Performance Evaluation of WMNs Using Hill Climbing Algorithm Considering Giant Component and Different Distributions

Chang, Xinyue; Oda, Tetsuya; Spaho, Evjola; Ikeda, Makoto; Barolli, Leonard; Xhafa, Fatos

doi:10.1007/978-94-007-6996-0_17

Cited by 1 publication

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the previous works in [7][8][9][10] were two-stage methods for static RNP problems, which maximize the first term and then the second term. The concerned problem focuses on a dynamic scenario, which is generally resolved by simultaneously maximizing the two terms [14,15].…”

Section: Fitness Evaluationmentioning

confidence: 99%

“…This paper is concerned with the problem of router node placement (RNP) in WMNs [6], and this line of research has moved from static WMN scenarios [7][8][9][10][11] to the dynamic WMN scenarios [12]. Note that the RNP problem has been studied in other fields of optimization (e.g., facility planning and logistics), and the geographic concern in wireless networks has as well, e.g., [13].…”

Section: Introductionmentioning

confidence: 98%

“…Xhafa et al [8] proposed a simulated annealing approach, and then Xhafa et al [9] proposed a tabu search approach to avoid the solution search from falling into local optimal solutions. Chang et al [10] proposed a hill-climbing algorithm. Our previous work in [11] further proposed a simulated annealing approach with momentum terms for the static WMN-RNP problem with mesh client priority constraint.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Social-aware dynamic router node placement in wireless mesh networks

Lin

Tseng

et al. 2015

Wireless Netw

View full text Add to dashboard Cite

The problem of dynamic router node placement (dynRNP) in wireless mesh networks (WMNs) is concerned with determining a dynamic geographical placement of mesh routers to serve mobile mesh clients at different times, so that both network connectivity (i.e., the greatest topology subgraph component size) and client coverage (i.e., the number of the served mesh clients) are maximized. Mesh clients are wireless devises associated with users, and in real world, the users with same interests or some social relationship have higher chance to gather and move together geographically, i.e., they form a community, and the WMN with multiple communities can be regarded as a social network. Therefore, this paper investigates the so-called social-aware WMN-dynRNP problem assuming that mesh routers should be aware of the social community structure of mesh clients to dynamically adjust their placement to improve network performance. To cope with this problem, this paper proposes a social-based particle swarm optimization approach, which additionally includes a social-supporting vector to direct low-loading mesh routers to support the heavy-loading mesh routers in the same topology subgraph component (community), so as to dynamically adopt to the social community behavior of mesh clients. As compared with the previous approach, our experimental results show that the proposed approach is capable of effectively reducing number of the unserved mesh clients and increasing network connectivity in dynamic social scenarios.

show abstract