Wireless multimedia sensor networks, security and key management

Wireless Multimedia Sensor Networks (WMSNs) have emerged and shifted the focus from the typical scalar wireless sensor networks to networks with multimedia devices that are capable to retrieve video, audio, images, as well as scalar sensor data. WMSNs are able to deliver multimedia content due to the availability of inexpensive CMOS cameras and microphones coupled with the significant progress in distributed signal processing and multimedia source coding techniques. These mentioned characteristics, challenges, and requirements of designing WMSNs open many research issues and future research directions to develop protocols, algorithms, architectures, devices, and testbeds to maximize the network lifetime while satisfying the quality of service requirements of the various applications. In this thesis dissertation, we outline the design challenges of WMSNs and we give a comprehensive discussion of the proposed architectures and protocols for the different layers of the communication protocol stack for WMSNs along with their open research issues. Also, we conduct a comparison among the existing WMSN hardware and testbeds based on their specifications and features along with complete classification based on their functionalities and capabilities. In addition, we introduce our complete classification for content security and contextual privacy in WSNs. Our focus in this field, after conducting a complete survey in WMSNs and event privacy in sensor networks, and earning the necessary knowledge of programming sensor motes such as Micaz and Stargate and running simulation using NS2, is to design suitable protocols meet the challenging requirements of WMSNs targeting especially the routing and MAC layers, secure the wirelessly exchange of data against external attacks using proper security algorithms: key management and secure routing, defend the network from internal attacks by using a light-weight intrusion detection technique, protect the contextual information from being leaked to unauthorized parties by adapting an event unobservability scheme, and evaluate the performance efficiency and energy consumption of employing the security algorithms over WMSNs

A survey of flooding, gossip routing, and related schemes for wireless multi- hop networks

Flooding is an essential and critical service in computer networks that is used by many routing protocols to send packets from a source to all nodes in the network. As the packets are forwarded once by each receiving node, many copies of the same packet traverse the network which leads to high redundancy and unnecessary usage of the sparse capacity of the transmission medium. Gossip routing is a well-known approach to improve the flooding in wireless multi-hop networks. Each node has a forwarding probability p that is either statically per-configured or determined by information that is available at runtime, e.g, the node degree. When a packet is received, the node selects a random number r. If the number r is below p, the packet is forwarded and otherwise, in the most simple gossip routing protocol, dropped. With this approach the redundancy can be reduced while at the same time the reachability is preserved if the value of the parameter p (and others) is chosen with consideration of the network topology. This technical report gives an overview of the relevant publications in the research domain of gossip routing and gives an insight in the improvements that can be achieved. We discuss the simulation setups and results of gossip routing protocols as well as further improved flooding schemes. The three most important metrics in this application domain are elaborated: reachability, redundancy, and management overhead. The published studies used simulation environments for their research and thus the assumptions, models, and parameters of the simulations are discussed and the feasibility of an application for real world wireless networks are highlighted. Wireless mesh networks based on IEEE 802.11 are the focus of this survey but publications about other network types and technologies are also included. As percolation theory, epidemiological models, and delay tolerant networks are often referred as foundation, inspiration, or application of gossip routing in wireless networks, a brief introduction to each research domain is included and the applicability of the particular models for the gossip routing is discussed

