The efficient designs of Wireless Sensor Network protocols and intelligent Machine Learning algorithms, together have led to the advancements of various systems and applications for Smart Environments. By definition, Smart Environments are the typical physical worlds used in human daily life, those are seamlessly embedded with smart tiny devices equipped with sensors, actuators and computational elements. Since human user is a key component in Smart Environments, human motion activity patterns have key importance in building sensor network systems and applications for Smart Environments. Motivated by this, in this thesis my work is focused on human motion activity-aware sensor networks for Smart Environments. The main contributions of this thesis are in two important aspects: (i) Designing event activity context-aware sensor networks for efficient performance optimization as well as resource usage; and (ii) Using binary motion sensing sensor networks' collective data for device-free real-time tracking of multiple users. Firstly, I describe the design of our proposed event activity context-aware sensor network protocols and system design for Smart Environments. The main motivation behind this work is as follows. A sensor network, unlike a traditional communication network, provides high degree of visibility into the environmental physical processes. Therefore its operation is driven by the activities in the environment. In long-term operations, these activities usually show certain patterns which can be learned and effectively utilized to optimize network design. In this thesis I have designed several novel protocols: (i) ActSee for activity-aware radio duty-cycling, (ii) EAR for activity-aware and energy balanced routing, and (iii) ActiSen complete working system with protocol suites for activity-aware sensing/ duty-cycling/ routing. Secondly, I have proposed and designed FindingHuMo (Finding Human Motion), a Machine Learning based real-time user tracking algorithm for Smart Environments using Sensor Networks. This work has been motivated by increasing adoption of sensor network enabled Ubiquitous Computing in key Smart Environment applications, like Smart Healthcare. Our proposed FindingHuMo protocol and system can perform device-free tracking of multiple (unknown and variable number of) users in the hallway environments, just from non-invasive and anonymous binary motion sensor data.

Some industrial applications require deterministic and bounded gathering delays. We focus on the joint time slots and channel assignment that minimizes the time of data collection and provides conflict-free schedules. This assignment allows nodes to sleep in any slot where they are not involved in transmissions. Hence, these schedules save the energy budjet of sensors. We calculate the minimum number of time slots needed to complete raw data convergecast for a sink equipped with multiple radio interfaces and heterogeneous nodes traffic. We also give optimal schedules that achieve the optimal bounds. We then propose MODESA, a centralized joint slots and channels assignment algorithm. We prove the optimality of MODESA in specific topologies. Through simulations, we show that MODESA is better than TMCP, a centralized subtree based scheduling algorithm. We improve MODESA with different strategies for channels allocation. In addition, we show that the use of a multi-path routing reduces the time of data collection .Nevertheless, the joint time slot and channels assignment must be able to adapt to changing traffic demands of the nodes ( alarms, additional requests for temporary traffic ) . We propose AMSA , an adaptive joint time slots and channel assignment based on incremental technical solution. To address the issue of scalability, we propose, WAVE, a distributed scheduling algorithm for convergecat that operates in centralized or distributed mode. We show the equivalence of schedules provided by the two modes.

Reducing the energy consumption of available resources is still a problem to be solved in Wireless Sensor Networks (WSNs). Many types of existing routing protocols are developed to save power consumption. In these protocols, cluster-based routing protocols are found to be more energy efficient. A cluster head is selected to aggregate the data received from root nodes and forwards these data to the base station in cluster-based routing. The selection of cluster heads should be efficient to save energy. In our proposed protocol, we use static clustering for the efficient selection of cluster heads. The proposed routing protocol works efficiently in large as well as small areas. For an optimal number of cluster head selection we divide a large sensor field into rectangular clusters. Then these rectangular clusters are further grouped into zones for efficient communication between cluster heads and a base station. We perform MATLAB simulations to observe the network stability, throughput, energy consumption, network lifetime and the number of cluster heads. Our proposed routing protocol outperforms in large areas in comparison with the LEACH, MH-LEACH, and SEP routing protocols.

A Wireless Sensor Network (WSN) is a network composed of a large number of nodes that sense, collect, transmit, and deliver data to where it is needed. Considering the variety of applications, the varied efficiency of WSNs in different environments, and their ability to interact with its surrounding, there are still many challenges to be met and problems to be solved. Overcoming these challenges requires a protocol that is tasked with providing and designing a system that is highly efficient, thus saving energy. In WSNs with Mobile-Actors, the task is first to find an effective way to decrease the length of the tour that the M-Actor follows for data gathering. Nonetheless, this short length should be with a guarantee to access all nodes in the networks to collect the sensory data. In this thesis, we propose a protocol that contributes to reducing energy consumption in WSNs by decreasing the M-Actor path and by using Rendezvous Points (RPs) that are distributed around the network. In addition, the proposed protocol increases the network lifetime by consuming less energy in comparison with a similar protocol and offers reasonable spending time for data collection. All of that with guarantee of offering an access for all nodes inside the network to exchange their data with the M-Actors by the suggested RP algorithm. One or more nodes can be represented by a single RP that provide connectivity to all nodes in it wireless range. In case where more M-Actors are used, less time is required for traversing the network for data gathering. It is shown in this research the tour time can be reduced significantly by using more than one M-Actors.

