Link Cost Research Articles

Hardware implementation of dynamic fuzzy logic based routing in Network-on-Chip

This paper presents the hardware implementation of a generic fuzzy logic-based adaptive routing scheme for both buffered and bufferless Networks-on-Chip (NoC). The routing scheme considers the dynamic traffic load and power consumption on neighboring router links to select the output port of an incoming flit. Specifically, fuzzy logic control is used to build a simple, generic, and efficient nonlinear control law that dynamically calculates the input link cost. Basing the link cost on traffic load and power consumption and not on empty buffer slots, makes the proposed algorithm applicable to both buffered and bufferless NoCs. Hardware implementation in ASIC and FPGA technologies demonstrate that the hardware area overhead imposed by the fuzzy control logic is from minimal to negligible for practical flit sizes and scales excellently with network size. Furthermore, since the fuzzy control logic is not in the router critical path, it imposes no additional latency. Finally, we demonstrate the efficiency of the proposed routing scheme through simulative evaluation against representative conventional counterparts.

INVESTIGATION ON ENERGY BASED DATA GATHERING APPROACH FOR WSN

Wireless Sensor Networks plays a vital role in all emerging areas of Wireless Platforms like Interne of Things (IoT), WiFi, WiMAX etc. Sensor nodes are communicated with or without the presence of administrator. Data gathering is a major issue in WSN which influences the throughput, energy and data delivery. In previous research, there was not taken efforts to focus on balanced data gathering. In this research, we propose Reliable Energy Efficient Data Gathering Approach (REEDGA) to balance data gathering and overhead. To achieve this, proposed work consists of three phases. In first phase, estimation of information gathering is implemented through stable paths. Stable paths are found based on link cost. In second phase, data gathering phase is initialized to save energy in the presence of mobile sensor nodes. Overhead is kept low while keeping round trip time of gathered data. From the analytical simulation using NS2, the proposed approach achieves better performance in terms of data delivery rate, data gathering rate, throughput, delay, link availability and control overhead.

Distributed data association in smart camera network via dual decomposition

One of the fundamental requirements for pedestrian surveillance using smart camera network is the correct association of each person's observations generated on different camera nodes to the person's track. Recently, distributed data association methods that involve only local information processing on each camera node and mutual information exchanging between neighboring cameras have attracted many research interests due to their superiority in large scale applications. In this paper, we propose a new method that performs global data association in a distributed manner by fusing the appearance and spatio-temporal measurements of objects captured by all camera nodes in the entire network. Specifically, we formulate the data association problem in smart camera networks as an Integer Programming problem by introducing a set of linking variables, and propose two distributed algorithms, namely L-DD and Q-DD, to solve the Integer Programming problem using the dual decomposition technique. In our algorithms, the original Integer Programming problem is decomposed into several subproblems, which can be solved locally on each smart camera. Different subproblems reach consensus on their solutions in a rigorous way by adjusting their parameters iteratively based on the projected subgradient optimization. The proposed method is simple and flexible, in that (i) we can incorporate any feature extraction and matching technique into our framework to calculate the similarity between observations, which corresponds to the costs of links in our model, and (ii) we can decompose the original problem in any way as long as the resulting subproblem can be solved independently and efficiently. We show the competitiveness of our method in both accuracy and speed by theoretical analysis and experimental comparison with state-of-the-art algorithms.

FW-AODV : An Optimized AODV Routing Protocol for Wireless Mesh Networks

In this paper we present a new optimized Adhoc on Demand Distance Vector reactive routing approach for Wireless Mesh Networks namely FW-AODV. The algorithm uses fuzzy logic based integrated link cost based cost measure to evaluate route cost. The proposed FW-AODV approach has modified the working of Adhoc on Demand Distance Vector Routing (AODV) routing protocol. The AODV routing protocol becomes unreliable as the mobility of nodes and size of Wireless Mesh Network (WMN) increases. The proposed approach is able to negotiate well with the node mobility and network issues. AODV is one of the most popular and commonly used reactive routing protocols. On the other hand proactive routing techniques increase control traffic of the network and require more bandwidth. The proposed FW-AODV approach has changed the least cost route evaluation process and the maintenance phase of AODV. The proposed approach was implemented in MATLAB and was validated on different network scenarios. The FW-AODV is compared with AODV, BAT and Ant Colony Optimization (ACO) based approaches. The of proposed approach proved to be much superior than the other three approaches. Keywords: Reactive Routing, AODV, FW-AODV, BAT, proactive routing, ACO.

