We consider a transportation system of heterogeneously connected vehicles, where not all vehicles are able to communicate. Heterogeneous connectivity in transportation systems is coupled to practical constraints such that (i) not all vehicles may be equipped with devices having communication interfaces, (ii) some vehicles may not prefer to communicate due to privacy and security reasons, and (iii) communication links are not perfect and packet losses and delay occur in practice. In this context, it is crucial to develop control algorithms by taking into account the heterogeneity. In this paper, we particularly focus on making traffic phase scheduling decisions. We develop a connectivity-aware traffic phase scheduling algorithm for heterogeneously connected vehicles that increases the intersection efficiency (in terms of the average number of vehicles that are allowed to pass the intersection) by taking into account the heterogeneity. The simulation results show that our algorithm significantly improves the efficiency of intersections as compared to the baselines.
Our previous work @cite_2 investigates the impact of the blocking problem at intersections, characterizes the waiting times, and develops a shortest delay routing algorithm in transportation systems. As compared to this work, in this paper, we develop a connectivity-aware traffic phase scheduling algorithm by taking into account heterogeneous communications.
@cite_2
2280210143
The blocking problem naturally arises in transportation systems as multiple vehicles with different itineraries share available resources. In this paper, we investigate the impact of the blocking problem to the waiting time at the intersections of transportation systems. We assume that different vehicles, depending on their Internet connection capabilities, may communicate their intentions (e.g., whether they will turn left or right or continue straight) to intersections (specifically to devices attached to traffic lights). We consider that information collected by these devices are transmitted to and processed in a cloud-based traffic control system. Thus, a cloud-based system, based on the intention information, can calculate average waiting times at intersections. We consider this problem as a queuing model, and we characterize average waiting times by taking into account (i) blocking probability, and (ii) vehicles' ability to communicate their intentions. Then, by using average waiting times at intersection, we develop a shortest delay algorithm that calculates the routes with shortest delays between two points in a transportation network. Our simulation results confirm our analysis, and demonstrate that our shortest delay algorithm significantly improves over baselines that are unaware of the blocking problem. We numerically investigate jamming transitions in complex heterogeneous networks. Inspired by Internet routing protocols, we study a general model that incorporates local traffic information through a tunable parameter. The results show that whether the transition from a low-traffic regime to a congested phase is of first- or second-order type is determined by the protocol at work. The microscopic dynamics reveals that these two radically different behaviors are due to the way in which traffic jams propagate through the network. Our results are discussed in the context of Internet dynamics and other transport processes that take place on complex networks and provide insights for the design of routing policies based on traffic awareness in communication systems. We investigate the problem of designing delay-aware joint flow control, routing, and scheduling algorithms in general multi-hop networks for maximizing network utilization. Since the end-to-end delay performance has a complex dependence on the high-order statistics of cross-layer algorithms, earlier optimization-based design methodologies that optimize the long term network utilization are not immediately well-suited for delay-aware design. This motivates us in this work to develop a novel design framework and alternative methods that take advantage of several unexploited design choices in the routing and the scheduling strategy spaces. In particular, we reveal and exploit a crucial characteristic of back pressure-type controllers that enables us to develop a novel link rate allocation strategy that not only optimizes long-term network utilization, but also yields loop free multi-path routes between each source-destination pair. Moreover, we propose a regulated scheduling strategy, based on a token-based service discipline, for shaping the per-hop delay distribution to obtain highly desirable end-to-end delay performance. We establish that our joint flow control, routing, and scheduling algorithm achieves loop-free routes and optimal network utilization. Our extensive numerical studies support our theoretical results, and further show that our joint design leads to substantial end-to-end delay performance improvements in multi-hop networks compared to earlier solutions. In this paper, we develop an energy efficient routing scheme that takes into account the interference created by existing flows in the network. Unlike previous works, we explicitly study the impact of routing a new flow on the