Design o f Wireless Ad Hoc System u sing Adaptive Multi Hop Link State Optimal Routing Protocol

Now a days for the radio network communication multi-hop routing is used. This multi-hop routing technique covers larger coverage area. Therefore to reach at specific location data is transferred in form of packets from one node to other node. But for the transmission of radio signals over the large distance, large number of transreceivers are required and these transreceivers requires large power to operate. As a result, multi-hop routing can saves energy over separate routing. Therefore it is necessity to design a cost effective multi-hop routing technique for successful transmission of ratio packet data. In this paper a hop by hop adaptive link state optional routing (HALO) is explained. It is the first packet transmitting solution with hop by hop and link state routing, which reduces the cost of transporting data across a packet switch network[3]. The triple model is designed for multi hop packet routing. In this work each node of network iteratively and separately improves the small part of traffic bound. This algorithm finds the shortest path of specific location for every iteration and it is calculated by the marginal cost of the various links of network. The marginal link cost is used to calculate the shortest path between the node and the destination location. This marginal link cost is gathered from link state updates. The various networks changes are automatically identified by the adaptive method which is used in this paper. Due to this the exchange between the packets on wrong node is reduced over the overhead traffic. To validate these theoretical results the experimental evaluations and mathematical calculations are also reported in this work. Net beans java is the programmed use in this proposed project.

Generalized Link-Cost Function and Network Design for Dedicated Truck-Platoon Lanes to Improve Energy, Pavement Sustainability, and Traffic Efficiency

Recent development of autonomous and connected trucks (ACT) has provided the freight industry with the option of using truck platooning to improve fuel efficiency, traffic throughput, and safety. However, closely spaced and longitudinally aligned trucks impose frequent and concentrated loading on pavements, which often accelerates pavement deterioration and increases the life cycle costs for the highway agency. Also, effectiveness of truck platooning can be maximized only in dedicated lanes; and its benefits and costs need to be properly balanced between stakeholders. This paper proposes a network-design model to optimize (i) placement of dedicated truck-platoon lanes and toll price in a highway network, (ii) pooling and routing of ACT traffic from multiple origins and destinations to utilize these lanes, and (iii) configuration of truck platoons within these lanes (e.g., lateral displacements and vehicle separations). The problem is formulated as an integrated bi-level optimization model. The upper level makes decisions on converting existing highway lanes into dedicated platoon lanes, as well as setting user fees. The lower-level decisions are made by independent shippers regarding the choice of routes and use of platoon lanes vs. regular lanes; and they collectively determine truck traffic in all lanes. Link-cost functions for platoon lanes are obtained by simultaneously optimizing, through dynamic programming, pavement-rehabilitation activities and platoon configuration in the pavement's life cycle. A numerical case study is used to demonstrate the applicability and performance of the proposed model framework over the Illinois freeway system. It is shown that the freight traffic is effectively channelized on a few corridors of platoon lanes and, by setting proper user fees to cover pavement-rehabilitation costs, systemwide improvements for both freight shippers and highway agencies can be achieved.

On Stochastic-User-Equilibrium-Based Day-to-Day Dynamics

Researchers have proposed many different concepts and models to study day-to-day dynamics. Some models explicitly model travelers’ perceiving and learning on travel costs, and some other models do not explicitly consider the travel cost perception but instead formulate the dynamics of flows as the functions of flows and measured travel costs (which are determined by flows). This paper investigates the interconnection between these two types of day-to-day models, in particular, those models whose fixed points are a stochastic user equilibrium. Specifically, a widely used day-to-day model that combines exponential-smoothing learning and logit stochastic network loading (called the logit-ESL model in this paper) is proved to be equivalent to a model based purely on flows, which is the logit-based extension of the first-in-first-out dynamic of Jin [Jin W (2007) A dynamical system model of the traffic assignment problem. Transportation Res. Part B Methodological 41(1):32–48]. Via this equivalent form, the logit-ESL model is proved to be globally stable under nonseparable and monotone travel cost functions. Moreover, the model of Cantarella and Cascetta is shown to be equivalent to a second-order dynamic incorporating purely flows and is proved to be globally stable under separable link cost functions [Cantarella GE, Cascetta E (1995) Dynamic processes and equilibrium in transportation networks: Towards a unifying theory. Transportation Sci. 29(4):305–329]. Further, other discrete choice models, such as C-logit, path-size logit, and weibit, are introduced into the logit-ESL model, leading to several new day-to-day models, which are also proved to be globally stable under different conditions.

