Clustering routing algorithm of wireless sensor networks based on Bayesian game

To avoid uneven energy consuming in wireless sen- sor networks, a clustering routing model is proposed based on a Bayesian game. In the model, Harsanyi transformation is introduced to convert a static game of incomplete information to the static game of complete but imperfect information. In addition, the existence of Bayesian nash equilibrium is proved. A clustering routing algorithm is also designed according to the proposed model, both cluster head distribution and residual energy are considered in the design of the algorithm. Simulation results show that the algorithm can balance network load, save energy and prolong network lifetime effectively.

A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm

In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life.

Efficient Clustering Routing Algorithm Based on Opportunistic Routing

Based on the analysis of the existing classic clustering routing algorithm HEED, this paper proposes an efficient dynamic clustering routing algorithm ED-HEED. In the cluster selection process, in order to optimize the network topology and select more proper nodes as the cluster head, the proposed clustering algorithm considers the shortest path prediction of the node to the destination sink and the congestion situation. In the data transmission procedure, the high-efficiency CEDOR opportunistic routing algorithm is applied into the ED-HEED as the data transmission mode between cluster headers. A novel adaptive dynamic clustering mechanism is also considered into the algorithm, as well as the data redundancy and security control. Our Simulation demonstrates that the ED-HEED algorithm can reduce the energy consumption, prolong the network life and keep the security and availability of the network compared with the HEED algorithm.

Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

Wireless sensor network(WSN)includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications.It comprises a massive set of nodes with restricted energy and processing abilities.Energy dissipation is a major concern involved in the design of WSN.Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms.In order to design an energy aware cluster-based route planning scheme,this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing(HBAC-AVOR)protocol for WSN.The presented HBAC-AVOR model mainly aims to cluster the nodes in WSN effectually and organize the routes in an energy-efficient way.The presented HBAC-AVOR model follows a two stage process.At the initial stage,the HBAC technique is exploited to choose an opti-mal set of cluster heads(CHs)utilizing afitness function involving many input parameters.Next,the AVOR approach was executed for determining the optimal routes to BS and thereby lengthens the lifetime of WSN.A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR protocol.On comparing with existing techniques,the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

Clustering routing algorithm of self-energized wireless sensor networks based on solar energy harvesting

Aiming at the problems of existing clustering routing algorithm of self-energized wireless sensor networks（WSNs） on fixed threshold for resurrection, incapacitates reappoint cluster head in the next round and lack of election limit, this paper proposes a novel clustering routing algorithm for self-energized WSNs clustering routing algorithm based on solar energy harvesting（CRBS） algorithm. The algorithm puts forward a threshold sensitive resurrection mechanism, reviving the node when harvesting energy reaches the set soft or hard energy threshold. Meanwhile, combined with current energy harvesting level, cluster head node can decide whether to reappoint the cluster head in the next round. What＇s more, CRBS optimizes the cluster head election threshold to limit the incompetent node in election. Combined with the solar energy harvesting simulation, the results show that CRBS algorithm can better keep the default cluster head proportion, and outperforms energy balanced clustering with self-energization（EBCS） algorithm in terms of surviving nodes number and the success ratio of data transmission

Energy-Efficient Routing Algorithm Based on Small-World Characteristics

Water quality sensor networks are widely used in water resource monitoring.However,due to the fact that the energy of these networks cannot be supplemented in time,it is necessary to study effective routing protocols to extend their lifecycle.To address the problem of limited resources,a routing optimization algorithm based on a small-world network model is proposed.In this paper,a small-world network model is introduced for water quality sensor networks,in which the short average path and large clustering coefficient of the model are used to construct a super link.A short average path can reduce the network’s energy consumption,and a large coefficient can improve its fault-tolerance ability.However,the energy consumption of the relay nodes near the heterogeneous node is too great,and as such the energy threshold and non-uniform clustering are constructed to improve the lifecycle of the network.Simulation results show that,compared with the low-energy adaptive clustering hierarchy routing algorithm and the best sink location clustering heterogeneous network routing algorithm,the proposed improved routing model can effectively enhance the energy-utilization.The lifecycle of the network can be extended and the data transmission amount can be greatly increased.

