Ant Colony-Based Energy-Aware Multipath Routing Algorithm for Wireless Sensor Networks

Xia, Songzhu; Wu, Su

doi:10.1109/kam.2009.68

Cited by 15 publications

(4 citation statements)

References 11 publications

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Section: Pheromone Concentration Update Modelmentioning

confidence: 99%

“…At present, the common pheromone concentration updating models are Ant-Density System(ADS), Ant-Quantity System(AQS) and Ant-Cycle System(ACS) [10]. ADS model and AQS model adopt local updating strategy, while ACS model adopts global updating strategy [10]. Considering the solution speed and obstacle avoidance ability of the algorithm, this paper adopts AQS model as the prototype.…”

Section: Pheromone Concentration Update Modelmentioning

confidence: 99%

See 1 more Smart Citation

Path Planning of Self-driving Vehicles Combining Ant Colony and DWA Algorithms in Complex Dense Obstacles

Niu,

Shen,

Wei

et al. 2024

EAI Endorsed Trans Energy Web

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INTRODUCTION: To solve the problems of low quality and weak global optimization of the DWA algorithm, especially the problems of unreasonable path planning and the inability to give consideration to speed and driving safety in the process of vehicles passing through dense obstacles, this paper proposed an improved DWA algorithm based on ant colony algorithm. OBJECTIVES: The traffic capacity and computing efficiency of Self-driving Vehicles in complex dense obstacles can be greatly improved. METHODS: Through the obstacle density and distance information obtained by high-precision sensors on the vehicle, the speed objective function is updating in real time by using ant colony algorithm. And the maneuverability and safety performance of vehicles passing through are considering by the way. RESULTS: The experimental results show that this method can obviously improve the vehicle's traveling ability and uneven path planning in the case of dense obstacles, and the number of iterations of the algorithm is reduced by more than 16%. CONCLUSION: The improved DWA algorithm integrated with the ant colony algorithm can effectively improve the operating efficiency of the algorithm, reduce the distance the car must go around outside the obstacles, and improve Car driving safety. The effectiveness and universality of the improved DWA algorithm were verified through experiments.

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Section: Pheromone Concentration Update Modelmentioning

confidence: 99%

Section: Pheromone Concentration Update Modelmentioning

confidence: 99%

Path Planning of Self-driving Vehicles Combining Ant Colony and DWA Algorithms in Complex Dense Obstacles

Niu,

Shen,

Wei

et al. 2024

EAI Endorsed Trans Energy Web

View full text Add to dashboard Cite

show abstract

“…Ant Colony Optimization, a hierarchical protocol based-Energy Aware Multipath Routing Algorithm (ACO-EAMRA) was being proposed by Xia andWu in 2009 (Xia &Wu, 2009). The motivation behind the development of this algorithm is to conserve the energy to be utilized by sensor nodes during transmission.…”

Section: Ant Colony Optimization Based-energy-aware Multipath Routing...mentioning

confidence: 99%

A Comprehensive Review of Ant Colony Optimization (ACO) based Energy-Efficient Routing Protocols for Wireless Sensor Networks

Nayyar

Singh

2014

International Journal of Wireless Networks and Broadband Technologies

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Wireless Sensor Networks (WSNs) have always been a hot area of researchers for finding more solutions towards making WSN network more energy efficient and reliable. Energy efficient routing is always a key challenging task to enhance the network lifetime and balance energy among the sensor nodes. Various solutions have been proposed in terms of energy efficient routing via protocol development, various techniques have also been incorporated like Genetic Algorithm, Swarm Intelligence etc. The main aim of this research paper to study all the routing protocols which are energy efficient and are based on Ant Colony Optimization (ACO). This paper also highlights the pros and cons of each of routing protocol which has been developed on lines of Energy Efficiency and has also been compared among one another to find which protocol outwits one another. Further, we conclude that Swarm Intelligence being a novel and bio-inspired field has contributed as well as contributing much in the area of improving routing issues of sensor networks.

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“…The management of power available for sensor nodes as been dealt with to an extent using Ant Based Routing [8][9][10][11][12][13][14][15][16], which utilizes the behavior of real ants searching for food through pheromone deposition while dealing with problems that need to find paths to goals. The simulating behavior of ant colony leads to optimization of network parameters for the WSN routing process to provide maximum network lifetime.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Profile for Energy Harvested WSNs

Zungeru¹,

Ang²,

Prabaharan³

et al. 2016

Green Mobile Devices and Networks

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Radio Frequency (RF) Energy Harvesting holds a promising future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. Reducing power consumption has become a major challenge in wireless sensor networks. As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks is an emerging and active research area. This chapter presents a practical approach for RF Energy harvesting and management of the harvested and available energy for wireless sensor networks using the Improved Energy Efficient Ant Based Routing Algorithm (IEEABR) as our proposed algorithm. The chapter looks at measurement of the RF power density, calculation of the received power, storage of the harvested power, and management of the power in wireless sensor networks. The routing uses IEEABR technique for energy management. Practical and real-time implementations of the RF Energy using Powercast™ harvesters and simulations using the energy model of our Libelium Waspmote to verify the approach were performed. The chapter concludes with performance analysis of the harvested energy, comparison of IEEABR and other traditional energy management techniques, while also looking at open research areas of energy harvesting and management for wireless sensor networks.

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Ant Colony-Based Energy-Aware Multipath Routing Algorithm for Wireless Sensor Networks

Cited by 15 publications

References 11 publications

Path Planning of Self-driving Vehicles Combining Ant Colony and DWA Algorithms in Complex Dense Obstacles

Path Planning of Self-driving Vehicles Combining Ant Colony and DWA Algorithms in Complex Dense Obstacles

A Comprehensive Review of Ant Colony Optimization (ACO) based Energy-Efficient Routing Protocols for Wireless Sensor Networks

Energy Consumption Profile for Energy Harvested WSNs

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