Solution Route Research Articles

Comparison of the new version of DEEC protocol to extend WSN lifetime

Wireless sensor networks (WSNs) are used in many different fields. One of the most recent and rapidly expanding applications is the Internet of Things (IoT), which enables the connectivity of numerous objects or devices over the Internet. However, WSNs have a bigger concern about battery power than mobile ad hoc networks, which shortens the network's lifetime. As a result, extensive research has been done on lowering WSNs' energy usage. Designing a hierarchical clustering algorithm is one of many methods for lowering the energy consumption of WSNs. Given the restricted hardware capabilities of the sensor nodes, modeling and building energy-efficient routing solutions to enhance the overall network lifetime has emerged as one of the most crucial approaches in wireless sensor networks. Cluster-based heterogeneous routing protocols, a typical feature of routing technology, have demonstrated efficacy in managing topology, energy consumption, data collection or fusion, dependability, or stability in distributed sensor networks. The existing Distributed Energy-Efficient Clustering (DEEC) clustering protocol is changed in this study by switching the power level between the nodes and adding a threshold limit for cluster head selection. The number of packets sent to a base station increases by 843% when comparing the proposed improved DEEC protocol to the existing DEEC protocol, increasing the number of living nodes to more than 10,000 rounds, or over 30,000 rounds, which can be used to extend the WSN lifetime. The suggested algorithm outperforms alternative energy-efficient protocols in terms of stability period and network lifetime under various scenarios of area, energy, and node density.

FastGR: Global Routing on CPU–GPU With Heterogeneous Task Graph Scheduler

Running time is a key metric across the standard physical design flow stages. However, with the rapid growth in design sizes, routing runtime has become the runtime bottleneck in the physical design flow. As a result, speeding routing becomes a critical and pressing task for IC design automation. Aside from the running time, we need to evaluate the quality of the global routing solution since a poor global routing engine degrades the solution performance after the entire routing stage. This work takes both of them into consideration. We propose a global routing framework with GPU-accelerated routing algorithms and a heterogeneous task graph scheduler, called FastGR, to accelerate the procedure of the modern global router and improve its effectiveness. Its runtime-oriented version FastGRL achieves 2.489× speedup compared with the stateof-the-art global router. Furthermore, the GPU-accelerated Lshape pattern routing algorithm used in FastGRL can contribute to 9.324× speedup over the sequential algorithm on CPU. Its quality-oriented version FastGRH offers a 27.855% improvement of the number of shorts over the runtime-oriented version and still gets 1.970× faster than the most advanced global router.

Decision Support System for Optimizing Rastra Distribution Routes Using Genetic Algorithm

Vehicle Routing Problem (VRP) is an optimal route design from a group of vehicles that deliver goods to a set of customers with a certain demand. VRP was widely studied as part of solving the distribution efficiency which minimizes the cost of traveled vehicle. Bulog Subdivre South Surabaya distribute Rastra to every village which has constraint of Multi depot and Split delivery (MDSDVRP). This study aims to minimize the traveled distance of MDSDVRP (Rastra distribution) using Genetic Algorithm (GA) and to find out the efficiency of the route solution. The research covers the steps to solve MDSDVRP using GA to generate feasible and efficient solution route. Then development of a Decision Support System (DSS) that applies the algorithm is implemented on web platform and the result of route solution is presented on the mobile platform. The system testing is carried out to test the user satisfaction (83.8%) which found that overall users were considered very agree, good, like for each component of user satisfaction. The traveled distance is compared between GA route and routes of the original data from 2013-2017. The efficiency of GA was evaluated and found that the traveled distance from the previous route is reduced by 3.7% (444 km) and in 2017 is reduced the distance traveled by 9.5 % (1,093 km). The GA can generate a better solution and optimize the distance than the original route.