A Bio-Inspired Approach to Traffic Network Equilibrium Assignment Problem.

Finding an equilibrium state of the traffic assignment plays a significant role in the design of transportation networks. We adapt the path finding mathematical model of slime mold Physarum polycephalum to solve the traffic equilibrium assignment problem. We make three contributions in this paper. First, we propose a generalized Physarum model to solve the shortest path problem in directed and asymmetric graphs. Second, we extend it further to resolve the network design problem with multiple source nodes and sink nodes. At last, we demonstrate that the Physarum solver converges to the user-optimized (Wardrop) equilibrium by dynamically updating the costs of links in the network. In addition, convergence of the developed algorithm is proved. Numerical examples are used to demonstrate the efficiency of the proposed algorithm. The superiority of the proposed algorithm is demonstrated in comparison with several other algorithms, including the Frank-Wolfe algorithm, conjugate Frank-Wolfe algorithm, biconjugate Frank-Wolfe algorithm, and gradient projection algorithm.

Routing in Wireless Mesh Networks based on Termites' Intelligence

Wireless Mesh Network (WMN) is envisaged to be key component of next generation wireless networks which can effectively cope with the ever increasing fast and vast growing need to access data and avail services over the network. These networks provide infrastructure less high speed internet access to the end users and have a cutting edge over the existing networks. Routing, being most critical issue in their implementation. The dynamic network conditions impose setbacks in the selection of optimal path. There is exigent requirement to tackle these routing issues in context of these networks. In this paper, the authors apply a nature inspired soft computing based meta heuristic technique called Termite Colony Optimization in WMN to find an optimal route based on the link cost. TCO approach is inspired by the emergent behaviour exhibited by the natural termite colony swarms for mound building. Experimentally TCO shows faster converges over some existing algorithms.

A Route Selection Approach Based on Neighbor Dependency Estimated by CSI in Multi-hop Wireless Networks

Unreasonable route selection approach in Multi-hop Wireless Networks (MHWNs) may result in continual routing reconstruction, poor utilization of channel bandwidth and high control overhead. How to select a neighboring node as the next hop and how to evaluate the cost of a link will greatly affect the transmission reliability. In wireless communication, the Channel State Information (CSI) refers to channel properties of a link and describes how a signal propagates from the transmitter to the receiver and represents the combined effect of scattering, fading and power decay with distance, which can represent the transmission characteristics of the wireless communication links. According to the neighbor dependency evaluated by CSI based on Exponential Smoothing Method (ESM),a neighbor dependency-based reliable routing algorithm is presented in this paper.Finally, the performance of the reliable routing protocol based on neighbor dependency (RPBND) is tested in OPNET, and compared with the classical AODV algorithm and the improved AODV based on link Cost (CAODV) algorithm. The simulation results show the RPBND protocol has better performance.

Strategic Formation of Heterogeneous Networks

We establish a network formation game for the Internet’s autonomous system (AS) interconnection topology. The game includes different types of players, accounting for the heterogeneity of ASs in the Internet. In this game, the utility of a player depends on the network structure, e.g., the distances between nodes and the cost of links. Our model is versatile and can accommodate various configurations: whether monetary transfers are allowed, whether survivability requirements are imposed or not, and if so, whether failures are frequent or rare. We analyze static properties of the game as well as its dynamic evolution. We provide a dynamic analysis of topological quantities, and explain the prevalence of some “network motifs” in the Internet connectivity graph. We assess our predictions with real-world data.

