Heterogeneous Routing Research Articles

Optimal Firefly based Balanced Energy Efficient Multi-hop (BEEM) Algorithm in Wireless Sensor Network

Life time of sensor network is very crucial and hot topic of research in wireless sensor network (WSN) from past to future. It is crucial due to system recharging and replacing the sensors are difficult and costly affair. Clustering provides some solution to extend the network lifetime. Existing clustering algorithms, such as LEACH and other heterogeneous routing protocol, can significantly minimize the power consumption on each sensor and prolong the network lifetime but not consideration of coverage network area. Balanced Energy Efficient Multi-hop (BEEM) algorithm has implemented to simulated WSN network and the selection of the cluster head on the basis of firefly (FF) optimization algorithm. Performance of the proposed hybrid Algorithm is well suited in terms of energy consumptions, stability period, network lifetime, throughput, Alive & Dead Nodes & other parameters. Proposed algorithm has showed improved result in energy consumption with firefly-BEEM over the existing BEEM.

Real-time scheduling under heterogeneous routing for industrial Internet of Things

Industrial Internet of Things (IIoT) provide a promising opportunity for building efficient industrial wireless systems by leveraging the growing ubiquity of sensor nodes. However, IIoT exhibits strict reliability and has a high requirement for real-time communications. Furthermore, different criticality level flows are coexisted in many IIoT. The system must guarantee the reliability and real-time performance of high-criticality flows to avoid disasters. To address this issue, this paper first proposes a heterogeneous routing model in which both source and graph routing coexist. Then, we propose Relative-execution Deadline First (RDF) scheduling and prove it has a better performance than the fixed priority (FP) and earliest deadline first (EDF) methods. By extending RDF in mixed-criticality IIoT, a Mixed-Criticality Relative-execution Deadline scheduling algorithm (MCRD) has proposed, which can further improve system schedulability by making a trade-off between reliability and real-time performance. Simulation and experiment results show that our approach outperforms the existing methods.

Continental Scale Heterogeneous Channel Flow Routing Strategy for Operational Forecasting Models

AbstractThe benefits of operational forecasting models to the general public are numerous. They support water management decisions, provide the opportunity to mitigate the impacts of weather‐ and flood‐related disasters and potentially save lives and properties. Channel flow routing is a key component of these models and affects their ability to forecast flood depth, duration, and extent. Continental scale channel flow routing within the operational forecasting environments encounters a broad spectrum of hydraulic characteristics. Deploying computationally demanding approaches, such as the dynamic wave, should be limited in time and space to conditions where the inertia terms are significant (typically in low‐gradient environments and whenever backwater effects are prominent); otherwise, efficient and robust methods, e.g., Kinematic, Muskingum‐Cunge or diffusive waves should be the default. The heterogeneous routing approach presented here provides a framework to evaluate the balance between friction, inertia, and pressure and strategically triggers the appropriate wave approximation. The strategy recommended here is to activate the appropriate wave approximation based on the ambient hydraulic conditions, and smoothly transitions among these approximations. This strategy, if successfully implemented, would strike a balance among the performance metrics of operational forecasting models, namely, computational efficiency, accuracy, and minimization of computational instabilities.

