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.

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.

In today’s era of the Internet of Things, it is crucial to study the real-time dependencies of the web, its failures and time delays. Today, smart grids, sensible homes, wise water networks, intelligent transportation, infrastructure systems that connect our world over are developing fast. The shared vision of such systems is typically associated with one single conception Internet of Things (IoT), where through the deployment of sensors, the entire physical infrastructure is firmly fastened with information and communication technologies; where intelligent observation and management is achieved via the usage of networked embedded devices. The performance of a real-time control depends not only on the reliability of the hardware and software used but also on the time delay in estimating the output, because of the effects of computing time delay on the control system performance. For a given fixed sampling interval, the delay and loss issues are the consequences of computing time delay. The delay problem occurs when the computing time delay is non-zero but smaller than the sampling interval, while the loss problem occurs when the computing time delay is greater than, or equal to, the sampling interval, i.e., loss of the control output. These two queries are analyzed as a means of evaluating real-time control systems. First, a general analysis of the effects of computing time delay is presented along with necessary conditions for system stability. In this thesis, we will focus on the experimental study of the closed loop control system in the internet of things to determine the cycle time constraints in case of link failure.

Wireless Sensor Networks(WSNs) have gained a lot of attention because of their potential to immerse deeper into people' lives. The applications of WSNs range from small home environment networks to large habitat monitoring. These highly diverse scenarios impose different requirements on WSNs and lead to distinct design and implementation decisions. This thesis presents an optimization framework for WSN design which selects a proper set of protocols and number of nodes before a practical network deployment. A Genetic Algorithm(GA)-based Sensor Network Design Tool(SNDT) is proposed in this work for wireless sensor network design in terms of performance, considering application-specific requirements, deployment constrains and energy characteristics. SNDT relies on offine simulation analysis to help resolve design decisions. A GA is used as the optimization tool of the proposed system and an appropriate fitness function is derived to incorporate many aspects of network performance. The configuration attributes optimized by SNDT comprise the communication protocol selection and the number of nodes deployed in a fixed area. Three specific cases : a periodic-measuring application, an event detection type of application and a tracking-based application are considered to demonstrate and assess how the proposed framework performs. Considering the initial requirements of each case, the solutions provided by SNDT were proven to be favourable in terms of energy consumption, end-to-end delay and loss. The user-defined application requirements were successfully achieved.

A wireless sensor network is a network consisting of spatially distributed, sometimeautonomous sensors, communicating wirelessly to cooperatively achieve some task. For example, a wireless sensor network may be used for habitat monitoring to ascertain the environment’s temperature, pressure, humidity, etc. In order for a wireless sensor network to provide such data, one needs to ensure there is connectivity between nodes. That is, nodes can communicate to exchange information. To analyze connectivity between sensors, the radio communication range of each sensor, also called the communication footprint, needs to be known. To date, the models used to analyze a sensor’s radio communication footprint have been overly simplistic (i.e., isotropic) and thus yield results not found in practice. Footprints are highly dependent on the deployment environments, which are typically heterogeneous and non-isotropic in structure. In this work, a ‘weak-monotonicity’ (W-M) model is leveraged to represent a footprint’s non-isotropic behavior. The work also considers the heterogeneity of the environment through the use of the log-normal shadowing model. In particular, the usable percentage of the W-M footprint (the area where the power exceeds the receiver threshold) in such environments is considered through analysis and simulation. We then develop an enhanced footprint model which overlays multiple W-M patterns and use this method to represent experimental propagation data. The work also considers the use of graph theory methods to analyze the connectivity of randomly deployed networks in nonhomogeneous, non-isotropic environments.

