Showing papers on "Routing (electronic design automation) published in 2016"

Intelligent Device-to-Device Communication in the Internet of Things

Abstract: Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.

Partial recharge strategies for the electric vehicle routing problem with time windows

Abstract: The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is an extension to the well-known Vehicle Routing Problem with Time Windows (VRPTW) where the fleet consists of electric vehicles (EVs) Since EVs have limited driving range due to their battery capacities they may need to visit recharging stations while servicing the customers along their route The recharging may take place at any battery level and after the recharging the battery is assumed to be full In this paper, we relax the full recharge restriction and allow partial recharging (EVRPTW-PR), which is more practical in the real world due to shorter recharging duration We formulate this problem as a 0–1 mixed integer linear program and develop an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it efficiently We apply several removal and insertion mechanisms by selecting them dynamically and adaptively based on their past performances, including new mechanisms specifically designed for EVRPTW and EVRPTW-PR These new mechanisms include the removal of the stations independently or along with the preceding or succeeding customers and the insertion of the stations with determining the charge amount based on the recharging decisions We test the performance of ALNS by using benchmark instances from the recent literature The computational results show that the proposed method is effective in finding high quality solutions and the partial recharging option may significantly improve the routing decisions

Body area networks: A survey

Abstract: Body Area Networks are an effective solution for communication in ubiquitous health systems. BAN's can be applied into fields of military, defense, telecomm etc. Such networks are thus being researched to provide better routing techniques in and around the body. This paper discusses architecture of BAN along with the requirements and challenges. Various routing protocols available are discussed in section V. Various routing algorithms are discussed with their limitations and advantages.

ProjecToR: Agile Reconfigurable Data Center Interconnect

Abstract: We explore a novel, free-space optics based approach for building data center interconnects. It uses a digital micromirror device (DMD) and mirror assembly combination as a transmitter and a photodetector on top of the rack as a receiver (Figure 1). Our approach enables all pairs of racks to establish direct links, and we can reconfigure such links (i.e., connect different rack pairs) within 12 us. To carry traffic from a source to a destination rack, transmitters and receivers in our interconnect can be dynamically linked in millions of ways. We develop topology construction and routing methods to exploit this flexibility, including a flow scheduling algorithm that is a constant factor approximation to the offline optimal solution. Experiments with a small prototype point to the feasibility of our approach. Simulations using realistic data center workloads show that, compared to the conventional folded-Clos interconnect, our approach can improve mean flow completion time by 30-95% and reduce cost by 25-40%.

Deploying chains of virtual network functions: On the relation between link and server usage

Abstract: Recently, Network Function Virtualization (NFV) has been proposed to transform from network hardware appliances to software middleboxes. Normally, a demand needs to invoke several Virtual Network Functions (VNFs) in a particular order following the service chain along a routing path. In this paper, we study the joint problem of VNF placement and path selection to better utilize the network. We discover that the relation between the link and server usage plays a crucial role in the problem. We first propose a systematic way to elastically tune the proper link and server usage of each demand based on network conditions and demand properties. In particular, we compute a proper routing path length, and decide, for each VNF in the service chain, whether to use additional server resources or to reuse resources provided by existing servers. We then propose a chain deployment algorithm to follow the guidance of this link and server usage. Via simulations, we show that our design effectively adapts resource usage to network dynamics, and, hence, serves more demands than other heuristics.

Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations

Abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). This paper first proposes a new time-discretized multi-commodity network flow model for the VRPPDTW based on the integration of vehicles’ carrying states within space–time transportation networks, so as to allow a joint optimization of passenger-to-vehicle assignment and turn-by-turn routing in congested transportation networks. Our three-dimensional state–space–time network construct is able to comprehensively enumerate possible transportation states at any given time along vehicle space–time paths, and further allows a forward dynamic programming solution algorithm to solve the single vehicle VRPPDTW problem. By utilizing a Lagrangian relaxation approach, the primal multi-vehicle routing problem is decomposed to a sequence of single vehicle routing sub-problems, with Lagrangian multipliers for individual passengers’ requests being updated by sub-gradient-based algorithms. We further discuss a number of search space reduction strategies and test our algorithms, implemented through a specialized program in C++, on medium-scale and large-scale transportation networks, namely the Chicago sketch and Phoenix regional networks.

Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks

Abstract: In rechargeable sensor networks (RSNs), energy harvested by sensors should be carefully allocated for data sensing and data transmission to optimize data gathering due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the data transmission. In this paper, we strive to optimize data gathering in terms of network utility by jointly considering data sensing and data transmission. To this end, we design a data gathering optimization algorithm for dynamic sensing and routing (DoSR), which consists of two parts. In the first part, we design a balanced energy allocation scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then in the second part, we propose a distributed sensing rate and routing control (DSR2C) algorithm to jointly optimize data sensing and data transmission, while guaranteeing network fairness. In DSR2C, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. Furthermore, since recomputing the optimal data sensing and routing strategies upon change of energy allocation will bring huge communications for information exchange and computation, we propose an improved BEAS to manage the energy allocation in the dynamic environments and a topology control scheme to reduce computational complexity. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithms in comparison with existing algorithms.

Routing in Delay Tolerant Network

Abstract: At present, the internet has grown exponentially due to its wide connectivity with different sets of devices. The only issue is the tolerances towards delay which leads to disconnection in case the delay is above tolerance level. Delay Tolerant Network (DTN) is the latest development to sustain longer delays by allowing disconnected operations. Among the various problems like buffering, resources allocation and energy consumption, routing is a major issue. The present study reviews many proposals regarding routing problem and its solution and the state of the art analysis is presented. General Terms Networks, delay, flooding, forwarding.

Electric Vehicle Routing Problem

Abstract: This paper presents a general Electric Vehicle Routing Problem (EVRP) that finds the optimal routing strategy with minimal travel time cost and energy cost as well as number of EVs dispatched. This is the first EVRP model to consider the vehicle load effect on battery consumption. As demonstrated with a case study in Austin TX, the effect of vehicle load on routing strategy cannot be ignored. Compared to diesel truck VRP, EVRP has comparable travel time and distance but long en-route re-charging time, which translates into a considerable amount of additional labor cost. Lastly, the network topology greatly affects the routing strategies.

Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization

Abstract: Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities A key to success for a bike sharing systems is the effectiveness of rebalancing operations, that is, the efforts of restoring the number of bikes in each station to its target value by routing vehicles through pick-up and drop-off operations There are two major issues for this bike rebalancing problem: the determination of station inventory target level and the large scale multiple capacitated vehicle routing optimization with outlier stations The key challenges include demand prediction accuracy for inventory target level determination, and an effective optimizer for vehicle routing with hundreds of stations To this end, in this paper, we develop a Meteorology Similarity Weighted K-Nearest-Neighbor (MSWK) regressor to predict the station pick-up demand based on large-scale historic trip records Based on further analysis on the station network constructed by station-station connections and the trip duration, we propose an inter station bike transition (ISBT) model to predict the station drop-off demand Then, we provide a mixed integer nonlinear programming (MINLP) formulation of multiple capacitated bike routing problem with the objective of minimizing total travel distance To solve it, we propose an Adaptive Capacity Constrained K-centers Clustering (AdaCCKC) algorithm to separate outlier stations (the demands of these stations are very large and make the optimization infeasible) and group the rest stations into clusters within which one vehicle is scheduled to redistribute bikes between stations In this way, the large scale multiple vehicle routing problem is reduced to inner cluster one vehicle routing problem with guaranteed feasible solutions Finally, the extensive experimental results on the NYC Citi Bike system show the advantages of our approach for bike demand prediction and large-scale bike rebalancing optimization

