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. We study the process of the spread of an infection among mobile nodes moving on a finite, grid based map. A random walk and a novel adversarial model are considered as two extreme cases of node mobility. With N nodes, we present analytical and simulation results for both mobility models for a square grid map with size √G × √G. A key finding is that with random mobility the total time to infect all nodes decreases with N while with an adversarial model we observe a reverse trend. Specifically, the random case results in a total infection time of Θ(GlogGlogN (N) as opposed to the adversarial case where the total infection time is found to be Θ(√(Glog(N). We also explore the possibility of emulating such an infection process as a mobile interaction game with wireless sensor motes, and the above results are complimented by traces obtained from an empirical study with humans as players in an outdoor field. In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. Though this is a strategically important task in many traffic monitoring and routing systems, the problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. In most cases, we cannot find a trajectory exactly traversing a query path either. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem, subject to a tradeoff between the length of a path and the number of trajectories traversing the path (i.e., support). Third, we need to instantly answer users' queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation. To address these challenges, we model different drivers' travel times on different road segments in different time slots with a three dimension tensor. Combined with geospatial, temporal and historical contexts learned from trajectories and map data, we fill in the tensor's missing values through a context-aware tensor decomposition approach. We then devise and prove an object function to model the aforementioned tradeoff, with which we find the most optimal concatenation of trajectories for an estimate through a dynamic programming solution. In addition, we propose using frequent trajectory patterns (mined from historical trajectories) to scale down the candidates of concatenation and a suffix-tree-based index to manage the trajectories received in the present time slot. We evaluate our method based on extensive experiments, using GPS trajectories generated by more than 32,000 taxis over a period of two months. The results demonstrate the effectiveness, efficiency and scalability of our method beyond baseline approaches. We examine the fundamental properties that determine the basic performance metrics for opportunistic communications. We first consider the distribution of intercontact times between mobile devices. Using a diverse set of measured mobility traces, we find as an invariant property that there is a characteristic time, order of half a day, beyond which the distribution decays exponentially. Up to this value, the distribution in many cases follows a power law, as shown in recent work. This power law finding was previously used to support the hypothesis that intercontact time has a power law tail, and that common mobility models are not adequate. However, we observe that the timescale of interest for opportunistic forwarding may be of the same order as the characteristic time, and thus, the exponential tail is important. We further show that already simple models such as random walk and random waypoint can exhibit the same dichotomy in the distribution of intercontact time as in empirical traces. Finally, we perform an extensive analysis of several properties of human mobility patterns across several dimensions, and we present empirical evidence that the return time of a mobile device to its favorite location site may already explain the observed dichotomy. Our findings suggest that existing results on the performance of forwarding schemes based on power law tails might be overly pessimistic.