Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

ECO-BAT: A New Routing Protocol for Energy Consumption Optimization Based on BAT Algorithm in WSN

Wireless sensor network (WSN) has been widely used due to its vastrange of applications. The energy problem is one of the important problems influencingthe complete application. Sensor nodes use very small batteries as a powersource and replacing them is not an easy task. With this restriction, the sensornodes must conserve their energy and extend the network lifetime as long as possible.Also, these limits motivate much of the research to suggest solutions in alllayers of the protocol stack to save energy. So, energy management efficiencybecomes a key requirement in WSN design. The efficiency of these networks ishighly dependent on routing protocols directly affecting the network lifetime.Clustering is one of the most popular techniques preferred in routing operations.In this work we propose a novel energy-efficient protocol for WSN based on a batalgorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithmfor WSN) to prolong the network lifetime. We use an objective function thatgenerates an optimal number of sensor clusters with cluster heads (CH) to minimizeenergy consumption. The performance of the proposed approach is comparedwith Low-Energy Adaptive Clustering Hierarchy (LEACH) and EnergyEfficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interestingin terms of energy-saving and prolongation of the network lifetime.

Reducing energy consumption optimization selection of path transmission routing algorithm in opportunistic networks

Opportunistic networks are random networks and do not communicate with each other among respective communication areas.This situation leads to great difficulty in message transfer.This paper proposes a reducing energy consumption optimal selection of path transmission(OSPT) routing algorithm in opportunistic networks.This algorithm designs a dynamic random network topology,creates a dynamic link,and realizes an optimized selected path.This algorithm solves a problem that nodes are unable to deliver messages for a long time in opportunistic networks.According to the simulation experiment,OSPT improves deliver ratio,and reduces energy consumption,cache time and transmission delay compared with the Epidemic Algorithm and Spray and Wait Algorithm in opportunistic networks.

A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

Vehicle routing optimization algorithm based on time windows and dynamic demand

To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.

A load-balanced minimum energy routing algorithm for Wireless Ad Hoc Sensor Networks

Wireless Ad Hoc Sensor Networks (WSNs) have received considerable academia research attention at present. The energy-constraint sensor nodes in WSNs operate on limited batteries, so it is a very important issue to use energy efficiently and reduce power consumption. To maximize the network lifetime, it is essential to prolong each individual node’s lifetime through minimizing the transmission energy consumption, so that many minimum energy routing schemes for traditional mobile ad hoc network have been developed for this reason. This paper presents a novel minimum energy routing algorithm named Load-Balanced Minimum Energy Routing (LBMER) for WSNs considering both sensor nodes’ energy consumption status and the sensor nodes’ hierarchical congestion levels, which uses mixture of energy balance and traffic balance to solve the problem of “hot spots” of WSNs and avoid the situation of “hot spots” sensor nodes using their energy at much higher rate and die much faster than the other nodes. The path router established by LBMER will not be very congested and the traffic will be distributed evenly in the WSNs. Simulation results verified that the LBMER performance is better than that of Min-Hop routing and the existing minimum energy routing scheme MTPR (Total Transmission Power Routing).

Energy Economic Dispatch for Photovoltaic-Storage via Distributed Event-Triggered Surplus Algorithm

This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.

Energy and Throughput Optimized, Cluster Based Hierarchical Routing Algorithm for Heterogeneous Wireless Sensor Networks

We propose a novel cluster based distributed routing algorithm in a generalized form for heterogeneous wireless sensor networks. Heterogeneity with respect to number/types of communication interfaces, their data rates and that with respect to energy dissipation model have been exploited for energy and throughput efficiency. The algorithm makes routing assignment optimized for throughput and energy and has a complexity of N/K*logN+k2logk approximately, where N is the number of nodes and k is the number of kcluster heads. Performance experiments confirm the effectiveness of throughput and energy optimizations. The importance of choosing an optimal cluster radius has been shown. The energy consumption in the network scales up well with respect to the network size.

LOA-RPL:Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained devices.Clustering is an effective technique for saving energy by reducing duplicate data.In a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a network.However,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH node.Consequently,early battery depletion is produced near the sink.To overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this study.LOA-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean distance.CH selection is performed using LOA.Route establishment is implemented using residual energy information.An extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and throughput.The performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and throughput.The proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and RISA-RPL.LOA-RPL is also highly energy-efficient compared with other similar routing protocols.

