Planning device deployment is a fundamental issue in implementing wireless sensor network (WSN) applications. This design practice determines types, numbers and locations of devices in order to build a powerful and effective system using devices of limited energy supply and constrained capacities. The deployment plan decides the limits of many intrinsic properties of a WSN, such as coverage, connectivity, cost, and lifetime. In this thesis, we address the device deployment planning issues related to large-scale WSN systems. We consider a typical deployment planning scenario in a heterogeneous two-tier WSN composed of sensor nodes and relay nodes. Sensor nodes form the lower tier of the network and are responsible for providing satisfactory sensing coverage to the application. Relay nodes form the upper tier of the network and they are responsible for forwarding data from sensor nodes to the base station. As so, relay nodes should provide reliable connectivity to sensor nodes for an extended period of time. We therefore address the sensor node deployment in terms of the sensing coverage and relay node deployment in terms of the communication connectivity and system lifetime. For sensor node deployment, we propose a coverage-guaranteed sensor node deployment design technique. Using this technique, the sensing coverage is complete even if sensor nodes are randomly dispersed within a bounded range from its target locations according to a given grid pattern. In order to curb the increased cost due to extra sensor nodes that are used in the coverage-guaranteed deployment, while still maintaining a high-quality sensing coverage, we further study the probabilistic properties of the grid-based sensor node deployment in the presence of deployment errors. For relay node deployment, we propose to extend the system lifetime by distributing relay nodes according to a density function, which is optimized in response to the energy consumption rate, so that the energy is dissipated at an approximately same rate across the network. We further craft the deployment density function to reconcile the needs of balanced energy consumption and strong sensor node connectivity. The techniques proposed in this thesis fill the blank of available literature and can serve as guidelines for WSN designers, solution providers and system integrators of WSN applications. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-01-15 09:33:53.917

Wireless sensor networks have applications in many places from wildlife environments to urban areas. Implementation of such a network is a challenging task because each specific application may require different constraints and objectives. To better meet the application requirements, cognitive wireless sensor network has been recently introduced. However, almost all the previous work in this area has been in theory or by simulation. Hence there is a demand to provide implementable ideas of cognition, implement, and analyze the results. The goal of this thesis is to implement a cognitive wireless sensor network with application in environment monitoring which is aware of the surrounding environment, updates its information based on the dynamic changes in the network status, makes appropriate decisions based on the gained awareness, and forwards required actions to involving nodes. An implementable cognitive idea is proposed based on the characteristics and goals of a cognitive system. Since transmission is one of the most power consuming processes in sensor nodes and non-efficient transmissions of data can lead to a shorter lifetime, this work tries to schedule nodes' transmission rate by the means of cognition and benefits from efficient scheduling of the redundant nodes to improve lifetime. To enhance a wireless sensor network with cognition, new nodes should be added to the architecture called cognitive nodes. Cognitive nodes will take care of most of the tasks in the cognition process while still there is a need to add a level of cognition to each individual node. The main contribution of this work is that it provides an implementable approach to cognition in wireless sensor networks, proposes a low complexity and low cost implementable idea for cognition, addresses implementation issues, and provides experimental results of different setups of the cognitive wireless sensor network. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-09-27 00:38:12.455

Prolonging the battery lifetime of sensors has been one of the most important issues in wireless sensor networks (WSNs). With the development of Wireless Power Transfer (WPT) technology, sensors can be recharged and possibly have infinite lifetime. One common approach to achieving this is having a wireless charging vehicle (WCV) move in the system coverage area and charge sensors nearby when it stops. The duration that the WCV stays at each charging location, the amount of traffic that each sensor carries, and the transmission power of individual sensors are closely related, and their joint optimization affects not only the data transmissions in the WSN but also energy consumption of the system. This problem is formulated as a mixed integer and nonconvex optimization problem. Different from existing work that either solves similar problems using genetic algorithms or considers charging sensors based on clusters, we consider the optimum charging time for each sensor, and solve the joint communication and charging problem optimally. Numerical results demonstrate that our solution can significantly reduce the average power consumption of the system, compared to the cluster-based charging solution. / Thesis / Master of Applied Science (MASc) / In a wireless sensor network (WSN), sensor nodes monitor the physical environment and forward the collected data to a data sink for further processing. Sensors are battery powered and, therefore, prolonging the lifetime of their batteries is critically important. In a rechargeable WSN (RWSN), prolonging the battery lifetime of sensors is achieved through reducing communication energy and recharging the batteries periodically. Reducing the communication energy consumption is done through choosing the best forwarding sensors (i.e., routing) for data collected by each sensor and deciding the transmission power of each sensor (i.e., power allocation). Recharging the batteries is achieved through harvesting energy from external sources. In this thesis, we consider a RWSN that uses wireless power transfer as the energy harvesting technology and jointly optimizes charging and communications in order to minimize the power consumption of the RWSN.

