Joint Routing Research Articles

When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale–Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

A Joint Routing and Channel Assignment Scheme for Hybrid Wireless-Optical Broadband-Access Networks

In this paper, we investigate mechanisms for improving the quality of communications in wireless-optical broadband access networks (WOBAN), which present a promising solution to meet the growing needs for capacity of access networks. This is achieved by using multiple gateways and multi-channel operation along with a routing protocol that effectively reduces the effect of radio interference. We present a joint route and channel assignment scheme with the objective of maximizing the end-to-end probability of success and minimizing the end-to-end delay for all active upstream traffic in the WOBAN. Performance evaluations of the proposed scheme are presented using ns-2 simulations, which show that the proposed scheme improves the network throughput up to three times and reduces the traffic delay by six times in presence of 12 channels and four network interface cards (NICs), compared to a single channel scenario.

Optimization of the latency in networks SDN

Unlike traditional networks, software-defined networking (SDN) are characterized by a physical separation of the control and the transfer plan. Thus, a centralized controller communicates the functions of control plan to each device via the OpenFlow Protocol whenever he is asked or that it deems appropriate. This impact strongly the latency time which is important for new services or multimedia applications. In order to optimize the time of transmission in network data with SDN, the proactive approach based on the algorithm back - pressure is usually oered. However, we note that the proactive approach while reducing strongly this time, does not account settings such as the failure of a node part of the way to transfer or the breaking of a bond that greatly increases the latency time. In this document, we will propose a joint routing approach based on proactive and reactive routing. This in order to optimize the routing functions by simply placing the traffic where capacity allows, in order to avoid congestion of highly stressed parts of the network taking into account the failures and significantly reduce the time latency. Simulation results show that our proposal allows a considerable reduction of latency even when there are failures in the network.

Quantifying the environmental and economic benefits of cooperation: A case study in temperature-controlled food logistics

Inefficient road transportation causes unnecessary costs and polluting emissions. This problem is even more severe in refrigerated transportation, in which temperature control is used to guarantee the quality of the products. Organizing logistics cooperatively can help decrease both the environmental and the economic impacts. In Joint Route Planning (JRP) cooperation, suppliers and customers jointly optimize routing decisions so that cost and emissions are minimized. Vendor Managed Inventory (VMI) cooperation extends JRP cooperation by optimizing routing and inventory planning decisions simultaneously. However, in addition to their economic advantages, VMI and JRP may also yield environmental benefits. To test this assertion, we perform a case study on cooperation between a number of supermarket chains in the Netherlands. The data of this case study are analyzed to quantify both the economic and environmental benefits of implementing cooperation via JRP and VMI, using vehicle routing and an inventory routing models. We found that JRP cooperation can substantially reduce cost and emissions compared with uncooperative routing. In addition, VMI cooperation can further reduce cost and emissions, but minimizing cost and minimizing emissions no longer result in the same solution and there is a trade-off to be made.

Towards Low-Cost Yet High-Performance Sensor Networks by Deploying a Few Ultra-fast Charging Battery Powered Sensors.

The employment of mobile vehicles to charge sensors via wireless energy transfer is a promising technology to maintain the perpetual operation of wireless sensor networks (WSNs). Most existing studies assumed that sensors are powered with off-the-shelf batteries, e.g., Lithium batteries, which are cheap, but it takes some non-trivial time to fully charge such a battery (e.g., 30–80 min). The long charging time may incur long sensor dead durations, especially when there are many lifetime-critical sensors to be charged. On the other hand, other studies assumed that every sensor is powered with an ultra-fast charging battery, where it only takes some trivial time to replenish such a battery, e.g., 1 min, but the adoption of many ultra-fast sensors will bring about high purchasing cost. In this paper, we propose a novel heterogeneous sensor network model, in which there are only a few ultra-fast sensors and many low-cost off-the-shelf sensors. The deployment cost of the network in the model is low, as the number of ultra-fast sensors is limited. We also have an important observation that we can significantly shorten sensor dead durations by enabling the ultra-fast sensors to relay more data for lifetime-critical off-the-shelf sensors. We then propose a joint charging scheduling and routing allocation algorithm, such that the longest sensor dead duration is minimized. We finally evaluate the performance of the proposed algorithm through extensive simulation experiments. Experimental results show that the proposed algorithm is very promising and the longest sensor dead duration by it is only about 10% of those by existing algorithms.

