Sequential Routing Research Articles

Improved reptile search algorithm with sequential assignment routing based false packet forwarding scheme for source location privacy protection on wireless sensor networks

Source Location Privacy (SLP) in Wireless Sensor Networks (WSNs) refers to a set of techniques and strategies used to safeguard the anonymity and confidentiality of the locations of sensor nodes (SNs) that are the source of transmitted data within the network. This protection is important in different WSN application areas like environmental monitoring, surveillance, and healthcare systems, where the revelation of the accurate location of SNs can pose security and privacy risks. Therefore, this study presents metaheuristics with sequential assignment routing based false packet forwarding scheme (MSAR-FPFS) for source location privacy protection (SLPP) on WSN. The contributions of the MSAR-FPFS method revolve around enhancing SLP protection in WSNs through the introduction of dual-routing, SAR technique with phantom nodes (PNs), and an optimization algorithm. In the presented MSAR-FPFS method, PNs are used for the rotation of dummy packets using the SAR technique, which helps to prevent the adversary from original data transmission. Next, the MSAR-FPFS technique uses an improved reptile search algorithm (IRSA) for the optimal selection of routes for real packet transmission. Moreover, the IRSA technique computes a fitness function (FF) comprising three parameters namely residual energy (RE), distance to BS (DBS), and node degree (ND). The experimental evaluation of the MSAR-FPFS system was investigated under different factors and the outputs show the promising achievement of the MSAR-FPFS system compared to other existing models.

The Effect of Symmetry on the De-Anonymizability of Social Networks

This paper studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from source needs to be delivered to multiple receivers within an imposed delay constraint. Prior art on static WSNs minimizes the multicast delay via the construction of a multicast tree that approximates Steiner tree in length, which, however, may be invalidated by the time-varying network topology of WSNs with uncertain link states. Moreover, for multicast in WSNs with link uncertainty, the possible link failure necessitates a suitable measurement of the uncertain communication distance and calls for the performance guarantee in both delay and delivery ratio. In this work, by modeling a WSN as a random graph with each link associated with a transmission probability, we propose FlowerCast, an efficient multicast scheme, to jointly minimize the expected multicast delay and to maximize the expected delivery ratio of multicast under delay constraint. The core of FlowerCast is to quantify the uncertain communication distance by the expected transmission delay of a time-varying path, based on which a delay-optimal multicast tree is constructed in accordance with the directionality of delay. Candidate paths with high expected delivery ratio and low expected delay are then selected in a distributed manner to conditionally connect adjacent multicast members and thus transform the multicast tree into a multicast flower. Despite the NP-hardness of optimal candidate paths addition, the transformation with the highest expected delivery ratio of multicast under delay constraint can be guaranteed through a pseudo-polynomial time derandomization-based greedy approach. We further demonstrate the time and energy efficiency of FlowerCast through asymptotic analysis. To make full use of the possible overlapping links in a multicast flower, a hybrid routing strategy is presented to wisely switch between sequential routing and synchronous routing for extra enhancement of the multicast performance. Extensive experiments on various datasets verify the superiority of FlowerCast and hybrid routing over baselines and indicate their wide applicability to practical scenarios.

FlowerCast: Efficient Time-sensitive Multicast in Wireless Sensor Networks with Link Uncertainty