A Model for Sustainable Courier Services: Vehicle Routing with Exclusive Lanes

In Southeast Asian cities, it is common for logistic companies to operate a heterogeneous fleet of delivery vehicles with motorcycles being the preferred vehicle to handle the final phase of delivery. In such scenarios, heterogeneous fleet vehicle routing problem (HFVRP) is generally applied to plan an optimal delivery. However, in many downtown cores of large and rapidly developing Southeast Asian cities, HFVRP is neither viable nor reliable because of road usage restrictions. The purpose of this article is to develop and test a different approach that accurately takes these restrictions into account and provides viable and more sustainable results. Restrictions in this paper refer to situations of urban areas in Vietnam where (i) certain vehicle types are prohibited in specified areas or where narrow alleyways limit the utilization of vehicles that exceed the road capacity and (ii) certain roads are exclusive to certain vehicle types. In networks, limited access and exclusive lanes are represented as links, or arcs, exclusive to one or another. Taking these limitations into consideration, we have developed a unique model, which we have termed Vehicle Routing Problem with Exclusive Links (VRP-EL). The model was validated and tested for its performance on scenarios with varying ratios of exclusive links. Scenarios up to 500 customers were tested on a meta-heuristic algorithm, simulated annealing. VRP-EL produces realistic outcomes. Limiting certain links to be selected according to vehicle types increases overall travel distance. However, this increase outweighs the cost of re-planning and rerouting had they not been constrained initially. The reduction in traveling distance leads to fossil fuel reduction for the overall system. The estimation of reduced carbon emissions through applying the proposed model is presented. Considering the severe traffic congestion and carbon emissions caused by motorcycles in Vietnam, the proposed model leads to a sustainable road environment.

An Approach to Optimize Homogeneous and Heterogeneous Routing Protocols in WSN Using Sink Mobility

Clustered routing protocols with static sink suffer from energy hole problem. Sensor nodes (SNs) closer to the sink node work as a conveyer for faraway nodes, and these nodes deplete their energy very fast which causes an energy hole in the network. Using the mobility of the sink node is one of the best approaches to eradicate energy hole problem and to improve the lifetime of the network in wireless sensor networks. A framework which uses mobile sink concept with 4- or 8-sojourn-location path patterns in addition to one centralized static sink has been proposed to maximize the lifetime of the network by diverting a load of SN to nearby static or mobile sink. Furthermore, the performance of the presented framework has been compared with both homogeneous and heterogeneous network protocols named as LEACH-C and DEEC, respectively. Simulation results demonstrate that the proposed framework of sink mobility is much energy efficient and improves the network’s lifetime as compared to LEACH-C and DEEC.

Energy-efficient routing model for water pipeline monitoring based on wireless sensor networks

In wireless sensor networks (WSNs), water pipeline monitoring is one of the domain applications that require lower energy consumption. Sensor nodes have to sense environment parameters and route the leak data to a central node in low power consumption. Several routing techniques have been proposed to enhance the network lifetime in WSNs. In this paper, we propose a heterogeneous two-tiered routing model in low power consumption. Two routing algorithms are applied in each tier of this model. To evaluate the performance capability of the proposed solution, comparison results based on the simulation tool Cooja applied in the WSN operating system Contiki are presented. We detail, also, a new prototyping based on several platforms to validate the proposed approach. This prototype is experienced in a real water pipeline testbed designed for our application.

Hybrid heterogeneous routing scheme for improved network performance in WSNs for animal tracking

Wireless Sensor Networks (WSNs) experiences several technical challenges such as limited energy, short transmission range, limited storage capacities, and limited computational capabilities. Moreover, the sensor nodes are deployed randomly and massively over an inaccessible or hostile region. Hence, WSNs are vulnerable to adversaries and are usually operated in a dynamic and unreliable environment. Animal tracking using wireless sensors is one such application of WSN where power management plays a vital role. In this paper, an energy-efficient hybrid routing method is proposed that divides the whole network into smaller regions based on sensor location and chooses the routing scheme accordingly. The sensor network consists of a base station (BS) located at a distant place outside the network, and a relay node is placed inside the network for direct communications from nodes nearer to it. The nodes are further divided into two categories based on the supplied energy; such that the ones located far away from BS and relay have higher energy than the nodes nearer to them. The network performance of the proposed method is compared with protocols like LEACH, SEP, and SNRP, considering parameters like stability period, throughput and energy consumption. Simulation result shows that the proposed method outperforms other methods with better network performance.

