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Radio resources of each network are usually managed independently The lack of cooperative resource management can cause signi cant performance degradation due to inter-WLAN interference [2] Moreover, unbalanced loads among multiple networks can incur congestion in a few WLANs while foregoing unused excess resources in others Hence, resource sharing among multiple WLANs can make the best use of available radio resources and accommodate more users systemwide At the same time, WLAN providers may also bene t from being able to improve service quality and network utilization through cooperative WLAN resource management WLANs and WPANs often operate in a shared spectrum, the 24-GHz unlicensed industrial, scienti c, and medical (ISM) band When a WLAN such as the IEEE 80211b is colocated with a WPAN such as Bluetooth or the IEEE 80215.

Once the dynamic clustering offers this interesting opportunity of modifying the granularity of the model, in the developed strategy we also strive for the maintenance of continuity of the information granules, meaning that when revising the granularity of the model, we re ne or generalize the clusters starting from the con guration of information granules encountered in the past This algorithmic aspect is of particular relevance when dealing with iterative schemes of fuzzy clustering (eg, Fuzzy C-Means, FCM) where the results are affected by the initial conditions (say randomly initialized partition matrix) the method starts with The chunks (sets) of data that we use to form information granules and describe their dynamics are referred to as data snapshots In particular, the problem of segmentation of temporal data [3 5, 9] falls under the same rubric as one of the possible scenarios of dynamic clustering.

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4 low rate WPAN (LR-WPAN), the issue of coexistence between different wireless networks needs to be considered to ensure their performance requirements are maintained [3 6] Colocation of wireless services may occur under a number of different scenarios For example, a WPAN can be deployed to support a sensor array within the same location as an established WLAN Alternatively, a hierarchical network structure based on the strengths of each wireless service is straightforward to envision, where WPANs support local connectivity and WLANs provide the backbone for multiple WPANs Therefore, resource management of WLANs will operate in a dynamic radio frequency (RF) environment often involving a diverse set of colocated wireless services Colocated wireless networks operating in the same unlicensed frequency band can cause interference to each other because of spectral overlap Interference sources will impact mobile stations differently due to variations in RF path loss.

Even if stations are at xed locations, dynamics in the environment will signi cantly impact the RF propagation characteristics These variations make it dif cult and costly, in terms of radio resources, to maintain performance requirements Hence, it is imperative that the dynamic effects of interference be incorporated into network management and control decision making..

Fuzzy clustering [6, 12, 15, 18] can be regarded as a viable alternative to consider when dealing with dynamic data granulation; however, its generic version does require some essential changes to address the dynamics of the available data Our ultimate objective is to revisit and redevelop Fuzzy C-Means (FCM) so that the generic algorithm could be ef ciently used in the framework of dynamic data analysis In particular, we will be interested in dynamically adjusting the number of clusters for each temporal segment of the data (the number could be decreased so some clusters could be merged; the number of clusters could be also increased, leading to the split of some clusters) Further, we consider a weighted distance function being optimized as a part of the FCM optimization (which leads to the selection of the relevant attributes-features).

Listing 6-1 (continued)

Figure 7.4 Schematic diagram of some of the most likely conversions between aerosol and PSC. STS = supercooled ternary solution (i.e. H2 SO4 /HNO3 /H2 O), SAT = sulphuric acid tetrahydrate and NAT = nitric acid trihydrate. (Evidence for the various steps comes from observations, laboratory studies and theoretical studies including: [17, 34, 42 46] for step 1, [47] for step 2, [48] for step 3, [49] for step 4, [50, 51 53] for step 5, [51, 52, 54] for step 6 and [55 57] for step 7. I am not aware of any specialist literature for step 8, which is usually regarded as a barrierless mass transfer problem, following Toon et al. [58] and the general ice-cloud literature.)

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