Energy-Aware Connectivity Restoration Mechanism for Cyber-Physical Systems of Networked Sensors and Robots

Baroudi, Uthman; Al-Darwbi, Mohammed Y.; Younis, Mohamed

doi:10.1109/jsyst.2020.2970649

Cited by 22 publications

(17 citation statements)

References 25 publications

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“…The designed strategy uses a radial basis function neural network with an unscented Kalman Filter. Baroudi et al proposed a fuzzy-logic-based node relocation technique to maintain the connectivity of a WSN, along with other optimization objectives [ 17 ]. The authors evaluated their presented algorithm on a testbed of Khepera IV robots.…”

Section: Related Workmentioning

confidence: 99%

PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks

Akram

Dagdeviren

Dağdeviren

et al. 2021

Sensors

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A Wireless Sensor Network (WSN) is connected if a communication path exists among each pair of sensor nodes (motes). Maintaining reliable connectivity in WSNs is a complicated task, since any failure in the nodes can cause the data transmission paths to break. In a k-connected WSN, the connectivity survives after failure in any k-1 nodes; hence, preserving the k-connectivity ensures that the WSN can permit k-1 node failures without wasting the connectivity. Higher k values will increase the reliability of a WSN against node failures. We propose a simple and efficient algorithm (PINC) to accomplish movement-based k-connectivity restoration that divides the nodes into the critical, which are the nodes whose failure reduces k, and non-critical groups. The PINC algorithm pickups and moves the non-critical nodes when a critical node stops working. This algorithm moves a non-critical node with minimum movement cost to the position of the failed mote. The measurements obtained from the testbed of real IRIS motes and Kobuki robots, along with extensive simulations, revealed that the PINC restores the k-connectivity by generating optimum movements faster than its competitors.

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Section: Related Workmentioning

confidence: 99%

PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks

Akram

Dagdeviren

Dağdeviren

et al. 2021

Sensors

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show abstract

“…1. Total travel distance (TTD) is the sum of the traveled distances by a robot to accomplish all its assigned tasks (6). The distance traveled by a robot R i that is assigned k number of tasks is computed as follows:…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Furthermore, it is more challenging if tasks must be assigned in a distributed manner in real-time as they appear. Multirobot task allocation (MRTA) has been employed in diverse applications and disciplines: object detection [4], search and rescue scenario [5], network connectivity restoration [6], just to mention a few examples. Combining MART with fuzzy logic has also witnessed a lot of attention and activities.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot Task Allocation System: Fuzzy Auction-Based and Adaptive Multi-threshold Approaches

Al-Shaboti

Baroudi

2021

SN COMPUT. SCI.

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Auction-based and threshold-based are the prevalent approaches for multi-robot distributed task allocation problem. We study the performance of these two approaches under a multi-objective dynamic task allocation scenario. The fuzzy inference system (FIS) is used in the auction-based approach to convert the objectives into a representative bid value. Experiments reveal that FIS auction-based outperforms the adaptive threshold-based approach in terms of load balancing. In contrast, the adaptive threshold-based approach produces better results in terms of traveled distance. Moreover, both approaches can achieve the same quality satisfaction objective.

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“…Cyber-physical systems are one of the emerging areas for networked and cloud robotic systems. [68] presented a distributed node repositioning algorithm based on fuzzy logic for addressing the issue of simultaneous failures of wireless network connectivity in harsh environments and evaluated it via conducting experiments using Khepera IV robots. Their scheme saves energy while restoring and maintaining network connectivity in wireless sensor and robot networks (WSRN) which is a special class of Cyber-physical systems.…”

Section: Challenges and Future Outlooksmentioning

confidence: 99%

From Networked Robotics to Cloud and Big Data Supercharged Robotics: A Survey and Analysis

Chowdhury

Yan

2021

Preprint

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In recent years, with the prosperity of big data and cloud computing, robotics is evolving from conventional networked robotics to internet-scale connected, big data driven, and cloud resources supercharged multi-robot systems. In this survey paper, we present a survey of an advancing, pioneering, and multi-disciplinary ﬁeld of research at the intersection of wireless sensor networks (WSN), robotics and big data. We discuss the concepts of networked robots. Networked robots refer to multiple robots working together in coordination with diﬀerent types of embedded computers, sensors, and human users. Networked robotics allows multiple robots and supporting entities to execute tasks that are well beyond the capabilities of a single robot. The recent initiation of cloud technologies is opening new prospects for the provisioning of advanced robotic services based on the cooperation of some connected robots, smart environments and devices powered by the huge computational and storage capability of the cloud servers. We have recently witnessed the emergence of cloud computing on one hand and robotics platforms on the other hand. These two areas have been merging and resulting in the cloud robotics model to oﬀer more distant services. Since networked robots require high computational and processing power, big data is becoming a dominant factor in networked and cloud robotics. This survey paper elaborates the primary concepts of networked and cloud robotics, their applications, challenges as well as the importance of big data in robotics, particularly, networked and cloud robotics and the noteworthy works in these areas.

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Energy-Aware Connectivity Restoration Mechanism for Cyber-Physical Systems of Networked Sensors and Robots

Cited by 22 publications

References 25 publications

PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks

PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks

Multi-robot Task Allocation System: Fuzzy Auction-Based and Adaptive Multi-threshold Approaches

From Networked Robotics to Cloud and Big Data Supercharged Robotics: A Survey and Analysis

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