Decision Analysis on IoV Routing Transmission and Energy Efficiency Optimization Algorithm with AmBC

The improvement of the quality and efficiency of vehicle wireless network data transmission is always a key concern in the Internet of Vehicles(IoV).Routing transmission solved the limitation of transmission distance to a certain extent.Traditional routing algorithm cannot adapt to complex traffic environment,resulting in low transmission efficiency.In order to improve the transmission success rate and quality of vehicle network routing transmission,make the routing algorithm more suitable for complex traffic environment,and reduce transmission power consumption to improve energy efficiency,a comprehensive optimized routing transmission algorithm is proposed.Based on the routing transmission algorithm,an optimization algorithmbased on road condition,vehicle status and network performance is proposed to improve the success rate of routing transmission in the IoV.Relative distance difference and density are used as decision-making indicators to measure Road Side Unit(RSU)assisted transmission.And the Ambient backscatter communication(AmBC)technology and energy collection are used to reduce the energy consumption of routing relay transmission.An energy collection optimization algorithm is proposed to optimize the energy efficiency of AmBC and improve the energy efficiency of transmission.Simulation results show that the proposed routing optimization algorithm can effectively improve the success rate of packet transmission in vehicular ad hoc networks(VANETs),and theAmBC optimization algorithmcan effectively reduce energy consumption in the transmission process.The proposed optimization algorithm achieves comprehensive optimization of routing transmission performance and energy efficiency.

Echo Location Based Bat Algorithm for Energy Efficient WSN Routing

Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes popular.WSN has marked its importance in both practical and research domains.Energy is the most significant resource,the important challenge in WSN is to extend its lifetime.The energy reduction is a key to extend the network’s lifetime.Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN.In this paper,an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm(ECHO-BAT).ECHO-BAT works in two stages.First Stage clusters the sensor nodes and identifies tentativeCluster Head(CH)along with the entropy value using BAT algorithm.The second stage aims to find the nodes if any,with high residual energy within each cluster.CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network’s lifetime.The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Zoning Clustering Algorithm(PEZCA)and Chaotic Firefly Algorithm CH(CFACH)in terms of lifetime of network,death of first nodes,death of 125th node,death of the last node,network throughput and execution time.Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures.The overall delivery ratio has also significantly optimized and improved by approximately 8%,proving the proposed approach to be an energy efficient WSN.

SI-INSPIRED ENERGY AWARE QoS ROUTING TREE FOR WSN

A heuristic theoretical optimal routing algorithm （TORA） is presented to achieve the data-gathering structure of location-aided quality of service （QoS） in wireless sensor networks （WSNs）. The construction of TORA is based on a kind of swarm intelligence （SI） mechanism, i. e. , ant colony optimization. Firstly, the ener- gy-efficient weight is designed based on flow distribution to divide WSNs into different functional regions, so the routing selection can self-adapt asymmetric power configurations with lower latency. Then, the designs of the novel heuristic factor and the pheromone updating rule can endow ant-like agents with the ability of detecting the local networks energy status and approaching the theoretical optimal tree, thus improving the adaptability and en- ergy-efficiency in route building. Simulation results show that compared with some classic routing algorithms, TORA can further minimize the total communication energy cost and enhance the QoS performance with low-de- lay effect under the data-gathering condition.

Real-time Threshold Routing Algorithm Based on Wireless Sensor Network

[Objective] This study was to design an intelligent greenhouse real-time monitoring system based on the core technology of Internet of Things in order to meet the needs of agricultural informatization and intellectualization. [Method] Based on the application characteristics of Wireless Sensor Network （WSN）, the intelligent greenhouse monitoring system was designed. And for the incompleteness strategy of load balancing in the Low-Energy Adaptive Clustering Hierarchy （LEACH）, a Real- time Threshold Routing Algorithm （RTRA） was proposed. [Result] The performance of network lifetime and network delay of RTRA were tested in MATLAB and found that, within the same testing environment, RTRA can save nodes energy consumption, prolong network lifetime, and had better real-time performance than LEACH. The al- gorithm satisfies the crops＇ requirements on real-time and energy efficiency in the greenhouse system. [Conclusion] For the good performance on real-time, the de- signed intelligent greenhouse real-time monitoring system laid the foundation for the research and development of agricultural informatization and intellectualization.

