Security, trust and cooperation in wireless sensor networks

Wireless sensor networks are a promising technology for many real-world applications such as critical infrastructure monitoring, scientific data gathering, smart buildings, etc.. However, given the typically unattended and potentially unsecured operation environment, there has been an increased number of security threats to sensor networks. In addition, sensor networks have very constrained resources, such as limited energy, memory, computational power, and communication bandwidth. These unique challenges call for new security mechanisms and algorithms. In this dissertation, we propose novel algorithms and models to address some important and challenging security problems in wireless sensor networks. The first part of the dissertation focuses on data trust in sensor networks. Since sensor networks are mainly deployed to monitor events and report data, the quality of received data must be ensured in order to make meaningful inferences from sensor data. We first study a false data injection attack in the distributed state estimation problem and propose a distributed Bayesian detection algorithm, which could maintain correct estimation results when less than one half of the sensors are compromised. To deal with the situation where more than one half of the sensors may be compromised, we introduce a special class of sensor nodes called \textit{trusted cores}. We then design a secure distributed trust aggregation algorithm that can utilize the trusted cores to improve network robustness. We show that as long as there exist some paths that can connect each regular node to one of these trusted cores, the network can not be subverted by attackers. The second part of the dissertation focuses on sensor network monitoring and anomaly detection. A sensor network may suffer from system failures due to loss of links and nodes, or malicious intrusions. Therefore, it is critical to continuously monitor the overall state of the network and locate performance anomalies. The network monitoring and probe selection problem is formulated as a budgeted coverage problem and a Markov decision process. Efficient probing strategies are designed to achieve a flexible tradeoff between inference accuracy and probing overhead. Based on the probing results on traffic measurements, anomaly detection can be conducted. To capture the highly dynamic network traffic, we develop a detection scheme based on multi-scale analysis of the traffic using wavelet transforms and hidden Markov models. The performance of the probing strategy and of the detection scheme are extensively evaluated in malicious scenarios using the NS-2 network simulator. Lastly, to better understand the role of trust in sensor networks, a game theoretic model is formulated to mathematically analyze the relation between trust and cooperation. Given the trust relations, the interactions among nodes are modeled as a network game on a trust-weighted graph. We then propose an efficient heuristic method that explores network heterogeneity to improve Nash equilibrium efficiency

Influence Analysis towards Big Social Data

Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation

Consensus-based Time Synchronization Algorithms for Wireless Sensor Networks with Topological Optimization Strategies for Performance Improvement