Many domains, such as geographical and biological sciences, can benefit from the ability of wireless sensor networks to provide long term, high temporal and spatial resolution sensing. Such networks must be able to trade off various requirements against each other to extend network lifetime while still providing useful, good quality data. The challenges faced by equipment in the field can very unpredictable and therefore a wireless sensor network should be able to cope with these challenges and return to a balanced state. Using readily available, low-cost components, this work was inspired by the human endocrine systems ability to maintain homeostasis, or balance, in a large number of parameters simultaneously. This work developed a number of endocrine inspired methods. These were aimed both at improving the power usage of nodes in a wireless sensor network and improving the quality of the data collected. Methods for improving power consumption and data quality were achieved. These methods were successfully deployed, for the purposes of environmental monitoring on a mesh network consisting of 20 nodes, for a period of almost 6 months. Analysis showed that the use of power by individual nodes was improved and that the endocrine inspired methods, aimed at improving data quality, were successful. Node lifetimes were extended, duplicate data reduced and the quality of data improved. The use of low-cost, readily available components was largely successful, and challenges and changes to these components were discussed.

The coverage and energy issues are the fundamental problems which prevent the development of wireless sensor networks. In order to accurately evaluate the monitoring quality (coverage), one needs to model the interactive of sensors, phenomenons and the environment. Furthermore, in collaborative with scheduling algorithm and computer optimization, protocols can improve the overall monitoring quality and prolong the lifetime of network. This thesis is an investigation of coverage problem and its relative applications in the wireless sensor networks. We first discuss the realistic of current boolean sensing model and propose an irregular sensing model used to determine the coverage in the area with obstacles. We then investigate a joint problem of maintaining the monitoring quality and extending the lifetime of network by using scheduling schemes. Since the scheduling problem is NP hard, genetic algorithm and Markov decision process are used to determine an achievable optimal result for the joint problem of coverage-preserving and lifetime-prolong. In order to avoid the cost of centralized or distributed scheduling algorithms, a localized coverage-preserving scheduling algorithm is proposed by exploring the construction process of Voronoi diagram. Besides exploring the coverage characteristic in a static wireless sensor network, we investigate the coverage problem when the mobile elements are introduced into network. We consider the single-hop mobile data gathering problem with the energy efficiency and data freshness concerns in a wireless sensor network where the connectivity cannot be maintained. We first investigate the upper/lower bound of the covering time for a single collector to cover the monitoring area. Through our investigation we show that for a bounded rectangle area a hexagon walk could explore the area more efficiently than a random walk when the edges of area are known. We then propose a virtual force mobile model (VFM) in which the energy consumption for data transmission is modeled as a virtual elastic force and used to guide of mobile collectors to move to optimal positions for energy saving.

Using multi-agent systems to execute a variety of missions such as environmental monitoring and target tracking has been made possible by the advances in control techniques and computational capabilities. Communication abilities between agents allow them to coact and execute several coordinated missions, among which there is optimal coverage. The optimal coverage problem has several applications in engineering theory and practice, as for example in environmental monitoring, which belongs to the broad class of resource allocation problems, in which a finite number of mobile agents have to be deployed in a given spatial region with the assignment of a sub-region to each agents with respect to a suitable coverage metric. The coverage metric encodes the sensing performance of individual agent with respect to points inside the domain of interest, and a distribution of risk density. Usually the risk density function measures the relative importance assigned to inner regions. The optimal coverage problem in which the risk density is time-invariant has been widely studied in previous research. The solution to this class of problems is centroidal Voronoi tessellation, in which each agent is located on the centroid of the related Voronoi cell. However, there are many scenarios that require to be modelled by time-varying risk density rather than time-invariant one, as for example in area coverage problems where the environment evolves independently of the evolution for the robotic agents deployed to cover the area. In this work, the changing environment is modeled by a time-varying density function which is governed by a convection-diffusion equation. Mixed boundary conditions are considered to model a scenario in which a diffusive substance (e.g., oil from a leaking event or radioactive material from a nuclear accident) enters the area with convective component from the boundary. A non-autonomous feed- back law is employed whose generated trajectories maximize the coverage metric. The asymptotic stability of the multi-agent system is proven by using Barbalat’s lemma, and then theoretical predictions are illustrated by several simulations that represent idealized scenarios.

