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This project studied the performance of ad hoc wireless sensor networks with respect to communications, event detection and localization. A combination of experiments and simulation were used. The experiments were conducted on a test bed consisting of over 200 sensor nodes. A probabilistic definition of connectivity in heterogeneous wireless sensor networks (WSNs) has been developed. Connectivity between two nodes is defined as whether a given packet or a series of packets can be delivered before a specific deadline with at least a given probability. Using this definition, admission control, routing and other network services considering timing requirements were built. Improvements in embedded system design and low-power wireless communication techniques have enabled the realization of large-scale systems that can directly interact with the environment without any human interaction. These systems are called cyber-physical systems. We have developed a cyber-physical system architecture, a novel event-based system design concept, and new theories for reasoning about time and space. Iterative decoding techniques have lead to a class of near-capacity achieving codes called low-density parity check codes. Theoretical constructs were developed to better describe these codes and to enable more accurate prediction of their performance, particularly at the short block lengths used in sensor networks.

Data gathering is a network communication task in which all of the network’s nodes send their individual messages to a distinguished sink node. In cognitive radio ad hoc and sensor wireless networks (CR-AHSWNs), unlicensed secondary users (SUs) opportunistically use channels when the licensed primary users are not using them. Therefore, the channels available to each SU vary with time and location, which makes the development of data gathering algorithms for CR-AHSWNs challenging. In this thesis, a data gathering protocol for CR-AHSWNs is proposed. The protocol consists of several distributed SU action selection and channel selection algorithms. An algorithm that can reduce the data gathering delay by selecting message forwarding SUs is also proposed. Finally, an algorithm that calculates an estimate of the successful data gathering ratio (SDGR) is proposed. The SDGR is affected by each SU’s channel availability and network collisions, and the exact value is extremely challenging to calculate.

Data gathering is a network communication task in which all of the network’s nodes send their individual messages to a distinguished sink node. In cognitive radio ad hoc and sensor wireless networks (CR-AHSWNs), unlicensed secondary users (SUs) opportunistically use channels when the licensed primary users are not using them. Therefore, the channels available to each SU vary with time and location, which makes the development of data gathering algorithms for CR-AHSWNs challenging. In this thesis, a data gathering protocol for CR-AHSWNs is proposed. The protocol consists of several distributed SU action selection and channel selection algorithms. An algorithm that can reduce the data gathering delay by selecting message forwarding SUs is also proposed. Finally, an algorithm that calculates an estimate of the successful data gathering ratio (SDGR) is proposed. The SDGR is affected by each SU’s channel availability and network collisions, and the exact value is extremely challenging to calculate.

Data gathering is a network communication task in which all of the network’s nodes send their individual messages to a distinguished sink node. In cognitive radio ad hoc and sensor wireless networks (CR-AHSWNs), unlicensed secondary users (SUs) opportunistically use channels when the licensed primary users are not using them. Therefore, the channels available to each SU vary with time and location, which makes the development of data gathering algorithms for CR-AHSWNs challenging. In this thesis, a data gathering protocol for CR-AHSWNs is proposed. The protocol consists of several distributed SU action selection and channel selection algorithms. An algorithm that can reduce the data gathering delay by selecting message forwarding SUs is also proposed. Finally, an algorithm that calculates an estimate of the successful data gathering ratio (SDGR) is proposed. The SDGR is affected by each SU’s channel availability and network collisions, and the exact value is extremely challenging to calculate.

One of the key concerns with location-aware Ad-hoc Wireless Sensor Networks (AWSNs) is how sensor nodes determine their position. The inherent power limitations of an AWSN along with the requirement for long network lifetimes makes achieving fast and power-efficient localization vital. This research examines the cost (in terms of power) of network irregularities on communications and localization in an AWSN. The number of data bits transmitted and received are significantly affected by varying levels of mobility, node degree, and network shape. The concurrent localization approach, used by the APS-Euclidean algorithm, has significantly more accurate position estimates with a higher percentage of nodes localized, while requiring 50% less data communications overhead, than the Map-Growing algorithm. Analytical power models capable of estimating the power required to localize are derived. The average amount of data communications required by either of these algorithms in a highly mobile network with a relatively high degree consumes less than 2.0% of the power capacity of an average 560mA-hr battery. This is less than expected and contrary to the common perception that localization algorithms consume a significant amount of a node's power.

We have made excellent progress on studying appropriate signal processing and communication strategies for networks of dispersed nodes where each node may be equipped with a sensor. One area of focus has been on developing appropriate signal processing and communication strategies for networks which focus on target detection or hypothesis testing problems. There are many important military and non-military applications of this paradyme. In particular, hypothesis testing, often called signal detection, is a fundamental sensor networking application which is key for solving many important problems including improved monitoring, control and repair of the human body, buildings, bridges, energy production facilities, the environment and other critical infrastructure, while also providing important contributions to homeland security, law enforcement, disaster prediction/avoidance and defense related problems. We have developed very promising approaches for saving battery power for important military applications employing sensors. For example, we developed a highly efficient approach which achieves the same performance as the optimum energy unconstrained approach but which saves energy proportional to the number of sensors employed provided the signal to be detected is observed with sufficient signal-to-noise ratio. For such cases, the average number of sensor transmissions saved over the optimum unconstrained energy approach is larger than half the number of sensors employed without any loss in performance.

In the context of this project, we worked on the development of a cross-layer resource management system over single hop and multihop Direct-Sequence Code Division Multiple Access (DS-CDMA) Visual Sensor Networks (VSNs). The target was the optimal determination and allocation of the source and channel coding rates and the power levels among the nodes of the considered network. For the efficient allocation of the nodes' transmission parameters, we applied various distortion-related criteria, drawing inspiration from game theory as well, and investigated fairness and efficiency under different perspectives for each of them. Computational intelligence optimization algorithms and reinforcement learning techniques were applied in order to solve the resulting optimization problems. All the solutions provided by the examined schemes were Pareto-optimal and thus, the choice about the most fair and efficient scheme is problem-dependent. Some of the approaches that were taken into account for further improving the end-to-end video distortion of a wireless multiple-access VSN included the time scheduling of transmitting nodes for interference mitigation, the optimal Instantaneous Decoding Refresh (IDR) frame placement during encoding, the content-based node prioritization, and the weighted bi-objective optimization for measuring the trade-off between video quality and consumed transmission power. The evaluation of all proposed approaches showed that enhanced end-to-end video quality can be achieved, by keeping power consumption at reasonable levels, when compared to the case of assigning equal values to the transmission parameters of all nodes constituting the network.

In this thesis we present LaSLAT, a sensor network algorithm that uses range measurements between sensors and a moving target to simultaneously localize the sensors, calibrate sensing hardware, and recover the target's trajectory. LaSLAT is based on a Bayesian filter that updates a probability distribution over the parameters of interest as measurements arrive. The algorithm is distributable and requires a fixed amount of storage space with respect to the number of measurements it has incorporated. LaSLAT is easy to adapt to new types of hardware and new physical environments due to its use of intuitive probability distributions: one adaptation demonstrated in this thesis uses a mixture measurement model to detect and compensate for bad acoustic range measurements due to echoes. We present results from a centralized implementation of LaSLAT using a network of Cricket sensors. In both 2D and 3D networks, LaSLAT is able to localize sensors to within several centimeters of their ground truth positions while recovering a range measurement bias for each sensor and the complete trajectory of the mobile.