energy consumption of the network. Under certain assumptions on how links are scheduled, we can show that our proposed algorithm is asymptotically (in time) optimal in terms of minimizing the average energy consumption. We also develop a distributed version of the algorithm. Our algorithm automatically detours around a congested area in the network, which helps mitigate network congestion and improve overall network performance. Using simulations, we show that the routes chosen by our algorithm (centralized and distributed) are more energy efficient than the state of the art. We show how to route on very large public transportation networks (up to half a billion arcs) with average query times of a few milliseconds. We take into account many realistic features like: traffic days, walking between stations, queries between geographic locations instead of a source and a target station, and multi-criteria cost functions. Our algorithm is based on two key observations: (1) many shortest paths share the same transfer pattern, i.e., the sequence of stations where a change of vehicle occurs; (2) direct connections without change of vehicle can be looked up quickly. We precompute the respective data; in practice, this can be done in time linear in the network size, at the expense of a small fraction of non-optimal results. We have accelerated public transportation routing on Google Maps with a system based on our ideas. We report experimental results for three data sets of various kinds and sizes. The throughput of wireless networks can be significantly improved by multi-channel communications compared with single-channel communications since the use of multiple channels can reduce interference influence. In this paper, we study interference-aware topology control and QoS routing in IEEE 802.11-based multi-channel wireless mesh networks with dynamic traffic. Channel assignment and routing are two basic issues in such networks. Different channel assignments can lead to different network topologies. We present a novel definition of co-channel interference. Based on this concept, we formally define and present an effective heuristic for the minimum INterference Survivable Topology Control (INSTC) problem which seeks a channel assignment for the given network such that the induced network topology is interference-minimum among all K-connected topologies. We then formulate the Bandwidth-Aware Routing (BAR) problem for a given network topology, which seeks routes for QoS connection requests with bandwidth requirements. We present a polynomial time optimal algorithm to solve the BAR problem under the assumption that traffic demands are splittable. For the non-splittable case, we present a maximum bottleneck capacity path routing heuristic. Simulation results show that compared with the simple common channel assignment and shortest path routing approach, our scheme improves the system performance by 57 on average in terms of connection blocking ratio. Data centers are a cost-effective infrastructure for hosting Cloud and Grid applications, but they do incur tremendous energy cost and CO2 emissions. Today's data center network architectures such as Fat-tree and BCube are over-provisioned to guarantee large network capacity and meet peak performance requirement. Networks suffer from inefficient power usage when data center traffic is not high. A solution to this problem is the adoption of network management platform such as OpenNaaS, which can be augmented with energy-aware capabilities. We developed a component for energy monitoring and routing in OpenNaaS. Energy-aware OpenNaaS can support different types of OpenFlow controller; it inherits and enhances network management capabilities, e.g. dynamically obtaining power and topology.In this paper we also discuss the evaluation and selection of energy-aware routing strategies based on an initial prototype of energy-aware OpenNaaS. The target strategies are fine-grained as they combine flow routing algorithms that make routing decisions for the flows and flow scheduling algorithms that schedule the flows on the same link. Differently from previous routing work which focuses on power-minimization problem in data center networks, we aim to optimize energy consumption. Our simulation shows that the combination of priority-based shortest routing and exclusive flow scheduling achieves about 5 -35 higher energy efficiency without performance degradation. Autonomous driving is likely to be the heart of urban transportation in the future. Autonomous vehicles have the potential to increase the safety of passengers and also to make road trips shorter and more enjoyable. As the first steps toward these goals, many car manufacturers are investing in designing and equipping their vehicles with advanced driver-assist systems. Road intersections are considered to be serious bottlenecks of urban transportation, as more than 44 of all reported crashes in U.S. occur within intersection areas which in turn lead to 8,500 fatalities and approximately 1 million injuries every year. Furthermore, the impact of road intersections on traffic delays leads to enormous waste of human and natural resources. In this paper, we therefore focus on intersection management in Intelligent Transportation