LA-MHR: Learning Automata Based Multilevel Heterogeneous Routing for Opportunistic Shared Spectrum Access to Enhance Lifetime of WSN

In wireless sensor networks (WSNs), optimal energy utilization is one of the most crucial issues which needs special attention. It has been observed from the existing literature that communication among sensor nodes (SNs) consumes more energy than computation. Therefore, an efficient mechanism needs to be designed for energy conservation during communication among different SNs. To address these gaps, we propose a learning automata-based multilevel heterogeneous routing (LA-MHR) scheme for WSNs. In an LA-MHR, S-model-based LA is used for cluster heads (CHs) selection. A base station (BS) is used to allocate the cognitive radio spectrum to selected CHs. Moreover, single-hop communication among different SNs is used as multihop communication. It suffers from energy holes problem in WSNs. Based upon the initial energy of SNs, these are divided into intermediate, advanced, super-intermediate, and super-advanced categories. The performance of LA-MHR is evaluated by varying the locations of BS and heterogeneity parameters of SNs. Extensive simulations are performed to evaluate the performance of LA-MHR. Performance evaluation results show that both the network lifetime and stability of LA-MHR are increased by more than 10% as compared to other competing preexisting protocols such as EHE-LEACH, E-SEP, LA-EEHSC, and MCR.

An Optimization Model for Heterogeneous Vehicle Routing and Scheduling Problem with Fixed Cost and Green Reverse Logistics Network Using Genetic Algorithm

Vehicle routing problem aims to find the optimal routes that must be traveled by a fleet of vehicles to satisfy the demand of the customers. In this research, the vehicle routing and scheduling problem is developed for a heterogeneous fleet with the fixed cost of applying vehicles and earliness and tardiness costs in a green reverse logistics network. Since the complexity order of these problems is higher than that of the polynomial ones, this problem is known as NP-hard. As the problem dimensions increase, the exact solving time of the problem increases considerably. Thus, metaheuristic methods are proposed to approximately solve these problems. After developing mixed integer nonlinear model, the Genetic Algorithm (GA) is used to find the near-optimal solutions for the large-scale cases. Finally, the performance of the GA is investigated for several examples by comparing its computation time and solution quality with the computation time and exact solution of the LINGO software. According to the results, the developed GA has an acceptable performance in providing solutions with minimum error in a rational time.

Hybrid and Multi-Hop Advanced Zonal-Stable Election Protocol for Wireless Sensor Networks

Wireless sensor network comprises of thousands of sensor nodes that are deployed in an area to monitor the physical environment. These sensor nodes have limited energy as they are battery powered. To reduce this problem, several routing protocols are designed in order to minimize energy consumption and increase the lifetime of the sensor network. In this paper, we introduced a new heterogeneous routing protocol known as advanced zonal stable election protocol (AZ-SEP) in which communication of sensor nodes with the base station is hybrid, i.e., some nodes communicate directly, while others use clustering mechanism to transmit data to base station. The dimensions of the field are unknown and are divided into three zones on the basis of nodes energy. We examined Z-SEP protocol regarding cluster head selection and communication of cluster head with the base station and introduced a new mechanism of cluster head selection on the basis of residual energy and distance from the base station. The communication between nodes with the base station occurs in a multi-hop fashion. Furthermore, the AZ-SEP is evaluated by taking different evaluation scenarios such as the changing position of the base station, skewed nodes, and variable node energy to compare it with the parent protocol. We implemented the AZ-SEP and compare it with the traditional routing protocol like Z-SEP and SEP protocol. The proposed protocol and its parent protocol are compared through simulation using simulator MATLAB 2014a. The simulation results show that our proposed protocol increases the stability period by increasing the number of alive nodes, packet delivery ratio, and optimizes average energy consumption as related to the existing protocols.