HydroCast: Pressure Routing for Underwater Sensor Networks

Abstract: A Sensor Equipped Aquatic (SEA) swarm is a sensor cloud that drifts with water currents and enables 4-D (space and time) monitoring of local underwater events such as contaminants, marine life, and intruders. The swarm is escorted on the surface by drifting sonobuoys that collect data from the underwater sensors via acoustic modems and report it in real time via radio to a monitoring center. The goal of this study is to design an efficient anycast routing algorithm for reliable underwater sensor event reporting to any surface sonobuoy. Major challenges are the ocean current and limited resources (bandwidth and energy). In this paper, these challenges are addressed, and HydroCast, which is a hydraulic-pressure-based anycast routing protocol that exploits the measured pressure levels to route data to the surface sonobuoys, is proposed. This paper makes the following contributions: a novel opportunistic routing mechanism to select the subset of forwarders that maximizes the greedy progress yet limits cochannel interference and an efficient underwater dead end recovery method that outperforms the recently proposed approaches. The proposed routing protocols are validated through extensive simulations.

Dynamic information routing in complex networks

Abstract: Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.

A real-life Multi Depot Multi Period Vehicle Routing Problem with a Heterogeneous Fleet: Formulation and Adaptive Large Neighborhood Search based Matheuristic

Abstract: In this paper, a new rich Vehicle Routing Problem that could arise in a real life context is introduced and formalized: the Multi Depot Multi Period Vehicle Routing Problem with a Heterogeneous Fleet. The goal of the problem is to minimize the total delivery cost. A heterogeneous fleet composed of vehicles with different capacity, characteristics (i.e. refrigerated vehicles) and hourly costs is considered. A limit on the maximum route duration is imposed. Unlike what happens in classical multi-depot VRP, not every customer may/will be served by all the vehicles or from all the depots. The planning horizon, as in most real life applications, consists of multiple periods, and the period in which each route is performed is a variable of the problem. The set of periods, within the time horizon, in which the delivery may be carried out is known for each customer. A Mixed Integer Programming (MIP) formulation for MDMPVRPHF is presented in this paper, and an Adaptive Large Neighborhood Search (ALNS) based Matheuristic approach is proposed, in which different destroy operators are defined. Computational results, pertaining to realistic instances, which show the effectiveness of the proposed method, are provided.

50th Anniversary Invited Article—Future Research Directions in Stochastic Vehicle Routing

Abstract: Stochastic vehicle routing, which deals with routing problems in which some of the key problem parameters are not known with certainty, has been an active, but fairly small research area for almost 50 years. However, over the past 15 years we have witnessed a steady increase in the number of papers targeting stochastic versions of the vehicle routing problem (VRP). This increase may be explained by the larger amount of data available to better analyze and understand various stochastic phenomena at hand, coupled with methodological advances that have yielded solution tools capable of handling some of the computational challenges involved in such problems. In this paper, we first briefly sketch the state-of-the-art in stochastic vehicle routing by examining the main classes of stochastic VRPs (problems with stochastic demands, with stochastic customers, and with stochastic travel or service times), the modeling paradigms that have been used to formulate them, and existing exact and approximate solution meth...

Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network

Abstract: Due to the promising application of collecting information from remote or inaccessible location, wireless sensor networks pose big challenge for data routing to maximize the communication with more energy efficient. Literature presents different cluster-based energy aware routing protocol for maximizing the life time of sensor nodes. Accordingly, an energy efficient clustering mechanism, based on artificial bee colony algorithm and factional calculus is proposed in this paper to maximize the network energy and life time of nodes by optimally selecting cluster-head. The hybrid optimization algorithm called, multi-objective fractional artificial bee colony is developed to control the convergence rate of ABC with the newly designed fitness function which considered three objectives like, energy consumption, distance travelled and delays to minimize the overall objective. The performance of the proposed FABC-based cluster head selection is compared with LEACH, PSO and ABC-based routing using life time, and energy. The results proved that the proposed FABC maximizes the energy as well as life time of nodes as compared with existing protocols.