Wireless Sensor Networks(WSNs)have received considerable attention in recent years because of its broad area of applications.In the same breadth,it also faces many challenges.Time synchronization is one of those fundamental challenges faced by WSN being a distributed system.It is a service by which all nodes in the network will share a common notion of time.It is a prerequisite for correctness of other protocols and services like security,localization and tracking protocols.Several approaches have been proposed in the last decade for time synchronization in WSNs.The well-known methods are based on synchronizing to a reference(root)node's time by considering a hierarchical backbone for the network.However,this approach seems to be not purely distributed,higher accumulated synchronization error for the farthest node from the root and subjected to the root node failure problem.Recently,consensus based approaches are gaining popularity due its computational lightness,robustness, and distributed nature.In this thesis,average consensus-based time synchronization algorithms are proposed,aiming to improve the performance metrics like number of iterations for convergence,total synchronization error,local synchronization error,message complexity,and scalability.Further,to cope up with energy constraint environment, Genetic algorithm based topological optimization strategies are proposed to minimize energy consumption and to accelerate the consensus convergence of the existing consensus-based time synchronization algorithms.All algorithms are analyzed mathematically and validated through simulation in MATLAB based PROWLER simulator.Firstly,a distributed Selective Average Time Synchronization (SATS) algorithm is proposed based on average consensus theory.The algorithm is purely distributed(runs at each node),and each node exploits a selective averaging with the neighboring node having maximum clock difference. To identify the neighboring node with maximum clock difference,every node broadcasts a synchronization initiation message to the neighboring nodes at its local oscillation period and waits for a random interval to get the synchronization acknowledgment messages.After receiving acknowledgment messages,a node estimates relative clock value and sends an averaging message to the selected node.The iteration continues until all nodes reach an acceptable synchronization error bound. The optimal convergence of the proposed SATS algorithm is analyzed and validated through simulation and compared with some state-of-the-art,average consensus based time synchronization algorithms. Furthermore, it is observed that most of the consensus-based time synchronization algorithms are one-hop in nature, i.e., the algorithms iterate by averaging with one-hop neighbors' clock value. In a sparse network with a lower average degree of connectivity, these algorithms show poor performance. In order to have better convergence on the sparse network, a multi-hop SATS algorithm is proposed. The basic principle of multi-hop SATS algorithm remains same as that of SATS algorithm, i.e., performing selective averaging with the neighboring node, having maximum clock difference. But, in this case, the search for neighboring node goes beyond one hop. The major challenge lies in multi-hop search is the end-to-end delay which increases with the increase in hop count. So, to search a multi-hop neighboring node with maximum clock difference and with minimum and bounded end-to-end delay, a distributed, constraint-based dynamic programming approach is proposed for multi-hop SATS algorithm. The performance of the proposed multi-hop SATS algorithm is compared with some one-hop consensus time synchronization algorithms. Simulation results show notable improvement in terms of convergence speed, total synchronization error within a restricted hop count. The trade-off with the increase in number of hops is also studied. The well-known consensus-based time synchronization algorithms are ``all node based'', i.e., every node iterates the algorithm to reach the synchronized state. This increases the overall message complexity and consumption of energy. Further, congestion in the network increases due to extensive synchronization message exchanges and induces the delay in the network. The delay induced in the message exchange is the main source of synchronization error and slows down the convergence speed to the synchronized (consensus) state. Hence, it is desirable that a subset of sensors along with a reasonable number of neighboring sensors should be selected in such a way that the resultant logical topology will accelerate the consensus algorithm with optimal message complexity and minimizes energy consumption. This problem is formulated as topological optimization problem which is claimed to be NP-complete in nature. Therefore, Genetic Algorithm (GA) based approaches are used to tackle this problem. Considering dense network topology, a single objective GA-based approach is proposed and considering sparse topology, a multi-objective Random Weighted GA based approach is proposed. Using the proposed topological optimization strategy, significant improvements are observed for consensus-based time synchronization algorithms in terms of average number of messages exchanged, energy consumption, and average mean square synchronization error

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Context-Aware Message-Level Rumour Detection with Weak Supervision

Social media has become the main source of all sorts of information beyond a communication medium. Its intrinsic nature can allow a continuous and massive flow of misinformation to make a severe impact worldwide. In particular, rumours emerge unexpectedly and spread quickly. It is challenging to track down their origins and stop their propagation. One of the most ideal solutions to this is to identify rumour-mongering messages as early as possible, which is commonly referred to as "Early Rumour Detection (ERD)". This dissertation focuses on researching ERD on social media by exploiting weak supervision and contextual information. Weak supervision is a branch of ML where noisy and less precise sources (e.g. data patterns) are leveraged to learn limited high-quality labelled data (Ratner et al., 2017). This is intended to reduce the cost and increase the efficiency of the hand-labelling of large-scale data. This thesis aims to study whether identifying rumours before they go viral is possible and develop an architecture for ERD at individual post level. To this end, it first explores major bottlenecks of current ERD. It also uncovers a research gap between system design and its applications in the real world, which have received less attention from the research community of ERD. One bottleneck is limited labelled data. Weakly supervised methods to augment limited labelled training data for ERD are introduced. The other bottleneck is enormous amounts of noisy data. A framework unifying burst detection based on temporal signals and burst summarisation is investigated to identify potential rumours (i.e. input to rumour detection models) by filtering out uninformative messages. Finally, a novel method which jointly learns rumour sources and their contexts (i.e. conversational threads) for ERD is proposed. An extensive evaluation setting for ERD systems is also introduced

