Lifetime maximization in mobile sensor networks with energy harvesting

Yu, Shengwei; Lee, C. S. George

doi:10.1109/icra.2011.5979588

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…However, considering the relatively low efficiency of energy harvesters [159], the NL maximization and power allocation mechanisms still play a significant role in keeping the network functional for an extended duration. This beneficial contribution of energy harvesters in extending the NL can be formulated as part of an optimization problem, as demonstrated in [109], [169]. As part of the solution, the key challenges of designing energy harvesting aided cellular networks discussed in [119] may be taken into consideration in order to guarantee the increased battery-lifetime of wireless devices.…”

Section: Future Research Ideasmentioning

confidence: 99%

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

544

229

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Abstract-Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.

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Section: Future Research Ideasmentioning

confidence: 99%

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

544

229

View full text Add to dashboard Cite

show abstract

“…Reactive algorithms can also offer a cheaper alternative for disposable robots to be utilised in harmful or infeasible environments for humans or animals and that could destroy or require the robots to be disposed of afterwards. Other applications are environments that are too widespread for other more complex robots to be deemed cost-effective or in long-term autonomous robots operating in unstructured environments [39,40]. Some applications for these algorithms include: spotting light sources which would enable them to use solar panels to recharge the batteries; detecting pollutants or hazardous gases in the air and identifying the source; discovering toxins or hazardous chemicals in water; spotting radiation, fire and overheating.…”

Section: Chemotaxis and Reactive Algorithmsmentioning

confidence: 99%

A minimal biologically-inspired algorithm for robots foraging energy in uncertain environments

Andrade

Boyle

2020

Robotics and Autonomous Systems

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This work details the design and simulation results of a bioinspired minimalist algorithm based on C. elegans, using autonomous agents to forage for attractant energy sources. The robotic agents are energy-constrained and depend on the energy they forage to recharge their batteries, which is significant as the foraging task is one of the canonical testbeds for cooperative robotics.The algorithm consists of 6 input parameters which were simulated and optimised in 9 unbounded environments of varying difficulty levels, containing attractant sources which robots would then have to forage from to maintain energy levels and survive the entirety of the simulation.The robots running the algorithm were then optimised using Evolutionary Algorithms and the best solutions in all 9 environments were categorized with the use of clustering techniques. The clustering results highlighted the different strategies which emerged. Ultimately across the 9 environments, 6 different strategies have been identified. The results demonstrate the applicability of the proposed algorithm to localise attractant sources and harvest energy in different scenarios using the same core algorithm.

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“…In order to increase the battery life of AMRs, a variety of approaches have been proposed by related work. Previous studies have proposed to find the most energy-efficient path for robots to move to a certain location or to cover a large area [2,3,5,8,15,47,49]. Other solutions coordinate various AMRs to optimize their charging scheduling [6,20,21,23,24,34,[37][38][39].…”

Section: Related Workmentioning

confidence: 99%

E2m

Liu

Chen

Brocanelli

et al. 2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

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Autonomous mobile robots (AMRs) have been widely utilized in industry to execute various on-board computer-vision applications including autonomous guidance, security patrol, object detection, and face recognition. Most of the applications executed by an AMR involve the analysis of camera images through trained machine learning models. Many research studies on machine learning focus either on performance without considering energy efficiency or on techniques such as pruning and compression to make the model more energy-efficient. However, most previous work do not study the root causes of energy inefficiency for the execution of those applications on AMRs. The computing stack on an AMR accounts for 33% of the total energy consumption and can thus highly impact the battery life of the robot. Because recharging an AMR may disrupt the application execution, it is important to efficiently utilize the available energy for maximized battery life. In this paper, we first analyze the breakdown of power dissipation for the execution of computer-vision applications on AMRs and discover three main root causes of energy inefficiency: uncoordinated access to sensor data, performance-oriented model inference execution, and uncoordinated execution of concurrent jobs. In order to fix these three inefficiencies, we propose E2M, an energy-efficient middleware software stack for autonomous mobile robots. First, E2M regulates the access of different processes to sensor data, e.g., camera frames, so that the amount of data actually captured by concurrently executing jobs can be minimized. Second, based on a predefined per-process performance metric (e.g., safety, accuracy) and desired target, E2M manipulates the process execution period to find the best energy-performance trade off. Third, E2M coordinates the execution of the concurrent processes to maximize the total contiguous sleep time of the computing hardware for maximized energy savings. We have implemented a prototype of E2M on HydraOne, a real-world AMR. Our experimental results show that, compared to several baselines, E2M leads to 24% energy savings for the computing platform, which translates into an extra 11.5% of battery time and 14 extra minutes of robot runtime, with a

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Lifetime maximization in mobile sensor networks with energy harvesting

Cited by 9 publications

References 14 publications

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

A minimal biologically-inspired algorithm for robots foraging energy in uncertain environments

E2m

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