Systems (ITS) research. In the future, when dealing with autonomous vehicles, it is critical to address safety and throughput concerns that arise from autonomous driving through intersections and roundabouts. Our goal is to provide vehicles with a safe and efficient passage method through intersections and roundabouts. We have been investigating vehicle-to-vehicle (V2V) communications as a part of co-operative driving in the context of autonomous driving. We have designed and developed efficient and reliable intersection protocols to avoid vehicle collisions at intersections and increase traffic throughput. In this paper, we introduce new V2V intersection protocols to achieve the above goals. We show that, in addition to intersections, these protocols are also applicable to vehicle crossings at roundabouts. Additionally, we study the effects of position inaccuracy of commonly-used GPS devices on some of our V2V intersection protocols and suggest required modifications to guarantee their safety and efficiency despite these impairments. Our simulation results show that we are able to avoid collisions and also increase the throughput of the intersections up to 87.82 compared to common traffic-light signalized intersections. In this paper, we present a framework for analyzing routing performance in delay tolerant networks (DTNs). Differently from previous work, our framework is aimed at characterizing the exact distribution of relevant performance metrics, which is a substantial improvement over existing studies characterizing either the expected value of the metric, or an asymptotic approximation of the actual distribution. In particular, the considered performance metrics are packet delivery delay, and communication cost, expressed as number of copies of a packet circulating in the network at the time of delivery. Our proposed framework is based on a characterization of the routing process as a stochastic coloring process and can be applied to model performance of most stateless delay tolerant routing protocols, such as epidemic, two-hops, and spray and wait. After introducing the framework, we present examples of its application to derive the packet delivery delay and communication cost distribution of two such protocols, namely epidemic and two-hops routing. Characterizing packet delivery delay and communication cost distribution is important to investigate fundamental properties of delay tolerant networks. As an example, we show how packet delivery delay distribution can be used to estimate how epidemic routing performance changes in presence of different degrees of node cooperation within the network. More specifically, we consider fully cooperative, noncooperative, and probabilistic cooperative scenarios, and derive nearly exact expressions of the packet delivery rate (PDR) under these scenarios based on our proposed framework. The comparison of the obtained packet delivery rate estimation in the various cooperation scenarios suggests that even a modest level of node cooperation (probabilistic cooperation with a low probability of cooperation) is sufficient to achieve 2-fold performance improvement with respect to the most pessimistic scenario in which all potential forwarders drop packets. Energy Efficient Ethernet, as defined by the IEEE 802.3az standard, has shown not to be as efficient as originally expected given the large values of the transition times between the active and sleep power modes. In fact, EEE performs nearly optimal only when the link load is either very low or very high, but never at medium loads. So, in order to achieve large power savings, then it is necessary to design a flow allocation algorithm that allocates traffic demands on links that avoid medium traffic loads on links, since these are far from optimal. This work defines EEE-FA, an energy-aware flow allocation algorithm that computes the best possible route in terms of energy consumption for a given network load condition. Essentially, EEE-FA computes the K-shortest paths for a given traffic demand and evaluates the consumption impact of allocating the traffic demand on each of them, in order to further select that route with minimum energy consumption impact for a given network status. This algorithm is compared with shortest path routing and it is shown that important energy savings may be achieved, however at the expense of increasing the global network traffic load and the average number of hops per demand as a consequence of using sub-optimal (in terms of distance) routes. We investigate optical network unit (ONU) grant scheduling techniques for multichannel Ethernet passive optical networks (EPONs), such as wavelength division multiplexed (WDM) EPONs. We take a scheduling theoretic approach to solving the grant scheduling problem. We introduce a two-layer structure of the scheduling problem and investigate techniques to be used at both layers. We present an extensive ONU grant scheduling simulation study that provides: 1) insight into the nature of the ONU grant scheduling problem and 2) indication of which scheduling techniques are best for certain conditions. We find that the choice of scheduling framework has typically the largest impact on average queueing delay and achievable channel