Deep Reinforcement Learning-Based Network Routing Technology for Data Recovery in Exa-Scale Cloud Distributed Clustering Systems

Research has been conducted to efficiently transfer blocks and reduce network costs when decoding and recovering data from an erasure coding-based distributed file system. Technologies using software-defined network (SDN) controllers can collect and more efficiently manage network data. However, the bandwidth depends dynamically on the number of data transmitted on the network, and the data transfer time is inefficient owing to the longer latency of existing routing paths when nodes and switches fail. We propose deep Q-network erasure coding (DQN-EC) to solve routing problems by converging erasure coding with DQN to learn dynamically changing network elements. Using the SDN controller, DQN-EC collects the status, number, and block size of nodes possessing stored blocks during erasure coding. The fat-tree network topology used for experimental evaluation collects elements of typical network packets, the bandwidth of the nodes and switches, and other information. The data collected undergo deep reinforcement learning to avoid node and switch failures and provide optimized routing paths by selecting switches that efficiently conduct block transfers. DQN-EC achieves a 2.5-times-faster block transmission time and 0.4-times-higher network throughput than open shortest path first (OSPF) routing algorithms. The bottleneck bandwidth and transmission link cost can be reduced, improving the recovery time approximately twofold.

Past and Future Trends in Medical Spas: A Co-Word Analysis

The present paper consists of a co-word analysis of the previous research in the medical spa field published in the Web Science Core Collection database. The study’s main purpose is to identify the past trends in the medical spa field from the tourist and medical perspectives and to anticipate the future research focuses in the field. In this regard, the article is based on four objectives that create a descriptive picture of the research in the medical spa area, such as (i) studying the current state of the art, (ii) analysing the most visible articles in the field, (iii) highlighting the leading research interests in medical spa research and (iv) anticipating new possible research trends that link cost-effective medical spa activity to COVID-19 post-recovery treatments. A total of 627 articles, published between 1997 and 2021 (March), were analysed, and the data were interpreted using the VOS Viewer software. The study results indicate that high interest in medical spas started to become observable in 2015, when the funding bodies became interested in this field and began supporting publishing and research regarding medical spas. The main subjects investigated in previous studies were related to the specific issues of the industry and tourism activity. They also considered the medical approach of the spa and the use of natural resources in treating different diseases. Except for these main interests, since 2020, it has started to become evident that another approach in the published studies may lead to a new trend in research. The study results show that researchers have begun to investigate the possibility of using medical spa resorts to aid post-COVID-19 recovery, which is considered a cost-efficient option for completing traditional treatment. This new focus in research proves that the medical spa field can rebrand itself as playing a supportive role in national healthcare systems in countries with a long tradition in balneotherapy, and gives a new developing trajectory to the medical spa industry.

A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty

The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization of TTP and VSP with uncertainty. In this paper, a novel multiobjective optimization approach with the objectives of minimizing the passenger travel cost, the vehicle scheduling cost, and the incompatible trip-link cost is proposed. Meanwhile, a multiobjective hybrid algorithm, which is a combination of the self-adjust genetic algorithm (SGA), large neighborhood search (LNS) algorithm, and Pareto separation operator (PSO), is applied to solve the integrated optimization problem. The experimental results show that the approach outperforms existing approaches in terms of service level and robustness.

Energy-aware routing based on link utilization in domain network

Aimed at the characteristic of the Software Defined Network (SDN), several green routing algorithms are proposed. However, there are many drawbacks consisted in the existing algorithms. Therefore, we propose a self-adaptive energy saving routing algorithm (LAR) which is based on residual bandwidth of links and SDN. The proposed algorithm makes the link utilization which is changing in real time as the link cost. It would obtain the topology information and link status to optimize and prune the topology for reducing the computing time of routing algorithm before selecting routing path. After a period of time, the incoming flows will automatically be gathered in heavily-loaded links. The links without traffic will be switched off while the whole network connectivity and QoS are guaranteed. Simulation results show that it is possible to reduce considerable energy consumption during off-peak hours and link energy saving can be up to 55%. And, the algorithm has the distinct advantage in terms of complexity and network performance comparing with related schemes.

Assignment of Freight Truck Shipment on the U.S. Highway Network

With the ever-increasing demand for freight movements, nationwide freight shipments between geographical regions by freight trucks need to be investigated since they comprise the largest share of total freight movements in the United States. To this end, the procedures for freight truck shipment demand network assignment on the entire U.S. highway network considering congestion effect are discussed, and the results are explained in detail, with visual illustrations. A fundamental traffic assignment model with a convex combinations algorithm is proposed to solve the nationwide freight truck shipment assignment problem under the user equilibrium principle. A link cost function is modified, considering the traffic volume that already exists on U.S. highways. A case study is conducted using big data including the entire U.S. highway network and freight shipment information in 2007. Total and average freight shipment costs for both truck and rail transportation for a specific origin–destination pair in the database are computed to compare the characteristics of these two major freight transportation modes in the United States. Application of the proposed model could be possible to address many other related problems, such as improvement of highway infrastructure, and reductions in traffic congestion and vehicle emissions.

Efficient Placement of Splitters in Optical WDM Network

In this paper, we investigate the splitter placement problem in an optical WDM network. The goal is to select a given number of MC nodes in the network such that the overall link cost of a multicast session is minimized. We present an exact formulation in integer linear programming ( ILP ) to find a set of trees that connects a source to a set of destination nodes. Then, four algorithms based on network topology metrics are proposed to select a given number of MC nodes in the network such that the overall link cost of a multicast session is minimized. The efficiency of the proposed algorithms is verified by simulation results.