Integrating wireless technologies into intra-vehicular communication

With the emergence of connected and autonomous vehicles, sensors are increasingly deployed within car. Traffic generated by these sensors congest traditional intra-vehicular networks, such as CAN buses. Furthermore, the large amount of wires needed to connect sensors makes it hard to design cars in a modular way. These limitations have created impetus to use wireless technologies to support intra-vehicular communication. In this dissertation, we tackle the challenge of designing and evaluating data collection protocols for intra-car networks that can operate reliably and efficiently under dynamic channel conditions. First, we evaluate the feasibility of deploying an intra-car wireless network based on the Backpressure Collection Protocol (BCP), which is theoretically proven to be throughput-optimal. We uncover a surprising behavior in which, under certain dynamic channel conditions, the average packet delay of BCP decreases with the traffic load. We propose and analyze a queueing-theoretic model to shed light into the observed phenomenon. As a solution, we propose a new protocol, called replication-based LIFO-backpressure (RBL). Analytical and simulation results indicate that RBL dramatically reduces the delay of BCP at low load, while maintaining its high throughput performance. Next, we propose and implement a hybrid wired/wireless architecture, in which each node is connected to either a wired interface or a wireless interface or both. We propose a new protocol, called Hybrid-Backpressure Collection Protocol (Hybrid-BCP), for the intra-car hybrid networks. Our testbed implementation, based on CAN and ZigBee transceivers, demonstrates the load balancing and routing functionalities of Hybrid-BCP and its resilience to DoS attacks. We further provide simulation results, obtained based on real intra-car RSSI traces, showing that Hybrid-BCP can achieve the same performance as a tree-based protocol while reducing the radio transmission power by a factor of 10. Finally, we present TeaCP, a prototype Toolkit for the evaluation and analysis of Collection Protocols in both simulation and experimental environments. TeaCP evaluates a wide range of standard performance metrics, such as reliability, throughput, and latency. TeaCP further allows visualization of routes and network topology evolution. Through simulation of an intra-car WSN and real lab experiments, we demonstrate the functionality of TeaCP for comparing different collection protocols

Destination-based Routing and Circuit Allocation for Future Traffic Growth

Internet traffic continues to grow relentlessly, driven largely by increasingly high- \\ resolution video streaming, the increasing adoption of cloud computing, the emergence of 5G networks, and the ever-growing reach of social media and social networks. Existing networks use packet switching to route packets on a hop-by-hop basis from the source to the destination. However, they suffer from two shortcomings. First, in existing networks, packets are routed along a fixed shortest path using the Open Shortest Path First (OSPF) protocol or obliviously load-balanced across equal-cost paths using the Equal-Cost Multi-Path (ECMP) protocol. These routing protocols do not fully utilize the network capacity because they do not adapt to network congestions in their routing decisions. Second, although studies have shown that the majority of packets processed by Internet routers are pass-through traffic, packets nonetheless have to be queued and routed at every hop in existing networks, which unnecessarily adds substantial delays and processing costs.In this thesis, we present two new approaches to overcome these shortcomings. First, we propose new backpressure-based routing algorithms which use only shortest-path routes when they are sufficient to accommodate the given traffic load, but will incrementally expand routing choices as needed to accommodate increasing traffic loads. This avoids the poor delay performance inherent in backpressure-based routing algorithms where packets may take long detours under light or moderate loads, and still retains the notable advantage, the network-wide optimal throughput, because packets are adaptively routed along less congested paths.Second, we propose a unified packet and circuit switched network in which the underlying optical transport is used to circuit-switch pass-through traffic by means of pre-established circuits. This avoids unnecessary packet queuing delays and processing costs at each hop. We propose a novel convex optimization framework based on a new destination-based multicommodity flow formulation for the allocation of circuits in such unified networks

PACE: Simple Multi-hop Scheduling for Single-radio 802.11-based Stub Wireless Mesh Networks