Route, Modulation Format, MIMO, and Spectrum Assignment in Flex-Grid/MCF Transparent Optical Core Networks

In this paper, we target an optimal multiple-input multiple-output digital signal processing (MIMO-DSP) assignment to super-channels affected by intercore crosstalk (ICXT) in multicore fiber (MCF) enabled transparent optical core networks. MIMO-DSP undoes ICXT effects, but can be costly with high core density MCFs. Hence, its implementation in the network must be carefully decided. We address our objective as a joint route, modulation format, MIMO and spectrum assignment (RMMSA) problem, for which integer linear programming formulations are provided to optimally solve it in small network scenarios. Moreover, several heuristic approaches are also proposed to solve large-scale problem instances with good accuracy. Their goal is to minimize both network spectral requirements and the amount of MIMO equalized super-channels, taking a crosstalk-free space division multiplexing (SDM) solution as a reference, for example, based on parallel single mode fibers [i.e., a multifiber (MF) scenario]. For our evaluation, we consider several state-of-the-art MCF prototypes and different network topologies. The obtained results, with the considered MCFs, disclose that in national backbone networks, the desirable percentage of super-channels with MIMO equalization to match the performance of an equivalent crosstalk-free SDM solution ranges from 0% to 36, while in continental-wide networks this range raises from 0% to 56%. In addition, in the case of a nonideal MIMO (with a 3 dB/km of crosstalk compensation), such percentages range from 0% to 28% and from 0% to 45% in national and continental-wide backbone networks, respectively, experimenting a performance gap up to 12% with respect to the MF reference scenario.

A Kind of Joint Routing and Resource Allocation Scheme Based on Prioritized Memories-Deep Q Network for Cognitive Radio Ad Hoc Networks.

Cognitive Radio (CR) is a promising technology to overcome spectrum scarcity, which currently faces lots of unsolved problems. One of the critical challenges for setting up such systems is how to coordinate multiple protocol layers such as routing and spectrum access in a partially observable environment. In this paper, a deep reinforcement learning approach is adopted for solving above problem. Firstly, for the purpose of compressing huge action space in the cross-layer design problem, a novel concept named responsibility rating is introduced to help decide the transmission power of every Secondary User (SU). In order to deal with problem of dimension curse while reducing replay memory, the Prioritized Memories Deep Q-Network (PM-DQN) is proposed. Furthermore, PM-DQN is applied to solve the joint routing and resource allocation problem in cognitive radio ad hoc network for minimizing the transmission delay and power consumption. Simulation results illustrates that our proposed algorithm can reduce the end-to-end delay, packet loss ratio and estimation error while achieving higher energy efficiency compared with traditional algorithm.

Autonomous Vehicle Logistic System: Joint Routing and Charging Strategy

The smart city embraces gradual adoption of autonomous vehicles (AVs) into the intelligent transportation system. Contributed by their full-fledged controllability, AVs can respond to instantaneous situations with high efficiency and flexibility. In this paper, we propose a novel AV logistic system (AVLS) to accommodate logistic demands for smart cities. We focus on determining the optimal routes for the governed AVs in consideration of various requirements imposed by the vehicles, logistic requests, renewable generations, and the underlying transportation system. By coordinating their routes and charging schedules, the system can effectively utilize the renewable energy generated by distributed generations. We formulate the joint routing and charging problem in the form of quadratic-constrained mixed integer linear program. To improve its scalability, we develop a distributed solution method via dual decomposition. We conduct extensive simulations to evaluate the performance of proposed system and solution methods. The results show that AVLS can effectively utilize excessive renewable energy while accomplishing all logistic requests. The distributed solution can develop near-optimal solutions with compelling improvement in computational speed.