This article studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from the source needs to be delivered to multiple receivers within an imposed delay constraint. Prior art on static WSNs minimizes the multicast delay via the construction of a multicast tree that approximates the Steiner tree in length, which, however, may be invalidated by the time-varying network topology of WSNs with uncertain link states. Moreover, for multicast in WSNs with link uncertainty, the possible link failure necessitates a suitable measurement of the uncertain communication distance and calls for the performance guarantee in both delay and delivery ratio. In this work, by modeling a WSN as a random graph with each link associated with a transmission probability, we propose FlowerCast, an efficient multicast scheme, to jointly minimize the expected multicast delay and to maximize the expected delivery ratio of multicast under delay constraint. The core of FlowerCast is to quantify the uncertain communication distance by the expected transmission delay of a time-varying path, based on which a delay-optimal multicast tree is constructed in accordance with the directionality of delay. Candidate paths with a high expected delivery ratio and low expected delay are then selected in a distributed manner to conditionally connect adjacent multicast members and thus transform the multicast tree into a multicast flower. Despite the NP-hardness of optimal candidate paths’ addition, the transformation with the highest expected delivery ratio of multicast under delay constraint can be guaranteed through a pseudo-polynomial time derandomization-based greedy approach. We further demonstrate the time and energy efficiency of FlowerCast through asymptotic analysis. To make full use of the possible overlapping links in a multicast flower, a hybrid routing strategy is presented to wisely switch between sequential routing and synchronous routing for extra enhancement of the multicast performance. Extensive experiments on various datasets verify the superiority of FlowerCast and hybrid routing over baselines and indicate their wide applicability to practical scenarios.

A distributed adaptive routing against selective forwarding attack in scale-free network considering cascading failure

Abstract To address the issues of data insecurity and unreliable transmission, redundancy-based data recovery can guarantee data security, but the increase of redundant data will reduce the robustness of network in the face of cascading failures. A distributed adaptive routing method in scale-free network is proposed to improve network resilience against selective forwarding attacks and the robustness against cascading failures. Based on the polynomial principle, the proposed routing method slices packets, adds redundancy reasonably and adopts multipath sequential routing method to completely send data to the destination node. The ability to resist selective forwarding attacks and robustness against cascading failures is investigated and analysed throughout the entire network operation. Simulation results show that our proposed routing method is not restricted by the number of disjoint paths, can maintain a higher data recovery ratio and resist effectively selective forwarding attacks, and also balances the network load well. Moreover, this routing method has a lower end-to-end latency for data transmission and is highly resistant to cascading failures under random and intentional attacks.

Single spikes drive sequential propagation and routing of activity in a cortical network.

Single spikes can trigger repeatable firing sequences in cortical networks. The mechanisms that support reliable propagation of activity from such small events and their functional consequences remain unclear. By constraining a recurrent network model with experimental statistics from turtle cortex, we generate reliable and temporally precise sequences from single spike triggers. We find that rare strong connections support sequence propagation, while dense weak connections modulate propagation reliability. We identify sections of sequences corresponding to divergent branches of strongly connected neurons which can be selectively gated. Applying external inputs to specific neurons in the sparse backbone of strong connections can effectively control propagation and route activity within the network. Finally, we demonstrate that concurrent sequences interact reliably, generating a highly combinatorial space of sequence activations. Our results reveal the impact of individual spikes in cortical circuits, detailing how repeatable sequences of activity can be triggered, sustained, and controlled during cortical computations.

A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study

A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study

Asynchronous Reinforcement Learning Framework and Knowledge Transfer for Net-Order Exploration in Detailed Routing

The net orders in detailed routing are crucial to routing closure, especially in most modern routers following the sequential routing manner with the rip-up and reroute scheme. In advanced technology nodes, detailed routing has to deal with complicated design rules and large problem sizes, making its performance more sensitive to the order of nets to be routed. In the literature, the net orders are mostly determined by simple heuristic rules tuned for specific benchmarks. In this work, we propose an asynchronous reinforcement learning (RL) framework to automatically search for optimal ordering strategies and a transfer learning (TL) algorithm to improve performance. By asynchronous querying, the router, pretraining the RL agents, and finetuning with the TL algorithm, we can generate high-performance routing sequences to achieve a 26% reduction in the DRC violations and a 1.2% reduction in the total costs compared with the state-of-the-art detailed router.

