Clock synchronization for Wireless Sensor Networks (WSNs) has attracted lots of attention due to its importance for operations in WSNs. In traditional centralized clock synchronization algorithms, all the local information should be transmitted to a fusion center for processing, and the results need to be forwarded back to each individual sensor, thus resulting in a heavy burden on communication and computation in the network. In addition, it also lacks of adaptability to link failures and dynamic changes in the network topology, which greatly prevents their use in WSNs. Hence, in this thesis, we focus on developing energy-efficient distributed clock synchronization algorithms for WSNs. Firstly, global clock synchronization problem is investigated with time-varying clock parameters (skew and offset) owing to imperfect oscillator circuits. A distributed Kalman filter is developed for clock parameters tracking. The proposed algorithm only requires each node to exchange limited information with its direct neighbors, thus is energy efficient, scalable with network size, and is robust against changes in network connectivity. A low-complexity distributed algorithm based on Coordinate-Descent with Bootstrap (CD-BS) is also proposed to provide rapid initialization of the tracking algorithm. Simulation results show that the proposed distributed tracking algorithm achieves the long-term accuracy for the clock parameters close to the Bayesian Cramer-Rao Lower Bound. Secondly, the problem of global clock synchronization for WSNs in the presence of unknown exponential delays is studied. The joint maximum likelihood estimator of clock offsets, clock skews and fixed delays of the network is first formulated as a global linear programming (LP) problem. Based on the Alternating Direction Method of Multipliers (ADMM), we propose a fully-distributed synchronization algorithm that has low communication overhead and computation cost. Simulation results show that the proposed algorithm achieves better accuracy than consensus algorithm and the distributed least squares algorithm, and can always converge to the centralized optimal solution. Finally, global clock synchronization for WSNs under the exponentially distributed delays is re-visited with the fast convergence min-sum algorithm. The synchronization problem is cast into an optimization problem represented by factor graph, and a closed-form expression of the messages passed between nodes are derived. Simulation results show that this distributed algorithm can approach the centralized LP solution with faster convergence speed compared to ADMM-based algorithm. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / This paper describes some of the security challenges faced by Wireless Sensor Networks (WSN). A classification and analysis of prominent attacks on the routing protocols of WSN is provided, along with a review of recent developments in the field to help mitigate the impact of these attacks.

Along with the rapid growth of Wireless Sensor Networks, a wide range of challenges have come to existence to make the network more robust and versatile. Gaining energy efficiency and maximizing network lifetime are the most important of all that can affect the performance of the network directly. In this thesis, a number of research aspects related to energy efficient data gathering have been investigated and some promising proposals are presented. In large, hierarchical multi-hop Wireless Sensor Networks, power consumption characteristics of the static sensor nodes and data traffic distribution across the network are largely determined by the node position and the adopted routing protocol. In this thesis, these phenomena of the network are addressed analytically and we proposed some methods to divide the monitoring field into partitions that act as the basis for even load distribution in the network. We proposed an algorithm to calculate the area of the partitions that exploits the energy efficient features of optimal transmission range. The partition works as the bedrock of the other proposals in this thesis. Considering the influential factors of the proximity and the recent state of the network, we also developed a routing protocol that minimises over all energy consumption of the network and is able to dynamically select a route to the sink. Further, we proposed a rotational order for data gathering scheme that works along with the routing protocol to ensure load balancing and to alleviate data congestion around the sink. Clustered organization of the nodes in sensor networks can further save energy consumption and facilitates scope for better network management. In this thesis, we address the fact that equal sized clusters can cause unbalanced data traffic around the sink. So, we propose a method to calculate suitable cluster radii in different regions of the monitoring field in order to form clusters of different sizes. To ensure unequal clusters in the field, a cluster construction procedure is also proposed targeting minimal data generation, minimal energy consumption and providing capacity for reliability preservation. Furthermore, the notion of redundant nodes and the outlines of a possible solution to identify and deactivate redundant nodes are explained in this thesis. Since the clusterheads play an important role as coordinators in the clusters, it is vital that there is a clusterhead in every cluster all the time. In this thesis, a message optimal and distributed leader election algorithm is proposed to select a new clusterhead in case of unexpected and unnoticed failure of a clusterhead node. Detailed analysis and simulation of the proposed methods clarify the effectiveness of the research. In comparison with other methods of similar kind, our methods confirm better balanced energy dissipation, energy efficient route selection, message optimal clusterhead selection and prolonged lifetime of the network.

Recent advances in wireless and sensing technologies have enabled the deployment of large scale Wireless Sensor Networks (WSNs) which have a wide range of scientific and commercial applications. However, due to the limited energy supply of sensor nodes, extending the network lifetime has become crucial for WSNs to deliver their promised benefits. Several proposals have aimed at this objective by designing energy efficient protocols at the physical, medium access, and network layers. While the proposed protocols achieve significant energy savings for individual sensor nodes, they fail to solve topology-related problems. An example of such problems is the bottlenecks around the sink, which is a direct result of multi-hop relaying: sensor nodes around the sink relay data generated all over the network which makes them deplete their energy much faster than other nodes. A natural solution to this problem is to have multiple mobile data collectors so that the load is distributed evenly among all nodes. We investigate this promising direction for balancing the load and, hence, prolonging the lifetime of the network. We design optimization schemes for routing and placement of mobile data collectors in WSNs. We show, by theoretical analysis and simulations, that our approach has the potential to prolong the lifetime of the network significantly. / Thesis (Ph.D, Computing) -- Queen's University, 2009-04-26 21:58:55.152

