23,778 research outputs found
A novel energy efficient wireless sensor network framework for object tracking
Object tracking is a typical application of Wireless Sensor Networks (WSNs), which
refers to the process of locating a moving object (or multiple objects) over time using
a sensor network. Object tracking in WSNs can be a time consuming and resource
hungry process due to factors, such as the amount of data generated or limited resources
available to the sensor network.
The traditional centralised approaches where a number of sensors transmit all information
to a base station or a sink node, increase computation burden. More recently static
or dynamic clustering approaches have been explored. Both clustering approaches suffer
from certain problems, such as, large clusters, redundant data collection and excessive
energy consumption. In addition, most existing object tracking algorithms mainly
focus on tracking an object instead of predicting the destination of an object.
To address the limitations of existing approaches, this thesis presents a novel framework
for efficient object tracking using sensor networks. It consists of a Hierarchical Hybrid
Clustering Mechanism (HHCM) with a Prediction-based Algorithm for Destinationestimation
(PAD). The proposed framework can track the destination of the object
without prior information of the objects movement, while providing significant reduction
in energy consumption. The costs of computation and communication are also
reduced by collecting the most relevant information and discarding irrelevant information
at the initial stages of communication. The contributions of this thesis are:
Firstly, a novel Prediction-based Algorithm for Destination-estimation (PAD) has
been presented, that predicts the final destination of the object and the path that
particular object will take to that destination. The principles of origin destination
(OD) estimation have been adopted to create a set of trajectories that a particular
object could follow. These paths are made up of a number of mini-clusters, formed
for tracking the object, combined together. PAD also contains a Multi-level Recovery
Mechanism (MRM) that recovers tracking if the object is lost. MRM minimises the
number of nodes involved in the recovery process by initiating the process at local level
and then expanding to add more nodes till the object is recovered.
Secondly, a network architecture called Hierarchical Hybrid Clustering Mechanism
(HHCM) has been developed, that forms dynamic mini-clusters within and across static
clusters to reduce the number of nodes involved in the tracking process and to distribute
the initial computational tasks amoung a larger number of mini-cluster heads.
Lastly, building upon the HHCM to create a novel multi-hierarchy aggregation and
next-step prediction mechanism to gather the most relevant data about the movement
of the tracked object and its next-step location, a Kalman-filter based approach for
prediction of next state of an object in order to increase accuracy has been proposed.
In addition, a dynamic sampling mechanism has been devised to collect the most
relevant data.
Extensive simulations were carried out and results were compared with the existing
approaches to prove that HHCM and PAD make significant improvements in energy
conservation. To the best of my knowledge the framework developed in unique and
novel, which can predicts the destination of the moving object without any prior historic
knowledge of the moving object
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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