Energy optimization based path selection algorithm for IEEE 802.11s Wireless Mesh Networks

Mhlanga, Martin Mafan; Olwal, Thomas O.; Mutanga, Murimo Bethel; Adigun, Matthew O.

doi:10.1109/afrcon.2011.6072125

Cited by 11 publications

(6 citation statements)

References 14 publications

(25 reference statements)

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“…In [141,[161][162][163][164]166], routing was optimized through access points or nodes that were installed in the rural area. However, the initial placement of these access points can be optimized depending on the characteristics of the rural area and the type of population agglomeration.…”

Section: ) Resource Management In Mesh/multihop Networkmentioning

confidence: 99%

“…In[141], the problem of joint routing and scheduling in a multihop network with directional antenna was formulated as a linear program, and a scheduling algorithm is proposed based on directed edge coloring in a multi-graph. In[163], an energy aware routing approach is presented for wireless mesh networks, such that the routing path selected is the one that maximizes network lifetime. The purpose is to perform efficient routing in rural areas where permanent energy availability is not guaranteed.…”

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confidence: 99%

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A Key 6G Challenge and Opportunity—Connecting the Base of the Pyramid: A Survey on Rural Connectivity

2020

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Providing connectivity to around half of the world population living in rural or underprivileged areas is a tremendous challenge, but also a unique opportunity. Access to the internet would provide the population living in these areas a possibility to progress on the educational, health, environment, and business levels. In this paper, a survey of technologies for providing connectivity to rural areas, and that can help address this challenge, is provided. Although access/fronthaul and backhaul techniques are discussed in the paper, it is noted that the major limitation for providing connectivity to rural and underprivileged areas is the cost of backhaul deployment. In addition, energy requirements and cost efficiency of the studied technologies are analyzed. In fact, the challenges faced for deploying an electricity network, as a prerequisite for deploying communication networks, are huge in these areas and they are granted an important share of the discussions in this paper. Furthermore, typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. The main

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Section: ) Resource Management In Mesh/multihop Networkmentioning

confidence: 99%

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confidence: 99%

A Key 6G Challenge and Opportunity—Connecting the Base of the Pyramid: A Survey on Rural Connectivity

2020

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“…In [118,137,138,139,140,142], routing was optimized through access points or nodes that were installed in the rural area. However, the initial placement of these access points can be optimized depending on the characteristics of the rural area and the type of population agglomeration.…”

Section: E Wifi/wimax/multihop/mesh Networkmentioning

confidence: 99%

A Key 6G Challenge and Opportunity - Connecting the Remaining 4 Billions: A Survey on Rural Connectivity

Yaacoub¹,

Alouini²

2019

Preprint

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DRAFT 4) Hyper Connected: This scenario corresponds to people enjoying state-of-the-art connectivity, e.g., at least 4G and possibly 5G cellular connectivity, and/or with high speed fixed DSL or FTTH or FTTN connectivity. These subscribers typically live in urban areas, and it is extremely difficult to find them in rural areas, due to the numerous barriers discussed previously. This paper surveys the literature aiming to provide basic connectivity to the unconnected population, or aiming to increase the connectivity level of those with basic connectivity ("Under Hence, the challenges for FSO in space are less critical, and indeed significantly better performance can be achieved, especially due to the fact that the optical signal is not traversing the atmosphere. In 2008, an optical inter-satellite link achieved a data rate transmission of 5.6 Gbps for distances up to 5, 000 km in the European Data Relay satellite System (EDRS). Newer terminals can transmit at a data rate of 1.8 Gbps for distances up to 45, 000 km. The intersatellite Ka band link achieved 300 Mbps [44]. In EDRS, LEO satellites relay the data from ground stations to GEO satellites using FSO for the GEO-LEO satellite communication, and RF bands for communication with the ground stations (similarly to the satellite-satellite Ka link, the satellite-ground Ka link achieved 300 Mbps) [44]. Some of the recent satellite constellations discussed in Section II-E, e.g., Starlink, are also expected to use FSO for its inter-satellite communication [45,46,47]. D. HAPs/Drones/UAVs/BalloonsTo solve the coverage problem in large rural areas without relying on the deployment of costly wired infrastructure for backhaul, HAPs and UAVs are generally recommended [48].UAVs are proposed in [49] to provide backhaul connectivity for ground 5G base stations. A steering algorithm to allow the antennas of the UAV and the ground BS to be dynamically steered towards each other is proposed in [49], and tested via an experimental setup. In[50], a multihop network of HAPs is proposed for providing backhaul connectivity to TVWS deployments in rural areas. The HAPs communicate with each other using mmWave frequencies, and with the ground stations using FSO.An interference alignment scheme to maximize the sum-rate capacity of HAPs communicating with GSs is proposed in [51]. The scheme assumes M HAP drones and N GSs, with no CSI at the HAPs. A tethered balloon with (M − 1) × (N − 1) antennas is used as a decode and forward (DF) relay with half-duplex operation: The HAPs transmit their data to the GSs first, then the tethered balloon relays the data after performing pre-coding [51].Aerostats filled with lighter than air gas are tethered to the ground and used to provide connectivity to rural areas in [52]. Although these tethered aerostats can be used to provide backhaul connectivity by communicating with each other using directive antennas, they were also used in [52] to provide fronthaul WiFi access by using high gain omnidirectional antennas.