Solving NP-hard facility location problems in wireless network planning is a common scenario. In our research, we study the Covering problem, a well known facility location problem with applications in wireless network deployment. We focus on networks with a sparse structure. First, we analyzed two heuristics of building Tree Decomposition based on vertex separator and perfect elimination order. We extended the vertex separator heuristic to improve its time performance. Second, we propose a dynamic programming algorithm based on the Tree Decomposition to solve the Covering problem optimally on the network. We developed several heuristic techniques to speed up the algorithm. Experiment results show that one variant of the dynamic programming algorithm surpasses the performance of the state of the art mathematical optimization commercial software on several occasions. / ix, 89 leaves : ill. ; 29 cm

In this thesis we directly consider an object tracking problem for wireless sensor networks (WSNs), called track persistence. Track persistence temporally extends the problem of object tracking by seeking to store and retrieve the entire history of an object. To provide an initial solution to track persistence, we develop two distinct algorithms. The first algorithm, update to sink, translates track persistence into a centralized problem. The second algorithm, a linked list-like algorithm, builds a dynamic data structure as the object traverses the network, and rebuilds the object history distributively upon demand. We conduct worst case analysis upon both of these algorithms. Finally, we implement a simulation environment and run a number of tests upon both algorithms. Track persistence is a very challenging problem, and this thesis contributes a pair of solutions which stand as a basis for future research. / Thesis (Master, Computing) -- Queen's University, 2010-09-09 12:56:50.921

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In this thesis, resource allocation and multiple access in cognitive radio (CR) and compressed sensing (CS)-based wireless networks are studied. Energy-efficiency oriented design becomes more and more important in wireless systems, which motivates us to propose a location-aware power strategy for single user and multiple users in CR systems and a CS-based processing in wireless sensor networks (WSNs) which reduces the number of data transmissions and energy consumption by utilizing sparsity of the transmitted data due to spatial correlation and temporal correlation. In particular, the work on location-aware power allocation in CR system gives a brief overview of the existing power allocation design in the literature and unifies them into a general power allocation framework. The impact of the network topology on the system performance is highlighted, which motivates us to propose a novel location-aware strategy that intelligently utilizes frequency and space opportunities and minimizes the overall power consumption while maintaining the quality of service (QoS) of the primary system. This work shows that in addition to exploring the spectrum holes in time and frequency domains, spatial opportunities can be utilized to further enhance energy efficiency for CR systems. Then the work of resource allocation is extended to finding the power strategy and channel allocation optimization for multiple secondary users in an orthogonal frequency division multiplexing (OFDM) based cognitive radio network. Three different spectrum access methods are considered and utilized adaptively according to the different locations of the secondary users, and we unify these spectrum access methods into a general resource allocation framework. An interference violation test is proposed to decide the parameters in this framework that indicate the set of licensed channels to be sensed. The proposed scheme intelligently utilizes frequency and space opportunities, avoids unnecessary spectrum sensing and minimizes the overall power consumption while maintaining the quality of service of the primary system. The uncertainty of channel state information between the secondary users (SUs) and the primary users (PUs) is also taken into account in the study of power and channel allocation optimization of the SUs. Simulation results validate the effectiveness of the proposed method in terms of energy efficiency and show that enhanced performance can be obtained by utilizing spatial opportunities. The work on CS-based WSNs considers the application of compressed sensing to WSNs for data measurement communication and reconstruction, where N sensor nodes compete for medium access to a single receiver. Sparsity of the sensor data in three domains due to time correlation, space correlation and multiple access are being utilized. A CS-based medium access control (MAC) scheme is proposed and an in depth analysis on this scheme from a physical layer perspective is provided to reveal the impact of communication signal-to-noise ratio on the reconstruction performance. We show the process of the sensor data converted to the modulated symbols for physical layer transmission and how the modulated symbols recovered via compressed sensing. This work further identifies the decision problem of distinguishing between active and inactive transmitters after symbol recovery and provides a comprehensive performance comparison between carrier sense multiple access and the proposed CSbased scheme. Moreover, a network data recovery scheme that exploits both spatial and temporal correlations is proposed. Simulation results validate the effectiveness of the proposed method in terms of communication throughput and show that enhanced performance can be obtained by utilizing the sensed signal’s temporal and spatial correlations. / Graduate