Revealing efficient battery-type redox reaction in MOF-derived porous sponge-like Co3O4 nanoarchitecture electrode material toward next-generation energy storage device

Developing metal oxides with advanced architectures has received extensive global attention and becoming an attractive contender for achieving high-performance electrode materials for efficient energy storing systems. Herein, metal organic framework (MOF) derived porous sponge-like Co3O4 architectures have been fabricated through a simple aqueous solution route combined with thermal treatment. The sponge-like unique morphology of Co3O4 architectures affords a high surface area with the appropriate porous feature and superior electronic conductivity. Further, it offers an effective pathway to expedite electron/ion transportation and alleviate volume changes. The porous sponge-like Co3O4 electrode reveals a large specific capacity of 434 C g−1 at a current density of 1 A g−1 with promising rate capability. Furthermore, the constructed hybrid supercapacitor (HSC; Co3O4//AC) depicts an excellent electrochemical performance with a specific capacity as high as 272 C g−1 at a current density of 1 A g−1. Moreover, the HSC achieves a large specific energy of 48.19 Wh kg−1 at a specific power of 710.76 W kg−1 and cyclic retention of 90.58% after 10,000 cycles. As a result, the remarkable electrochemical performance of the porous sponge-like Co3O4 architectures could provide a new strategy as a potential candidate for next-generation energy storage applications.

Development of Heuristic Approaches for Last-Mile Delivery TSP with a Truck and Multiple Drones

Unmanned Aerial Vehicles (UAVs) are gaining momentum in many civil and military sectors. An example is represented by the logistics sector, where UAVs have been proven to be able to improve the efficiency of the process itself, as their cooperation with trucks can decrease the delivery time and reduce fuel consumption. In this paper, we first state a mathematical formulation of the Travelling Salesman Problem (TSP) applied to logistic routing, where a truck cooperates synchronously with multiple UAVs for parcel delivery. Then, we propose, implement, and compare different sub-optimal routing approaches to the formulated mFSTSP (multiple Flying Sidekick Travelling Salesman Problem) since the inherent combinatorial computational complexity of the problem makes it unattractable for commercial Mixed-Integer Linear Programming (MILP) solvers. A local search algorithm, two hybrid genetic algorithms that permutate feasible and infeasible solutions, and an alternative ad-hoc greedy method are evaluated in terms of the total delivery time of the output schedule. For the sake of the evaluation, the savings in terms of delivery time over the well-documented truck-only TSP solution are investigated for each proposed routing solution, and this is repeated for two different scenarios. Monte Carlo simulations corroborate the results.

Visible-Light-driven Photocatalytic Properties of Copper(I) Oxide (Cu2O) and Its Graphene-based Nanocomposites

In this study, an improved process was proposed for the synthesis of structure-controlled Cu2O nanoparticles, using a simplified wet chemical method at room temperature. A chemical solution route was established to synthesize Cu2O crystals with various sizes and morphologies. The structure, morphology, and optical properties of Cu2O nanoparticles were analyzed by X-ray diffraction, SEM (scanning electron microscope), and UV-Vis spectroscopy. By adjusting the aqueous mixture solutions of NaOH and NH2OH•HCl, the synthesis of Cu2O crystals with different morphology and size could be realized. Strangely, it was found that the change in the ratio of de-ionized water and NaOH aqueous solution led to the synthesis of Cu2O crystals of different sizes, while the morphology of Cu2O crystals was not affected. The synthesized Cu2O crystal samples were used as photocatalysts for methyl orange (MO) dye decomposition, as a model molecule, to evaluate the photocatalytic activities. However, under 200 watts of a visible light source, there are four samples with and without graphene-based nanocomposite of Cu2O NPs. The results showed that, compared with roughly spherical, irregular but thick plates, brick and small granule spheres shaped Cu2O nanoparticles provided better activity. The Cu2O sample with irregular but thick platelet-like shapes, having an average particle size of 0.53 µm, exhibited excellent photocatalytic activity (99.08% degradation). In addition, by reducing the size of Cu2O particles and preparing their graphene composition, one can fabricate a sample (Cu2-Cu2Gr) with the highest efficiency which has significantly better photocatalytic activity in comparison to the others. This work represents an innovative strategy for pre-the-case production of nanomaterials with shapes and sizes, that is, Cu2O crystals, with excellent photocatalytic activity through compositing with graphene

