Heterogeneous Routing Research Articles

Distance-Distributed Energy Efficient Clustering (D-DEEC) Routing Protocol for Wireless Sensor Network

A wireless sensor network (WSN) composed of many tiny devices that rely on energy efficient routing protocols for extension of their lifetime. Many cluster-based routing protocols have been proposed based on heterogeneity in recent times. Indeed, these protocols are aiming at achieving energy efficiency, throughputs and better lifetime of the networks. However, two important factors that could have helped some of these protocols to achieve the above-mentioned aims are really missing. Factors such as the distance and average distance between nodes and the Base Station (BS) in selecting a cluster head needed to be considered. These were the major challenges that were identified in the Distributed Energy Efficient Clustering (DEEC) protocol after careful study. As a result, the throughputs and the lifetime of the scheme were affected. In this paper, a reviewed hierarchy-based heterogeneous routing protocol called Distance-DEEC (D-DEEC) is proposed to enhance the DEEC protocol. The new algorithm took into account the residual energy, distance of the individual nodes and average distance of all the nodes from the BS in selecting the Cluster Heads (CHs). This has allowed the protocol to select a cluster head that has high residual energy, is closer to the BS and at the same time not too far from its neighbours. The scheme also employed the sleep and awake approach to reduce energy dissipation. The technique allows the Base BS to calculate the maximum energy of distant nodes and determines when such nodes can transmit their report based on a given threshold energy, Eth . The performance of the proposed algorithm was evaluated using MatLab R2018a and the outcomes showed that, D-DEEC protocol outperformed TDEEC in terms of energy consumption, throughputs and the network lifetime.

Performance Analysis of LEACH, SEP and Z-SEP Protocols in Heterogeneous Wireless Sensor Network

Wireless sensor networks use thousands of sensors to monitor the physical environment. This sensor node consumes a limited amount of energy since it is battery-powered. Researchers regularly develop energy-efficient routing protocols to extend the lifetime of WSNs. The threshold-based cluster head (CH) selection techniques used by hierarchical routing protocols have been shown to extend network lifetime in many studies. Through the implementation of several routing protocols, sensor networks can be made to last longer and consume less energy. Z-SEP introduces a new heterogeneous routing protocol using a hybrid communication mechanism. Others use clustering mechanisms, while others use the base station directly. Energy efficiency and stability are improved by 11% and 19%, respectively, over Z-SEP

Performance Evaluation of Underwater Routing Protocols DHRP, LASR and DFR for Underwater Wireless Sensor Network using MATLAB

Communication issues in Underwater Wireless Sensor Networks (UWSNs) are the main problem. In this research paper and we proposed “Dolphin Heterogeneous Routing Protocol” (DHRP) and it determine the most efficient path to destination, it balance the energy and it increase the lifetime of nodes. Due to the lack of growth in underwater wireless communications, Communication cables are still used for underwater communication. The use of wires to ensure the communication of sensor nodes at the ocean's depths is extremely costly. In underwater wireless sensor networks, determining the optimum route to convey sensed data to the destination in the shortest amount of time has become a major difficulty (UWSN). Because of the challenging communication medium, UWSN routing protocols are incompatible with those used in traditional sensor networks. Existing routing protocols have the problem of requiring more energy to send data packets, as well as experiencing higher delays due to the selection of ineffective routes. This research introduces the Dolphin Heterogeneous Routing Protocol (DHRP) to tackle the routing issues faced by UWSN. The swarming behavior of dolphins in search of food is the inspiration for DHRP. In order to find the best route in UWSN, DHRP goes through six essential processes are initialization, searching, calling, reception, predation and termination.

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

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

NMSFRA: Heterogeneous routing protocol for balanced energy consumption in mobile wireless sensor network

Wireless sensor networks are widely used in various fields and consist of a large number of sensor nodes that are powered by batteries. As a result, energy efficiency is crucial for the effective operation of these networks. With the advancement of cross technology communication (CTC), the presence of heterogeneous wireless nodes, especially mobile nodes, in wireless sensor networks has increased, making traditional routing protocols for homogeneous wireless sensor networks inapplicable. To overcome the above issues, this paper proposes a heterogeneous wireless sensor network routing protocol for a balanced energy consumption, NMSFRA (NGO based mean shift fuzzy clustering routing algorithm). In order to mitigate the impact of uneven cluster distribution and unstable network links due to the mobility of nodes, the proposed protocol first divides the network into balanced clusters using the Mean Shift (MS) method. The Mamdani fuzzy inference system is then used to take node mobility into account. The protocol dynamically selects different parameters as inputs to the fuzzy logic, enabling it to choose cluster heads (CHs) based on the overall location of the clusters. Additionally, to extend the network lifetime, the protocol generates multi-hop forwarding paths using the Northern Goshawk Optimization (NGO) method. Finally, the paper presents experimental simulations carried out on three different network topologies and compares the results with existing protocols such as LEACH, EETPF, E-FUCA, and DE-SEP. The results show that the NMSFRA protocol extends the survival period of nodes by 235 rounds, 240 rounds, 364 rounds, and 190 rounds, on average, compared to the LEACH, EETPF, E-FUCA, and DE-SEP protocols, respectively. The network throughput also increased by 154.92%, 48.74%, 84.77%, and 106.77%, respectively. The NMSFRA protocol also showed better performance in terms of total residual energy, FND, HND, and average speed difference between nodes in the first 5 rounds. Therefore, the proposed NMSFRA protocol demonstrates superior performance in terms of network lifetime, energy utilization, throughput, and network stability.

