We propose Otiy, a node-centric location service that limits the impact of location updates generate by mobile nodes in IEEE802.11-based wireless mesh networks. Existing location services use node identifiers to determine the locator (aka anchor) that is responsible for keeping track of a node's location. Such a strategy can be inefficient because: (i) identifiers give no clue on the node's mobility and (ii) locators can be far from the source destination shortest path, which increases both location delays and bandwidth consumption. To solve these issues, Otiy introduces a new strategy that identifies nodes to play the role of locators based on the likelihood of a destination to be close to these nodes- i.e., locators are identified depending on the mobility pattern of nodes. Otiy relies on the cyclic mobility patterns of nodes and creates a slotted agenda composed of a set of predicted locations, defined according to the past and present patterns of mobility. Correspondent nodes fetch this agenda only once and use it as a reference for identifying which locators are responsible for the node at different points in time. Over a period of about one year, the weekly proportion of nodes having at least 50 of exact location predictions is in average about 75 . This proportion increases by 10 when nodes also consider their closeness to the locator from only what they know about the network.
In @cite_1 , Wu mine the mobility behavior nodely (operated by the nodes) from long term mobility history. From this information they evaluate the time-varying probability of the different nodely-defined regions. The prevalence of a region on the time is defined through a cost model. Finally, they obtain a vector @math of mobility which will define the region to be paged in function of the time. The location updates and paging schemes are approximately the same than the afore-presented proposals. Here, the prevalence of a particular area on the time is more flexible and accurate than ours. However, the complexity of the algorithm is more important. Furthermore, the length of the vector of mobility can be a lot more important than a division in time slots of equal duration.
@cite_1
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We propose a new location tracking strategy called behavior-based strategy (BBS) based on each mobile's moving behavior. With the help of data mining technologies the moving behavior of each mobile could be mined from long-term collection of the mobile's moving logs. From the moving behavior of each mobile, we first estimate the time-varying probability of the mobile and then the optimal paging area of each time region is derived. To reduce unnecessary computation, we consider the location tracking and computational cost and then derive a cost model. A heuristics is proposed to minimize the cost model through finding the appropriate moving period checkpoints of each mobile. The experimental results show our strategy outperforms fixed paging area strategy currently used in the GSM system and time-based strategy for highly regular moving mobiles.