IEEE 802.11-based Stub Wireless Mesh Networks (WMNs) are a cost-effective and flexible solution to extend wired network infrastructures. Yet, they suffer from two major problems: inefficiency and unfairness. A number of approaches have been proposed to tackle these problems, but they are too restrictive, highly complex, or require time synchronization and modifications to the IEEE 802.11 MAC. PACE is a simple multi-hop scheduling mechanism for Stub WMNs overlaid on the IEEE 802.11 MAC that jointly addresses the inefficiency and unfairness problems. It limits transmissions to a single mesh node at each time and ensures that each node has the opportunity to transmit a packet in each network-wide transmission round. Simulation results demonstrate that PACE can achieve optimal network capacity utilization and greatly outperforms state of the art CSMA/CA-based solutions as far as goodput, delay, and fairness are concerned

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

Self-organized backpressure routing for the wireless mesh backhaul of small cells

The ever increasing demand for wireless data services has given a starring role to dense small cell (SC) deployments for mobile networks, as increasing frequency re-use by reducing cell size has historically been the most effective and simple way to increase capacity. Such densification entails challenges at the Transport Network Layer (TNL), which carries packets throughout the network, since hard-wired deployments of small cells prove to be cost-unfeasible and inflexible in some scenarios. The goal of this thesis is, precisely, to provide cost-effective and dynamic solutions for the TNL that drastically improve the performance of dense and semi-planned SC deployments. One approach to decrease costs and augment the dynamicity at the TNL is the creation of a wireless mesh backhaul amongst SCs to carry control and data plane traffic towards/from the core network. Unfortunately, these lowcost SC deployments preclude the use of current TNL routing approaches such as Multiprotocol Label Switching Traffic Profile (MPLS-TP), which was originally designed for hard-wired SC deployments. In particular, one of the main problems is that these schemes are unable to provide an even network resource consumption, which in wireless environments can lead to a substantial degradation of key network performance metrics for Mobile Network Operators. The equivalent of distributing load across resources in SC deployments is making better use of available paths, and so exploiting the capacity offered by the wireless mesh backhaul formed amongst SCs. To tackle such uneven consumption of network resources, this thesis presents the design, implementation, and extensive evaluation of a self-organized backpressure routing protocol explicitly designed for the wireless mesh backhaul formed amongst the wireless links of SCs. Whilst backpressure routing in theory promises throughput optimality, its implementation complexity introduces several concerns, such as scalability, large end-to-end latencies, and centralization of all the network state. To address these issues, we present a throughput suboptimal yet scalable, decentralized, low-overhead, and low-complexity backpressure routing scheme. More specifically, the contributions in this thesis can be summarized as follows: We formulate the routing problem for the wireless mesh backhaul from a stochastic network optimization perspective, and solve the network optimization problem using the Lyapunov-driftplus-penalty method. The Lyapunov drift refers to the difference of queue backlogs in the network between different time instants, whereas the penalty refers to the routing cost incurred by some network utility parameter to optimize. In our case, this parameter is based on minimizing the length of the path taken by packets to reach their intended destination. Rather than building routing tables, we leverage geolocation information as a key component to complement the minimization of the Lyapunov drift in a decentralized way. In fact, we observed that the combination of both components helps to mitigate backpressure limitations (e.g., scalability,centralization, and large end-to-end latencies). The drift-plus-penalty method uses a tunable optimization parameter that weight the relative importance of queue drift and routing cost. We find evidence that, in fact, this optimization parameter impacts the overall network performance. In light of this observation, we propose a self-organized controller based on locally available information and in the current packet being routed to tune such an optimization parameter under dynamic traffic demands. Thus, the goal of this heuristically built controller is to maintain the best trade-off between the Lyapunov drift and the penalty function to take into account the dynamic nature of semi-planned SC deployments. We propose low complexity heuristics to address problems that appear under different wireless mesh backhaul scenarios and conditions..