Transmission Modeling with Smartphone-based Sensing

Infectious disease spread is difficult to accurately measure and model. Even for well-studied pathogens, uncertainties remain regarding the dynamics of mixing behavior and how to balance simulation-generated estimates with empirical data. Smartphone-based sensing data promises the availability of inferred proximate contacts, with which we can improve transmission models. This dissertation addresses the problem of informing transmission models with proximity contact data by breaking it down into three sub-questions. Firstly, can proximity contact data inform transmission models? To this question, an extended-Kalman-filter enhanced System Dynamics Susceptible-Infectious-Removed (EKF-SD-SIR) model demonstrated the filtering approach, as a framework, for informing Systems Dynamics models with proximity contact data. This combination results in recurrently-regrounded system status as empirical data arrive throughout disease transmission simulations---simultaneously considering empirical data accuracy, growing simulation error between measurements, and supporting estimation of changing model parameters. However, as revealed by this investigation, this filtering approach is limited by the quality and reliability of sensing-informed proximate contacts, which leads to the dissertation's second and third questions---investigating the impact of temporal and spatial resolution on sensing inferred proximity contact data for transmission models. GPS co-location and Bluetooth beaconing are two of those common measurement modalities to sense proximity contacts with different underlying technologies and tradeoffs. However, both measurement modalities have shortcomings and are prone to false positives or negatives when used to detect proximate contacts because unmeasured environmental influences bias the data. Will differences in sensing modalities impact transmission models informed by proximity contact data? The second part of this dissertation compares GPS- and Bluetooth-inferred proximate contacts by accessing their impact on simulated attack rates in corresponding proximate-contact-informed agent-based Susceptible-Exposed-Infectious-Recovered (ABM-SEIR) models of four distinct contagious diseases. Results show that the inferred proximate contacts resulting from these two measurement modalities are different and give rise to significantly different attack rates across multiple data collections and pathogens. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the frequency and scanning duration used for proximate-contact detection. The choice of a balanced sensing regime involves specifying temporal resolutions and interpreting sensing data---depending on circumstances such as the characteristics of a particular pathogen, accompanying disease, and underlying population. How will the temporal resolution of sensing impact transmission models informed by proximity contact data? Furthermore, how will circumstances alter the impact of temporal resolution? The third part of this dissertation investigates the impacts of sensing regimes on findings from two sampling methods of sensing at widely varying inter-observation intervals by synthetically downsampling proximity contact data from five contact network studies---with each of these five studies measuring participant-participant contact every 5 minutes for durations of four or more weeks. The impact of downsampling is evaluated through ABM-SEIR simulations from both population- and individual-level for 12 distinct contagious diseases and associated variants of concern. Studies in this part find that for epidemiological models employing proximity contact data, both the observation paradigms and the inter-observation interval configured to collect proximity contact data exert impacts on the simulation results. Moreover, the impact is subject to the population characteristics and pathogen infectiousness reflective (such as the basic reproduction number, $R_0$). By comparing the performance of two sampling methods of sensing, we found that in most cases, periodically observing for a certain duration can collect proximity contact data that allows agent-based models to produce a reasonable estimation of the attack rate. However, higher-resolution data are preferred for modeling individual infection risk. Findings from this part of the dissertation represent a step towards providing the empirical basis for guidelines to inform data collection that is at once efficient and effective. This dissertation addresses the problem of informing transmission models with proximity contact data in three steps. Firstly, the demonstration of an EKF-SD-SIR model suggests that the filtering approach could improve System Dynamics transmission models by leveraging proximity contact data. In addition, experiments with the EKF-SD-SIR model also revealed that the filtering approach is constrained by the limited quality and reliability of sensing-data-inferred proximate contacts. The following two parts of this dissertation investigate spatial-temporal factors that could impact the quality and reliability of sensor-collected proximity contact data. In the second step, the impact of spatial resolution is illustrated by differences between two typical sensing modalities---Bluetooth beaconing versus GPS co-location. Experiments show that, in general, proximity contact data collected with Bluetooth beaconing lead to transmission models with results different from those driven by proximity contact data collected with GPS co-location. Awareness of the differences between sensing modalities can aid researchers in incorporating proximity contact data into transmission models. Finally, in the third step, the impact of temporal resolution is elucidated by investigating the differences between results of transmission models led by proximity contact data collected with varying observation frequencies. These differences led by varying observation frequencies are evaluated under circumstances with alternative assumptions regarding sampling method, disease/pathogen type, and the underlying population. Experiments show that the impact of sensing regimes is influenced by the type of diseases/pathogens and underlying population, while sampling once in a while can be a decent choice across all situations. This dissertation demonstrated the value of a filtering approach to enhance transmission models with sensor-collected proximity contact data, as well as explored spatial-temporal factors that will impact the accuracy and reliability of sensor-collected proximity contact data. Furthermore, this dissertation suggested guidance for future sensor-based proximity contact data collection and highlighted needs and opportunities for further research on sensing-inferred proximity contact data for transmission models

Distributed Robotic Vision for Calibration, Localisation, and Mapping

This dissertation explores distributed algorithms for calibration, localisation, and mapping in the context of a multi-robot network equipped with cameras and onboard processing, comparing against centralised alternatives where all data is transmitted to a singular external node on which processing occurs. With the rise of large-scale camera networks, and as low-cost on-board processing becomes increasingly feasible in robotics networks, distributed algorithms are becoming important for robustness and scalability. Standard solutions to multi-camera computer vision require the data from all nodes to be processed at a central node which represents a significant single point of failure and incurs infeasible communication costs. Distributed solutions solve these issues by spreading the work over the entire network, operating only on local calculations and direct communication with nearby neighbours. This research considers a framework for a distributed robotic vision platform for calibration, localisation, mapping tasks where three main stages are identified: an initialisation stage where calibration and localisation are performed in a distributed manner, a local tracking stage where visual odometry is performed without inter-robot communication, and a global mapping stage where global alignment and optimisation strategies are applied. In consideration of this framework, this research investigates how algorithms can be developed to produce fundamentally distributed solutions, designed to minimise computational complexity whilst maintaining excellent performance, and designed to operate effectively in the long term. Therefore, three primary objectives are sought aligning with these three stages