Penerapan Algoritma Greedy Pada Pengaturan Shipping Buku Diknas PT. X

PT. X is a company engaged in manufacturing media printing and paper packaging. PT. X has a fixed product that is a Diknas book. The research is focused on the delivery of Diknas book for East Java region with a total time as the main constrain in determining the route which resulted in the minimum delivery cost of Diknas book by taking into account the capacity of the fleet used. The method used in determining the route with the optimal shipping cost is by the Greedy Algorithm method. Research shows that in zone 1 there are 4 shipping routes with range of shipping cost between Rp100,00/kg-Rp200,00/kg by using joint route, zone 2 there are 3 delivery routes with range of shipping cost between Rp190,00/kg-Rp205,00/kg, zone 3 there are 3 shipping routes with a range of shipping costs between Rp130,00/kg-Rp525,00/kg, zone 2 and zone 3 using separate route method and joint route.

Joint routing and aborting optimization of cooperative unmanned aerial vehicles

This paper incorporates the abort policy into the routing problem of unmanned aerial vehicles (UAV). In order to serve a number of targets, some UAVs can be deployed each visiting part of the targets. Different from other works on routing of UAVs, it is assumed that each UAV may experience shocks during the travel. In order to reduce the expected cost of UAV destruction, it is allowed that a UAV aborts the mission if it is found to have undergone too many shocks after it finishes serving a certain number of targets. The optimal routing plan together with the abort policy for each UAV are studied, with the objective to minimize the total cost consisting of the expected cost of UAV destruction and the expected cost for unvisited targets. Test case is used to illustrate the application of the framework.

Stochastic passenger train timetabling using a branch and bound approach

Abstract We investigate the integrated scheduling of freight and passenger trains in complex railway networks and the timetabling for passenger trains to improve the efficiency of the schedules. In the proposed model, the objective is to study the robust passenger train timetable considering the uncertainty in freight train departure times. The joint routing and scheduling are considered between the two types of trains. We propose a branch and bound framework with hybrid heuristics to solve the problem. On each search tree node, a Lagrangian method is first developed to solve for a relaxed passenger train schedule and then a feasibility recovery heuristic is applied. Then a labeling algorithm is proposed to jointly optimize the freight and passenger trains sequentially. Numerical experiments show the proposed solution approach outperforms other heuristics on actual railway networks.

A Data-Gathering Scheme with Joint Routing and Compressive Sensing Based on Modified Diffusion Wavelets in Wireless Sensor Networks.

Compressive sensing (CS)-based data gathering is a promising method to reduce energy consumption in wireless sensor networks (WSNs). Traditional CS-based data-gathering approaches require a large number of sensor nodes to participate in each CS measurement task, resulting in high energy consumption, and do not guarantee load balance. In this paper, we propose a sparser analysis that depends on modified diffusion wavelets, which exploit sensor readings’ spatial correlation in WSNs. In particular, a novel data-gathering scheme with joint routing and CS is presented. A modified ant colony algorithm is adopted, where next hop node selection takes a node’s residual energy and path length into consideration simultaneously. Moreover, in order to speed up the coverage rate and avoid the local optimal of the algorithm, an improved pheromone impact factor is put forward. More importantly, theoretical proof is given that the equivalent sensing matrix generated can satisfy the restricted isometric property (RIP). The simulation results demonstrate that the modified diffusion wavelets’ sparsity affects the sensor signal and has better reconstruction performance than DFT. Furthermore, our data gathering with joint routing and CS can dramatically reduce the energy consumption of WSNs, balance the load, and prolong the network lifetime in comparison to state-of-the-art CS-based methods.