On-demand QoS routing protocol based on energy and bandwidth requirement in Ad Hoc networks

Along with the emergence of real-time multi-media,interactive service,real-time voice and other services calling for high quality of service,there should be a good network to support those services.Most present route algo- rithms with computational complexity hardly consider the restriction of node energy,so it degrades the whole capabil- ity of network.Bandwidth guarantee is one of the most crucial factors in real-time application,and this paper brings forward a distributed on-demand QoS routing protocol based on energy and bandwidth requirement.This QoS routing protocol makes use of bandwidth calculation algorithm and analyzes its route mechanism.The simulation results veri- fy its validity.The QoS routing protocol improves the packet delivery fraction and average end-to-end delay,prolongs the network lifetime,enhances the network performance and satisfies the route requirement for ad hoc networks.

Multiple People Picking Assignment and Routing Optimization Based on Genetic Algorithm

In order to improve the picking efficiency,reduce the picking time,this paper take artificial picking operation of a certain distribution center which has double-area warehouse as the studying object.Discuss the picking task allocation and routing problems.Establish the TSP model of order-picking system.Create a heuristic algorithm bases on the Genetic Algorithm(GA)which help to solve the task allocating problem and to get the associated order-picking routes.And achieve the simulation experiment with the Visual 6.0C++platform to prove the rationality of the model and the effectiveness of the arithmetic.

QoS multicast routing algorithm based on GA

The multicast routing problem with multiple QoS constraints in networks with uncertain parameters is discussed, and a network model that is suitable to research such QoS multicast routing problem is described. The QMRGA, a multicast routing policy for Internet, mobile network or other highperformance networks is mainly presented, which is based on the genetic algorithm(GA), and can provide QoSsensitive paths in a scalable and flexible way in the network environment with uncertain parameters. The QMRGA can also optimize the network resources such as bandwidth and delay, and can converge to the optimal or nearoptimal solution within few iterations, even for the network environment with uncertain parameters. The incremental rate of computational cost can be close to a polynomial and is less than exponential rate. The performance measures of the QMRGA are evaluated by using simulations. The results show that QMRGA provides an available approach to QoS multicast routing in network environment with uncertain parameters.

A Memetic Algorithm With Competition for the Capacitated Green Vehicle Routing Problem

In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor(k NN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search(VNS) framework and the simulated annealing(SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station(AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-ofexperiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.

Unfolding analysis of LaBr3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration

With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study mainly examines a method to deconvolve the LaBr_3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration.In the algorithm, the full width at half maximum(FWHM)of full energy peak was calculated by the cubic spline interpolation algorithm and calibrated by a square root of a quadratic function that changes with the energy. Additionally, the detector response matrix was constructed to deconvolve the gamma spectrum. Furthermore, an improved SNIP algorithm was proposed to eliminate the background. In the experiment, several independent peaks of ^(152)Eu,^(137)Cs, and ^(60)Co sources were detected by a LaBr_3:Ce scintillator that were selected to calibrate the energy resolution. The Boosted Gold algorithm was applied to deconvolve the gamma spectrum. The results showed that the peak position difference between the experiment and the deconvolution was within ± 2 channels and the relative error of peak area was approximately within 0.96–6.74%. Finally, a ^(133) Ba spectrum was deconvolved to verify the efficiency and accuracy of the algorithm in unfolding the overlapped peaks.

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.

Multiple constraints-based QoS multicast routing: model and algorithms

Constraint-based multicast routing, which aims at identifying a path that satisfies a set of quality of service (QoS) constraints, has became a very important research issue in the areas of networks and distributed systems. In general, multi-constrained path selection with or without optimization is a NP-complete problem that can not be exactly solved in polynomial time. Hence, accurate constraints-based routing algorithms with a fast running time are scarce, perhaps even non-existent. The expected impact of such a constrained-based routing algorithm has resulted in the proposal of numerous heuristics and a few exact QoS algorithms. This paper aims to give a thorough, concise and fair evaluation of the most important multiple constraint-based QoS multicast routing algorithms known today, and it provides a descriptive overview and simulation results of these multi-constrained routing algorithms.