The Network As a Computer: A Framework for Distributed Computing Over IoT Mesh Networks

Pascale, Emanuele Di; Macaluso, Irene; Nag, Avishek; Kelly, Mark; Doyle, Linda

doi:10.1109/jiot.2018.2823978

Cited by 40 publications

(31 citation statements)

References 21 publications

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“…Similarly, traditional simulation protocols are tested over standalone battery-operated devices in terms of energy memory and execution of time [22]. Further, to utilize the computing power of IoT devices, Artificial Neural Networks (ANNs) are used for simultaneous data transfer in order to reduce latency and improve network lifetime [23]. To summarize, a comparison of IoT simulation frameworks is presented in Table II.…”

Section: Related Workmentioning

confidence: 99%

Toward Distributed Heterogeneous Simulation Using Internet of Things

Haseeb

Malik

Rahman

et al. 2019

IEEE Internet Things J.

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Section: Related Workmentioning

confidence: 99%

Toward Distributed Heterogeneous Simulation Using Internet of Things

Haseeb

Malik

Rahman

et al. 2019

IEEE Internet Things J.

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“…Therefore, the drawbacks of WSNs with centralized architectures deployed in large-scale monitoring applications are enormous bandwidth requirement and huge energy consumption as periodic transmission of raw data over long distances, via multiple hops to the base station leads to fast depletion of sensor node’s battery and shortens the lifespan of a WSN [ 7 , 9 , 10 ]. Other drawbacks include low reliability, longer response time, high bandwidth cost, low level data security and privacy [ 7 , 9 , 10 , 11 , 12 ]. Some works in the literature have demonstrated the feasibility of distributed computing in WSN and its promises of gain in performance and lower power consumption [ 5 , 7 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Other drawbacks include low reliability, longer response time, high bandwidth cost, low level data security and privacy [ 7 , 9 , 10 , 11 , 12 ]. Some works in the literature have demonstrated the feasibility of distributed computing in WSN and its promises of gain in performance and lower power consumption [ 5 , 7 , 11 , 12 , 13 ]. By performing more local computation, limiting exchanges only between neighboring nodes and reducing the number of messages that need to be transmitted, distributed computing in WSN has the potential of providing a solution to the drawbacks of the centralized approach [ 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Multi-sensor data fusion in WSN can either be done via a centralized, decentralized or in a distributed manner [ 37 , 41 ]. The centralized data fusion technique will require multi-hop communications, which have a higher probability of developing an energy hole in the network, thus shortening the lifespan of the WSN [ 12 , 14 , 15 ]. Thus, using distributed data fusion may increase the lifespan of the WSN as there is no central point for fusion and multi-hop communications will be eliminated entirely.…”

Section: Introductionmentioning

confidence: 99%

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A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

Nkemeni

Mieyeville

Tsafack

2020

Sensors

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Wireless Sensor Network (WSN) applications that favor more local computations and less communication can contribute to solving the problem of high power consumption and performance issues plaguing most centralized WSN applications. In this study, we present a fully distributed solution, where leaks are detected in a water distribution network via only local collaborations between a sensor node and its close neighbors, without the need for long-distance transmissions via several hops to a centralized fusion center. A complete approach that includes the design, simulation, and physical measurements, showing how distributed computing implemented via a distributed Kalman filter improves the accuracy of leak detection and the power consumption is presented. The results from the physical implementation show that distributed data fusion increases the accuracy of leak detection while preserving WSN lifetime.

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“…The authors based their study on one of the forms of wireless networks; namely, MESH networks [16,17]. These networks are particularly widely used in Internet of Things (IoT) type infrastructure; i.e., to ensure connectivity of sensor systems or communication in computer networks of the ad hoc type [18][19][20][21]. They can be used both in general access networks (networks in parks, on the streets, the natural environment, etc.…”

Section: Introductionmentioning

confidence: 99%

Phase Transitions in Wireless MESH Networks and Their Application in Early Detection of Network Coherence Loss

Paszkiewicz¹,

Bolanowski²,

Zapała³

2019

Applied Sciences

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The paper focuses on the phenomenon of phase transitions related to changes taking place directly in a network structure; i.e., in physical topology. Although these changes are related to the physical layer, they directly affect the functioning of higher layers, and thus the quality and efficiency of network transmission. The research presented herein was focused in particular on phase transformations in MESH type wireless topologies. The dependence between the values of radius (transmitter power) nodes and the coherence of the MESH network is presented. These considerations are important for understanding the dynamics of processes occurring in topological structures, and for optimizing the use of power sources for individual nodes. The paper also presents the use of phase transition analysis for early detection of network coherence loss at the physical layer level. A new mechanism for assessing the level of network communication and an active method of preventing its failure using the arbiter and a set of limited diagnostic information are described. The solution presented can be used both in Internet of Things (IoT) systems and Industry 4.0 to create a high level of reliability wireless connection infrastructure between mobile and fixed devices.

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The Network As a Computer: A Framework for Distributed Computing Over IoT Mesh Networks

Cited by 40 publications

References 21 publications

Toward Distributed Heterogeneous Simulation Using Internet of Things

Toward Distributed Heterogeneous Simulation Using Internet of Things

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

Phase Transitions in Wireless MESH Networks and Their Application in Early Detection of Network Coherence Loss

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