Analysing the Effect of Partner Characteristics on the Performance of Horizontal Carrier Collaborations

In order to survive under the ever increasing pressure to operate more efficiently, transportation companies are obliged to adopt a collaborative focus. Although organisations become increasingly aware of the inevitable character of horizontal collaboration, surveys report failure rates up to 70% for starting strategic partnerships. While a growing body of research acknowledges the importance of fit between partners for coalition sustainability, no extensive study has been performed on the numerical relationship between specific company traits and the performance of the alliances these organisations are involved in. This paper empirically investigates the impact of coalition characteristics on attainable collaborative savings in a joint route planning context. Factorial analysis of variance is applied to examine desirable coalition structures for horizontal carrier collaborations based on request sharing. Overall, our experiments suggest that carriers may reap significant operational benefits from sharing orders. However, the extent of these benefits highly depends on the characteristics of the partnering organisations, stressing the importance of careful partner selection.

Energy-Efficient Service Function Chain Provisioning

Network function virtualization (NFV) is a promising network architecture concept to reduce operational costs. In legacy networks, network functions, such as firewall or TCP optimization, are performed by specific hardware. In networks enabling NFV coupled with the software defined network (SDN) paradigm, virtual network functions (VNFs) can be implemented dynamically on generic hardware. This is of primary interest to implement energy-efficient solutions, in order to adapt the resource usage dynamically to the demand. In this paper, we study how to use NFV coupled with an SDN to improve the energy efficiency of networks.We consider a setting inwhich a flowhas to go through a service function chain, which is several network functions in a specific order.We propose an integer linear programming (ILP) formulation, an ILP-based heuristic, and a decomposition model that relies on joint routing and placement configuration to solve the problem. We show that virtualization provides between 22% and 62% of energy savings for networks of different sizes.

Joint routing and channel assignment using online learning in cognitive radio networks

Cognitive radio networks (CRNs) are the solution for the problem of underutilizing the licensed spectrum for which there are more requests in the last couple of decades. In CRNs, Secondary users (SUs) are permitted to access opportunistically the licensed spectrum owned by primary users (PUs). In this paper, we address the problem of joint routing and channel assignment for several flows generated by source SUs to a given destination. We consider a more realistic model based on Markov modulated Poisson process for modeling the PUs traffic at each channel and the SUs try to exploit short lived spectrum holes between the PUs packets at the selected channel. The SUs want to cooperatively minimize the end-to-end delay of source SUs flows meanwhile the quality of service requirements of the PUs would be met. To consider partial observation of SUs about PUs activity at all channels and quick adaptation of SUs decisions to environment changes and cooperative interaction of SUs, we use decentralized partially observable markov decision process for modeling the problem. Then, an online learning based scheme is proposed for solving the problem. Simulation results show that the performance of the proposed method and the optimal method is close to each other. Also, simulation results show that the proposed method greatly outperforms related works at control of interference to the PUs while maintains the end-to-end delay of SU flows in a low level.

A Joint Channel Selection and Routing Protocol for Cognitive Radio Network

In cognitive radio network, the activities of primary users will cause great influence on the stability of multiple hops routes between cognitive users. In this regard, a joint channel selection and routing protocol, termed as CSRP, is proposed to ensure route stability and reduce route latency between cognitive users. The channel availability based on historical information and the channel switching delay are used as the channel selection criteria to choose the end‐to‐end shortest route which possesses high data delivery probabilities and low delays. Besides, simulation results show that the proposed protocol has a better performance in terms of packet transmission delay and data delivery rate compared with the routing protocols based on delay (TDRP) and based on joint routing and channel allocation (PUB‐JRCA).