On the hydration of ternesite and the formation of thaumasite

The calcium sulfosilicate phase ternesite, previously considered as an undesired product in cement kilns, has recently gained attention due to its potential replacement of ye'elimite in calcium sulfoaluminate clinkers and the contribution of ternesite hydration products to the strength development of such clinkers. However, the potential formation of thaumasite during ternesite hydration, in particular in the presence of ettringite, has not yet been fully explored. In the present study synthesized ternesite was hydrated in the presence of ettringite, carbonated ettringite, portlandite and calcite. Thaumasite formed, together with gypsum and C-S-H, only in samples initially containing ettringite, both at 6.5 °C and at ambient temperature. Portlandite and calcite did not have a noticeable impact on the hydration of ternesite and the formation of thaumasite. The dissolution and/or carbonation of ettringite, together with its surface template effect, is suggested to be key to initiate thaumasite nucleation via the solid solution route.

Digital-Twin-Driven AGV Scheduling and Routing in Automated Container Terminals

Automated guided vehicle (AGV) scheduling and routing are critical factors affecting the operation efficiency and transportation cost of the automated container terminal (ACT). Searching for the optimal AGV scheduling and routing plan are effective and efficient ways to improve its efficiency and reduce its cost. However, uncertainties in the physical environment of ACT can make it challenging to determine the optimal scheduling and routing plan. This paper presents the digital-twin-driven AGV scheduling and routing framework, aiming to deal with uncertainties in ACT. By introducing the digital twin, uncertain factors can be detected and handled through the interaction and fusion of physical and virtual spaces. The improved artificial fish swarm algorithm Dijkstra (IAFSA-Dijkstra) is proposed for the optimal AGV scheduling and routing solution, which will be verified in the virtual space and further fed back to the real world to guide actual AGV transport. Then, a twin-data-driven conflict prediction method is proposed to predict potential conflicts by constantly comparing the differences between physical and virtual ACT. Further, a conflict resolution method based on the Yen algorithm is explored to resolve predicted conflicts and drive the evolution of the scheme. Case study examples show that the proposed method can effectively improve efficiency and reduce the cost of AGV scheduling and routing in ACT.

Hierarchical Cooperation and Load Balancing for Scalable Autonomous Vehicle Routing in Multi-Access Edge Computing Environment

When Connected Autonomous Vehicles (CAVs) request routing in a driving environment with Autonomous Intersection Management (AIM) systems, a routing planner collects the demands and optimizes the routes in a coordinated manner to reduce the overall travel times. In practice, however, the routing demands are massive, especially in a large-scale traffic network. As a result, the centralized routing planner fails to scale out to accommodate the growing requests, causing a severe scalability issue. This paper presents a holistic solution for scalable CAV routing by enabling hierarchical cooperation and load balancing in the Multi-Access Edge Computing (MEC) environment. The proposed system cooperatively plans CAV routes and dynamically balances loads in MECs to handle massive requests. According to the experiments, our system has a 15.68X higher routing capacity than the centralized routing system, and load balancing reduces 14.51% computation time of routing. The experiments show that our system is scalable for massive autonomous vehicle routing.

Incremental 3-D Global Routing Considering Cell Movement and Complex Routing Constraints

Placement and routing are two critical problems in VLSI physical design. However, there may be out-of-sync between the two problems considering congestion and wirelength. Therefore, it is desirable to design an efficient and highly coupled placement and routing engine to narrow the gap and minimize the mismatch between placement and routing. This paper proposes an incremental 3D global routing engine considering cell movement and complex routing constraints to relocate cells and reroute nets. We first apply a queue-based congestion-aware 3D maze routing with routing height restriction to improve the initial routing solution. Efficient multi-net-based location estimation is then presented to find the best location for each cell in multiple cell movement rounds. In each step of cell movement, we reroute nets for all candidate cell locations in parallel using a guided stack-based 3D routing algorithm while considering the routing constraints. Finally, we adopt an edge-adjusting technique to improve the routed wirelength further. Compared with the champion of the 2020 CAD Contest at ICCAD hu2020iccad and the state-of-the-art works, experiment results based on the contest benchmarks show that our proposed algorithm achieves the best routing wirelength and competitive runtime without maximum cell movement constraint.

Enabling Quasi-Static Reconfigurable Networks With Robust Topology Engineering

Many optical circuit switched data center networks (DCN) have been proposed in the last decade to attain higher capacity and topology reconfigurability, though commercial adoption of these architectures have been minimal. One major challenge these architectures face is the difficulty of handling uncertain traffic demands using commercial optical circuit switches (OCS) with high switching latency. Prior works have generally focused on developing fast-switching OCS prototypes to quickly react to traffic variations through frequent reconfigurations. This approach, however, adds tremendous complexity overhead to the control plane, and raises the barrier for commercial adoption of optical circuit switched data center networks. We propose, a robust topology and routing optimization framework for reconfigurable optical circuit switched data centers. co-optimizes topology and routing based on a convex set of traffic matrices, and offers strict throughput guarantees for any future traffic matrices bounded by the convex set. For the bursty traffic demands that are unbounded by the convex set, we employ a desensitization technique to reduce performance hit. This enables to generate topology and routing solutions capable of handling unexpected traffic changes without relying on frequent topology reconfigurations. Our extensive evaluations based on Facebook’s production DCN traces show that, even with daily reconfigurations which could be realized by current commercial MEMS-based OCSs from Calient Technologies, achieves about 20% lower max link utilization, and about 32% lower average hop count compared to cost-equivalent static topologies. Our work shows that adoption of reconfigurable topologies in commercial DCNs is feasible even without fast OCSs.

Optical and Structural Properties of 2D ZnO Nanosheets Synthesized Using Simple Chemical Solution Route

In the present paper, two-dimensional (2D) Zinc oxide (ZnO) nanosheets were prepared through a simple, single-step chemical solution method followed by annealing treatment without any surfactant, template or catalyst. The prepared ZnO nanosheets were characterized for their structural and optical properties by different techniques such as XRD, SEM with EDS, RAMAN, FTIR, UV-Visible and photoluminescence (PL) spectroscopy. The X-ray diffraction (XRD) spectra display sharp and highly intense peaks corresponding to the (101) plane, confirming the wurtzite crystal structure of ZnO. Scanning electron microscopy (SEM) images confirm the sheet-like structure of ZnO nanosheets with a thickness of approximately 23 nm. The elemental composition has been estimated through the Energy dispersive spectroscopy (EDS) technique. The basic reaction mechanism is discussed in detail. The stretching vibrations of Zn and O bonding were observed from FTIR spectra. The optical properties and optical bandgap have been obtained by UV-Visible and PL spectroscopy. The optical bandgap of ZnO is 3.20 eV from UV-Visible spectroscopy via Tauc’s plot. PL measurement confirming oxygen deficiency in ZnO nanosheets. These results of prepared ZnO nanosheets are expected to perform well in various applications such as photodetectors, sensors and in optoelectronic devices.

Finite Element Modeling of the Cycle Characteristics of Li–O2 Secondary Batteries Considering Surface- and Solution-Route Discharge Reactions

Model-based development is an effective approach in terms of obtaining design guidelines for lithium–oxygen secondary batteries (LOBs). In this study, a model simulating the charge/discharge cycle was developed using the finite element method, which enabled us to investigate the factors governing the cyclability of LOBs by conducting virtual cycle tests. In this modeling, we particularly considered that the discharge can proceed via two different routes: surface and solution routes. As an example demonstrating the usefulness of the virtual tests based on the developed model, we evaluated the effects of changes in parameters related to oxygen transport properties on the deposition and dissolution sites of lithium peroxide (Li2O2) generated in the porous cathode of an LOB via the surface and solution routes, respectively, and hence investigated the effects on the cyclability. The proposed model enables various validations that are extremely difficult to carry out using an experimental approach.

A Comparative Study of Single-Constraint Routing in Wireless Mesh Networks Using Different Dynamic Programming Algorithms

Finding the shortest route in wireless mesh networks is an important aspect. Many techniques are used to solve this problem like dynamic programming, evolutionary algorithms, weighted-sum techniques, and others. In this paper, we use dynamic programming techniques to find the shortest path in wireless mesh networks due to their generality, reduction of complexity and facilitation of numerical computation, simplicity in incorporating constraints, and their onformity to the stochastic nature of some problems. The routing problem is a multi-objective optimization problem with some constraints such as path capacity and end-to-end delay. Single-constraint routing problems and solutions using Dijkstra, Bellman-Ford, and Floyd-Warshall algorithms are proposed in this work with a discussion on the difference between them. These algorithms find the shortest route through finding the optimal rate between two nodes in the wireless networks but with bounded end-to-end delay. The Dijkstra-based algorithm is especially favorable in terms of processing time. We also present a comparison between our proposed single-constraint Dijkstra-based routing algorithm and the mesh routing algorithm (MRA) existing in the literature to clarify the merits of the former.

Dynamic multiple multicasts routing and wavelength assignment for realizing modified artificial fish model in mesh-based ONoC

Dynamic multiple multicasts widely exist in several applications of optical network-on-chip. However, there is no good solution for routing and wavelength assignment for multiple multicasts in the mesh-based network. This paper proposes a new routing strategy based on a modified artificial fish swarm algorithm. The modified artificial fish model can support unicast and multicast in the mesh-based network. The routing and wavelength assignment for multiple multicasts can be solved based on this model. Then, we design a layer-based algorithm to assign wavelength for multiple multicasts, which can utilize wavelength and area resources more effectively. Simulation results show that our scheme works better than the other tree-based schemes regarding average communication latency and power consumption. In general, our modified artificial fish swarm algorithm provides a universal platform to study different aspects of routing and wavelength assignment in mesh-based ONoC.

Terahertz topological photonic crystals with dual edge states for efficient routing.

Topological photonic crystals with robust pseudo-spin and valley edge states have shown promising and wide applications in topological waveguides, lasers, and antennas. However, the limited bandwidth and intrinsic coupling properties of a single pseudo-spin or valley edge state have imposed restrictions on their multifunctional applications in integrated photonic circuits. Here, we propose a topological photonic crystal that can support pseudo-spin and valley edge states simultaneously in a single waveguiding channel, which effectively broadens the bandwidth and enables a multipath routing solution for terahertz information processing and broadcasting. We show that distorted Kekulé lattices can open two types of bandgaps with different topological properties simultaneously by molding the inter- and intra-unit cell coupling of the tight-binding model. The distinct topological origins of the edge states provide versatile signal routing paths toward free space radiation or on-chip self-localized edge modes by virtue of their intrinsic coupling properties. Such a powerful platform could function as an integrated photonic chip with capabilities of broadband on-chip signal processing and distributions that will especially benefit terahertz wireless communications.

An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI Design

Global routing is a crucial step in the design of Very Large-Scale Integration (VLSI) circuits. However, most of the existing methods are heuristic algorithms, which cannot conjointly optimize the subproblems of global routing, resulting in congestion and overflow. In response to this challenge, an enhanced Deep Reinforcement Learning- (DRL-) based global router has been proposed, which comprises the following effective strategies. First, to avoid the overestimation problem generated by Q -learning, the proposed global router adopts the Double Deep Q -Network (DDQN) model. The DDQN-based global router has better performance in wire length optimization and convergence. Second, to avoid the agent from learning redundant information, an action elimination method is added to the action selection part, which significantly enhances the convergence performance of the training process. Third, to avoid the unfair allocation problem of routing resources in serial training, concurrent training is proposed to enhance the routability. Fourth, to reduce wire length and disperse routing resources, a new reward function is proposed to guide the agent to learn better routing solutions regarding wire length and congestion standard deviation. Experimental results demonstrate that the proposed algorithm outperforms others in several important performance metrics, including wire length, convergence performance, routability, and congestion standard deviation. In conclusion, the proposed enhanced DRL-based global router is a promising approach for solving the global routing problem in VLSI design, which can achieve superior performance compared to the heuristic method and DRL-based global router.