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Wireless sensor networks are used to collect sensor data in different applications such as environmental monitoring, smart building control, and health care applications. Wireless sensor nodes used are typically small, low-cost, and battery powered. The nodes are often hard to access after deployment, for example when they are in remote  locations. Another property of wireless sensor networks is that their operation is dependent on the environment they operate in, both due to the specific sensor readings but also due to the effects on communication by factors such as fading and radio interference. This makes it important to evaluate a wireless sensor network in its intendent target environment before final deployment. To enable experiments with wireless sensor networks in their target environment, we have designed and implemented a testbed called Sensei-UU. It is designed to allow WSN experiments to be repeated in different locations, thus exposing effects caused by the environment. To allow this, the testbed is designed to be easily moved between experimental sites. One type of WSN applications Sensei-UU is aimed to evaluate is protocols where nodes are mobile. Mobile testbed nodes are low-cost robots which follow a tape track on the floor. The localization accuracy of the robot approach is evaluated and is accurate enough to expose a protocol to fading phenoma in a repeatable manner. Sensei-UU has helped us develop a lightweight interference classification approach, SoNIC, which runs on standard motes. The approach only use information from a standard cc2420 chipset available when packets are received. We believe that the classification accuracy is good enough to motivate specific transmission techniques avoiding interference. / WISENET

The thesis focuses on enabling the communication between Wireless Sensor Networks and Internet-of-Things applications.  In order to achieve this goal, the first step has been to investigate the concept of the Internet-of-Things and then to understand how this scenario could be used to interconnect multiple Wireless Sensor Networks in order to develop context-aware applications which could handle sensor data coming from this type of network.  The second step was to design and implement a communication stack which enabled Wireless Sensor Networks to communicate with an Internet-of-Things platform. The CoAP protocol has been used as application protocol for the communication with the Wireless Sensor Networks. The solution has been developed in Java programming language and extended the sensor and actuator layer of the Sensible Things platform.  The third step of this thesis has been to investigate in which real world applications the developed solution could have been used. Next a Proof of Concept application has been implemented in order to simulate a simple fire detection system, where multiple Wireless Sensor Networks collaborate to send their temperature data to a control center. The last step was to evaluate the whole system, specifically the responsiveness and the overhead introduced by the developed communication stack.

In the multiratecasting problem in wireless sensor networks, the source sensor is usually required to report to multiple destinations at dif- ferent rates for each of them. We present a MST-based rate-aware cost-efficient multiratecast routing protocol (MSTRC). The proposed MSTRC examines only one set partition of destinations at each for- warding step. A message split occurs when the locally-built minimum spanning tree (MST) over the current node and the set of destina- tions has multiple edges originated at the current node. Destinations spanned by each of these edges are grouped together, and for each of these subsets the best neighbor is selected as the next hop. We also suggested a novel face recovery mechanism to deal with void ar- eas, when no neighbor provides positive progress toward destinations. It constructs a MST of current node and destinations without the progress via neighbors, and for each set partition of destinations cor- responding to an edge e in MST, the face routing keeps going until a node that is closer to one of these destinations is found, allowing for greedy continuation, while the process repeats for the remaining desti- nations similarly. Our experimental results demonstrate that MSTRC is highly rate-efficient in all scenarios, and unlike existing solutions, it is adaptive to destination rate deviations.

A major issue in distributed wireless sensor networks (WSNs) is the design of efficient distributed algorithms for network-wide dissemination of information acquired by individual sensors, where each sensor, by itself, is unable to access enough data for reliable decision making. Without a centralized fusion center, network-wide reliable inferencing can be accomplished by recovering meaningful global statistics at each sensor through iterative inter-sensor message passing. In this dissertation, we first consider the problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in a WSN, where each sensor receives a single snapshot of the field. An iterative distributed least-squares (DLS) algorithm is investigated with and without the consideration of node failures. In particular, without sensor node failures it is shown that every instantiation of the DLS algorithm converges, i.e., consensus is reached among the sensors, with the limiting agreement value being the centralized least-squares estimate. With node failures during the iterative exchange process, the convergence of the DLS algorithm is still guaranteed; however, an error exists be- tween the limiting agreement value and the centralized least-squares estimate. In order to reduce this error, a modified DLS scheme, the M-DLS, is provided. The M-DLS algorithm involves an additional weight compensation step, in which a sensor performs a one-time weight compensation procedure whenever it detects the failure of a neighbor. Through analytical arguments and simulations, it is shown that the M-DLS algorithm leads to a smaller error than the DLS algorithm, where the magnitude of the improvement dependents on the network topology. We then investigate the case when the observation or sensing mode is only partially known at the corresponding nodes, perhaps, due to their limited sensing capabilities or other unpredictable physical factors. Specifically, it is assumed that the observation validity at a node switches stochastically between two modes, with mode I corresponding to the desired signal plus noise observation mode (a valid observation), and mode II corresponding to pure noise with no signal information (an invalid observation). With no prior information on the local sensing modes (valid or invalid), we introduce a learning-based distributed estimation procedure, the mixed detection-estimation (MDE) algorithm, based on closed-loop interactions between the iterative distributed mode learning and the target estimation. The online learning (or sensing mode detection) step re-assesses the validity of the local observations at each iteration, thus refining the ongoing estimation update process. The convergence of the MDE algorithm is established analytically, and the asymptotic performance analysis studies shows that, in the high signal-to-noise ratio (SNR) regime, the MDE estimation error converges to that of an ideal (centralized) estimator with perfect information about the node sensing modes. This is in contrast with the estimation performance of a naive average consensus based distributed estimator (with no mode learning), whose estimation error blows up with an increasing SNR.