Wireless sensor actuator networks (WSANs) are becoming a solution for the implementation of control applications. Sensors and actuators can be deployed forming a large or dense network to monitor and control physical parameters or systems. However, this comes with challenges. Reliable data transmission and real-time communication constraints are the most significant challenges in WSANs for control applications because wireless networks are characterised by harsh transmission conditions. The use of WSANs for critical control applications has not gained sufficient progress as wireless networks are perceived to be totally unreliable and hence unsuitable. This makes reliable data transmission a priority in this research. Control applications will have a number of quality of service (QoS) requirements, such as requiring a very low packet-loss rate (PLR), minimum delay and guaranteed packet delivery. The overall goal of this research is to develop a framework that ensures reliable and real-time communication within the sensor network. A totally reliable network design involves ensuring reliability in areas such as the medium access control, connectivity, scalability, lifetime, clustering and routing with trade-offs such as energy consumption, system throughput and computational complexity. In this thesis, we introduce a unique method of improving reliability and real-time communication for control applications using a link quality routing mechanism which is tied into the ZigBee addressing scheme. ZigBee routing protocols do not consider link quality when making routing decisions. The results based on common network test conditions give a clear indication of the impact on network performance for various path loss models. The proposed link quality aware routing (LQAR) showed a highly significant 20.5% improvement in network delays against the ZigBee hierarchical tree routing (HTR) protocol. There is also a 17% improvement in the PLR. We also investigate variable sampling to mitigate the effects of delay in WSANs using a neural network delay predictor and observer based control system model. Our focus on variable sampling is to determine the appropriate neural network topology for delay prediction and the impact of additional neural network inputs such as PLR and throughput. The major contribution of this work is the use of typical obtainable delay series for training the neural network. Most studies have used random generated numbers which are not a correct representation of delays actually experienced in a wireless network. In addition, results show that the use of network packet loss information improves the prediction accuracy of delay. Our results show that adequate prediction of the time-delay series using the observer based variable sampling model influences the performance of the control system model under the assumptions and stated conditions.

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / The use of wireless data networks is ubiquitous in the consumer world. They have gained significant traction due to advantages afforded by the lack of wires. These same advantages can prove valuable in Flight Test for data acquisition. Sensor nodes are ideal candidates for low bandwidth wireless networks. Located in remote, hard to reach and hostile environments, wirelessly acquiring data from such sensor can solve a number of existing issues for FTI engineers. Implementing such wireless communication introduces a number of challenges such as guaranteeing reliable transfer of the sensor data and time synchronization of the remote nodes. This paper addresses wireless sensor acquisition, the associated challenges and discusses approaches and solutions to these problems.

published_or_final_version / Computer Science / Master / Master of Philosophy

Distributed imaging using sensor arrays is gaining popularity among various research and development communities. A common bottleneck within such an imaging sensor network is the large resulting data load. In applications for which transmission power and/or bandwidth are constrained, this can drastically decrease the network lifetime. In this dissertation, we consider a network of imaging sensors. We address the problem of energy-efficient communication of the resulting measurements. First, we develop a heuristic-based method that exploits the redundancy in the measurements of imaging sensors. The algorithm attempts to maximize the lifetime of the network without utilizing inter-sensor communication. Gains in network lifetime up to 114% are obtained when using the suggested algorithm with lossless compression. Our results also demonstrate that when lossy compression is employed, much larger gains are achieved. For example, when a normalized Root-Mean-Squared- Error of 0.78% can be tolerated in the received measurements, the network lifetime increases by a factor of 2.8, as compared to the lossless case. Second, we develop a novel theory for maximizing the lifetime of unicast multihop wireless sensor networks. An optimal centralized solution is presented in the form of an iterative algorithm. The algorithm attempts to find a Pareto Optimal solution. In the first iteration, the minimum lifetime of the network is maximized. If the solution is not Pareto Optimal a second iteration is performed which maximizes the second minimum lifetime subject to the minimum lifetime being maximum. At the nth iteration, the algorithm maximizes the nth minimum lifetime subject to the (n−1)th minimum lifetime being maximum, subject to the (n−2)th minimum lifetime being maximum, etc. The algorithm can be stopped at any iteration n. Third, we present a novel algorithm for the purpose of exploiting the inherent inter- and intra-sensor correlation in a network of imaging sensors while utilizing inter-sensor communication. This algorithm combines a collaborative compression method in conjunction with our cooperative multi-hop routing strategy in order to maximize the lifetime of the network. This CMT algorithm is demonstrated to achieve average gain in lifetime as high as 3.2 over previous methods. Finally, we discuss practical implementation considerations of our CMT algorithm. We first present some experimental results that illustrate the practicality of our method. Next, we develop a realistic optical model that permits us to consider a more heterogeneous network of cameras by allowing for varying resolution, intrinsic and extrinsic parameters, point-spread function and detector size. We show that our previous CMT algorithm can be extended to successfully operate in such a diverse imaging model. We propose new object-domain quality metrics and show that our proposed method is able to balance lifetime and fidelity according to expectations.