Scheduling algorithms for throughput maximization in data networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 215-226).This thesis considers the performance implications of throughput optimal scheduling in physically and computationally constrained data networks. We study optical networks, packet switches, and wireless networks, each of which has an assortment of features and constraints that challenge the design decisions of network architects. In this work, each of these network settings are subsumed under a canonical model and scheduling framework. Tools of queueing analysis are used to evaluate network throughput properties, and demonstrate throughput optimality of scheduling and routing algorithms under stochastic traffic. Techniques of graph theory are used to study network topologies having desirable throughput properties. Combinatorial algorithms are proposed for efficient resource allocation. In the optical network setting, the key enabling technology is wavelength division multiplexing (WDM), which allows each optical fiber link to simultaneously carry a large number of independent data streams at high rate. To take advantage of this high data processing potential, engineers and physicists have developed numerous technologies, including wavelength converters, optical switches, and tunable transceivers.(cont.) While the functionality provided by these devices is of great importance in capitalizing upon the WDM resources, a major challenge exists in determining how to configure these devices to operate efficiently under time-varying data traffic. In the WDM setting, we make two main contributions. First, we develop throughput optimal joint WDM reconfiguration and electronic-layer routing algorithms, based on maxweight scheduling. To mitigate the service disruption associated with WDM reconfiguration, our algorithms make decisions at frame intervals. Second, we develop analytic tools to quantify the maximum throughput achievable in general network settings. Our approach is to characterize several geometric features of the maximum region of arrival rates that can be supported in the network. In the packet switch setting, we observe through numerical simulation the attractive throughput properties of a simple maximal weight scheduler. Subsequently, we consider small switches, and analytically demonstrate the attractive throughput properties achievable using maximal weight scheduling. We demonstrate that such throughput properties may not be sustained in larger switches.(cont.) In the wireless network setting, mesh networking is a promising technology for achieving connectivity in local and metropolitan area networks. Wireless access points and base stations adhering to the IEEE 802.11 wireless networking standard can be bought off the shelf at little cost, and can be configured to access the Internet in minutes. With ubiquitous low-cost Internet access perceived to be of tremendous societal value, such technology is naturally garnering strong interest. Enabling such wireless technology is thus of great importance. An important challenge in enabling mesh networks, and many other wireless network applications, results from the fact that wireless transmission is achieved by broadcasting signals through the air, which has the potential for interfering with other parts of the network. Furthermore, the scarcity of wireless transmission resources implies that link activation and packet routing should be effected using simple distributed algorithms. We make three main contributions in the wireless setting. First, we determine graph classes under which simple, distributed, maximal weight schedulers achieve throughput optimality.(cont.) Second, we use this acquired knowledge of graph classes to develop combinatorial algorithms, based on matroids, for allocating channels to wireless links, such that each channel can achieve maximum throughput using simple distributed schedulers. Third, we determine new conditions under which distributed algorithms for joint link activation and routing achieve throughput optimality.by Andrew Brzezinski.Ph.D

Fast, Distributed Optimization Strategies for Resource Allocation in Networks

Many challenges in network science and engineering today arise from systems composed of many individual agents interacting over a network. Such problems range from humans interacting with each other in social networks to computers processing and exchanging information over wired or wireless networks. In any application where information is spread out spatially, solutions must address information aggregation in addition to the decision process itself. Intelligently addressing the trade off between information aggregation and decision accuracy is fundamental to finding solutions quickly and accurately. Network optimization challenges such as these have generated a lot of interest in distributed optimization methods. The field of distributed optimization deals with iterative methods which perform calculations using locally available information. Early methods such as subgradient descent suffer very slow convergence rates because the underlying optimization method is a first order method. My work addresses problems in the area of network optimization and control with an emphasis on accelerating the rate of convergence by using a faster underlying optimization method. In the case of convex network flow optimization, the problem is transformed to the dual domain, moving the equality constraints which guarantee flow conservation into the objective. The Newton direction can be computed locally by using a consensus iteration to solve a Poisson equation, but this requires a lot of communication between neighboring nodes. Accelerated Dual Descent (ADD) is an approximate Newton method, which significantly reduces the communication requirement. Defining a stochastic version of the convex network flow problem with edge capacities yields a problem equivalent to the queue stability problem studied in the backpressure literature. Accelerated Backpressure (ABP) is developed to solve the queue stabilization problem. A queue reduction method is introduced by merging ideas from integral control and momentum based optimization