The coverage and energy issues are the fundamental problems which prevent the development of wireless sensor networks. In order to accurately evaluate the monitoring quality (coverage), one needs to model the interactive of sensors, phenomenons and the environment. Furthermore, in collaborative with scheduling algorithm and computer optimization, protocols can improve the overall monitoring quality and prolong the lifetime of network. This thesis is an investigation of coverage problem and its relative applications in the wireless sensor networks. We first discuss the realistic of current boolean sensing model and propose an irregular sensing model used to determine the coverage in the area with obstacles. We then investigate a joint problem of maintaining the monitoring quality and extending the lifetime of network by using scheduling schemes. Since the scheduling problem is NP hard, genetic algorithm and Markov decision process are used to determine an achievable optimal result for the joint problem of coverage-preserving and lifetime-prolong. In order to avoid the cost of centralized or distributed scheduling algorithms, a localized coverage-preserving scheduling algorithm is proposed by exploring the construction process of Voronoi diagram. Besides exploring the coverage characteristic in a static wireless sensor network, we investigate the coverage problem when the mobile elements are introduced into network. We consider the single-hop mobile data gathering problem with the energy efficiency and data freshness concerns in a wireless sensor network where the connectivity cannot be maintained. We first investigate the upper/lower bound of the covering time for a single collector to cover the monitoring area. Through our investigation we show that for a bounded rectangle area a hexagon walk could explore the area more efficiently than a random walk when the edges of area are known. We then propose a virtual force mobile model (VFM) in which the energy consumption for data transmission is modeled as a virtual elastic force and used to guide of mobile collectors to move to optimal positions for energy saving.

Deployment of mobile nodes in a region of interest is a critical issue in building a mobile sensor network because it affects cost and detection capabilities of the system. The deployment of mobile sensors in essence is the movement of sensors from an initial position to a final optimal location. Considerable attention has recently been given to this deployment issue. Many of the distributed deployment schemes use the potential field method. In most cases, the negative gradient of the potential function becomes the feedback control input to a node. This assumes that the potential function is differentiable over the entire region. This assumption is valid primarily when the topology of the network is fixed. In this research, we analyze the stability of a network that uses piecewise smooth potential functions. A gravitation-like force is proposed to deploy a group of agents and to form a certain configuration. We use a nonsmooth version of the Lyapunov stability theory and LaSalle’s invariance principle to show asymptotic stability of the network which is governed by discontinuous dynamics. We propose a hierarchical structure using potential fields for mobile sensor network deployment. A group of mobile nodes first form a cluster using a potential field method and then cluster heads are used to establish a hexagonal structure that employs a higher level potential field. We consider specifically the problem of deploying a mobile sensor network so that a certain area coverage is realized and maintained. And we propose an algorithm for main taining the desired coverage that assumes the availability of a stochastic sensor model. The model reflects the decline of the sensor accuracy as the distance increases from the sensor. It is further assumed that each node’s sensor has a different sensing range to represent sensor performance deterioration due to power decay. The network deployment scheme combines artificial forces with individual sensor ranges. The validity and the effectiveness of the proposed algorithm are compared to the conventional methods in simulations. Simulation results confirm the effectiveness of the proposed algorithms with respect to a defined performance metric.

Wireless sensor network consists of a large number of sensors, which are capable of sensing, communication and data processing. In wireless sensor network, predictable or unpredictable death of sensor nodes may cause coverage and connectivity problems of the original network. In order to compensate the loss of coverage and connectivity, we propose a region-based movement scheme that divides the neighboring sensors of the dead sensor into a number of regions. The neighboring sensors are moved to repair the regions respectively by using the least mobility distance, and their existing coverage and connectivity are not jeopardized. Our work has better performance of maintaining coverage and connectivity of the network. By the results, our work can decrease the average mobility distance and coverage deterioration substantially.

This Master’s report presents the research leading to the development of a low power Wireless Sensor Network (WSN) and a discussion of an implementation of the WSN. This report assesses the power reduction techniques further by reviewing their influences upon functionality, throughput, latency, and data reliability. The software techniques were implemented on evaluation boards and actual performance gains were observed. Furthermore, the report provides insight into the selection of the processor, wireless protocol, and WSN architecture by comparing other options in regards to the power reduction, functionality, and data reliability. The architecture of the WSN consists of four sensor nodes, and a backbone router connected to a PC. The sensor nodes contain an application processor and a radio processor. The application processor is a Texas Instruments MSP430F5438 which is located on an MSP-EXP430F5438 evaluation board. The radio processor is a NIVIS Versa Node 210 that is located on a VS210 development board. The wireless protocol investigated is the ISA100.11a. / text