Automatic design system for generating routing layout of tubes, hoses, and cable harnesses in a commercial truck

Abstract Although many routing algorithms have been developed, it is difficult for designers in the automotive industry to adopt them because of the complicated preliminary steps that are required. This study presents a systematic framework for generating the routing layout of the tubes, hoses, and cable harnesses in a commercial truck. The routing layout design problem in a commercial truck is analysed and defined. For routing operations, a sequential graph-based routing algorithm is employed to rapidly provide a routing solution. Because a reference routing layout design does not exist in most engineering problems, a cell-based genetic algorithm combined with a modified maze algorithm is employed to generate a reference design. To consider the clamping condition of the routing components, a new fitness function in the genetic algorithm is implemented. The numerical study shows that the proposed routing algorithm provides a better reference routing layout design than the conventional algorithm. The proposed automatic design system was applied to the routing layout design problem of a commercial truck. It was demonstrated that the proposed framework satisfies all industrial practitioners’ functional requirements and provides a systematic method of solving the routing layout design problem, considering all its characteristics.

Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing.

ObjectiveMultitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer – impaired learning if tasks are not appropriately selected. We introduce a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them.Materials and MethodsUsing the MIMIC-III (Medical Information Mart for Intensive Care-III) dataset, we train deep neural network models to predict the onset of 6 endpoints including specific organ dysfunctions and general clinical outcomes: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay. We compare single-task (ST) models with naive multitask and SeqSNR in terms of discriminative performance and label efficiency.ResultsSeqSNR showed a modest yet statistically significant performance boost across 4 of 6 tasks compared with ST and naive multitasking. When the size of the training dataset was reduced for a given task (label efficiency), SeqSNR outperformed ST for all cases showing an average area under the precision-recall curve boost of 2.1%, 2.9%, and 2.1% for tasks using 1%, 5%, and 10% of labels, respectively.ConclusionsThe SeqSNR architecture shows superior label efficiency compared with ST and naive multitasking, suggesting utility in scenarios in which endpoint labels are difficult to ascertain.

Sequential routing framework: Fully capsule network-based speech recognition

Capsule networks (CapsNets) have recently gotten attention as a novel neural architecture. This paper presents the sequential routing framework which we believe is the first method to adapt a CapsNet-only structure to sequence-to-sequence recognition. Input sequences are capsulized then sliced by a window size. Each slice is classified to a label at the corresponding time through iterative routing mechanisms. Afterwards, losses are computed by connectionist temporal classification (CTC). During routing, the required number of parameters can be controlled by the window size regardless of the length of sequences by sharing learnable weights across the slices. We additionally propose a sequential dynamic routing algorithm to replace traditional dynamic routing. The proposed technique can minimize decoding speed degradation caused by the routing iterations since it can operate in a non-iterative manner without dropping accuracy. The method achieves a 1.1% lower word error rate at 16.9% on the Wall Street Journal corpus compared to bidirectional long short-term memory-based CTC networks. On the TIMIT corpus, it attains a 0.7% lower phone error rate at 17.5% compared to convolutional neural network-based CTC networks (Zhang et al., 2016).

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.

Sequential Routing-Loading Algorithm for Optimizing One-Door Container Closed-Loop Logistics Operations

One-door container type of vehicle is the main tool for urban logistics in Indonesia which may take the form of truck, car, or motorcycle container. The operations would be more effective when it is performed through pickup-delivery or forward-reverse at a time. However, there is difficulty to optimize the operation of routing and container loading processes in such a system. This article is proposing an improvement for algorithm for sequential routing- loading process which had been tested in the small datasets but not yet tested in the case of big data set and vehicle routing problem with time windows. The improvement algorithm is tested in big data set with the input of the vehicle routing problem with time windows (VRP-TW) using the solution optimization of the Simulated Annealing process with restart point procedure (SA-R) for the routing optimization and Genetic Algorithm (GA) to optimize the container loading algorithm. The large data sets are hypothetical generated data for 800-2500 single-sized products, 4 types of container capacity, and 100-400 consumer spots. As result, the performance of the proposed algorithm in terms of cost is influenced by the number of spots to be visited by the vehicle and the vehicle capacity. Limitations and further analysis are also described in this article.

Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks

With the development of 5G communication, massive multiple input multiple output (MIMO) technology is getting more and more attention. Massive MIMO uses a large amount of simultaneous transmitting and receiving antennas to reduce power consumption and raise the level of transmission quality. Meanwhile, the diversification of user equipment (UE) in the 5G environment also makes heterogeneous networks (HetNets) more prevalent. HetNets allow UE of different network standards to access small cells, while the base stations of small cells access a macro base station (BS) to form a multihop wireless heterogeneous backhaul network. However, how to effectively combine these two technologies by efficiently allocating the antennas of each BS during the route construction process of heterogeneous wireless backhaul networks is still an important issue that is yet to be solved. In this paper, we propose an algorithm called preallocated sequential routing (PSR). Based on the links’ channel conditions and the available antennas and location of BSs, it builds a wireless heterogeneous network backhaul topology and adjusts each link’s transmitting and receiving antennas to maximize total utility. Simulation results showed that the proposed algorithm significantly improved the overall utility and the utility of the outer area of heterogeneous networks.

Integrated routing optimization for post-disaster rapid-detailed need assessment

The post-disaster need assessment for the affected area is of significance for the effective distribution of relief supplies. This paper innovatively concentrates on the integrated routing optimization problem for the rapid need assessment stage and the detailed need assessment stage. A bi-objective model with consideration of the effects of site familiarity and route familiarity is proposed. The proposed nonlinear model is equivalently transformed into a mixed-integer linear programming. A tabu search algorithm is designed to solve the proposed model, and its solving accuracy is validated by comparing with the solution of CPLEX based on a small-scale problem. To demonstrate the efficiency of the proposed model and algorithm for large-scale problems, the case of Ya'an earthquake in China is investigated. The experimental results reveal that comparing to the separate routing optimization and the sequential routing optimization, the integrated routing optimization can achieve a better coordination between the decisions of two stages.

Sequential graph-based routing algorithm for electrical harnesses, tubes, and hoses in a commercial vehicle

The routing design of the various electrical wires, tubes, and hoses of a commercial vehicle requires a significant number of man-hours because of the variety of the commercial vehicles, frequent design changes of other vehicular components and the manual trial-and-error approaches. This study proposes a new graph-based routing algorithm to find the collision-free routing path in the constrained space of a commercial vehicle. Minimal spanning tree is adopted to connect multi-terminal points in a graph and Dijkstra’s algorithm is used to find the shortest route among the candidate paths; the design domain is divided into several sub-domains to simplify the graph and the proposed algorithm solves the routing problems in a sequential manner to deal intermediate points. Then, the proposed method was applied to the design of the routes for four different routing components of a commercial truck. The results indicate that the developed methodology can provide a satisfactory routing design satisfying all the requirements of the design experts in the automotive industry.

On modeling stochastic dynamic vehicle routing problems

Operations research requires models that unambiguously define problems and support the generation and presentation of solution methodology. In the field of dynamic routing, capturing the joint evolution of complex sequential routing decisions and stochastic information is challenging, leading to a situation where rigorous methods have outpaced rigorous models and thus making it difficult for researchers to engage in rigorous science. We provide a modeling framework that strongly connects application with method and that leverages the rich body of route-based planning and optimization. As a generalization of conventional Markov decision processes (MDPs), route-based MDPs augment the state space, action space, and reward structure to include routing information. Accordingly, route-based MDPs make it conceptually easier to connect dynamic routing problems with the route-based methods typically used to solve them – construct and revise routes as new information is learned. We anticipate route-based MDPs will facilitate more scientific rigor in dynamic routing studies, provide researchers with a common modeling language, allow for better inquiry, and improve classification and description of solution methods.

