Heterogeneous Interface Configuration in Wireless Mesh Networks

Wang, Junfang; Xie, Bin; Cai, Kan; Agrawal, Dharma P.

doi:10.1109/wcnc.2008.409

Cited by 2 publications

(2 citation statements)

References 7 publications

(8 reference statements)

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“…Given that path loss, node residual capacity [9] and monitored area are key factors [10][11][12] to realize network performance optimization and extend network lifetime, the constraint conditions are dealt with according to mathematical programming so that the optimized network has high accuracy. Construct the optimized model with maximizing the network lifetime as the target.…”

Section: Problem Descriptionmentioning

confidence: 99%

Coverage Optimization Strategy for WSN based on Energy-aware

Zhu¹,

Fan

Wen

et al. 2016

INT J COMPUT COMMUN

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In order to optimize the wireless sensor network coverage, this paper designs a coverage optimization strategy for wireless sensor network (EACS) based on energy-aware. Under the assumption that the geographic positions of sensor nodes are available, the proposed strategy consists of energy-aware and network coverage adjustment. It is restricted to conditions such as path loss, residual capacity and monitored area and according to awareness ability of sensors, it would adjust the monitored area, repair network hole and kick out the redundant coverage. The purpose is to balance the energy distribution of working nodes, reduce the number of "dead" nodes and balance network energy consumption. As a result, the network lifetime is expanded. Simulation results show that: EACS effectively reduces the number of working nodes, improves network coverage, lowers network energy consumption while ensuring the wireless sensor network coverage and connectivity, so as to balance network energy consumption.

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Section: Problem Descriptionmentioning

confidence: 99%

Coverage Optimization Strategy for WSN based on Energy-aware

Zhu¹,

Fan

Wen

et al. 2016

INT J COMPUT COMMUN

View full text Add to dashboard Cite

show abstract

“…In the calculation, the authors introduced a compensation factor to reflect the interference impact. Wang et al [17] proposed a heuristic interface placement algorithm to minimizing the total number of NICs in a WMN while guaranteeing traffic demand constraints. The algorithm first computed the upper bound of relay traffic and the lower bound of the number of NICs for each MR, then assigned these amount NICs to each MR, and finally validated the traffic demand constraints.…”

Section: Related Workmentioning

confidence: 99%

Joint Interface Placement and Channel Assignment in Multi-channel Wireless Mesh Networks

Luo

Yang

et al. 2012

2012 IEEE 10th International Symposium on Parallel and Distributed Processing With Applications

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In multi-channel wireless mesh networks (WMNs), it is significantly important to achieve efficient channel utilization. The channel assignment problem, which investigates how to seek a proper mapping between available channels and network interface cards (NICs) on mesh routers (MRs), is attracting more and more attention from the research community. However, most proposed channel assignment approaches assume that NICs are evenly placed on the MRs, and its amount is also pre-determined. Due to the non-uniform distribution of WMN traffic, the equal interface placement will inevitably cause that the bottleneck MRs suffer from NIC shortage, and MRs with less load only have a low utilization of NICs. Hence, in order to improve network performance and reduce network cost, interface placement should be considered carefully in the process of planning a WMN. In this paper, we investigate the joint interface placement and channel assignment problem, i.e., how to appropriately place NICs on MRs and assign channels to them. We first formulate the problem as a mixed integer linear programming (MILP) model, which aims to minimize the total number of NICs while satisfying logical link, link bandwidth, link interference and traffic demand constraints. Then, we propose an MILPbased heuristic algorithm, using iterated local search to obtain sub-optimal solutions efficiently. Finally, we conduct simulation experiments to compare the heuristic solutions with the optimal solutions, and the results show that our proposed heuristic algorithm can achieve good sub-optimal solutions. Moreover, the simulation results also demonstrate that the algorithm can be well applied in large-scale WMNs where the optimal solutions cannot be obtained, and can perform well under different traffic demands.

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Heterogeneous Interface Configuration in Wireless Mesh Networks

Cited by 2 publications

References 7 publications

Coverage Optimization Strategy for WSN based on Energy-aware

Coverage Optimization Strategy for WSN based on Energy-aware

Joint Interface Placement and Channel Assignment in Multi-channel Wireless Mesh Networks

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