Improved Enhanced Distributed Energy Efficient Clustering (iE-DEEC) Scheme for heterogeneous Wireless Sensor Network

Wireless Sensor Networks (WSNs) contain thousands of sensor nodes which are capable of conveying sensed and captured data from any deployment area to another well –resourced node called the Base station for analysis. Due to the ability of the sensor nodes to relay data from one place to the other, they are found applicable in several areas such as environmental monitoring, military surveillance and home intelligence. The operation of the heterogeneous routing algorithm, E-DEEC (Enhanced Distributed Energy-Efficient Clustering) for heterogeneous networks has been observed carefully. The E-DEEC scheme improved the DEEC algorithm by introducing other nodes called supernodes. It was realized that E-DEEC did not consider the proximity of the advanced nodes to the Base station in its election probabilities. This compelled the supernodes with higher residual energy but far from the Base station to dissipate a large amount of energy in conveying their reports to the Base station. In this research work, an improved heterogeneous algorithm, iE-DEEC, is proposed. The new scheme considered the distance of supernodes and the average distance of the nodes to the Base station (BS) in selecting the cluster heads (CHs). Also, different amplification energy levels have been implemented in this protocol to minimize the energy consumption during the communication between the CHs and BS and also between inter and intra cluster communication. MatLab 2017a was used for simulation to evaluate the performance of the new scheme and the existing protocol. The simulation results showed that the proposed protocol performed better than E-DEEC in terms of throughputs, residual energy and network lifetime.

The heterogeneous fleet location routing problem with simultaneous pickup and delivery and overloads

This paper addresses a new variant of the location routing problem (LRP), namely the heterogeneous fleet LRP with simultaneous pickup and delivery and overloads (HFLRPSPDO) which has not been previously tackled in literatures. In this problem, the heterogeneous fleet is comprised of vehicles with different capacities, and the vehicle overloads up to a specified upper bound is allowed. This paper proposes a polynomial-size mixed integer linear programming formulation for the problem in which a penalty function, allowing capacity violations of vehicles, is integrated into objective function. Furthermore, two heuristic algorithms, respectively based on tabu search and simulated annealing, are proposed to solve HFLRPSPDO. Computational results on simulated instances show the effectiveness of the proposed problem formulation and the efficiency of the proposed heuristic algorithms.

Multi-period heterogeneous vehicle routing considering carbon emission trading

This study considers the problem of determining heterogeneous vehicle routes in each period of a given planning horizon while satisfying service combinations, customer demands and vehicle capacities. The objective is to minimize the sum of vehicle operation costs and carbon emission trading cost/benefit, where the trading cost is incurred to purchase the carbon emission right if the total emission exceeds an upper limit in each period, while the trading benefit can be obtained by selling the right in each period, otherwise. A mixed integer programming model is developed to formulate the problem mathematically. Then, a tabu search algorithm is proposed that incorporates the characteristics of the heterogeneous and the period vehicle routing problems while considering the amount of carbon emission in each period. Computational experiments were done on modified benchmark instances and additional random instances, and the results show that the multi-period approach outperforms the existing single-period one in overall average. In particular, the test results show that the multi-period approach can reduce carbon emission more significantly than the single-period one without sacrificing the total cost.

An Optimization Routing Algorithm for Green Communication in Underground Mines

With the long-term dependence of humans on ore-based energy, underground mines are utilized around the world, and underground mining is often dangerous. Therefore, many underground mines have established networks that manage and acquire information from sensor nodes deployed on miners and in other places. Since the power supplies of many mobile sensor nodes are batteries, green communication is an effective approach of reducing the energy consumption of a network and extending its longevity. To reduce the energy consumption of networks, all factors that negatively influence the lifetime should be considered. The degree constraint minimum spanning tree (DCMST) is introduced in this study to consider all the heterogeneous factors and assign weights for the next step of the evaluation. Then, a genetic algorithm (GA) is introduced to cluster sensor nodes in the network and balance energy consumption according to several heterogeneous factors and routing paths from DCMST. Based on a comparison of the simulation results, the optimization routing algorithm proposed in this study for use in green communication in underground mines can effectively reduce the network energy consumption and extend the lifetimes of networks.