Rapid advances in hardware technology are making it possible to manufacture different types of Sensor Nodes (SN) that results in fast growing heterogeneous Wireless Sensor Networks (WSNs). These WSN’s are applicable for a wide range of applications relevant for military, industry and domestic use. However, WSNs have particular features such as scarce resources which can affect their performance. In addition, WSNs are subject to experience changes that can occur both within the condition of the network, due to factors such as node mobility or node failure (prevalent in harsh environments), and with regards to user requirements. Consequently, it is vital for WSNs to sense the current network conditions and user requirements to be able to perform efficiently. Cognition is necessarily introduced in WSNs as a response to this need. Cognition in the context of WSNs deals with the ability to be aware of the environment and user requirements and to proactively adapt to changes. This thesis proposes a hierarchical architecture along with a cognitive network management protocol capable of enabling cognition in WSNs. Specifically; this research introduces Cognitive Nodes (CN) into WSNs so that they can manage the cognitive network. The cognitive network management process is composed of three sub-processes: 1) scanning the network, 2) decision-making, and 3) updating the nodes from taken decisions.Scanning the network process aims to provide an awareness of current network conditions. Therefore, at the first execution, each CN creates a profile table for each node in its purview and updates the tables periodically during the network operation. In decision making process, CNs make necessary decisions in terms of the working state of SNs (active/sleep), the duration of this state, and the Frequency of Sensing (FoS). Decision making process uses an optimization scheme to find the optimal number of active SNs in order to prolong the lifetime of the network. Finally, the nodes will be informed of the taken decisions. Based on the simulation and implementation results, the proposed cognitive WSN shows a significant enhancement in terms of the network’s longevity, its ability to negotiate competing objectives, and its ability to serve users more efficiently. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-03-04 12:39:24.502

Sensor networks comprise resource-constrained wireless nodes with the capability of gathering information about their surroundings and have recently risen to prominence with the promise of being an effective computing platform for diverse applications, ranging from event detection to environmental monitoring. The database community proposed the use of sensor network query processors (SNQPs) as means to meet data collection requirements using a declarative query language. Declarative queries posed against a sensor network constitute an effective means to repurpose sensor networks and reduce the high software development costs associated with them. The range of sensor network applications is very broad. Such applications have diverse, and often conflicting, QoS expectations in terms of the delivery time of results, the acquisition interval at which data is collected, the total energy consumption of the deployment, or the network lifetime. The conflicting nature of these desiderata is aggravated by the resource-constrained nature of sensor networks as a computing fabric, making it particularly challenging to reconcile the trade-offs that arise. Previously, SNQPs have been focussed on evaluating queries as energy-efficiently as possible. There has been comparatively less work on attempting to meet a broad range of optimization goals and constraints that captured these QoS expectations. In this respect, previous work in SNQP has not aimed at being general purpose across the breadth of applications to which sensor networks have been applied. This PhD dissertation presents an approach for enabling QoS-awareness in SNQPs so that query evaluation plans are generated that exhibit good performance for a broader range of sensor network applications in terms of their QoS expectations. The research contributions reported here include (a) a functional decomposition of the decision-making steps required to compile a declarative query into a query evaluation plan in a sensor network setting; (b) algorithms to implement these decision-making steps; and (c) an empirical evaluation to show the benefits of QoS-awareness compared to a representative fixed-goal SNQP.

This thesis discusses the security of sensor networks. First, an overview of the security architectures of two dominant implementations of sensor networks in the market today is presented: the TinyOS stack and the IEEE 802.15.4 stack. Their similarities and differences are explored and their strength and limitations are discussed. Where applicable, comparisons are made with IEEE 802.11 Wireless LAN to highlight improvements and lessons learned. It is pointed out that in general, IEEE 802.15.4 offers better security, but replay protection is effectively missing in today's implementations and access control is poorly implemented. Consequently, TinyOS is still the better option for devices with severe resource constraints. Finally, as a tool to aid in the security analysis of sensor network, the design and implementation of a TinyOS sniffer is presented and captured frames for a simple sensor network application are analyzed for the purpose of validation.

The resilience of wireless sensor networks is investigated. A key concept is that scale-free network principles can be adapted to artificially create resilient wireless sensor networks. As scale-free networks are known to be resilient to errors but vulnerable to attack, a strategy using "cold-start" diversity is proposed to reduce the vulnerability to attacks. The IEEE 802.15.4 MAC and ZigBee protocols are investigated for their ability to form resilient clusters. Our investigation reveals there exists deficiencies in these protocols and the possibility of selfdirected and attack-directed denial-of-service is significant. Through insights gained, techniques are recommended to augment the protocols, increasing their resilience without major changes to the standard itself. Since both topological and protocol resilience properties are investigated, our results reveal important insights. Simulation of the physical and media access control layers using ns-2 is carried out to validate key concepts and approach.

In this thesis, a variety of different approaches are proposed to study the energy restoration problem in wireless sensor networks by one or more robots. First, we introduce an on-demand decentralized strategy performed by a robot that visits the sensors in a predefined circular order. We study it both analytically and experimentally analyzing the impact of various network parameters on network coverage, disconnection time, and time sensors have to wait to be served. We then introduce an optimal centralized approach as a benchmark to assess how close to optimal our on-demand strategy is, and we discover that, for sufficiently large networks, the on-demand strategy is indeed optimal. We then propose an even simpler mechanism where the robot simply moves blindly along the circular order, which is experimentally shown to be as efficient as the other two. The results above apply to arbitrary sensor network; we then consider a common special topology: a linear arrangement of sensors, were we propose three restoring mechanisms. We compare them experimentally discovering, once again, that the simplest approach is also the best, in most cases. We finally consider the case of multiple robots. We propose two strategies where the network is portioned among the robots and each robot takes care of a portion, and we compare those with a collaborative strategy where all robots work on the global network. The main general result of this study is that simple solutions are often as good as more sophisticated ones. In fact, a totally blind strategy where a robot simply moves around restoring energy on its way turns out to be as efficient as the best possible centralized solution for most networks.

University of Macau / Faculty of Science and Technology. / Department of Electrical and Computer Engineering