Narrow Results

The vehicle routing problem (VRP) and its variants are a class of network problems that have attracted the attention of many researchers in recent years, owing to their pragmatic approach to solving issues in logistics management. Most surveys/reviews of the extant literature often focus on specific variants or aspects of the VRP. However, a few reviews of the overall VRP literature are available. The focus of these papers is to identify which VRP literature characteristics are the most popular in recent studies. To this end, we analyze 229 articles published between 2015 and 2017. We provide a systematic literature review evaluating the Scenario Characteristics and Problem Physical Characteristics that are most frequently addressed by VRP researchers, the Type of Study and Data Characteristics that they address, the most cited works that constitute the theoretical pillars of the field, and details of three specific problem variants that have been studied extensively in recent years and their opportunities for future research.

Circulant graphs have been extensively investigated over the past 30 years because of their broad application to different fields of theory and practice. Two known surveys on circulant networks including a survey on undirected circulants have been published: by Bermond et al. [Distributed loop computer networks: A survey, J. Parallel Distributed Comput.24 (1995) 2–10] and by Hwang [A survey on multi-loop networks, Theoret. Comput. Sci.299 (2003) 107–121]. The present paper includes the results which have not been presented there, in particular the works of Russian researchers, and also a lot of new results obtained in the area of research of circulant networks. We focus on the survey connected with study of structural and communicative properties of circulant networks.

Wireless Sensor Networks (WSN) consists of numerous of low cost and less-energy sensor nodes that are responsible to gather and transmit the data packets from one node to destination point. WSN has a wide range of applications over agriculture, military, traffic monitoring, instrument surveillance, and security monitoring. In WSN, the nodes are located in a specific region to create a wireless network. The effective data communication among sensors is a challenging task because of different complex parameters. Typically, clustering is a well-preferred methodology to provide the effective communication by partitioning the nodes into different clusters. Every cluster possesses individual cluster head that transmits the data to other sensor nodes. Therefore, it is substantial to choose optimal cluster head and optimal route for effective transmission with less energy consumption and less delay. To increase the network efficiency and sink utilization, an energy aware routing algorithm called Fractional Competitive Fruit Fly Optimizer (FrCFFO) is designed, which is an integration of Fractional concept into the Competitive Fruit Fly Optimizer (CFFO). Here, the energy prediction is performed using Deep Quantum Neural Network (QNN). Effective CH selection and routing is done using the proposed FrCFFO and the fitness parameter is considered depending upon the factors like energy, distance, link lifetime, trust, and delay. Moreover, the developed FrCFFO has achieved effective performance with minimum delay of 0.098sec, maximum energy of 0.233J, and maximum PDR of 90.81%.

New technologies and the deployment of mobile and nomadic services are driving the emergence of complex communications networks, that have a highly dynamic behavior. This naturally engenders new route-discovery problems under changing conditions over these networks. Unfortunately, the temporal variations in the network topology are hard to be effectively captured in a classical graph model. In this paper, we use and extend a recently proposed graph theoretic model, which helps capture the evolving characteristic of such networks, in order to propose and formally analyze least cost journey (the analog of paths in usual graphs) in a class of dynamic networks, where the changes in the topology can be predicted in advance. Cost measures investigated here are hop count (shortest journeys), arrival date (foremost journeys), and time span (fastest journeys).

Logistics delivery companies typically deal with delivery problems that are strictly constrained by time while ensuring optimality of the solution to remain competitive. Often, the companies depend on intuition and experience of the planners and couriers in their daily operations. Therefore, despite the variability-characterizing daily deliveries, the number of vehicles used every day are relatively constant. This motivates us towards reducing the operational variable costs by proposing an efficient heuristic that improves on the clustering and routing phases. In this paper, a decision support system (DSS) and the corresponding clustering and routing methodology are presented, incorporating the driver’s experience, the company’s historical data and Google map’s data. The proposed heuristic performs as well as $k$-means algorithm while having other notable advantages. The superiority of the proposed approach has been illustrated through numerical examples.

The interconnetion network plays an important role in a parallel system. To avoid the edge number of the interconnect network scaling rapidly with the increase of dimension and achieve a good balance of hardware costs and properties, this paper presents a new interconnection network called exchanged $3$-ary $n$-cube ($E3C$). Compared with the $3$-ary $n$-cube structures, $E3C$ shows better performance in terms of many metrics such as small degree and fewer links. In this paper, we first introduce the structure of $E3C$ and present some properties of $E3C$; then, we propose a routing algorithm and obtain the diameter of $E3C$. Finally, we analyze the diagnosis of $E3C$ and give the diagnosibility under PMC model and MM* model.