A New Algorithm for Routing in Ad-hoc Network

In this paper, we propose a new algorithm for wireless mobile and ad-hoc network, which establishes dynamic cluster of nodes. The proposed algorithm, namely, the Mobility Sensitive Routing Protocol (MSRP), consists of routing in cluster and routing between clusters. Ad-hoc network can utilize MSRP to reduce information exchange and communication bandwidth, to shorten route acquisition delay, and to accommodate more nodes.

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the future.The fundamental problem with WSN is that it has afinite lifespan.Clustering a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data transmission.The cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunflower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is used.It does not provide better competence and out-put in the network.Therefore,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the network.Black hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used tofind the route among origin CH and destination.The open cache of nodes,trans-mission power,and proximity are used to improve the CH selection.The grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle food.The GWO method was inspired by this hunting habit.The proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale net-works.The HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

Three-stage algorithms for the large-scale dynamic vehicle routing problem with industry 4.0 approach

Companies are eager to have a smart supply chain especially when they have adynamic system. Industry 4.0 is a concept which concentrates on mobility andreal-time integration. Thus, it can be considered as a necessary component thathas to be implemented for a dynamic vehicle routing problem. The aim of thisresearch is to solve large-scale DVRP (LSDVRP) in which the delivery vehiclesmust serve customer demands from a common depot to minimize transit costswhile not exceeding the capacity constraint of each vehicle. In LSDVRP, it isdifficult to get an exact solution and the computational time complexity growsexponentially. To find near-optimal answers for this problem, a hierarchicalapproach consisting of three stages: “clustering, route-construction, routeimprovement”is proposed. The major contribution of this paper is dealing withLSDVRP to propose the three-stage algorithm with better results. The resultsconfirmed that the proposed methodology is applicable.

Energy aware hierarchical cluster-based routing protocol for WSNs

Wireless sensor networks（WSNs） are emerging as essential and popular ways of providing pervasive computing environments for various applications. Unbalanced energy consumption is an inherent problem in WSNs, characterized by multi-hop routing and a many-to-one traffic pattern. This uneven energy dissipation can significantly reduce network lifetime. In multi-hop sensor networks, information obtained by the monitoring nodes need to be routed to the sinks, the energy consumption rate per unit information transmission depends on the choice of the next hop node. In an energy-aware routing approach, most proposed algorithms aim at minimizing the total energy consumption or maximizing network lifetime. In this paper, we propose a novel energy aware hierarchical cluster-based（NEAHC） routing protocol with two goals： minimizing the total energy consumption and ensuring fairness of energy consumption between nodes. We model the relay node choosing problem as a nonlinear programming problem and use the property of convex function to find the optimal solution. We also evaluate the proposed algorithm via simulations at the end of this paper.

Joint Design of Clustering and In-cluster Data Route for Heterogeneous Wireless Sensor Networks

A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes.In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that,we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.

Multiple Routes Recommendation System on Massive Taxi Trajectories

This paper presents a cloud-based multiple-route recommendation system, xGo, that enables smartphone users to choose suitable routes based on knowledge discovered in real taxi trajectories. In modern cities, GPS-equipped taxicabs report their locations regularly, which generates a huge volume of trajectory data every day. The optimized routes can be learned by mining these massive repositories of spatio-temporal information. We propose a system that can store and manage GPS log files in a cloud-based platform, probe traffic conditions, take advantage of taxi driver route-selection intelligence, and recommend an optimal path or multiple candidates to meet customized requirements. Specifically, we leverage a Hadoop-based distributed route clustering algorithm to distinguish different routes and predict traffic conditions through the latent traffic rhythm. We evaluate our system using a real-world dataset（〉100 GB） generated by about 20 000 taxis over a 2-month period in Shenzhen, China. Our experiments reveal that our service can provide appropriate routes in real time and estimate traffic conditions accurately.