Recent advances in wireless sensor networks have led to an emergence of many routing protocols. Limited battery capacity of sensor nodes makes energy efficiency a major and challenge problem in wireless sensor networks. Thus, the routing protocols for wireless sensor networks must be energy efficient in order to maximise the network lifetime. In this thesis, we developed a centralised clustering, energy-efficient routing protocol for wireless sensor networks. Our protocol consists of a cluster head selection algorithm, a cluster formation scheme and a routing algorithm for the data transmission between cluster heads and the base station. The cluster head selection algorithm is performed by the base station using global information of the network. This algorithm aiming at choosing cluster heads that ensure both the intra-cluster data transmission and inter-cluster data transmission are energy-efficient. The cluster formation scheme is accomplished by exchanging messages between non-cluster-head nodes and the cluster head to ensure a balanced energy load among cluster heads. The routing algorithm is based on the optimal transmission range for the data transmission between cluster heads and the base station using multi-hop. The performance of our routing protocol is evaluated by comparing with three existing routing protocols on a simulation platform. The simulation results show that our protocol can achieve better performance in terms of energy efficiency and network lifetime. Because of the centralised algorithm and multi-hop routing, there is a small communication overhead and transmission delay when using our protocol. Since our protocol can save energy and prolong network lifetime, it is well suited for applications where energy and network lifetime are the primary considerations and small overhead and time delay can be tolerated. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1456494 / Thesis (M.Eng.Sc.) - University of Adelaide, School of Electrical and Electronic Engineering, 2009

<p>To predict the lifetime of wireless sensor networks before their installation is an important concern. The IEEE 802.15.4 standard is specifically meant to support long battery life time; still there are some precautions to be taken by which a sensor network system application based on the standard can be made to run for longer time periods.</p><p>This thesis defines a holistic approach to the problem of energy consumption in sensor</p><p>networks and suggests a choice of node architecture, network structure and routing</p><p>algorithm to support energy saving in the network. The idea and thrust of the thesis is that stand-alone measures such as selecting a low-power microcontroller with embedded transceiver will not alone be sufficient to achieve energy saving over the entire network. A comprehensive design study with energy saving as a primary task must be made. Focus given on the design objectives needs to look at different aspects – application code, network configuration code, routing algorithms etc to come up with an energy efficient network.</p>

The baseband part of IEEE 802.15.4 operated in 2.45 GHz mode is designed and implemented in this essay. First, the features of IEEE 802.15.4 WPAN(Wireless Personal Area Network), PHY layer and MAC Layer are introduced. Then the algorithm and VHDL of the baseband part of transceiver are designed and verified by FPGA board and logical analyzer.

In the environment of wireless sensor network, while one node want to send a message to another node, the most natural way is used broadcasting to distribute the message to the whole network. In the other words, as long as one node sends messages to the other node, its neighbor nodes can also listen to these messages, and then receive them. The advantage of broadcast networks is that can efficiently distribute data to multiple receivers. However, it has some drawbacks. A sensor network may be deployed in hostile environment where there are malicious attacks. The malicious attacker can send false messages to his neighbor nodes, and then rely on these neighbor nodes to distribute over the network. So if there are not any schemes of the security authentication in the message when a node wants to use broadcast, everyone can impersonate the sender and broadcast false messages. We call this a packet injection attack. So security is a main challenge in broadcast network. In order to authenticate a broadcast message¡Ait would conform to two conditions. First, insure that the data is transmitted from the claimed source. Second, the messages are not be modified en route. TESLA has been proposed to provide such services for sensor networks¡Ait mainly use time synchronization and delay disclosure key to protect encryption key¡CHowever, this scheme still has some drawbacks, so we propose some schemes to modify TESLA in this paper, and we will show these schemes can achieve better performance than previous ones.

Wireless sensor network has applications in environmental surveillance, healthcare, and military operations. Because the energy of sensor nodes is limited and nodes are unable to supply energy in real time, the purpose of many researches is to prolong lifetime of sensor network. Lifetime is times that the sink can collect data from all sensor nodes. When a user proposes a query, then the sink gathers data from all sensor nodes. The problem defined in the previous research is given a sensor network and residual energy of each node, and the energy consumption of transmitting a unit message between two nodes. Then this problem is to find a directed tree that maximize minimum residual energy. In this thesis, we define a new problem that given a sensor network and residual energy of each node, and the energy consumption of transmitting a unit message between two nodes. Then our problem is to find a path of each node, which maximize minimum residual energy. We prove this problem is NP-complete. We propose a heuristic algorithm and a similar heuristic algorithm for this problem.