Towards Immortal Wireless Sensor Networks by Optimal Energy Beamforming and Data Routing

The lifetime of a wireless sensor network (WSN) determines how long the network can be used to monitor the area of interest. Hence, it is one of the most important performance metrics for WSN. The approaches used to prolong the lifetime can be briefly divided into two categories: reducing the energy consumption, such as designing an efficient routing, and providing extra energy, such as using wireless energy transfer (WET) to charge the nodes. Contrary to the previous line of work where only one of those two aspects is considered, we investigate these two together. In particular, we consider a scenario where dedicated wireless chargers transfer energy wirelessly to sensors. The overall goal is to maximize the minimum sampling rate of the nodes while keeping the energy consumption of each node smaller than the energy it receives. This is done by properly designing the routing of the sensors and the WET strategy of the chargers. Although such a joint routing and energy beamforming problem is non-convex, we show that it can be transformed into a semi-definite optimization problem (SDP). We then prove that the strong duality of the SDP problem holds, and hence, the optimal solution of the SDP problem is attained. Accordingly, the optimal solution for the original problem is achieved by a simple transformation. We also propose a low-complexity approach based on pre-determined beamforming directions. Moreover, based on the alternating direction method of multipliers, the distributed implementations of the proposed approaches are studied. The simulation results illustrate the significant performance improvement achieved by the proposed methods. In particular, the proposed energy beamforming scheme significantly outperforms the schemes where one does not use energy beamforming, or one does not use optimized routing. A thorough investigation of the effect of system parameters, including the number of antennas, the number of nodes, and the number of chargers, on the system performance is provided. The promising convergence behavior of the proposed distributed approaches is illustrated.

Compressed Sensing Based Joint Rate Allocation and Routing Design in Wireless Sensor Networks

Compressed sensing for wireless sensor networks has attracted a lot of research attention in the last decade for its advantages in energy saving, robustness, and so on. Nevertheless, existing solutions mostly focus on the data compression performance while neglecting the energy efficiency. In this paper, we first present the joint resource allocation problem formulation based on compressed sensing. Then a distributed algorithm to compute the sampling rate and routes utilizing local network status is proposed. We conduct extensive experiments based on meteorological wireless sensor networks to verify the merit of our mechanism; it is shown that the proposed mechanism is able to achieve very high efficiency in terms of network lifetime and sensing quality compared with existing approaches.

AVB-Aware Routing and Scheduling of Time-Triggered Traffic for TSN

IEEE 802.1 time-sensitive networking (TSN) is a set of amendments to the IEEE 802.1 standard that enable safety-critical and real-time behavior over Ethernet for the industrial automation and automotive domains. Selected TSN mechanisms offer the possibility to emulate the well-known traffic classes found in mixed-criticality distributed systems: Time-triggered (TT) communication with low jitter and bounded end-to-end latency, audio-video-bridging (AVB) streams with bounded end-to-end latency, and general best-effort messages, which have no timing guarantees. Critical traffic is guaranteed via the global network schedule which is stored in so-called gate control lists (GCLs) and controls the timely behavior of frames for each queue of an egress port. Although researchers have started to propose approaches for the routing and scheduling (i.e., GCL synthesis) of TT traffic, all previous research has ignored lower priority real-time traffic, such as AVB, resulting in TT configurations that may increase the worst-case delays of AVB traffic, rendering it unschedulable. In this paper, we propose a joint routing and scheduling approach for TT traffic, which takes into account the AVB traffic, such that both TT and the AVB traffic are schedulable. We extensively evaluate our approach on a number of synthetic and realistic test cases.

Joint routing and scheduling for transmission service in software-defined full-duplex wireless networks

In recent years, full-duplex communication has been investigated in wireless networks to improve the quality of transmission service. Most existing work focused on the physical layer, with little consideration of the upper layers. In this paper, we address the issue of joint routing and link scheduling in software-defined full-duplex wireless networks, where an exclusive SDN controller node is involved. Firstly, we formulate the problem as an optimization problem, which tries to maximize the total throughput of the network. Due to the NP-hardness of this problem, we propose a heuristic algorithm that jointly consider the routing selection and link scheduling. For routing selection subroutine, we propose the minimum-cost routing algorithm (MinCostRo for short). We evaluate the performance in MATLAB, and compare it with the DRPA routing algorithm and the minimal maximal interference routing algorithm (MinMaxRo for short). It can be found that MinCostRo performs better than the two existing methods.