Object Tracking Using Wireless Sensor Network

Wireless sensor network consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, where each sensors have the ability to collect, process and store information. These characteristics allow WSN (Wireless Sensor Network) to be used in wide range of application such as area monitoring, environmental sensing, battlefield surveillance, NBC (Nuclear Biological Chemical) attack detection and so on. In certain applications where the sensor field is large and the available budget cannot provide enough sensors to fully cover the entire sensor field. This provides the motivation to deploy minimum number of sensors by connecting which the entire sensor field can be fully covered. In this paper we propose an approximation algorithm for grid coverage and a technique namely regular energy efficient monitoring to make the sensors in the minimum size wireless sensor network energy efficient in order to increase the network life time while tracking the object in the network. Simulation shows that the proposed algorithm provides a good solution for grid coverage and energy consumption

TRACKING OF MOVING OBJECT IN WIRELESS SENSOR NETWORK

A Wireless Sensor Network is a collection of sensor nodes distributed into a network to monitor the environmental conditions and send the sensed data to the Base Station. Wireless Sensor Network is one of the rapidly developing area in which energy consumption is the most important aspect to be considered while tracking, monitoring, reporting and visualization of data. An Energy Efficient Prediction-based Clustering algorithm is proposed to track the moving object in wireless sensor network. This algorithm reduces the number of hops between transmitter and receiver nodes and also the number of transmitted packets. In this method, the sensor nodes are statically placed and clustered using LEACH-R algorithm. The Prediction based clustering algorithm is applied where few nodes are selected for tracking which uses the prediction mechanism to predict the next location of the moving object. The Current Location of the target is found using Trilateration algorithm. The Current Location or Predicted Location is sent to active Cluster Head from the leader node or the other node. Based on which node send the message to the Cluster Head, the Predicted or Current Location will be sent to the base station. In real time, the proposed work is applicable in traffic tracking and vehicle tracking. The experiment is carried out using Network Stimulator-2 environment. Simulation result shows that the proposed algorithm gives a better performance and reduces the energy consumption

A Prediction based Energy-Efficient Tracking Method in Sensor Networks

I. INTRODUCTION Recently, an increasing interest in deploying wireless sensor networks (WSNs) for real-life applications. OTSN is mainly used to track certain objects in a monitored area and to report their position to the application's users. Object tracking, which is also called target tracking, is a major field of research in WSNs and has many real-life applications such as wild life monitoring, security applications for buildings and compounds to avoid interference or trespassing, and international border monitoring for prohibited crossings. Additionally, object tracking is measured one of the most challenging applications in WSNs due to its application requirements, which place a heavy load on the network resources, mainly energy consumption. The main task of an object tracking sensor network (OTSN) is to track a moving object and to report its latest location in the monitored area to the application in an acceptable timely manner, and this dynamic process of sensing and reporting keeps the network's resources under heavy pressure. However, there has been a very limited focus on the energy lost by the computing components, which are referred to as microcontroller unit (MCU) and the sensing components OTSN is considered as one of the most energy-consuming applications of WSNs. Due to this fact, there is a necessity to develop energy-efficient techniques that adhere to the application requirements of an objecttracking system, which reduce the total energy consumption of the OTSN while maintaining a tolerable missing rate level

Operational Design And Modelling Of Fire Event Tracking System In Wireless Sensor Networks

In recent years, WSNs have been widely used for monitoring of environmental changes as they are capable of combining their sensing of a phenomenon with their computational functions and operate with limited resources to accomplish an intended task. Sensors can cooperatively monitor the surrounding environment and provide data that help in realizing the time evolution of the phenomenon and anticipating its effects. Consequently, such information would facilitate performing control actions that meet the predetermined goals. As distributed computing enables the exchange of real time data statistics obtained from various sources that need to be combined together to infer real abnormal conditions for management decisions. As tracking of an event depends on the event type, high-accuracy localization of an event such as fire is a serious challenge, where most of traditional detection systems depend on visualization (cameras) in making their control decision. Moreover, those systems concern the continuous detection of fire and do not provide reliable and feasible mechanism for tracking fire spread. This paper presents the design and modelling of a fire event tracking system that consists of indoor distributed sensor nodes and a powerful intelligent processing unit (controller) to detect fire events and compute information to provide desired safety decisions. Whenever the temperature in a premise increases, the system deploys cooperative centralized control functions to collect and process data statistics related to the fire. It exchanges direction, velocity, and/or position to take proper decision such as evacuating people from fire areas to a safe exit

Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning

“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv

An Energy-Efficient and Reliable Data Transmission Scheme for Transmitter-based Energy Harvesting Networks

Energy harvesting technology has been studied to overcome a limited power resource problem for a sensor network. This paper proposes a new data transmission period control and reliable data transmission algorithm for energy harvesting based sensor networks. Although previous studies proposed a communication protocol for energy harvesting based sensor networks, it still needs additional discussion. Proposed algorithm control a data transmission period and the number of data transmission dynamically based on environment information. Through this, energy consumption is reduced and transmission reliability is improved. The simulation result shows that the proposed algorithm is more efficient when compared with previous energy harvesting based communication standard, Enocean in terms of transmission success rate and residual energy.This research was supported by Basic Science Research Program through the National Research Foundation by Korea (NRF) funded by the Ministry of Education, Science and Technology(2012R1A1A3012227)