A Comparative Analysis of ANN-based Homogeneous and Heterogeneous Routing Protocols for Selection of Cluster Head in WSN

Background: WSNs are composed of various tiny low battery power Sensor Nodes (SNs). It is required to sense the dynamic network environments, which may fluctuate due to external factors or the system model itself. Furthermore, sensory data is transmitted over the communication channel to the destination. Integration of Artificial Neural Network (ANN) in WSNs provides the facility to learn SNs and networks from past experiences and make the predictions based on them. The computational ability of ANN may help to choose an efficient route for data transfer in WSNs. Also, it is able to improve the performance of the network w.r.t power consumption, latency, throughput, etc. This work explores an efficient way to manage the energy of the SNs using an ANN-based routing approach for the selection of Cluster Head (CH). Methods: In this work, an ANN-based and algorithmic approach has been applied to select the CH. Various popular routing strategies such as LEACH-C, TEEN, SEP & DEEC have been used for simulation. LEACH-C and TEEN belong to the homogeneous category, while SEP and DEEC are categorized as heterogeneous. ANN based CH selection strategy has been proposed and compared with the other routing techniques. Results: ANN based CH selection strategy has been simulated and compared. Results revealed that CH election based on ANN will enhance the network lifetime. It is clear from the simulation results that ANN-DEEC is much more stable than protocols of the same category. Conclusion: ANN-DEEC outperforms five times more than LEACH-C, 2.5 times more than TEEN and SEP, twice DEEC and 10-20% more than EDDEC and Advanced-DEEC. ANN-DEEC transmits 71021 packets to BS more than any other protocol. other: Thanks

A Two-level clustering chain energy heterogeneous routing protocol for WSN

In view of the disadvantages of high energy consumption, short life cycle, low throughput and no application correlation in heterogeneous WSN, a two-level clustering chain routing protocol is proposed under the heterogeneous WSN of intelligent water-saving irrigation system of ecological agriculture in Hexi Gobi. The irrigation subnets are divides into clusters according to the actual layout in the wireless network and the nodes in the cluster form the chain on the basis of the minimum distance. The two-level cluster headers in reserve are selected by rate of the remaining energy of terminal nodes in cluster vs. average energy in local region. Eventually the level clusters heads and the two-level cluster headers are chosen according to the largest ratio of the remaining energy to the distance between the terminal nodes and the base station. Results in simulation show that the proposed algorithm is superior to PEGASIS and SEP in energy consumption, life cycle and throughput.

RUBER: Recoverable UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network

This paper is a sequel to a previous article by the authors in which UAV-based energy-efficient reconfigurable routing (UBER) scheme was proposed to address coverage loss and rapid energy depletion issues for smart wireless livestock sensor networks. Sensor node and route failure issues associated with the UBER scheme are therefore addressed in this research by proposing a recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme. RUBER scheme relies on an efficient fault detection and recycling technique, dynamic recovery mechanism, and robust route maintenance technique. Performance of RUBER was analyzed under low, medium and high failure rate network conditions. Performance indices employed for this assessment are failure detection ratio (FDR), failure recovery ratio (FRR), load balancing ratio (LBR), and packet delivery ratio (PDR). Analysis results demonstrated that keeping the failure rate below 10% led to performance improvements of 60.96%, 74.14%, 64.68%, and 60.74% for FDR, FRR, LBR, and PDR, respectively. Performance comparison of RUBER was conducted against UBER and hybrid heterogeneous routing (HYBRID) schemes. Performance metrics utilized for this comparative evaluation are average routing delay (ARD), energy dissipation ratio (EDR), routing overhead (ROH), fault tolerance index (FTI), nodal failure recovered (NFR), route failure recovered (RFR), and cluster survival ratio (CSR). With respect to best-case values, RUBER exhibited gains of 79.67%, 44.98%, 67.88%, 74.90%, 52.20%, 70.56%, and 52.52% over UBER and HYBRID in terms of ARD, EDR, ROH, FTI, NFR, RFR, and CSR respectively. Simulation experiments revealed the relative competitiveness of the RUBER scheme against the benchmarked schemes.