utilization. An offline scheduling framework is not work conserving and consequently wastes channel resources while waiting for all ONU REPORT messages before making access decisions. An online scheduling framework, although work conserving, does not provide the best performance since scheduling decisions are made with the information contained in a single ONU REPORT. We propose a novel online just-in-time (JIT) scheduling framework that is work conserving while increasing scheduling control by allowing the channel availability to drive the scheduling process. In online JIT, multiple ONU REPORTs can be considered together when making scheduling decisions, resulting in lower average queueing delay under certain conditions and a more effective service differentiation of ONUs. The paper copes with the reduction of network power consumption by the definition of new routing algorithms, able to take into account the energy consumed by the network devices. In particular, based on the power consumption characterization of the network devices obtained using the Energy Profile (EP) concept, the paper presents the analysis of the exact solution of the Energy Aware Routing (EAR) problem solved with a Mixed Integer Programming solver. The analysis is aimed at evaluating the impact on the performance of three relevant aspects of the problem: the approximation of the actual EP, the traffic load and the topology of the network. Furthermore, the paper proposes a heuristic solution of the EAR, denoted as Dijkstra-based Power Aware Routing Algorithm (DPRA), defined in order to cope with the complexity of the exact solution. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. Sensors in future smart cities will continuously monitor the environment in order to prevent critical situations and waste of resources or to offer new services to end users. Likely, the existing networks will not be able to sustain such a traffic without huge investments in the telecommunication infrastructure. One possible solution to overcome these problems is to apply the Delay Tolerant Network (DTN) paradigm. This paper presents the Sink and Delay Aware Bus (S&DA-Bus) routing protocol, a DTN routing protocol designed for smart cities able to exploit mobility of people, vehicles and buses roaming around the city. Particular attention is put on the public transportation system: S&DA-Bus takes advantage of the predictable and quasi-periodic mobility that characterizes it. The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. In particular, improving safety at intersections has been identified as a high priority due to the large number of intersection-related fatalities. This paper focuses on developing algorithms for estimating driver behavior at road intersections and validating them on real traffic data. It introduces two classes of algorithms that can classify drivers as compliant or violating. They are based on (1) support vector machines and (2) hidden Markov models, which are two very popular machine learning approaches that have been used successfully for classification in multiple disciplines. However, existing work has not explored the benefits of applying these techniques to the problem of driver behavior classification at intersections. The developed algorithms are successfully validated using naturalistic intersection data collected in Christiansburg, VA, through the U.S. Department of Transportation Cooperative Intersection Collision Avoidance System for Violations initiative. Their performances are also compared with those of three traditional methods, and the results show significant improvements with the new algorithms. Efficient elevator group control is an important issue for vertical transportation in high-rise buildings. From the engineering design perspective, regulation of average waiting time and journey time while considering energy consumption is an optimization problem. Alternatively to the conventional algorithms for scheduling and dispatching cars to hall calls, intelligent systems based methods have drawn much attention in the last years. This study aims to improve the elevator group control system's performance by applying genetic algorithm based optimization algorithms considering two systems. Firstly, average passenger waiting time is optimized in the conventional elevator systems in which a hall call is submitted by indicating the travel direction. Secondly, a recent development in elevator industry is considered and it is assumed that instead of direction indicators there are destination button panels at floors that allow passengers to specify their destinations. In this case optimization of average waiting time, journey time and car trip time is investigated. Two proposed algorithms have been applied considering preload conditions in a building with 20 floors and 4 cars. The simulation results have been compared with a previous study and conventional duplex algorithm. In this paper, we address the following question: given a specific placement of wireless nodes in physical space and a specific traffic workload, what is the maximum throughput that can be supported by the resulting network? Unlike previous work that has focused on computing asymptotic performance bounds under assumptions of homogeneity