Community Detection in Multimodal Networks

Community detection on networks is a basic, yet powerful and ever-expanding set of methodologies that is useful in a variety of settings. This dissertation discusses a range of different community detection on networks with multiple and non-standard modalities. A major focus of analysis is on the study of networks spanning several layers, which represent relationships such as interactions over time, different facets of high-dimensional data. These networks may be represented by several different ways; namely the few-layer (i.e. longitudinal) case as well as the many-layer (time-series cases). In the first case, we develop a novel application of variational expectation maximization as an example of the top-down mode of simultaneous community detection and parameter estimation. In the second case, we use a bottom-up strategy of iterative nodal discovery for these longer time-series, abetted with the assumption of their structural properties. In addition, we explore significantly self-looping networks, whose features are inseparable from the inherent construction of spatial networks whose weights are reflective of distance information. These types of networks are used to model and demarcate geographical regions. We also describe some theoretical properties and applications of a method for finding communities in bipartite networks that are weighted by correlations between samples. We discuss different strategies for community detection in each of these different types of networks, as well as their implications for the broader contributions to the literature. In addition to the methodologies, we also highlight the types of data wherein these ``non-standard" network structures arise and how they are fitting for the applications of the proposed methodologies: particularly spatial networks and multilayer networks. We apply the top-down and bottom-up community detection algorithms to data in the domains of demography, human mobility, genomics, climate science, psychiatry, politics, and neuroimaging. The expansiveness and diversity of these data speak to the flexibility and ubiquity of our proposed methods to all forms of relational data.Doctor of Philosoph

NP-hard networking problems : exact and approximate algorithms

An important class of problems that occur in different fields of research such as biology, linguistics or in the design of wireless communication networks, deal with the problem of finding an interconnection of a given set of objects. Additionaly, these networks should satisfy certain properties and minimize a certain cost function. In this thesis, we discuss such NP-hard networking problems in two parts. First, we mainly deal with the so-called Steiner minimum tree problem in Hamming metric. The computation of such trees has become a key tool for the reconstruction of the ancestral relationships of species. We give a new exact algorithm that clearly outperforms the branch and bound based method of Hendy and Penny which has been considered to be the fastest for the last 25 years. Further, we propose an extended model to cope with the case in which the ancestral relationships are best described by a non-tree structure. Finally, we deal with several problems occurring in the design of wireless ad-hoc networks: While minimizing the total power consumption of a wireless communication network, one wants to establish a messaging structure such that certain communication tasks can be performed. We show how approximate solutions can be found for these problems.In verschiedenen wissenschaftlichen Disziplinen, wie der Biologie, der Linguistik und dem Entwurf kabelloser Kommunikationsnetzwerke, wird man mit der Konstruktion von Verbindungsnetzwerken über einer gegebenen Menge von Objekten konfrontiert. Diese Netzwerke sollen bestimmte Eigenschaften erfüllen und gleichzeitig eine gegebene Kostenfunktion minimieren. In dieser Arbeit werden NP-schwere Netzwerkprobleme dieser Art behandelt. Die Arbeit untergliedert sich in zwei Teile. Im ersten Teil beschäftigen wir uns hauptsächlich mit dem so genannten Steinerbaumproblem in der Hamming-Metrik. Die Berechnung solcher Bäume hat sich als eines der Hauptwerkzeuge in der Rekonstruktion abstammungsgeschichtlicher Beziehungen zwischen Spezien herausgestellt. Wir geben einen neuen, exakten Algorithmus, welcher der Branch-and-Bound-Methode von Hendy und Penny deutlich überlegen ist. Diese galt in den letzten 25 Jahren als die schnellste Methode zur Berechnung solcher Bäume. Des Weiteren stellen wir ein erweitertes Modell vor, welches die Fälle behandelt, in denen die abstammungsgeschichtlichen Beziehungen bestmöglich durch eine nicht baumartige Struktur beschrieben wird. Im zweiten Teil beschäftigen wir uns mit verschiedenen Problemen, wie sie bei dem Entwurf kabelloser Ad-hoc-Netzwerke auftreten: Unter denjenigen Kommunikationsstrukturen, die bestimmte Kommunikationsarten zulassen, versucht man diejenige zu finden, welche die Stromaufnahme des Netzwerkes minimiert. Wir zeigen, wie für diese Probleme approximative Lösungen gefunden werden können