With the increasing complexity and demand for transportation networks, effective routing planning has attracted more and more attention. Different modes of transportation, such as the airplane, railway and so on, work together, forming a multi-modal transportation network. Therefore, this paper studies the routing and congestion problems in the multi-modal transportation network, and shows how to increase the network capacity as much as possible while saving time and economic costs, so as to avoid congestion and realize the effective use of different modes of transportation. This paper simulates the influence of two main factors on the network capacity, the parameter which shows the importance between time costs and economic costs, and the difference between different transportation modes. The results show the change of the network capacity when the time cost and economic cost are of different importance. There exists a critical point that can balance time costs and economic costs, so as to maximize network capacity. Then this paper further finds out the method of estimating the condition when the network takes the maximum capacity through theoretical analysis.

This paper develops an approach for detecting landslide using IoT. The simulation of IoT is the preliminary step that helps to collect data. The suggested Water Particle Grey Wolf Optimization (WPGWO) is used for the routing. The Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) are combined in the suggested method (WPGWO). The fitness is newly modeled considering energy, link cost, distance, and delay. The maintenance of routes is done to assess the dependability of the network topology. The landslide detection process is carried out at the IoT base station. In feature selection, angular distance is used. Oversampling is used to enrich the data, and Deep Residual Network (DRN) — used for landslide identification — is trained using the proposed Water Cycle Particle Swarm Optimization (WCPSO) method, which combines WCA and PSO. The proposed WCPSO-based DRN offered effective performance with the highest energy of 0.049J, throughput of 0.0495, accuracy of 95.7%, sensitivity of 97.2% and specificity of 93.9%. This approach demonstrated improved robustness and produced the global best optimal solution. For the proposed WPGWO, WCA, GWO, and PSO are linked to improve performance in determining the optimum routes. When comparing with existing methods the proposed WCPSO-based DRN offered effective performance.

One of the major significant problems in the existing techniques in Wireless Sensor Networks (WSNs) is Energy Efficiency (EE) because sensor nodes are battery-powered devices. The energy-efficient data transmission and routing to the sink are critical challenges because WSNs have inherent resource limitations. On the other hand, the clustering process is a crucial strategy that can rapidly increase network lifetime. As a result, WSNs require an energy-efficient routing strategy with optimum route election. These issues are overcome by using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm for Data Aggregation and Cluster-Based Routing in WSN (TFR-DLN-PMPOA-WSN) which is proposed to expand the network lifetime. Initially, Tasmanian Fully Recurrent Deep Learning Network (TFR-DLN) is proposed to elect the Optimal Cluster Head (OCH). After OCH selection, the three parameters, trust, connectivity, and QoS, are optimized for secure routing with the help of the Pelican Variable Marine Predators Optimization Algorithm (PMPOA). Finally, the proposed method finds the minimum distance among the nodes and selects the best routing to increase energy efficiency. The proposed approach will be activated in MATLAB. The efficacy of the TFR-DLN- PMPOA-WSN approach is assessed in terms of several performances. It achieves higher throughput, higher packet delivery ratio, higher detection rate, lower delay, lower energy utilization, and higher network lifespan than the existing methods.

Plant health monitoring is a very significant task in any agriculture-based environment. The Internet of Things (IoT) plays an important role in the monitoring of plant diseases. IoT is required to obtain data through sensor nodes for finding soil moisture and heat level. Even though different methods are available to monitor the health of plants, observing heat level and soil moisture still results a complex task. Thus, this paper introduces a novel chimp shuffled shepherd optimization (ChSSO) by the integration of chimp optimization algorithm (ChOA) and shuffled shepherd optimization (SSOA) to perform the selection of cluster head (CH) and routing process. The proposed ChSSO is trained using the deep LSTM which is developed for predicting soil moisture and heat level conditions in IoT network to monitor the health of plants. The proposed method obtained higher performance by the metrics, like testing accuracy and precision of 0.937, and 0.926 for 100 nodes and the values of 0.940, and 0.940 for 150 nodes using the LDAS dataset.