A Topology Control Strategy with Reliability Assurance for Satellite Cluster Networks in Earth Observation.

This article investigates the dynamic topology control problem of satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites’ relative position are involved in the link cost metric which is to give a selection criterion for choosing the most reliable data routing paths. Also, a cooperative work model with reliability is proposed for the situation of emergency EO missions. Based on the link cost metric and the proposed reliability model, a reliability assurance topology control algorithm and its corresponding dynamic topology control (RAT) strategy are established to maximize the stability of data transmission in the SCNs. The SCNs scenario is tested through some numeric simulations of the topology stability of average topology lifetime and average packet loss rate. Simulation results show that the proposed reliable strategy applied in SCNs significantly improves the data transmission performance and prolongs the average topology lifetime.

Physarum solver: a bio-inspired method for sustainable supply chain network design problem

A supplier of products and services aims to minimize the capacity investment cost and the operational cost incurred by unwanted byproducts, e.g. carbon dioxide emission. In this paper, we consider a sustainable supply chain network design problem, where the capacity and the product flow along each link are design variables. We formulate it as a multi-criteria optimization problem. A bio-inspired algorithm is developed to tackle this problem. We illustrate how to design a sustainable supply chain network in three steps. First, we develop a generalized model inspired by the foraging behaviour of slime mould Physarum polycephalum to handle the network optimization with multiple sinks. Second, we propose a strategy to update the link cost iteratively, thus making the Physarum model to converge to a user equilibrium. Third, we perform an equivalent operation to transform a system optimum problem into a corresponding user equilibrium problem so that it is solvable in the Physarum model. The efficiency of the proposed algorithm is illustrated with numerical examples.

Massive MIMO operation in partially centralized cloud radio access networks

Massive multiple-input multiple-output (MIMO) is considered as one of the key technologies in next generation cellular systems due to its higher multiplexing gain and energy efficiency. However, in a cloud radio access network (C-RAN) with massive MIMO, operation of many antennas incurs a huge amount of digital sampled data to be transported over the fronthaul link between a baseband unit (BBU) pool and each remote radio head (RRH). Accordingly, the bandwidth requirement for the fronthaul link increases in proportion to the number of antennas at the RRH, and it incurs enormous fronthaul link cost to operators. In this paper, we propose a new C-RAN architecture called massive MIMO partially-centralized C-RAN (MPC-RAN), which is designed for a more efficient use of fronthaul resources in massive MIMO operation. Differently from a conventional fully-centralized C-RAN (FC-RAN) where most L1/L2/L3 functions are performed at the BBU pool, some beamforming-related functions in the proposed MPC-RAN are performed at RRHs. We further provide an MPC-RAN operation strategy that aims to efficiently exploit the proposed C-RAN structure in massive MIMO systems. Specifically, we formulate a wireless sum-rate maximization problem for constrained fronthaul capacity and provide a heuristic algorithm that targets at obtaining optimal beamforming configuration and bandwidth allocation. The simulation results confirm that our proposed architecture requires less fronthaul link capacity compared to FC-RAN for the same transmission configuration, and our operation strategy allows for flexible utilization of capacity-limited fronthaul resources.

