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SEGMENTATION AND SAMPLING OF MOVING OBJECT TRAJECTORIES BASED ON REPRESENTATIVENESS

Platform : DOT NET

IEEE Projects Years : 2012 - 13

SEGMENTATION AND SAMPLING OF MOVING OBJECT TRAJECTORIES BASED ON REPRESENTATIVENESS

 

Abstract

 

Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub)trajectories in the MOD. In order to find the most representative subtrajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity  information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative subtrajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

 

 

 

Existing System:

 

 

 

This is a very challenging problem where very limited work has been carried out so far. An insightful solution to the problem would be an analyst to be able to supervise the sampling procedure, not only regarding the volume of the sampled data set, but also the properties of the data set that reveal the underlying movement patterns of the MOD.

 

We argue that this problem can be effectively tackled if interconnected to the previous two discussed problems. In other words, we propose an automatic method for sub trajectory sampling based on the “representativeness” of the sub trajectories.

 

In this approach, an analyst may request the top-k representative subtrajectories that best describe the MOD in an optimized way, where optimization is with respect to the “representativeness.”

 

 

 

Proposed System:

 

  • We propose an index-based global voting method that allows us to represent the representativeness of a trajectory in a MOD as a smooth continuous descriptor.
  • We introduce an algorithm for the automatic segmentation of trajectories into “homogenous” sub trajectories according to their “representativeness” in the MOD.
  • We define the problem of sub trajectory sampling in a MOD as an optimization problem and we propose a novel solution to tackle the problem.
  • Finally, we conduct a comprehensive set of experiments over synthetic and real trajectory data sets, in order to thoroughly evaluate our approach.

 

Software Requirements:

 

  • .Net
  • Language - C#.Net
  • Back End - SQL Server
  • Windows XP

 

Hardware Requirements:

 

  • RAM         : 512 Mb
  • Hard Disk : 80 Gb
  • Processor  : Pentium IV

 

 

 

Modules:

 

Key Management for Sensor Networks:

 

Key management for sensor networks has been extensively studied recently. There are pair wise key establishment schemes using a trusted third party (BS), exploiting the initial trustworthiness of newly deployed sensors , and based on the framework of probabilistic key predeployment .A may adopt one  of these pair wise key establishment schemes according to security requirements and resource constraints. Many logical-key-tree (LKH)-based group key management schemes have been proposed for secure multicast in wired networks, including LKH , ELK , subset difference, to name a few. Since these schemes were not designed for sensor networks, they are less optimized and less efficient when employed in sensor networks directly. A few schemes also discussed the management of group keys in sensor networks. In , an updated group key is distributed in a network through hop-by-hop encryption by trading computation for communication.

 

Network Model:

 

The Mining Group movement system also assumes that a sensor network is divided into cells (or grids) where each pair of nodes in neighboring cells can communicate directly with each other. Cell is the minimum unit for detecting events (referred to as detection cell) and for storing sensor data (referred to as storage cell). We assume the events of interest to the MSs are classified into multiple types. For example, when a sensor network is deployed for monitoring the activities and locations of the animals in a wild animal habitat, all the activities of a certain kind of animal may be considered as belonging to one event type. We do not assume a fixed BS in the network. Instead, a trusted MS may enter the network at an appropriate time and work as the network controller for collecting data or performing key management. We also assume the clocks of sensor nodes in a network are loosely synchronized based on an attack-resilient time synchronization protocol .

 

Location-Based Forwarding:

 

Location-based forwarding has been studied for both mobile ad hoc networks and sensor networks. The location- aided routing  was proposed to reduce the cost of discovery by restricted area flooding when the uncertainty about a destination is limited. Greedy routing schemes, e.g., GPSR , choose the next hop that provides most progress toward the destination. In these schemes, the delivery of packets is guaranteed by planarizing the network graph and applying detour algorithms which avoid obstacles using the “right hand rule” strategy. Niculescu and Nath  proposed trajectory-based routing, in which the source encodes trajectory to traverse and embeds it into each packet. Upon the arrival of each packet, intermediate nodes employ greedy forwarding techniques such that the packet follows its trajectory as much as possible. With this scheme,  routing becomes source-based while there is no need for maintaining routing tables at intermediate nodes. We note that the scheme is suitable for a regular shape trajectory, not for totally random shape trajectory.

 

Security Assumption:

 

We assume that an authorized MS has a mechanism to authenticate broadcast messages, and every node can verify the broadcast messages. We also assume that when an attacker compromises a node he can obtain all the sensitive keying material possessed by the compromised node. Note that although technically an attacker can compromise an arbitrary number of current generation of sensor nodes without much effort, we assume that only nodes in a small number of cells have been compromised. For instance, it may not be very easy for sensor nodes to be captured because of their geographic locations or their tiny sizes. Also, the attacker needs to spend longer time on compromising more sensor nodes, which may increase the chance of being identified. For simplicity, we say a cell is compromised when at least one node in the cell is compromised. To deal with the worst scenario, we allow an attacker to selectively compromise s cells. We assume the existence of anti-traffic analysis techniques if so required. If an attacker is capable of monitoring and collecting all the traffic in the network, he may be able to correlate the detection cells and the storage cells without knowing the mapping functions.

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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