Using geospatial information in sensor networks

… of Sensors, American Scientific Publishers (ASP), 2005

Eurasip Journal on Advances in Signal Processing, 2009

Monitoring a large area with stationary sensor networks requires a very large number of nodes which with current technology implies a prohibitive cost. The motivation of this work is to develop an architecture where a set of mobile sensors will collaborate with the stationary sensors in order to reliably detect and locate an event. The main idea of this collaborative architecture is that the mobile sensors should sample the areas that are least covered (monitored) by the stationary sensors. Furthermore, when stationary sensors have a "suspicion" that an event may have occurred, they report it to a mobile sensor that can move closer to the suspected area and can confirm whether the event has occurred or not. An important component of the proposed architecture is that the mobile nodes autonomously decide their path based on local information (their own beliefs and measurements as well as information collected from the stationary sensors in a neighborhood around them). We believe that this approach is appropriate in the context of wireless sensor networks since it is not feasible to have an accurate global view of the state of the environment.

Proceedings of the 13th International Conference on Software Technologies, 2018

Sensor networks are recently rapidly growing research area in wireless communications and distributed networks. A sensor network is a densely deployed wireless network of small, low cost sensors, which can be used in various applications like health, environmental monitoring, military, natural disaster relief, and finally gathering and sensing information in inhospitable locations to name a few. In this paper, we focus on one specific type of sensor network called MQTT, which stands for Message Queue Transport Telemetry. MQTT is an open source publisher/subscriber standard for M2M (Machine to Machine) communication. This makes it highly suitable for Internet of Things (IoT) messaging situations where power usage is at a premium or in mobile devices such as phones, embedded computers or microcontrollers. In its original state, MQTT is lacking the ability to broadcast geolocation as part of the protocol itself. In today's age of IoT however, it has become more pertinent to have geolocation as part of the protocol. In this paper, we add geolocation to the MQTT protocol and offer a revised version, which we call MQTT-G. We describe the protocol here and show where we were able to embed geolocation successfully.

2006 IEEE Region 5 Conference, 2006

As there are many applications of geosensor networks, these are widely used and attract many researchers. Geosensor networks are used in real world applications for object tracking, environmental monitoring and controlling events. These networks consist of set of sensor nodes placed in different locations. To monitor an environmental area a very basic issue that arise is the optimization of deployment. In this there is a problem in estimation of its spatial coverage. Coverage is the major issue, because if there are certain obstacles in the network which increase the deployment complexity and increases holes and uncovered area. These coverage holes should be detected and minimized or removed completely using coverage optimization process. Many researchers have been proposed optimization approaches. Some of these approaches use Vornoi diagram and Delaunay triangulation to identify the coverage holes in the network and optimize the sensor deployment in the area under consideration. But many of these methods reduces the performance of the network and also the quality of data. This paper gives an optimization framework by integrating spatial information while making the deployment. The approach is completely based on Vornoi approach including the spatial coverage estimation process. Finally the results have been presented with respect to performance of the network.

2010

In this paper, we propose and evaluate a distributed, energy-efficient, lightweight framework for target localization and tracking in wireless sensor networks. Since radio communication is the most energy-consuming operation, this framework aims to reduce the number of messages and the number of message collisions, while providing refined accuracy. The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on distance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimated location using the distance ratio, RVI localizes the target accurately with only three sensors' participation. After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a small area, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reporting frequency considering the target's movement so that we can reduce the number of report messages while maintaining tracking quality. Extensive simulation results show that the proposed framework combining RVI and the movement-adaptive report scheduling algorithm reduces the localization error and total number of the transmitted messages up to half of those of the existing approaches.

2000

Ad Hoc Networks

Large scale dense wireless sensor networks (WSNs) will be increasingly deployed in different classes of applications for accurate monitoring. Due to this high density of nodes, it is very likely that both spatially correlated information and redundant data can be detected by several nearby nodes, which can be exploited to save energy. In this work we consider the problem of constructing a spatial correlation aware dynamic and scalable routing structure for data collection and aggregation in WSNs. Although there are some solutions for data aggregation in WSNs, most of them build their structures based on the order of event occurrence. This can lead to both low quality routing trees and a lack of load balancing support, since the same tree is used throughout the network lifetime. To tackle these challenges we propose a novel algorithm called dYnamic and scalablE tree Aware of Spatial correlaTion (YEAST). Results show that the routing tree built by YEAST provides the best aggregation quality compared with other evaluated algorithms. With YEAST an event can be sensed with 97% accuracy, and 75% of the nodes’ residual energy can be saved within the phenomena area when compared with the classical approach for data collection.

2011

In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis.