Showing papers on "Routing (electronic design automation) published in 2021"

Efficient Content-Based Sparse Attention with Routing Transformers

Abstract: Self-attention has recently been adopted for a wide range of sequence modeling problems. Despite its effectiveness, self-attention suffers from quadratic compute and memory requirements with respect to sequence length. Successful approaches to reduce this complexity focused on attending to local sliding windows or a small set of locations independent of content. Our work proposes to learn dynamic sparse attention patterns that avoid allocating computation and memory to attend to content unrelated to the query of interest. This work builds upon two lines of research: it combines the modeling flexibility of prior work on content-based sparse attention with the efficiency gains from approaches based on local, temporal sparse attention. Our model, the Routing Transformer, endows self-attention with a sparse routing module based on online k-means while reducing the overall complexity of attention to O(n^{1.5}d) from O(n^2d) for sequence length n and hidden dimension d. We show that our model outperforms comparable sparse attention models on language modeling on Wikitext-103 (15.8 vs 18.3 perplexity) as well as on image generation on ImageNet-64 (3.43 vs 3.44 bits/dim) while using fewer self-attention layers. Additionally, we set a new state-of-the-art on the newly released PG-19 data-set, obtaining a test perplexity of 33.2 with a 22 layer Routing Transformer model trained on sequences of length 8192.

Efficient and Secure Routing Protocol Based on Artificial Intelligence Algorithms With UAV-Assisted for Vehicular Ad Hoc Networks in Intelligent Transportation Systems

Abstract: Vehicular Ad hoc Networks (VANETs) that are considered as a subset of Mobile Ad hoc Networks (MANETs) can be applied in the field of transportation especially in Intelligent Transportation Systems (ITS). The routing process in these networks is a challenging task due to rapid topology changes, high vehicle mobility and frequent disconnection of links. Therefore, developing an efficient routing protocol that satisfies restriction of delay and minimum overhead is faced with many difficulties and limitations. Also, the detection of malicious vehicles is a significant task in VANETs. To address these issues, using Unmanned Aerial Vehicles (UAVs) can be helpful to cope with these limitations. In this paper, operation of UAVs in ad hoc mode and their cooperation with vehicles in VANETs are studied to help in the process of routing and detection of malicious vehicles. A routing protocol named VRU is proposed that includes two distinct ways of routing of data: (1) delivering packets of data between vehicles with the help of UAVs using a protocol named VRU_vu, and (2) routing packet of data between UAVs using a protocol named VRU_u. The NS-2.35 simulator under Linux Ubuntu 12.04 is utilized in order to appraise the performance of VRU routing components in an urban scenario. Also, VanetMobiSim generator of mobility and MobiSim are used to produce the motions of vehicles and to produce the motions of UAVs, respectively. The performance analysis displays that VRU protocol can improve the packet delivery ratio by 16% and detection ratio by 7% compared to other reviewed routing protocol. Also, VRU protocol decreases end-to-end delay by an average of 13% and overhead by 40%.

Energy Efficient Smart Routing Based on Link Correlation Mining for Wireless Edge Computing in IoT

Abstract: Modern Internet of Things (IoT) applications are heavily data driven and often require reliable data streams to achieve high-quality data mining. The concept of edge computing is introduced to reduce data latency and communication bandwidth between the cloud server and IoT edge devices. However, inefficient routing that may cause transmission failure or unnecessary data (re)transmission is still a key obstacle to obtain good and reliable data mining results. In this paper, network coding combined with opportunistic routing is used to improve energy efficiency in wireless IoT infrastructure, considering the existence of link correlation. Studies have shown that packet receptions on wireless links are correlated, which is completely contrary to the assumption of link independence used in existing routing mechanisms. This assumption causes estimation errors in the calculation of expected number of transmissions for forwarders, which further affects the selection of forwarder set, and ultimately affects the performance of the protocol. We propose an intra-session network coding mechanism based on the mining of link correlation. A novel smart routing method is proposed to accurately estimate the number of transmissions required by forwarders, together with an algorithm for selecting a forwarder set with more optimal number of transmissions. Simulation results demonstrate that the proposed mechanism can achieve fewer transmissions and offer more energy efficient communications for wireless edge IoT applications.

An Intelligent Route Computation Approach Based on Real-Time Deep Learning Strategy for Software Defined Communication Systems

Abstract: Software Defined Networking (SDN) is regarded as the next generation paradigm as it simplifies the structure of the data plane and improves the resource utilization. However, in current Software Defined Communication Systems (SDCSs), the maximum or minimum metric value based routing strategies come from traditional networks, which lack the ability of self-adaptation and do not efficiently utilize the computation resource in the controllers. To solve these problems, in this paper, we utilize the deep learning technique to conduct the routing computation for the SDCSs. Specifically, in our proposal, the considered Convolutional Neural Networks (CNNs) are adopted to intelligently compute the paths according to the input real-time traffic traces. To reduce the computation overhead of the central controller and improve the adaptation of CNNs to the changing traffic pattern, we consider an online training manner. Analysis shows that the computation complexity can be significantly reduced through the online training manner. Moreover, the simulation results demonstrate that our proposed CNNs are able to compute the appropriate paths combinations with high accuracy. Furthermore, the adopted periodical retraining enables the deep learning structures to adapt to the traffic changes.

UAV Communication Networks Issues: A Review

Abstract: The unmanned aerial vehicle communication networks (UAVCN) comprises of a collection of unmanned aerial vehicles (UAVs) to build a network that can be used for many applications. These nodes autonomously fly in free space in ad-hoc mode to carry out the mission. However, the UAVs face some challenging issues during collaboration and communication. These nodes have high speed, hence the communication links fail to route the traffic that affects the routing mechanism. Therefore, UAVCN communication affecting the quality of service and facing the performance issue. Power is another major problem to limit and optimize the use of power, the energy-efficient mechanism is needed. In this paper, an attempt is made to explore the issues of unmanned aerial vehicle communication networks: UAVCN characteristics, UAVCN design issues, UAVCN applications, routing protocols, quality of service, power issue and identify the future open research areas which could be considered for further research to explore the UAVCN technology.

Explicit Evolutionary Multitasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem

Abstract: Recently, evolutionary multitasking (EMT) has been proposed in the field of evolutionary computation as a new search paradigm, for solving multiple optimization tasks simultaneously. By sharing useful traits found along the evolutionary search process across different optimization tasks, the optimization performance on each task could be enhanced. The autoencoding-based EMT is a recently proposed EMT algorithm. In contrast to most existing EMT algorithms, which conduct knowledge transfer across tasks implicitly via crossover, it intends to perform knowledge transfer explicitly among tasks in the form of task solutions, which enables the employment of task-specific search mechanisms for different optimization tasks in EMT. However, the autoencoding-based explicit EMT can only work on continuous optimization problems. It will fail on combinatorial optimization problems, which widely exist in real-world applications, such as scheduling problem, routing problem, and assignment problem. To the best of our knowledge, there is no existing effort working on explicit EMT for combinatorial optimization problems. Taking this cue, in this article, we thus embark on a study toward explicit EMT for combinatorial optimization. In particular, by using vehicle routing as an illustrative combinatorial optimization problem, the proposed explicit EMT algorithm (EEMTA) mainly contains a weighted $l_{1}$ -norm-regularized learning process for capturing the transfer mapping, and a solution-based knowledge transfer process across vehicle routing problems (VRPs). To evaluate the efficacy of the proposed EEMTA, comprehensive empirical studies have been conducted with the commonly used vehicle routing benchmarks in multitasking environment, against both the state-of-the-art EMT algorithm and the traditional single-task evolutionary solvers. Finally, a real-world combinatorial optimization application, that is, the package delivery problem (PDP), is also presented to further confirm the efficacy of the proposed algorithm.

Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks

Abstract: Existing routing protocols for wireless sensor networks (WSNs) focus primarily either on energy efficiency, quality of service (QoS), or security issues. However, a more holistic view of WSNs is needed, as many applications require both QoS and security guarantees along with the requirement of prolonging the lifetime of the network. The limited energy capacity of sensor nodes forces a tradeoff to be made between network lifetime, QoS, and security. To address these issues, an ant colony optimization based QoS aware energy balancing secure routing (QEBSR) algorithm for WSNs is proposed in this article. Improved heuristics for calculating the end-to-end delay of transmission and the trust factor of the nodes on the routing path are proposed. The proposed algorithm is compared with two existing algorithms: distributed energy balanced routing and energy efficient routing with node compromised resistance. Simulation results show that the proposed QEBSR algorithm performed comparatively better than the other two algorithms.

Leveraging Communicating UAVs for Emergency Vehicle Guidance in Urban Areas

Abstract: The response time to emergency situations in urban areas is considered as a crucial key in limiting material damage or even saving human lives. Thanks to their “bird’s eye view” and their flexible mobility, Unmanned Aerial Vehicles (UAVs) can be a promising candidate for several vital applications. Under these perspectives, we investigate the use of communicating UAVs to detect any incident on the road, provide rescue teams with their exact locations, and plot the fastest path to intervene, while considering the constraints of the roads. To efficiently inform the rescue services, a robust routing scheme is introduced to ensure a high level of communication stability based on an efficient backbone, while considering both the high mobility and the restricted energy capacity of UAVs. This allows both predicting any routing path breakage prior to its occurrence, and carrying out a balanced energy consumption among UAVs. To ensure a rapid intervention by rescue teams, UAVs communicate in an ad hoc fashion with existing vehicles on the ground to estimate the fluidity of the roads. Our system is implemented and evaluated through a series of experiments. The reported results show that each part of the system reliably succeeds in achieving its planned objective.

Sway: Traffic-Aware QoS Routing in Software-Defined IoT

Abstract: In this paper, we propose a traffic-aware quality-of-service (QoS) routing scheme in software-defined internet of things (SDIoT) network. The proposed scheme exploits the unique features of software-defined networking (SDN), such as flow-based nature, and network flexibility, in order to fulfill QoS requirements of each flow in the network. We consider two types of QoS routing strategies—delay-sensitive and loss-sensitive—for incoming packets from end-devices in the network. The former is devised to deal with delay-sensitive flows, and the latter deals with loss-sensitive flows, in order to maximize the overall network performance. We propose a greedy approach based on Yen's K-shortest paths algorithm to compute the optimal forwarding path, while considering the QoS requirements of each packet. Consequently, the SDN controller deploys adequate flow-rules at the forwarding devices in the network. Extensive simulation results show that the proposed scheme significantly reduces the end-to-end delay and the percentage of flows which violate QoS constraints compared to the benchmarks considered in the study. It is also observed that the proposed scheme adequately satisfies the QoS requirements for both type of flows in contrast to the existing schemes. In particular, with 2000 flows in the network, the proposed scheme achieves 13%, 14% and 15% (with AttMpls topology) and 38%, 37% and 39% (with Goodnet topology) reduction in QoS violated flows as compared to the existing LARAC, SPD, and MRC schemes, respectively.

A Survey of Routing Protocols for Underwater Wireless Sensor Networks

Abstract: Underwater wireless sensor network (UWSN) is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. However, UWSNs suffer from various limitations and challenges: high ocean interference and noise, high propagation delay, narrow bandwidth, dynamic network topology, and limited battery energy of sensor nodes. The design of routing protocols is one of the solutions to address these issues. A routing protocol can efficiently transfer the data from the source node to the destination node in the network. This article presents a review of underwater routing protocols for UWSNs. We classify the existing underwater routing protocols into three categories: energy-based, data-based, and geographic information-based protocols. In this article, we summarize the underwater routing protocols proposed in recent years. The proposed protocols are described in detail and give advantages and disadvantages. Meanwhile, the performance of different underwater routing protocols is analyzed in detail. Besides, we also present the research challenges and future directions of underwater routing protocols, which can help the researcher better explore in the future.

Trust Aware Secure Energy Efficient Hybrid Protocol for MANET

Abstract: Mobile ad hoc network (MANETs) is infrastructure-less, self-organizing, fast deployable wireless network, so they truly are exceptionally appropriate for purposes between special outside occasions, communications in locations without a radio infrastructure, crises, and natural disasters, along with military surgeries. Security could be the primary weak spot in manet on account of this flexibility of structures and always changing dynamic topology, that will be very exposed to your selection of strikes like eavesdropping, routing, and alteration of programs. MANET is affected with security issues, surpassing Quality of services (QoS). So, intrusion tracking which modulates your system to recognize some other violation weakness would be that the top approach to guarantee security for MANET. Detecting intrusions has a critical part in supplying protections and functions as beyond layer of defenses against access. Power collapse of the cellular node maybe not merely alter the node alone but its capacity to forwards packets which is based on total system life. This also caused the institution of the routing protocol to its stable optimal choice of this multi-path to increase the navigation MANETs. Provision of energy-efficient and secure routing is a challenge given the changing topology and restricted resources of this kind of network. To address the energy efficiency and security we suggest a trust-based secure energy efficient navigation in MANETs employing the hybrid algorithm, cat slap single-player algorithm (C-SSA), that selects the best jumps in advancing the routing. In the beginning, the fuzzy clustering is put on, and the cluster heads (CHs) are picked predicated maximum worth of indirect, direct, and recent trust. Predicated on trust threshold worth nodes additionally discovered. Even the CHs are participated from the multi hop routing, and the assortment of the best route relies upon the projected hybrid protocol, and that selects the best routes determined by the delay, throughput, along with connectivity within this course. The proposed method obtained a minimal energy of 0.11m joules, a negligible delay of 0.005 msec, a maximum throughput of 0.74 bps, a maximum packet delivery ratio of 0.99 %, and a maximum detection rate of 90%. The proposed method compared with the existing techniques in the presence and absent of the selective packet dropping attack.

Cooperative Beam Routing for Multi-IRS Aided Communication

Abstract: Intelligent reflecting surface (IRS) has been deemed as a transformative technology to achieve smart and reconfigurable environment for wireless communication. This letter studies a new IRS-aided communication system, where multiple IRSs assist in the communication between a multi-antenna base station (BS) and a remote single-antenna user by multi-hop signal reflection. Specifically, by exploiting the line-of-sight (LoS) link between nearby IRSs, a multi-hop cascaded LoS link between the BS and user is established where a set of IRSs are selected to successively reflect the BS’s signal, so that the received signal power at the user is maximized. To tackle this new problem, we first present the closed-form solutions for the optimal active and cooperative passive beamforming at the BS and selected IRSs, respectively, for a given beam route. Then, we derive the end-to-end channel power, which unveils a fundamental trade-off in the optimal beam routing design between maximizing the multiplicative passive beamforming gain and minimizing the multi-reflection path loss. To reconcile this trade-off, we recast the IRS selection and beam routing problem as an equivalent shortest simple-path problem in graph theory and solve it optimally. Numerical results show significant performance gains of the proposed algorithm over benchmark schemes and also draw useful insights into the optimal beam routing design.

Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks

Abstract: This paper surveys the energy-efficient routing protocols in wireless sensor networks (WSNs). It provides a classification and comparison following a new proposed taxonomy distinguishing nine categories of protocols, namely: Latency-aware and energy-efficient routing, next-hop selection, network architecture, initiator of communication, network topology, protocol operation, delivery mode, path establishment and application type. We analyze each class, discuss its representative routing protocols (mechanisms, advantages, disadvantages…) and compare them based on different parameters under the appropriate class. Simulation results of LEACH, Mod-LEACH, iLEACH, E-DEEC, multichain-PEGASIS and M-GEAR protocols, conducted under the NS3 simulator, show that the routing task must be based on various intelligent techniques to enhance the network lifespan and guarantee better coverage of the sensing area.

Q-Learning-Based Data-Aggregation-Aware Energy-Efficient Routing Protocol for Wireless Sensor Networks

Abstract: The energy consumption of the routing protocol can affect the lifetime of a wireless sensor network (WSN) because tiny sensor nodes are usually difficult to recharge after they are deployed. Generally, to save energy, data aggregation is used to minimize and/or eliminate data redundancy at each node and reduce the amount of the overall data transmitted in a WSN. Furthermore, energy-efficient routing is widely used to determine the optimal path from the source to the destination, while avoiding the energy-short nodes, to save energy for relaying the sensed data. In most conventional approaches, data aggregation and routing path selection are considered separately. In this study, we consider the degrees of the possible data aggregation of neighbor nodes when a node needs to determine the routing path. We propose a novel Q-learning-based data-aggregation-aware energy-efficient routing algorithm. The proposed algorithm uses reinforcement learning to maximize the rewards, defined in terms of the efficiency of the sensor-type-dependent data aggregation, communication energy and node residual energy, at each sensor node to obtain an optimal path. We used sensor-type-dependent aggregation rewards. Finally, we performed simulations to evaluate the performance of the proposed routing method and compared it with that of the conventional energy-aware routing algorithms. Our results indicate that the proposed protocol can successfully reduce the amount of data and extend the lifetime of the WSN.

A Hybrid of Deep Reinforcement Learning and Local Search for the Vehicle Routing Problems

Abstract: Different variants of the Vehicle Routing Problem (VRP) have been studied for decades. State-of-the-art methods based on local search have been developed for VRPs, while still facing problems of slow running time and poor solution quality in the case of large problem size. To overcome these problems, we first propose a novel deep reinforcement learning (DRL) model, which is composed of an actor, an adaptive critic and a routing simulator. The actor, based on the attention mechanism, is designed to generate routing strategies. The adaptive critic is devised to change the network structure adaptively, in order to accelerate the convergence rate and improve the solution quality during training. The routing simulator is developed to provide graph information and reward with the actor and adaptive cirtic. Then, we combine this DRL model with a local search method to further improve the solution quality. The output of the DRL model can serve as the initial solution for the following local search method, from where the final solution of the VRP is obtained. Tested on three datasets with customer points of 20, 50 and 100 respectively, experimental results demonstrate that the DRL model alone finds better solutions compared to construction algorithms and previous DRL approaches, while enabling a 5- to 40-fold speedup. We also observe that combining the DRL model with various local search methods yields excellent solutions at a superior generation speed, comparing to that of other initial solutions.

Artificial Bee Colony Optimization Algorithm for Enhancing Routing in Wireless Networks

Abstract: The throughput of wireless multi-channel networks are enhanced using artificial intelligence algorithm. The performance of the network may be improved while reducing the interference. This technique involves three steps namely creation of wireless environment specific model, performance optimization using the right tools and improvement of routing by selecting the performance indicators cautiously. Artificial bee colony optimization algorithm and its evaluative features positively affects communication in wireless networks. The simple behavior of bee agents in this algorithm assist in making synchronous and decentralized routing decisions. The advantages of this algorithm is evident from the MATLAB simulations. The nature inspired routing algorithm offers improved performance when compared to the existing state-of-the-art models. The simple agent model can improve the performance values of the network. The breadth first search variant is utilized for discovery and deterministic evaluation of multiple-paths in the network increasing the overall routing protocol output.

Electric vehicle routing problem with machine learning for energy prediction

Abstract: Routing electric commercial vehicles requires taking into account their limited driving range, which is affected by several uncertain factors such as traffic conditions. This paper presents the time-dependent Electric Vehicle Routing Problem with Chance-Constraints (EVRP-CC) and partial recharging. The routing method is divided into two stages, where the first finds the best paths and the second optimizes the routes. A probabilistic Bayesian machine learning approach is proposed for predicting the expected energy consumption and variance for the road links, paths and routes. Hence it is possible to consider the uncertainty in energy demand by planning charging within a confidence interval. The energy estimation is validated with data from electric buses driving a public transport route in Gothenburg-Sweden as well as with realistic simulations for 24 hours traffic in the city of Luxembourg connected to a high fidelity vehicle model. Routing solutions are compared with a deterministic formulation of the problem similar to the ones found in the literature. The results indicate high accuracy for the energy prediction as well as energy savings and more reliability for the routes.

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

Abstract: 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.

Optimizing nonlinear charging times of electric vehicle routing with genetic algorithm

Abstract: With the rising share of electric vehicles used in the service industry, the optimization of their specific constraints is gaining importance. Lowering energy consumption, time of charging and the strain on the electric grid are just some of the issues that must be tackled, to ensure a cleaner and more efficient industry. This paper presents a Two-Layer Genetic Algorithm (TLGA) for solving the capacitated Multi-Depot Vehicle Routing Problem with Time Windows (MDVRPTW) and Electric Vehicles (EV) with partial nonlinear recharging times (NL) – E-MDVRPTW-NL. Here, the optimization goal is to minimize driving times, number of stops at electric charging stations and time of recharging while taking the nonlinear recharging times into account. This routing problem closes the gap between electric vehicle routing problem research on the one hand and its applications to several problems with numerous real-world constraints of electric vehicles on the other. Next to the definition and the formulation of the E-MDVRPTW-NL, this paper presents the evolutionary method for solving this problem using the Genetic Algorithm (GA), where a novel two-layer genotype with multiple crossover operators is considered. This allows the GA to not only solve the order of the routes but also the visits to electric charging stations and the electric battery recharging times. Various settings of the proposed method are presented, tested and compared to competing meta-heuristics using well-known benchmarks with the addition of charging stations.

Dynamic Route Discovery Using Modified Grasshopper Optimization Algorithm in Wireless Ad-Hoc Visible Light Communication Network

Abstract: In recent times, visible light communication is an emerging technology that supports high speed data communication for wireless communication systems. However, the performance of the visible light communication system is impaired by inter symbol interference, the time dispersive nature of the channel, and nonlinear features of the light emitting diode that significantly reduces the bit error rate performance. To address these problems, many environments offer a rich infrastructure of light sources for end-to-end communication. In this research paper, an effective routing protocol named the modified grasshopper optimization algorithm is proposed to reduce communication interruptions, and to provide alternative routes in the network without the need of previous topology knowledge. In this research paper, the proposed routing protocol is implemented and analyzed using the MATLAB environment. The experimental result showed that the proposed routing protocol adapts to dynamic changes in the communication networks, like obstacles and shadows. Hence, the proposed protocol achieved better performance in data transmission in terms of throughput, packet delivery ratio, end-to-end delay, and routing overhead. In addition, the performance is analyzed by varying the number of nodes like 50, 100, 250, and 500. From the experimental analysis, the proposed routing protocol achieved maximum of 16.69% and minimum of 2.20% improvement in packet delivery ratio, and minimized 0.80 milliseconds of end-to-end delay compared to the existing optimization algorithms.

Efficient-CapsNet: capsule network with self-attention routing.

Abstract: Deep convolutional neural networks, assisted by architectural design strategies, make extensive use of data augmentation techniques and layers with a high number of feature maps to embed object transformations. That is highly inefficient and for large datasets implies a massive redundancy of features detectors. Even though capsules networks are still in their infancy, they constitute a promising solution to extend current convolutional networks and endow artificial visual perception with a process to encode more efficiently all feature affine transformations. Indeed, a properly working capsule network should theoretically achieve higher results with a considerably lower number of parameters count due to intrinsic capability to generalize to novel viewpoints. Nevertheless, little attention has been given to this relevant aspect. In this paper, we investigate the efficiency of capsule networks and, pushing their capacity to the limits with an extreme architecture with barely 160 K parameters, we prove that the proposed architecture is still able to achieve state-of-the-art results on three different datasets with only 2% of the original CapsNet parameters. Moreover, we replace dynamic routing with a novel non-iterative, highly parallelizable routing algorithm that can easily cope with a reduced number of capsules. Extensive experimentation with other capsule implementations has proved the effectiveness of our methodology and the capability of capsule networks to efficiently embed visual representations more prone to generalization.

An Improved IDAF-FIT Clustering Based ASLPP-RR Routing with Secure Data Aggregation in Wireless Sensor Network

Abstract: In recent years, Wireless Sensor Network (WSN) became a key technology for monitoring and tracking applications in a wide application range. Still, an energy-efficient data gathering protocol has become the most challenging issue. This is because each sensor node in the network is equipped with limited energy resources. To achieve better energy efficiency, better network communication, and minimized delay, clustering is introduced. Therefore, the clustering-based techniques for data gathering play a vital role in terms of energy-saving and increasing the lifetime of the network due to cluster head election and data aggregation. In this proposed methodology, the Integration of Distributed Autonomous Fashion with Fuzzy If-then Rules (IDAF-FIT) algorithm is proposed for clustering, and also the Cluster Head (CH) is elected in the meanwhile. After that, to transmit the packet from source to the destination node by choosing an optimal path, the routing concept is initiated. For this purpose, an Adaptive Source Location Privacy Preservation Technique using Randomized Routes (ASLPP-RR) is presented for routing. Also, Secure Data Aggregation based on Principle Component Analysis (SDA-PCA) algorithm is performed with end-to-end confidentiality and integrity. Finally, the security of confidential data is analyzed properly to obtain a better result than the existing approaches. The overall performance of the proposed methodology when compared with existing is expressed in terms of 20% higher packet delivery ratio, 15% lower packet dropping ratio, 18% higher residual energy, 22% higher network lifetime, and 16% lower energy consumption.

Formulating and Solving Routing Problems on Quantum Computers

Abstract: The determination of vehicle routes fulfilling connectivity, time, and operational constraints is a well-studied combinatorial optimization problem. The NP-hard complexity of vehicle routing problems has fostered the adoption of tailored exact approaches, matheuristics, and metaheuristics on classical computing devices. The ongoing evolution of quantum computing hardware and the recent advances of quantum algorithms (i.e., VQE, QAOA, and ADMM) for mathematical programming make decision-making for routing problems an avenue of research worthwhile to be explored on quantum devices. In this article, we propose several mathematical formulations for inventory routing cast as vehicle routing with time windows and comment on their strengths and weaknesses. The optimization models are compared from a quantum computing perspective, specifically with metrics to evaluate the difficulty in solving the underlying quadratic unconstrained binary optimization problems. Finally, the solutions obtained on simulated quantum devices demonstrate the relative benefits of different algorithms and their robustness when put into practice.