Near optimal routing protocols for source location privacy in wireless sensor networks: modelling, design and evaluation

Wireless Sensor Networks (WSNs) are collections of small computing devices that are used to monitor valuable assets such as endangered animals. As WSNs communicate wirelessly they leak information to malicious eavesdroppers. When monitoring assets it is important to provide Source Location Privacy (SLP), where the location of the message source must be kept hidden. Many SLP protocols have been developed by designing a protocol using intuition before evaluating its performance. However, this does not provide insight into how to develop optimal approaches. This thesis will present an alternate approach where the SLP problem is modelled using different techniques to give an optimal output. However, as this optimal output is typically for a restricted scenario, algorithms that trade optimality for generality are subsequently designed. Four main contributions are presented. First, an analysis is performed based on entropy and divergence to gain insight into how to reduce the information an attacker gains via the use of competing paths, and ways to compare the information loss of arbitrary routing protocols. Secondly, the SLP problem is modelled using Integer Linear Programming. The model result guides the design of a generic protocol called ILPRouting that groups messages together to reduce the moves an attacker makes. Thirdly, a timing analysis of when events occur is used to dynamically determine fake source parameters for the Dynamic and DynamicSPR algorithms. These fake sources lure the attacker to their location instead of the real source. Finally, the first SLP-aware duty cycle is investigated, and implemented for DynamicSPR to make it more energy efficient. These techniques are evaluated through simulations and deployments on WSN testbeds to demonstrate their effectiveness

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Distributed navigation of multi-robot systems for sensing coverage

A team of coordinating mobile robots equipped with operation specific sensors can perform different coverage tasks. If the required number of robots in the team is very large then a centralized control system becomes a complex strategy. There are also some areas where centralized communication turns into an issue. So, a team of mobile robots for coverage tasks should have the ability of decentralized or distributed decision making. This thesis investigates decentralized control of mobile robots specifically for coverage problems. A decentralized control strategy is ideally based on local information and it can offer flexibility in case there is an increment or decrement in the number of mobile robots. We perform a broad survey of the existing literature for coverage control problems. There are different approaches associated with decentralized control strategy for coverage control problems. We perform a comparative review of these approaches and use the approach based on simple local coordination rules. These locally computed nearest neighbour rules are used to develop decentralized control algorithms for coverage control problems. We investigate this extensively used nearest neighbour rule-based approach for developing coverage control algorithms. In this approach, a mobile robot gives an equal importance to every neighbour robot coming under its communication range. We develop our control approach by making some of the mobile robots playing a more influential role than other members of the team. We develop the control algorithm based on nearest neighbour rules with weighted average functions. The approach based on this control strategy becomes efficient in terms of achieving a consensus on control inputs, say heading angle, velocity, etc. The decentralized control of mobile robots can also exhibit a cyclic behaviour under some physical constraints like a quantized orientation of the mobile robot. We further investigate the cyclic behaviour appearing due to the quantized control of mobile robots under some conditions. Our nearest neighbour rule-based approach offers a biased strategy in case of cyclic behaviour appearing in the team of mobile robots. We consider a clustering technique inside the team of mobile robots. Our decentralized control strategy calculates the similarity measure among the neighbours of a mobile robot. The team of mobile robots with the similarity measure based approach becomes efficient in achieving a fast consensus like on heading angle or velocity. We perform a rigorous mathematical analysis of our developed approach. We also develop a condition based on relaxed criteria for achieving consensus on velocity or heading angle of the mobile robots. Our validation approach is based on mathematical arguments and extensive computer simulations

Efficient Passive Clustering and Gateways selection MANETs

Passive clustering does not employ control packets to collect topological information in ad hoc networks. In our proposal, we avoid making frequent changes in cluster architecture due to repeated election and re-election of cluster heads and gateways. Our primary objective has been to make Passive Clustering more practical by employing optimal number of gateways and reduce the number of rebroadcast packets

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems