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Data Management and Wireless Transport for Large Scale Sensor Networks
Today many large scale sensor networks have emerged, which span many different sensing applications. Each of these sensor networks often consists of millions of sensors collecting data and supports thousands of users with diverse data needs. Between users and wireless sensors there are often a group of powerful servers that collect and process data from sensors and answer users\u27 requests. To build such a large scale sensor network, we have to answer two fundamental research problems: i) what data to transmit from sensors to servers? ii) how to transmit the data over wireless links? Wireless sensors often can not transmit all collected data due to energy and bandwidth constraints. Therefore sensors need to decide what data to transmit to best satisfy users\u27 data requests. Sensor network users can often tolerate some data errors, thus sensors may transmit data in lower fidelity but still satisfy users\u27 requests. There are generally two types of requests-raw data requests and meta-data requests. To answer users\u27 raw data requests, we propose a model-driven data collection approach, PRESTO. PRESTO splits intelligence between sensors and servers, i.e., resource-rich servers perform expensive model training and resource-poor sensors perform simple model evaluation. PRESTO can significantly reduce data to be transmitted without sacrificing service quality. To answer users\u27 meta-data request, we propose a utility-driven multi-user data sharing approach, MUDS. MUDS uses utility function to unify diverse meta-data metrics. Sensors estimate utility value of each data packet and sends packets with highest utility first to improve overall system utility. After deciding what data to transmit from sensors to servers, the next question is how to transmit these data over wireless links. Wireless transport often suffers low bandwidth and unstable connectivity. In order to improve wireless transport, I propose a clean-slate re-design of wireless transport, Hop. Hop uses reliable perhop block transfer as a building block and builds all other components including hidden-terminal avoidance, congestion avoidance, and end-to-end reliability on top of it. Hop is built based on three key ideas: a) hop-by-hop transfer adapts to the lossy and highly variable nature of wireless channel significantly better than end-to-end transfer, b) the use of blocks as the unit of control is more efficient over wireless links than the use of packets, and c) the duplicated functionality in different layers in the network stack should be removed to simplify the protocol and avoid complex interaction
Optimizing Network Coding Algorithms for Multiple Applications.
Deviating from the archaic communication approach of treating information as a fluid moving through pipes, the concepts of Network Coding (NC) suggest that optimal throughput of a multicast network can be achieved by processing information at individual network nodes. However, existing challenges to harness the advantages of NC concepts for practical applications have prevented the development of NC into an effective solution to increase the performance of practical communication networks. In response, the research work presented in this thesis proposes cross-layer NC solutions to increase the network throughput of data multicast as well as video quality of video multicast applications. First, three algorithms are presented to improve the throughput of NC enabled networks by minimizing the NC coefficient vector overhead, optimizing the NC redundancy allocation and improving the robustness of NC against bursty packet losses. Considering the fact that majority of network traffic occupies video, rest of the proposed NC algorithms are content-aware and are optimized for both data and video multicast applications. A set of content and network-aware optimization algorithms, which allocate redundancies for NC considering content properties as well as the network status, are proposed to efficiently multicast data and video across content delivery networks. Furthermore content and channel-aware joint channel and network coding algorithms are proposed to efficiently multicast data and video across wireless networks. Finally, the possibilities of performing joint source and network coding are explored to increase the robustness of high volume video multicast applications. Extensive simulation studies indicate significant improvements with the proposed algorithms to increase the network throughput and video quality over related state-of-the-art solutions. Hence, it is envisaged that the proposed algorithms will contribute to the advancement of data and video multicast protocols in the future communication networks
Opportunistic Routing in Multihop Wireless Networks: Capacity, Energy Efficiency, and Security
Opportunistic routing (OR) takes advantages of the spatial diversity and broadcast nature of wireless networks to combat the time-varying links by involving multiple neighboring nodes (forwarding candidates) for each packet relay. This dissertation studies the properties, energy efficiency, capacity, throughput, protocol design and security issues about OR in multihop wireless networks. Firstly, we study geographic opportunistic routing (GOR), a variant of OR which makes use of nodes\u27 location information. We identify and prove three important properties of GOR. The first one is on prioritizing the forwarding candidates according to their geographic advancements to the destination. The second one is on choosing the forwarding candidates based on their advancements and link qualities in order to maximize the expected packet advancement (EPA) with different number of forwarding candidates. The third one is on the concavity of the maximum EPA in respect to the number of forwarding candidates. We further propose a local metric, EPA per unit energy consumption, to tradeoff the routing performance and energy efficiency for GOR. Leveraging the proved properties of GOR, we propose two efficient algorithms to select and prioritize forwarding candidates to maximize the local metric. Secondly, capacity is a fundamental issue in multihop wireless networks. We propose a framework to compute the end-to-end throughput bound or capacity of OR in single/multirate systems given OR strategies (candidate selection and prioritization). Taking into account wireless interference and unique properties of OR, we propose a new method of constructing transmission conflict graphs, and we introduce the concept of concurrent transmission sets to allow the proper formulation of the maximum end-to-end throughput problem as a maximum-flow linear programming problem subject to the transmission conflict constraints. We also propose two OR metrics: expected medium time (EMT) and expected advancement rate (EAR), and the corresponding distributed and local rate and candidate set selection schemes, the Least Medium Time OR (LMTOR) and the Multirate Geographic OR (MGOR). We further extend our framework to compute the capacity of OR in multi-radio multi-channel systems with dynamic OR strategies. We study the necessary and sufficient conditions for the schedulability of a traffic demand vector associated with a transmitter to its forwarding candidates in a concurrent transmission set. We further propose an LP approach and a heuristic algorithm to obtain an opportunistic forwarding strategy scheduling that satisfies a traffic demand vector. Our methodology can be used to calculate the end-to-end throughput bound of OR in multi-radio/channel/rate multihop wireless networks, as well as to study the OR behaviors (such as candidate selection and prioritization) under different network configurations. Thirdly, protocol design of OR in a contention-based medium access environment is an important and challenging issue. In order to avoid duplication, we should ensure only the best receiver of each packet to forward it in an efficient way. We investigate the existing candidate coordination schemes and propose a fast slotted acknowledgment (FSA) to further improve the performance of OR by using a single ACK to coordinate the forwarding candidates with the help of the channel sensing technique. Furthermore, we study the throughput of GOR in multi-rate and single-rate systems. We introduce a framework to analyze the one-hop throughput of GOR, and provide a deeper insight on the trade-off between the benefit (packet advancement, bandwidth, and transmission reliability) and cost (medium time delay) associated with the node collaboration. We propose a local metric named expected one-hop throughput (EOT) to balance the benefit and cost. Finally, packet reception ratio (PRR) has been widely used as an indicator of the link quality in multihop wireless networks. Many routing protocols including OR in wireless networks depend on the PRR information to make routing decision. Providing accurate link quality measurement (LQM) is essential to ensure the right operation of these routing protocols. However, the existing LQM mechanisms are subject to malicious attacks, thus can not guarantee to provide correct link quality information. We analyze the security vulnerabilities in the existing link quality measurement (LQM) mechanisms and propose an efficient broadcast-based secure LQM (SLQM) mechanism, which prevents the malicious attackers from reporting a higher PRR than the actual one. We analyze the security strength and the cost of the proposed mechanism
Network streaming and compression for mixed reality tele-immersion
Bulterman, D.C.A. [Promotor]Cesar, P.S. [Copromotor
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Compressive Data Gathering in Wireless Sensor Networks
The thesis focuses on collecting data from wireless sensors which are deployed randomly in a region. These sensors are widely used in applications ranging from tracking to the monitoring of environment, traffic and health among others. These energy constrained sensors, once deployed may receive little or no maintenance. Hence gathering data in the most energy efficient manner becomes critical for the longevity of wireless sensor networks (WSNs).
Recently, Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in WSN; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the forwarding load across the network. This is particularly true due to the benefits obtained from in-network data compression. With CDG, the central unit, instead of receiving data from all sensors in the network, it may receive very few compressed or weighted sums from sensors, and eventually recovers the original data.
To prolong the lifetime of WSN, in this thesis, we present data gathering methods based on CDG. More specifically, we propose data gathering schemes using CDG by building up data aggregation trees from sensor nodes to a central unit (sink). Our problem aims at minimizing the number of links in the forwarding trees to minimize the number of overall transmissions. First, we mathematically formulate the problem and solve it using optimization program. Owing to its complexity, we present real-time algorithmic (centralized and decentralized) methods to efficiently solve the problem. We also explore the benefits one may obtain when jointly applying compressive data gathering with network coding in a wireless sensor network. Finally, and in the context of compressive data gathering, we study the problem of joint forwarding tree construction and scheduling under a realistic interference model, and propose some efficient distributed methods for solving it. We also present a primal dual decomposition method, using the theory of column generation, to solve this complex problem
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