Solution epitaxy of polarization-gradient ferroelectric oxide films with colossal photovoltaic current

Solution growth of single-crystal ferroelectric oxide films has long been pursued for the low-cost development of high-performance electronic and optoelectronic devices. However, the established principles of vapor-phase epitaxy cannot be directly applied to solution epitaxy, as the interactions between the substrates and the grown materials in solution are quite different. Here, we report the successful epitaxy of single-domain ferroelectric oxide films on Nb-doped SrTiO3 single-crystal substrates by solution reaction at a low temperature of ~200 oC. The epitaxy is mainly driven by an electronic polarization screening effect at the interface between the substrates and the as-grown ferroelectric oxide films, which is realized by the electrons from the doped substrates. Atomic-level characterization reveals a nontrivial polarization gradient throughout the films in a long range up to ~500 nm because of a possible structural transition from the monoclinic phase to the tetragonal phase. This polarization gradient generates an extremely high photovoltaic short-circuit current density of ~2.153 mA/cm2 and open-circuit voltage of ~1.15 V under 375 nm light illumination with power intensity of 500 mW/cm2, corresponding to the highest photoresponsivity of ~4.306×10−3 A/W among all known ferroelectrics. Our results establish a general low-temperature solution route to produce single-crystal gradient films of ferroelectric oxides and thus open the avenue for their broad applications in self-powered photo-detectors, photovoltaic and optoelectronic devices.

Solving the multicommodity flow problem using an evolutionary routing algorithm in a computer network environment.

The continued increase in Internet traffic requires that routing algorithms make the best use of all available network resources. Most of the current deployed networks are not doing so due to their use of single path routing algorithms. In this work we propose the use of a multipath capable routing algorithm using Evolutionary Algorithms (EAs) that take into account all the traffic going over the network and the link capacities by leveraging the information available at the Software Defined Network (SDN) controller. The designed routing algorithm uses Per-Packet multipath routing to make the best use of the network's resources. Per-Packet multipath is known to have adverse affects when used with TCP, so we propose modifications to the Multipath TCP (MPTCP) protocol to overcome this. Network simulations are performed on a real world network model with 41 nodes and 60 bidirectional links. Results for the EA routing solution with the modified MPTCP protocol show a 29% increase in the total network Goodput, and a more than 50% average reduction in a flow's end-to-end delay, when compared to OSPF and standard TCP under the same network topology and flow request conditions.

Multihop Task Routing in UAV-Assisted Mobile-Edge Computing IoT Networks With Intelligent Reflective Surfaces

The cooperation between Unmanned Aerial Vehicles (UAVs) and ground Mobile Edge Computing (MEC) servers in processing tasks is becoming one of the main research trends of MEC networks. Despite the advantages of UAV-assisted MEC, it is restricted by the limited battery capacity and sensitive energy consumption of UAVs. Unlike the previous works where UAVs are allowed to either process tasks locally or offload them to ground MEC servers, in this paper, we propose a multi-hop task routing solution for Internet of Things (IoT) networks in which a UAV can also relay to another UAV with better connection to a ground MEC server. Furthermore, the UAV can make benefit of existing Intelligent Reflective Surfaces (IRSs) to further improve tasks offloading and reduce energy consumption. We show that the problem of minimizing the total energy of UAVs is NP-hard, and we propose a graph-based heuristic solution to solve it. Simulation results show that the proposed graph-based solution outperforms the traditional no UAV-UAV relaying scheme, especially when IRSs are deployed. Furthermore, a Convolutional Neural Network (CNN) is devised to reduce the delay of finding the decisions for the UAVs at the centralized coordinator. Simulations show that the CNN achieves very close energy consumption performance and a remarkable reduction in execution time compared to the graph-based heuristic solution.