UBER: UAV-Based Energy-Efficient Reconfigurable Routing Scheme for Smart Wireless Livestock Sensor Network.

This paper addresses coverage loss and rapid energy depletion issues for wireless livestock sensor networks by proposing a UAV-based energy-efficient reconfigurable routing (UBER) scheme for smart wireless livestock sensor networking applications. This routing scheme relies on a dynamic residual energy thresholding strategy, robust cluster-to-UAV link formation, and UAV-assisted network coverage and recovery mechanism. The performance of UBER was evaluated using low, normal and high UAV altitude scenarios. Performance metrics employed for this analysis are network stability (NST), load balancing ratio (LBR), and topology fluctuation effect ratio (TFER). Obtained results demonstrated that operating with a UAV altitude of 230 m yields gains of 31.58%, 61.67%, and 75.57% for NST, LBR, and TFER, respectively. A comparative performance evaluation of UBER was carried out with respect to hybrid heterogeneous routing (HYBRID) and mobile sink using directional virtual coordinate routing (MS-DVCR). The performance indicators employed for this comparative analysis are energy consumption (ENC), network coverage (COV), received packets (RPK), SN failures detected (SNFD), route failures detected (RFD), routing overhead (ROH), and end-to-end delay (ETE). With regard to the best-obtained results, UBER recorded performance gains of 46.48%, 47.33%, 15.68%, 19.78%, 46.44%, 29.38%, and 58.56% over HYBRID and MS-DVCR in terms of ENC, COV, RPK, SNFD, RFD, ROH, and ETE, respectively. The results obtained demonstrated that the UBER scheme is highly efficient with competitive performance against the benchmarked CBR schemes.

A New Multi-Objective Integrated Supply Chain Model with Environmentally-Friendly Heterogeneous Vehicle Routing

A New Multi-Objective Integrated Supply Chain Model with Environmentally-Friendly Heterogeneous Vehicle Routing

Randomized greedy algorithm for helicopter optimization in the energy industry: a practical approach to model development and solution deliverance

Randomized greedy algorithm for helicopter optimization in the energy industry: a practical approach to model development and solution deliverance

Energy Efficient Node Deployment for Heterogeneous Wireless Sensor Networks

Wireless Sensor nodes are power-constrained devices and the lifetime of the nodes is significantly influenced by the deployment of the nodes. In the appropriate deployment of the nodes leads to inefficient network energy utilization which results in a minimized network lifetime. To enhance the network lifetime within the limited battery power, hierarchical heterogeneous routing has been proposed by various researchers and have significantly prolonged the network lifetime. In heterogeneous wireless sensor networks, the researchers have addressed mainly energy efficiency of the network but node deployment has been rarely addressed. Node deployment is a critical issue in heterogeneous wireless sensor networks since the nodes in these networks are energized with different energy levels. The simulation results obtained proved that the appropriate deployment of nodes can lead to a substantial amount of energy conservation.

Energy Efficient Node Deployment for Heterogeneous Wireless Sensor Networks

Wireless Sensor nodes are power-constrained devices and the lifetime of the nodes is significantly influenced by the deployment of the nodes. In the appropriate deployment of the nodes leads to inefficient network energy utilization which results in a minimized network lifetime. To enhance the network lifetime within the limited battery power, hierarchical heterogeneous routing has been proposed by various researchers and have significantly prolonged the network lifetime. In heterogeneous wireless sensor networks, the researchers have addressed mainly energy efficiency of the network but node deployment has been rarely addressed. Node deployment is a critical issue in heterogeneous wireless sensor networks since the nodes in these networks are energized with different energy levels. The simulation results obtained proved that the appropriate deployment of nodes can lead to a substantial amount of energy conservation.

Mixed-Integer Linear Programming Model by Linear Approximation for a Strike Package-to-Target Assignment Problem

This study deals with an aircraft-to-target assignment (ATA) problem considering the modern air operation environment, such as the strike package concept, multiple targets for a sortie, and the strike packages’ survivability. For the ATA problem, this study introduces a novel mathematical model in which a heterogeneous vehicle routing problem (HVRP) and a weapon-to-target assignment (WTA) problem are conceptually integrated. The HVRP generates the flight routes for strike packages because this study confirms that the survivability of a strike package depends on the path, and the WTA problem evaluates the likelihood of successful target destruction of assigned weapons. Although the first version of the model is developed as a mixed-integer nonlinear programming (MINLP) model, this study attempts to convert it to a mixed-integer linear programming (MILP) model using the logarithmic transformation and piecewise linear approximation methods. For an ATA problem, this activity could provide an opportunity to use the excellent existing algorithms for searching the optimal solution of LP models. To maximize the operational effectiveness, the MILP model simultaneously determines the following for each strike package: (a) composition type, (b) targets, (c) flight route, (d) types, and (e) quantity of weapons for each target.