Medical information system, like the Internet of Medical Things (IoMT), has gained more attention in recent decades. Disease diagnosis is an important facility of the medical healthcare system. Wearable devices become popular in a wide range of applications in the health monitoring system and this has stimulated the increasing growth of IoMT. Recently, a smart healthcare system has been more effective, and various methods have been developed to classify the disease at the beginning stage. To capture the patient’s information and detect the disease, a new framework is designed using the developed Conditional Auto regressive Mayfly Algorithm (CAMA)-based Deep Residual Network (DRN). Initially, pre-processing is done by the T2FCS filtering technique to increase the image quality by eliminating noises. The second step is segmentation. Here, the segmentation of brain tumor is done using U-Net. After that, data augmentation is performed to enhance image dimensions using the techniques, such as flipping, shearing, and translation to solve the issues of data samples. After processing the data augmentation mechanism, the next step is brain tumor detection, which is done using DRN. Here, DRN is trained by the proposed CAMA, which is the integration of conditional auto regressive value at risk (CAViaR) with the mayfly algorithm (MA). The developed model reduces computational complexity and increases effectiveness and robustness. The proposed CAMA-based DRN outperformed with an utmost testing accuracy of 0.921, sensitivity of 0.931, specificity of 0.928, distance of 52.842 and trust of 0.697.

Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters.

To perceive the data utilizing sensor nodes, wireless sensor network (WSN) consists of several nodes connected to a wireless channel. However, the sink node, also known as a base station (BS), provides power to the WSN and acts as an access node for a number of the network’s sensor devices. Weather monitoring, field surveillance, and the collection of meteorological data are just a few of the various uses for WSN. The energy of each node directly affects how long a wireless network will last. So, to increase the lifespan of WSN, effective routing is required. Using the suggested Taylor sea lion optimization-based deep belief network (TSLnO-based DBN), the ultimate purpose of this research is to build a method for energy-aware communication in WSN. In the setup stage, cluster head (CH) is chosen using a hybrid optimization technique called ant lion whale optimization (ALWO), which is created by fusing the whale optimization algorithm (WOA) and ant lion optimizer (ALO). It is important to note that CH’s selection criteria are solely based on fitness factors such as energy and distance. The second phase, known as the steady state step, is when the updating of energy and trust takes place. In the prediction phase, the network classifier is trained using a newly created optimization method called TSLnO, and the age of neighbor nodes is predicted by estimating the energy of neighbors using DBN. By combining the Taylor Series and the sea lion optimization (SLnO) method, the proposed TSLnO is produced. The communication/route discovery phase, which occurs in the fourth phase, is where the path through nearby nodes is chosen. The maintenance phase of the route is the fifth phase.

Typically, wireless sensor networks (WSNs) are used to monitor as well as detect different kinds of objects in realistic monitoring, where security remains as a major confront. Estimation of node trust is established to be an effectual way of enhancing the security, thus aiding in nodes collaboration and decision-making in wireless and wired networks. Nevertheless, conventional methods of trust management generally highlight on trust modeling and fail to notice the overhead issues. In this paper, a security aware ring cluster routing technique is introduced. The routing is undergone based on the multi-objectives including trust (security) parameters, energy, and distance. Here, the trust parameters include both the direct trust evaluation and indirect trust evaluation. Thereby, the lifetime of the network gets maximized even with secured manner. An innovative Self-Adaptive Deer Hunting Optimization (SA-DHO) is presented in this study because the optimization plays a significant role in selecting the neighbors as ring nodes. Finally, the superiority of the suggested approach is demonstrated in relation to various measures.

The wireless sensor network (WSN) assists an extensive range of sensor nodes and enables several real-time uses. Congestion on the WSN is based on high pocket traffic and low wireless communication capabilities under network topology. Highly loaded nodes will consume power quickly and increase the risk of the network going offline or breaking. Additionally, loss of packet and buffer overflows would result in an outcome of increased end-to-end delay, performance deterioration of heavily loaded nodes, and transport communication loss. In this paper, a novel congestion control system is proposed to diminish the congestion on network and to enhance the throughput of the network. Initially, cluster head (CH) selection is achieved by exhausting K-means clustering algorithm. After the selection of cluster head, an efficient approach for congestion management is designed to select adaptive path by using Adaptive packet rate reduction (APTR) algorithm. Finally, Ant colony optimization (ACO) is utilized for enhancement of wireless sensor network throughput. The objective function increases the wireless sensor network throughput by decreasing the congestion on network. The proposed system is simulated with (Network Simulator NS-2). The proposed K-means C-ACO-ICC-WSN attains higher throughput 99.56%, 95.62% and 93.33%, lower delay 4.16%, 2.12% and 3.11% and minimum congestion level 1.19%, 2.33% and 5.16% and the proposed method is likened with the existing systems as Fuzzy-enabled congestion control through cross layer protocol exploiting OABC on WSN (FC-OABC-CC-WSN), Optimized fuzzy clustering at wireless sensor networks with improved squirrel search algorithm (FLC-ISSA-CC-WSN) and novel energy-aware clustering process through lion pride optimizer (LPO) and fuzzy logic on wireless sensor networks (EAC-LPO-CC-WSN), respectively. Finally, the simulation consequences demonstrate that proposed system may be capable of minimizing that congestion level and improving the throughput of the network.