or randomness in the network topology and or workload, we work with any given network and workload specified as inputs.A key issue impacting performance is wireless interference between neighboring nodes. We model such interference using a conflict graph, and present methods for computing upper and lower bounds on the optimal throughput for the given network and workload. To compute these bounds, we assume that packet transmissions at the individual nodes can be finely controlled and carefully scheduled by an omniscient and omnipotent central entity, which is unrealistic. Nevertheless, using ns-2 simulations, we show that the routes derived from our analysis often yield noticeably better throughput than the default shortest path routes even in the presence of uncoordinated packet transmissions and MAC contention. This suggests that there is opportunity for achieving throughput gains by employing an interference-aware routing protocol. Today's heterogeneous architectures bring together multiple general-purpose CPUs and multiple domain-specific GPUs and FPGAs to provide dramatic speedup for many applications. However, the challenge lies in utilizing these heterogeneous processors to optimize overall application performance by minimizing workload completion time. Operating system and application development for these systems is in their infancy. In this article, we propose a new scheduling and workload balancing scheme, HDSS, for execution of loops having dependent or independent iterations on heterogeneous multiprocessor systems. The new algorithm dynamically learns the computational power of each processor during an adaptive phase and then schedules the remainder of the workload using a weighted self-scheduling scheme during the completion phase. Different from previous studies, our scheme uniquely considers the runtime effects of block sizes on the performance for heterogeneous multiprocessors. It finds the right trade-off between large and small block sizes to maintain balanced workload while keeping the accelerator utilization at maximum. Our algorithm does not require offline training or architecture-specific parameters. We have evaluated our scheme on two different heterogeneous architectures: AMD 64-core Bulldozer system with nVidia Fermi C2050 GPU and Intel Xeon 32-core SGI Altix 4700 supercomputer with Xilinx Virtex 4 FPGAs. The experimental results show that our new scheduling algorithm can achieve performance improvements up to over 200p when compared to the closest existing load balancing scheme. Our algorithm also achieves full processor utilization with all processors completing at nearly the same time which is significantly better than alternative current approaches. Advances in artificial intelligence, sensors and big data management have far-reaching societ al impacts. As these systems augment our everyday lives, it becomes increasing-ly important for people to understand them and remain in control. We investigate how HCI researchers can help to develop accountable systems by performing a literature analysis of 289 core papers on explanations and explaina-ble systems, as well as 12,412 citing papers. Using topic modeling, co-occurrence and network analysis, we mapped the research space from diverse domains, such as algorith-mic accountability, interpretable machine learning, context-awareness, cognitive psychology, and software learnability. We reveal fading and burgeoning trends in explainable systems, and identify domains that are closely connected or mostly isolated. The time is ripe for the HCI community to ensure that the powerful new autonomous systems have intelligible interfaces built-in. From our results, we propose several implications and directions for future research to-wards this goal. Population is steadily increasing worldwide, resulting in intractable traffic congestion in dense urban areas. Adaptive traffic signal control (ATSC) has shown strong potential to effectively alleviate urban traffic congestion by adjusting signal timing plans in real time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay). Efficient and robust ATSC can be designed using a multiagent reinforcement learning (MARL) approach in which each controller (agent) is responsible for the control of traffic lights around a single traffic junction. Applying MARL approaches to the ATSC problem is associated with a few challenges as agents typically react to changes in the environment at the individual level, but the overall behavior of all agents may not be optimal. This paper presents the development and evaluation of a novel system of multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC). MARLIN-ATSC offers two possible modes: 1) independent mode, where each intersection controller works independently of other agents; and 2) integrated mode, where each controller coordinates signal control actions with neighboring intersections. MARLIN-ATSC is tested on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto, ON, Canada, for the morning rush hour. The results show unprecedented reduction in the average intersection delay ranging from 27 in mode 1 to 39 in mode 2 at the network level and travel-time savings of 15 in mode 1 and 26 in mode 2, along the busiest routes in Downtown Toronto.