Smartphone Architecture for Edge-Centric IoT Analytics

Marah, Bockarie Daniel; Jing, Zilong; Ma, Tinghuai; Al-Sabri, Raeed; Anaadumba, Raphael; Al-Dhelaan, Abdullah; Al-Dhelaan, Mohammed

doi:10.3390/s20030892

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…In this context, several machine learning algorithms have been employed, including linear regression [9,10], decision trees [11,12], random forests [13], support vector machines (SVMs) [14,15], and neural networks [16][17][18][19][20][21]. However, these traditional machine learning algorithms suffer from various limitations that impede their effectiveness [22,23].…”

Section: Mgataf: Multi-channel Graph Attentionmentioning

confidence: 99%

MGATAF: Multi-channel Graph AttentionNetwork with Adaptive Fusion forCancer-Drug Response Prediction

Saeed,

Xing,

AlBadani

et al. 2024

Preprint

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Drug response prediction is critical in personalized medicine, aiming todetermine the most effective and safe treatments for individual patients.Traditional prediction methods relying on demographic and genetic dataoften fall short in accuracy and robustness. Recent graph-based models,while promising, frequently neglect the critical role of atomic interactionsand fail to integrate drug fingerprints with SMILES for comprehensivemolecular graph construction. We introduces MGATAF (MultimodalMulti-channel Graph Attention Network and Adaptive Fusion), a frame-work designed to enhance drug response predictions by capturing bothlocal and global interactions among graph nodes. MGATAF improvesdrug representation by integrating SMILES and fingerprints, resulting inmore precise predictions of drug effects. The methodology involves con-structing multimodal molecular graphs, employing multi-channel graph attention networks to capture diverse interactions, and using adap-tive fusion to integrate these interactions at multiple abstraction levels.Empirical results demonstrate MGATAF’s superior performance com-pared to traditional and other graph-based techniques. For example,on the GDSC dataset, MGATAF achieved a 5.12% improvement in thePearson correlation coefficient (PCC), reaching 0.9312 with an RMSE of0.0225.Similary, in new cell-line tests, MGATAF outperformed baselineswith a PCC of 0.8536 and an RMSE of 0.0321 on the GDSC dataset,and a PCC of 0.7364 with an RMSE of 0.0531 on the CCLE dataset

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Section: Mgataf: Multi-channel Graph Attentionmentioning

confidence: 99%

MGATAF: Multi-channel Graph AttentionNetwork with Adaptive Fusion forCancer-Drug Response Prediction

Saeed,

Xing,

AlBadani

et al. 2024

Preprint

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show abstract

“…At edge nodes, conventional approaches propose to offload heavy tasks that cannot be handled by end-users, collecting data and locally pre-processing it before sending aggregated data to the cloud [16]. Recently, some authors have stated the edge/fog architectures are well suited for stream processing systems [14,[19][20][21]. Cardellini et al [14] made Storm suitable for execution in a geographically distributed environments.…”

Section: Edge Computing Architecturesmentioning

confidence: 99%

SLedge: Scheduling and Load Balancing for a Stream Processing EDGE Architecture

et al. 2022

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Natural disasters have a significant impact on human welfare. In recent years, disasters are more violent and frequent due to climate change, so their impact may be higher if no preemptive measures are taken. In this context, real-time data processing and analysis have shown great potential to support decision-making, rescue, and recovery after a disaster. However, disaster scenarios are challenging due to their highly dynamic nature. In particular, we focus on data traffic and available processing resources. In this work, we propose SLedge—an edge-based processing model that enables mobile devices to support stream processing systems’ tasks under post-disaster scenarios. SLedge relies on a two-level control loop that automatically schedules SPS’s tasks over mobile devices to increase the system’s resilience, reduce latency, and provide accurate outputs. Our results show that SLedge can outperform a cloud-based infrastructure in terms of latency while keeping a low overhead. SLedge processes data up to five times faster than a cloud-based architecture while improving load balancing among processing resources, dealing better with traffic spikes, and reducing data loss and battery drain.

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“…Edge computing overhauls cloud computing—a centralized paradigm of AIoT [ 26 , 27 ]. The studies in [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] have featured HEMSs.…”

Section: Introductionmentioning

confidence: 99%

“…This technology has recently been developed and recognized as a promising, efficient decentralized paradigm of AIoT [ 26 , 35 , 36 ]. Edge computing, which aims to displace data science analytics in the cloud as close as possible to the edge of the network [ 27 , 37 ], can prevent network latency for latency-sensitive IoT applications (where network connectivity is not always available) [ 38 , 39 ] and fulfill the lack of location awareness as well as data mobility for IoT end devices [ 36 ] deployed in practical fields of interest (i.e., real-time responsiveness from heterogeneous sensor data for local interpretable and actionable data insights can be guaranteed). Edge computing has been foreseen as a remedy to alleviate the various issues of cloud computing [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

Chen

Chang³

et al. 2021

Sensors

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Electricity is a vital resource for various human activities, supporting customers’ lifestyles in today’s modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium’s Niagara Framework® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable.

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Smartphone Architecture for Edge-Centric IoT Analytics

Cited by 9 publications

References 27 publications

MGATAF: Multi-channel Graph AttentionNetwork with Adaptive Fusion forCancer-Drug Response Prediction

MGATAF: Multi-channel Graph AttentionNetwork with Adaptive Fusion forCancer-Drug Response Prediction

SLedge: Scheduling and Load Balancing for a Stream Processing EDGE Architecture

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

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