Featured Collection Introduction: National Water Model III

Featured Collection Introduction: National Water Model III

A CAPACITATED HETEROGENEOUS VEHICLE ROUTING PROBLEM FOR PHARMACEUTICAL PRODUCTS DELIVERY

This paper describes an optimal way for retail pharmacies to deliver medical products for customers. Heterogeneous vehicle routing problem (HVRP) is one of the options for the optimal solution of the problem. This is a well-established question of combinatorial optimisation, in which a sequence in cars with varying capabilities departs from a central warehouse to satisfy a variety of customers with specific locations. The study sets out a model for the optimal transportation of medicinal products by a private company based in Medan City, Indonesia. The HVRP has time slots, delivery and allocation of fleets over a short period of time. The objective is to minimize the overall cost of transport moving across the planning horizon. The method is formulated as a linear mixed integer system, and an appropriate response requires a feasible search approach.

Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach

Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra’s algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm.

Green last mile distribution system: Heterogeneous fleet vehicle routing problem with time window and external cost

The development of e-commerce business in Jakarta, Indonesia, in recent years has made the Last Mile Delivery (LMD) business sector develop rapidly. Increased demand for LMD makes the resulting kilometer trips even greater, resulting in negative externalities. On the other hand, logistics costs in Indonesia are only affected by vehicle operating costs and no external cost component. Optimization of LMD services that take into account internal and external costs is needed to minimize the total cost of LMD and in reducing the impact of negative externalities. The purpose of this paper is to optimize the LMD distribution system on the Heterogeneous Fleet Vehicle Routing Problem with Time Window and External Costs (HFVRPTW-EC) models. The optimization is done by applying the HFVRPTW-EC model using data from one of the parcel deliveries companies in Jakarta and then doing a simulation by forming several operational scenarios. The results show that the optimization of LMD has reduced internal and external costs by more than 50% compared to existing conditions. The detailed results show that, for the short-term program, a scenario with a one-tier distribution system and type of motorcycle vehicle can reduce total costs compared to existing conditions by 66.22% on a peak day and 59.41% on off-peak day. Whereas for long-term program optimization, scenarios with multiple tier distribution systems and types of motorized vehicles for drop mileage and pick up truck for stem mileage can reduce total costs by 69.23% on a peak day and 60.24% on off-peak day.

Three Level Heterogeneous Zonal Stable Election Protocol for Energy Efficiency in Wireless Sensor Networks

Sensor battery limitation has always been the most challenging hurdle for wireless sensor networks. Many energy effcient routing protocols have been proposed to overcome this issue in homogeneous networks where sensor nodes start with the same initial energy. When sensor nodes have different amount of initial energy, the network is heterogeneous and it becomes complicated to design an energy effcient routing protocol to save nodes energy and prolong network lifetime. Herein, we propose a three level heterogeneous routing protocol to boost network stability period of wireless sensor networks. The network model splits up into five zones according to nodes initial energies and distance to base station. For data communication, the proposed model relies on two types of communications: Direct and Multi-Hop. The choice of the type of communication is made according to nodes initial energy and their distance to the base station. The clustering scheme is used just in the zones that contains nodes with higher energies. The simulation of our proposed scheme is done using Matlab simulator and the results are compared to the conventional heterogeneous routing protocols 3-level heterogeneous Stable Election Protocol and 3-level Modified Low Energy Adaptive Clustering Hierarchy.

Green hybrid fleets using electric vehicles: solving the heterogeneous vehicle routing problem with multiple driving ranges and loading capacities

The introduction of Electric Vehicles (EVs) in modern fleets facilitates green road transportation. However, the driving ranges of EVs are limited by the duration of their batteries, which arise new operational challenges. Hybrid fleets of gas and EVs might be heterogeneous both in loading capacities as well as in driving-range capabilities,whichmakes the design of efficient routing plans a difficult task. In this paper, we propose a newMulti-Round IteratedGreedy (MRIG) metaheuristic to solve the Heterogeneous Vehicle Routing Problem with Multiple Driving ranges and loading capacities (HeVRPMD). MRIG uses a successive approximations method to offer the decision maker a set of alternative fleet configurations,with different distance-based costs and green levels. The numerical experiments show that MRIG is able to outperform previous works dealing with the homogeneous version of the problem, which assumes the same loading capacity for all vehicles in the fleet. The numerical experiments also confirm that the proposed MRIG approach extends previous works by solving a more realistic HeVRPMD and provides the decision-maker with fleets with higher green levels.