Energy aware routing with link disjoint backup paths

Energy aware routing has attracted a wide spread attention in the last decade. However, relatively concentrated traffic after applying energy aware strategies makes the network be vulnerable to link failure and sudden traffic bursts. It is a challenge to take enhancing network reliability into account while keeping low energy consumption. Routing each source-to-terminal request via two disjoint paths can be a trade-off. In this paper, we propose a problem called Energy Aware Routing with Link Disjoint Backup Paths (EAR-LDBP) to minimize router power consumption when backup sharing is not allowed during off-peak hours. Each request is routed via an active path. A link disjoint backup path is computed for each request and can be used to route traffic in case of single link failure on the active path. A 0–1 integer linear programming model that can guarantee the network resilience to single link failure is formulated to minimize the power of used links and nodes while putting idle ones to sleep under constraints of link utilization and a link disjoint backup path for each request. We analyze the complexity of EAR-LDBP and show its Np-hardness. We prove that there is no polynomial-time constant-factor approximation algorithm even for two requests. Four different versions of EAR-LDBP (link/node-disjoint, link cost only/link and node cost) are shown to be equivalent and polynomial-time reducible to each other. Then two algorithms are proposed to solve this problem: 2 Stage Greedy Algorithm (2SG) and Greedy Two Link Disjoint Paths Algorithm (G2DP) with an iterative deletion strategy to improve the solution. For comparison purpose, we also use the state of the art reliable energy aware routing algorithm 2DP-NF in unsplittable way. Simulation results on synthetic networks and real network of Internet2 show the effectiveness of our algorithms. Particularly, our algorithms can save more energy (up to 39%) while using less computation time as compared to 2DP-NF (less than 1% at best).

Stochastic traffic assignment of mixed electric vehicle and gasoline vehicle flow with path distance constraints

Abstract: This paper addresses a general stochastic user equilibrium (SUE) traffic assignment problem (TAP) for transport networks with electric vehicles (EV), where EV paths are restricted by the EV driving range limits. A minimization model for path-constrained SUE is first proposed as an extension of path-constrained deterministic user equilibrium (DUE) TAP, which also extends the existing general SUE models with link-based constraints to path-based constraints. The resulting SUE model and solution algorithm can be used for other conditions with similar path-based constraints. The equilibrium conditions reveal that any path cost in the network is the sum of corresponding link costs and a path specific out-of-range penalty term, while path out-of-range term should equal to zero to ensure feasible flows. We develop a modified method of successive averages (MSA) with a predetermined step size sequence where both multinomial logit and multinomial probit based loading procedure are applied to solve the TAP. The suggested methods incorporate K-shortest paths algorithm to generate the path set on a need basis. Finally, two numerical examples are presented to verify the proposed model and solution algorithms.

Stochastic Multi-Objective Evacuation Model Under Managed and Unmanaged policies

Abstract Natural and man-created disasters, such as hurricanes, earthquakes, tsunamis, accidents and terrorist attacks, require evacuation and assistance routes. Evacuation routes are mostly based on the capacities of the road network. However, in extreme cases, such as earthquakes, road network infrastructure may adversely be affected, and may not supply their required capacities. If for various situations, the potential damage for critical roads can be identified in advance, it is possible to develop an evacuation model, that can be used in various situations. This paper focuses on the development of a model for the design of an optimal evacuation network which simultaneously minimizes retrofit costs of critical links (bridges, tunnels, etc.) and evacuation time. The model considers infrastructures’ vulnerability (as a stochastic function which is dependent on the event location and magnitude), road network, transportation demand and evacuation areas. Furthermore, the model evaluates the benefits of managed evacuation (system optimum) when compared to unmanaged evacuation (user equilibrium). The paper presents a mathematic model for the presented problem. However, since an optimal solution cannot be found within a reasonable timeframe, a heuristic model is presented as well. This heuristic model is based on evolutionary algorithms, which also provides a mechanism for solving the problem as a multi-objective stochastic problem. Using a real-world data, the algorithm is evaluated and compared to the unmanaged evacuation conditions. The results clearly demonstrate the advantages of managed evacuation, as the average travel time can be reduced by 5% to 30%.

Consistency Between Convergence of Dynamic Assignment and Stochasticity of Microsimulation: Implication for Number of Runs

Dynamic transportation models route vehicles by using the principles of dynamic user equilibrium. These models include a dynamic network loading (DNL) module that is used to evaluate link costs. However, an element of stochasticity creeps into the modeling framework when the analytical dynamic assignment (DA) procedure is used along with a stochastic microscopic DNL. A methodologically correct way of approaching this problem is by solving the entire DA with a microscopic DNL (DA-microDNL) model until convergence for a given random seed and then repeating the process with different seed values. This paper proposes an approach to determine the minimum number of replications of the DA-microDNL model to determine a statistically valid estimate of the measure of effectiveness (MOE). The approach was tested on a small and medium-size network having different demand and network characteristics. Results show that running the integrated DA-microDNL framework for a minimum number of replications provides a statistically significant MOE at much lower computation time. The consistent estimates obtained by using this approach would provide robust information to transportation planners and practitioners in evaluating the impacts of several policy decisions on network performance.