In order to alleviate traffic congestion on multilayer networks, designing an efficient routing strategy is one of the most important ways. In this paper, a novel routing strategy is proposed to reduce traffic congestion on two-layer networks. In the proposed strategy, the optimal paths in the physical layer are chosen by comprehensively considering the roles of nodes’ degrees of the two layers. Both numerical and analytical results indicate that our routing strategy can reasonably redistribute the traffic load of the physical layer, and thus the traffic capacity of two-layer complex networks are significantly enhanced compared with the shortest path routing (SPR) and the global awareness routing (GAR) strategies. This study may shed some light on the optimization of networked traffic dynamics.

The rise of socio-technical systems in which humans interact with various forms of Artificial Intelligence, including assistants and recommenders, multiplies the possibility for the emergence of large-scale social behavior, possibly with unintended negative consequences. In this work, we discuss a particularly interesting case, i.e., navigation services’ impact on urban emissions, showing through simulations that the sum of many individually “optimal” choices may have unintended negative outcomes because such choices influence and interfere with each other on top of shared resources. To prove this point, we demonstrate how the introduction of a random component in the path suggestion phase may help to relieve the effect of collective and individual choices on the urban environment in terms of urban emissions.

In Wireless Sensor Network (WSN), node localization is a crucial need for precise data gathering and effective communication. However, high energy requirements, long inter-node distances and unpredictable limitations create problems for traditional localization techniques. This study proposes an innovative two-stage approach to improve localization accuracy and maximize route selection in WSNs. In the first stage, the Self-Adaptive Binary Waterwheel Plant Optimization (SA-BWP) algorithm is used to evaluate a node’s trustworthiness to achieve accurate localization. In the second stage, the Gazelle-Enhanced Binary Waterwheel Plant Optimization (G-BWP) method is employed to determine the most effective data transfer path between sensor nodes and the sink. To create effective routes, the G-BWP algorithm takes into account variables like energy consumption, shortest distance, delay and trust. The goal of the proposed approach is to optimize WSN performance through precise localization and effective routing. MATLAB is used for both implementation and evaluation of the model, which shows improved performance over current methods in terms of throughput, delivery ratio, network lifetime, energy efficiency, delay reduction and localization accuracy in terms of various number of nodes and rounds. The proposed model achieves highest delivery ratio of 0.97, less delay of 5.39, less energy of 23.3 across various nodes and rounds.

Routing algorithm is an important factor and a key technology for Flying Ad hoc Networks (FANETs), which can safeguard FANET network communication. In FANETs, the fast-changing nature of FANET network topology makes the traditional Mobile Ad-hoc Networks (MANET) network routing algorithm not directly usable, which leads to challenges for the design of routing algorithms. In this paper, when the FANET nodes need to communicate, the route search program performs route search based on the ant colony algorithm, so as to obtain a stable route with high efficiency. Making use of NS3 for simulation of ACA, Dynamic Source Routing (DSR), and Ad hoc On demand Distance Vector Routing (AODV), the results of simulation show that ACA can improve FANET performance.

The applications of wireless sensor network (WSN) are growing very rapidly, so utilizing the energy in an efficient manner is a challenging task as the battery life of nodes in WSN is very limited. For enhancing the lifetime of the network, various clustering protocols have been proposed earlier. In this paper, a clustering protocol named Energy Efficient Clusterhead Selection Scheme (ECSS) is proposed. The proposed ECSS protocol focusses on selecting an energy-efficient cluster head (CH), which helps in enhancing the overall lifetime and performance of the network. The proposed ECSS protocol uses the energy levels of nodes for the CH selection process. The proposed protocol is designed for the heterogeneous environment and it aims in minimizing the energy usage in the network and thereby improving the lifespan of the network. To measure the performance of the proposed ECSS protocol, the comparison is performed with the various existing protocols using MATLAB simulator. The results of simulation show that the proposed ECSS protocol has enhanced the network lifespan, throughput, and energy usage of the network as contrasted to the existing protocols.