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“…Hence, they proposed an algorithm with one more metric called energy for path selection. Mhalanga et al [9] introduced an energy optimization based path selection algorithm for IEEE 802.11s WMNs that will maximize the network lifetime addressing the self configuration and cost effectiveness constraints of WMN. Authors Xi Chen et.al., [13] proposed an enhancement to the default 802.11 Power Save Mode (PSM), called M-PSM, it provides the additional power-saving opportunities by considering user mobility and detailed traffic condition.…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Power Save Mode based AODV Routing for IEEE 802.11s Wireless Mesh Networks

Prakash¹,

Nagabhushan²,

Krinkin³

2016

IJCA

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Owing to the self organizing, self configuring and self healing capabilities, Wireless Mesh Nwetworks (WMN) have emerged as most powerful architectures in recent times. Wireless Mesh Networks operate with limited battery resources. In 802.11s, a mobile station(STA) can act as router as well as client that results in increased energy consumption during routing process. The degradation of energy at STA can result in low network performance due to relay STA failure. In order to increase network performance there is a need to reduce the energy consumption rate of STA. To save energy, 802.11s has introduced a mechanism called power save mode(PSM) which switches STA mode from active to light sleep or deep sleep mode when STA is not involved in transmission. This ensures saving of energy at each STA during routing process. Several models have been proposed by researchers to improve the energy saving mechanisms in 802.11s. Adhoc On Demand Distance Vector (AODV) routing algorithm is used for routing purpose in WMN. In this routing protocol, regular usage of the same node during routing may lead to battery failure and result in low network lifetime. Hence in this work we propose a novel Energy Aware Power Save Mode-AODV (EAPSM-AODV) routing model that switches among power save modes based on battery status of a node before participating in AODV routing. Our proposed routing model refers to the mechanism that takes available remaining energy and energy draining rate of a node into account to decide on relaying traffic. It consist of four units namely Routing, Path choosing, Mode selector and Energy units. Experiments have been conducted using NS3 simulator by considering various situations such as keeping nodes in static position and moving nodes dynamically in a simulation area. The results shows that our proposed model significantly improves network performance and increase in network lifetime.

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Energy optimization based path selection algorithm for IEEE 802.11s Wireless Mesh Networks

Cited by 11 publications

References 14 publications

A Key 6G Challenge and Opportunity—Connecting the Base of the Pyramid: A Survey on Rural Connectivity

A Key 6G Challenge and Opportunity—Connecting the Base of the Pyramid: A Survey on Rural Connectivity

A Key 6G Challenge and Opportunity - Connecting the Remaining 4 Billions: A Survey on Rural Connectivity

Energy Aware Power Save Mode based AODV Routing for IEEE 802.11s Wireless Mesh Networks

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