A REVIEW: FUZZY LOGIC CONTROLLER (FLC) IN WIRELESS SENSOR NETWORKS (WSN)

The routing mechanisms are designed to inter-connect the nodes across the networks in dynamic domain. The wireless routing protocols must be empowered with dynamic memory management, which can be deployed with cooperative proactive routing or partial reactive routing mechanisms, which manages the different routing information on run-time memory. The multiple paths can also procured in routing table, which can be used to deploy the load balancing among the multiple paths selected using multipath reactive routing mechanism. In this paper, we have studied the various routing mechanism, which includes the multipath routing, personalized routing, inter-domain routing, joint traffic splitting, rate control mechanism and cognitive heterogeneous routing mechanisms. The comparative study of the latter models is performed, which are observed for their functional merits and demerits. The projected improvement has been observed in the form of segmental routing mechanism, which can handle the micro routes in the inter-domain routing mechanism for WSN.

Multi-objective optimization using genetic algorithm based clustering for multi- depot heterogeneous fleet vehicle routing problem with time windows

Efficient routing and scheduling of vehicles has significant economic implications for both the public and private sectors. For this purpose, we propose in this study a decision support system which aims to optimise the classical capacitated vehicle routing problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that we call the multi-depot heterogeneous vehicle routing problem with time window (MDHVRPTW) by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles...., and we solve this problem by proposing a new scheme based on the application of the bio-inspired genetic algorithm heuristics and by embedding a clustering algorithm within a VRPTW optimisation frame work, that we will specify later. Computational experiments with the benchmark test instances confirm that our approach produces acceptable quality solutions compared with the best previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that our proposed genetic algorithm is effective in solving the MDHVRPTW problem and hence has a great potential.

A rich heterogeneous fleet vehicle routing problem with flexible time windows: a case study of dairy supply chain

Routing is a particularly important concept of cargo transport and has always enticed the interest of researchers and decision makers from different industries. The rich vehicle routing problem considers more realistic features of the real-world problems than the conventional problem. This article aims at solving a vehicle routing problem with heterogeneous fleet and flexible hard and soft time windows where flexible time windows allow for early and tardy deliveries in a given tolerance interval considering earliness and tardiness penalties through defining the earliness and tardiness as decision variables. In this paper, the proposed mathematical programming model is verified through several numerical examples using GAMS software. In order to demonstrate the applicability of the model, a real-world case study is provided. The results show the high potential of the proposed model for real-life application. Specifically, by applying the results of the case study, a 156,000 USD annual cost reduction is possible.

Heterogeneous Multi-Hop Energy Routing Protocol with Ant Bee Fuzzy Clustering Process in Wireless Network (Journal of Computational and Theoretical Nanoscience, Vol. 14(9), pp. 4320–4325 (2017))

Heterogeneous Multi-Hop Energy Routing Protocol with Ant Bee Fuzzy Clustering Process in Wireless Network (Journal of Computational and Theoretical Nanoscience, Vol. 14(9), pp. 4320–4325 (2017))

WSNs heterogeneous cluster routing based on distributed fuzzy logic inference

In this paper, the wireless sensor network of heterogeneous non-uniform distribution is studied, and sun energy harvesting model ISHE based on intensity of illumination is proposed. In ISHE model, the light intensity sensor is used to read solar illuminance under the current environment, and then, energy size obtained by solar energy supply node at specified period can be estimated based on corresponding relation between illuminance and irradiance. The experiment results show that invalidation round rate of the first node LEACH in the algorithm proposed in the paper is 86.2% higher than the average value and invalidation round rate of the last node is 65.9% higher than the average value; network coverage rate of the algorithm in 86% of lifetime is 38% higher than LEACH; network throughput of the algorithm proposed in the thesis at round 2,000 is five times as that of LEACH.

Modeling a Variant of Periodic Heterogeneous Vehicle Routing Problem with Driver Scheduling

Modeling a Variant of Periodic Heterogeneous Vehicle Routing Problem with Driver Scheduling