Optimal Hyperpaths With Non-Additive Link Costs

Optimal Hyperpaths With Non-Additive Link Costs

Multiclass, Multicriteria Dynamic Traffic Assignment with Path-Dependent Link Cost and Entropy-Based Risk Preference

Challenges arise in dynamic traffic assignment (DTA) when heterogeneous users evaluate choices on the basis of multiple interrelated criteria such as travel time and travel time uncertainty. This paper proposes a density-based formulation along with a stochastic quasigradient projection (SQGP) solution scheme with the aid of a traffic simulator. Path-dependent link cost is proposed to allow for the objective function formulation and more tractable analysis. The criteria in the discussion and the case study are travel time (link-additive), monetary cost (non additive), and travel time uncertainty (path-dependent link-additive). An information entropy-based uncertainty measure is proposed because of concerns about using conventional measures such as variability and reliability. The case study shows stochastic and efficient convergence, demonstrates the ability of SQGP to bypass local optima, and exemplifies the significant effect of using path-independent and path-dependent link costs to forecast traffic pattern and toll revenue. The results also suggest that a pricing strategy aimed at optimizing travel time and reliability for different user classes should consider travel time correlations between toll segments and the adjacent no-toll segments if enumerating paths is practically infeasible.

リンクコスト設定と最短経路計算による災害後トラフィック分布の簡易推定手法の検討

リンクコスト設定と最短経路計算による災害後トラフィック分布の簡易推定手法の検討

On characterizing the relationship between route choice behavior and optimal traffic control solution space

Abstract Explicitly including the dynamics of users’ route choice behaviour in optimal traffic control applications has been of interest for researchers in the last five decades. This has been recognized as a very challenging problem, due to the added layer of complexity and the considerable non-convexity of the resulting problem, even when dealing with simple static assignment and analytical link cost functions. In this work we establish a direct behavioural connection between the different shapes and structures emerging in the solution space of such problems and the underlying route choice behaviour. We specifically investigate how changes in the active equilibrium route set exert direct influence on the solution space's structure and behaviour. Based on this result, we then formulate and validate a constrained version of the original problem, yielding desirable properties in terms of solution space regularity.

Towards Energy-Efficient Routing in Satellite Networks

Satellite networks are drawing more and more attention, since they can provide various services to everywhere on the earth. Communication devices in satellites are typically powered by solar panels and battery cells, which are carefully designed to guarantee power supply and avoid deficiency. However, we find that unrestrained use of energy will cause a satellite to age quickly, because the number of recharge/discharge of battery cells is limited. Due to the extremely high cost of satellites, the development of energy-efficient satellite routing to save energy and prolong satellite lifetimes has become significantly important. In this paper, we do comprehensive studies. First, we model the power consumption of a space router, power supply by solar panels, and aging of battery cells formally. Second, we define the energy-efficient satellite routing (EESR) problem, and prove that the EESR problem is NP-hard. Then, we develop three algorithms to gradually solve the EESR problem. GreenSR-B is a baseline algorithm which computes link costs iteratively to compute a routing that minimizes the total recharge/discharge cycle number. GreenSR-A selects space routers to switch into sleep mode to improve energy conservation. GreenSR jointly considers energy efficiency and QoS requirements of path length and the maximum link utilization ratio. We evaluate our algorithms by simulations on a low earth orbit satellite network with real Internet usage traces. The results show that GreenSR can prolong the lifetime of satellite battery cells by more than 40%, with little increment in path length and a small link utilization ratio.