An Energy-Efficient Routing Algorithm for Software-Defined Wireless Sensor Networks

Abstract: Recent significant research on wireless sensor networks (WSNs) has led to the widespread adoption of software-defined WSNs (SDWSNs), which can be reconfigured even after deployment. In this paper, we propose an energy-efficient routing algorithm for SDWSNs. In this algorithm, to make the network to be functional, control nodes are selected to assign different tasks dynamically. The selection of control nodes is formulated as an NP-hard problem, taking into consideration of the residual energy of the nodes and the transmission distance. To tackle the NP-hard problem, an efficient particle swarm optimization algorithm is proposed. Simulation results show that the proposed algorithm performs well over other comparative algorithms under various scenarios.

A Branch-Price-and-Cut Algorithm for the Inventory-Routing Problem

Abstract: The inventory-routing problem IRP integrates two well-studied problems, namely, inventory management and vehicle routing. Given a set of customers to service over a multiperiod horizon, the IRP consists of determining when to visit each customer, which quantity to deliver in each visit, and how to combine the visits in each period into feasible routes such that the total routing and inventory costs are minimized. In this paper, we propose an innovative mathematical formulation for the IRP and develop a state-of-the-art branch-price-and-cut algorithm for solving it. This algorithm incorporates known and new families of valid inequalities, including an adaptation of the well-known capacity inequalities, as well as an ad hoc labeling algorithm for solving the column generation subproblems. Through extensive computational experiments on a widely used set of 640 benchmark instances involving between two and five vehicles, we show that our branch-price-and-cut algorithm clearly outperforms a state-of-the-art branch-and-cut algorithm on the instances with four and five vehicles. In this instance set, 238 were still open before this work and we proved optimality for 54 of them.

Arc Routing: Problems, Methods, and Applications

Abstract: This book provides a thorough and up-to-date discussion of arc routing by world-renowned researchers. Organized by problem type, the book offers a rigorous treatment of complexity issues, models, algorithms, and applications. Arc Routing: Problems, Methods, and Applications opens with a historical perspective of the field and is followed by three sections that cover complexity and the Chinese Postman and the Rural Postman problems; the Capacitated Arc Routing Problem and routing problems with min-max and profit maximization objectives; and important applications, including meter reading, snow removal, and waste collection. Audience: This book will be of interest to practitioners, researchers, and graduate students in operations research, engineering, logistics, mathematics, and business.

SCRP: Stable CDS-Based Routing Protocol for Urban Vehicular Ad Hoc Networks

Abstract: This paper addresses the issue of selecting routing paths with minimum end-to-end delay (E2ED) for nonsafety applications in urban vehicular ad hoc networks (VANETs). Most existing schemes aim at reducing E2ED via greedy-based techniques (i.e., shortest path, connectivity, or number of hops), which make them prone to the local maximum problem and to data congestion, leading to higher E2ED. As a solution, we propose SCRP, which is a distributed routing protocol that computes E2ED for the entire routing path before sending data messages. To do so, SCRP builds stable backbones on road segments and connects them at intersections via bridge nodes. These nodes assign weights to road segments based on the collected information of delay and connectivity. Routes with the lowest aggregated weights are selected to forward data packets. Simulation results show that SCRP outperforms some of the well-known protocols in literature.

Understanding individual routing behaviour.

Abstract: Knowing how individuals move between places is fundamental to advance our understanding of human mobility (Gonzalez et al. 2008 Nature 453, 779–782. (doi:10.1038/nature06958)), improve our urban in...

Vehicle routing problems with multiple trips

Abstract: This paper presents a survey on the Multi-Trip Vehicle Routing Problem (MTVRP) and on related routing problems where vehicles are allowed to perform multiple trips. The first part of the paper focuses on the MTVRP. It gives an unified view on mathematical formulations and surveys exact and heuristic approaches. The paper continues with variants of the MTVRP and other families of routing problems where multiple trips are sometimes allowed. For the latter, it specially insists on the motivations for having multiple trips and the algorithmic consequences. The expected contribution of the survey is to give a comprehensive overview on a structural property of routing problems that has seen a strongly growing interest in the last few years and that has been investigated in very difierent areas of the routing literature.