The Inefficiency of Nash and Subgame Perfect Equilibria for Network Routing

This paper provides new bounds on the quality of equilibria in finite congestion games with affine cost functions, specifically for atomic network routing games. It is well known that the price of anarchy equals exactly 5/2 in general. For symmetric network routing games, it is at most (5n − 2)/(2n + 1), where n is the number of players. This paper answers to two open questions for congestion games. First, we show that the price of anarchy bound (5n − 2)/(2n + 1) is tight for symmetric network routing games, thereby answering a decade-old open question. Second, we ask whether sequential play and subgame perfection allows to evade worst-case Nash equilibria, and thereby reduces the price of anarchy. This is motivated by positive results for congestion games with a small number of players, as well as recent results for other resource allocation problems. Our main result is the perhaps surprising proof that subgame perfect equilibria of sequential symmetric network routing games with linear cost functions can have an unbounded price of anarchy. We complete the picture by analyzing the case with two players: we show that the sequential price of anarchy equals 7/5 and that computing the outcome of a subgame perfect equilibrium is NP-hard.

A hierarchical adaptive routing algorithm of wireless sensor network based on software-defined network

The target tracking issue has always been the hotspot in wireless sensor network, and with the emergence of new application in multimedia and real-time transmission, new requirements are proposed for transmission performance of target tracking routing; therefore, a software-defined network–based hierarchical adaptive routing algorithm of wireless sensor network is proposed in this article. The algorithm takes into account both network energy and throughput, uses Hopfield neural network algorithm to calculate the optimal path among adjacent clusters as a local routing (LR), and builds the Multi-choice Knapsack Problem model based on local paths to realize end-to-end global routing, in order to realize the routing of tracking target information under multi-objective conditions. The test bed includes physical and simulation tests. Experimental results show that the proposed algorithm is superior to low energy adaptive clustering hierarchy (LEACH) and Sequential Assignment Routing under different test scenarios.

A multi-user decision support system for online city bus tour planning

Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology, the smartphone, the Internet and Wi-Fi have given a boost to the tourism industry. The city bus tour (CBT) service is one of the most successful businesses in the tourism industry. However, there exists no smart decision support system determining the most efficient way to plan the itinerary of a CBT. In this research, we report on the ongoing development of a mobile application (app) and a website for tourists, hoteliers and travel agents to connect with city bus operators and book/purchase the best CBT both in terms of cost and time. Firstly, the CBT problem is formulated as an asymmetric sequential three-stage arc routing problem. All places of interest (PoI) and pickup/dropout points are identified with arcs of the network (instead of nodes), each of which can be visited at least once (instead of exactly once). Secondly, the resulting pure integer programming (IP) problem is solved using a leading optimization software known as General Algebraic Modeling System (GAMS). The GAMS code developed for this project returns: (1) the exact optimal solution identifying the footprints of the city bus relative to all the arcs forming the minimal cost network; (2) the augmenting paths corresponding to the pickup stage, the PoI visiting stage and the drop-off stage. Finally, we demonstrate the applicability of the mobile app/website via a pilot study in the city of Melbourne (Australia). All the computations relative to the initial tests show that the ability of the app to answer users’ inquiries in a fraction of a minute.

Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics

ABSTRACTGoods movement is one of the fastest growing transportation sectors, affecting both economic and environmental sustainability, particularly in dense urban areas with traffic congestion and air pollution. To meet this challenge, urban public agencies have paid attention to policies and systems to facilitate efficient and sustainable city logistics. This paper proposes a modeling framework to consider both spatial–temporal constraints and a means to calibrate the model from observable data, based on an adaptation of an activity-based passenger model called the household activity pattern problem. Conceptual comparisons with a state-of-the-art freight forecasting methodology are made using an example. Application of the model is illustrated through formulating and implementing a Sequential Selective Vehicle Routing Problem associated with drayage truck activities at the San Pedro Bay Ports in Southern California.