy harvesting operation of sensor devices and provide fairness for data transmission opportunities to sensor devices. REE-MAC uses two types of superframes that operate simultaneously in different frequency bands: the wireless energy transfer (WET) superframe and wireless information transfer (WIT) superframe. At the beginning of each superframe, the coordinator estimates the change in the residual energy of individual sensor devices caused by their energy consumption and energy harvesting during the previous superframe. It then determines the devices’ charging priorities, based on which it allocates dedicated power slots (DPSs) within the WET superframe. The simulation results demonstrated that REE-MAC exhibits superior performance for the harvested energy, average freezing time, and fairness to existing representative WPSN MAC protocols.

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“…Only Sensor Device 4 was used in the 500 MB case, and Sensor Devices 3 and 4 were used in the 1 GB case. The difference in residual energy among sensor devices can be mathematically represented by the fairness index F [35]. The fairness index is calculated as Furthermore, the lost blocks were retransmitted without any retransmission request messages in the VPSF.…”

Section: Implementation and Performance Evaluationmentioning

confidence: 99%

Design and Implementation of Virtual Private Storage Framework Using Internet of Things Local Networks

2020

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This paper presents a virtual private storage framework (VPSF) using Internet of Things (IoT) local networks. The VPSF uses the extra storage space of sensor devices in an IoT local network to store users’ private data, while guaranteeing expected network lifetime, by partitioning the storage space of a sensor device into data and system volumes and, if necessary, logically integrating the extra data volumes of the multiple sensor devices to virtually build a single storage space. When user data need to be stored, the VPSF gateway divides the original data into several blocks and selects the sensor devices in which the blocks will be stored based on their residual energy. The blocks are transmitted to the selected devices using the modified speedy block-wise transfer (BlockS) option of the constrained application protocol (CoAP), which reduces communication overhead by retransmitting lost blocks without a retransmission request message. To verify the feasibility of the VPSF, an experimental implementation was conducted using the open-source software libcoap. The results demonstrate that the VPSF is an energy-efficient solution for virtual private storage because it averages the residual energy amounts for sensor devices within an IoT local network and reduces their communication overhead.

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Asymmetric Directional Multicast for Capillary Machine-to-Machine Using mmWave Communications

Cited by 9 publications

References 33 publications

Efficient Management of Multicast Traffic in Directional mmWave Networks

Efficient Management of Multicast Traffic in Directional mmWave Networks

Residual Energy Estimation-Based MAC Protocol for Wireless Powered Sensor Networks

Design and Implementation of Virtual Private Storage Framework Using Internet of Things Local Networks

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