A Review of Energy Hole Mitigating Techniques in Multi-Hop Many to One Communication and its Significance in IoT Oriented Smart City Infrastructure

Shafique, Tamoor; Gantassi, Rahma; Soliman, Abdel-Hamid; Amjad, Anas; Hui, Zhan-Qiang; Choi, Yonghoon

doi:10.1109/access.2023.3327311

Cited by 6 publications

(4 citation statements)

References 127 publications

(299 reference statements)

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“…The dominant consumer of energy is the network node's radio operations [52], [53]. Recent techniques to limit energy consumption in radio operations can be classified as: improved transceiver circuit design, transmission power control, cognitive radios and energy-efficient routing [54]. Energy-efficient topology and routing methods have proven to be an emerging area for WSN energy management.…”

Section: Related Workmentioning

confidence: 99%

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Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Shafique,

Soliman,

Amjad

2024

IEEE Access

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Due to the technological advancements in wireless communication and their continuously increasing applications in collaborative and cooperative smart infrastructures, energy efficient data collection using wireless devices, has gained significant importance recently. Modern wireless sensor networks refer to network of low-powered and energy-constrained Internet of Things (IoT) devices. Although data collection using hierarchical routing with clustered network improves energy efficiency but introduces energy holes in the region closer to the data gathering center due to heavy relaying load on cluster heads. In this paper, first an improved data gathering center deployment technique for heterogeneous networks has been proposed. Technique for Order of Preference by Similarity to Ideal Solutions (TOPSIS), a multi-criteria decisionmaking technique, is used to determine the optimal location for the deployment of data gathering center. The proposed technique is adaptive to various shaped networks as required by IoT and increases energy efficiency. Secondly, an unequal clustering based on transmission distances has been proposed. Moreover, cubical, and spherical segmentation schemes for 3D heterogeneous networks have been proposed that assist the formation of unequal clusters. Finally, a shape independent data rate-based segmentation has been proposed that further extends the adaptability and scalability of the proposed unequal clustering. The results demonstrate that the proposed data traffic-based shape independent adaptive clustering scheme increases network lifetime up to 14.2% and 18.8% as compared to Fuzzy Logic based unequal clustering and IUCR respectively. It also reduces the overall network energy consumption up to 61.4% as compared to the state-of-the art unequal clustering methods.INDEX TERMS Balanced energy routing, Energy Holes, Internet of Things, Shape independent clustering, Scalable clustering protocol, Unequal clustering, 3D Wireless Sensor Network.

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Section: Related Workmentioning

confidence: 99%

“…Due to the increasing applications of WSN assisted IoT, scalability and adaptivity of the energy efficient hierarchical routing methods are important quality of service criterions [54]. Typically, sensing devices are deployed arbitrarily or in a pre-planned manner in a region of interest [19].…”

Section: Sink Node Deploymentmentioning

confidence: 99%

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Shafique,

Soliman,

Amjad

2024

IEEE Access

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“…[7] Shafique et al conducted "A Review of Energy Hole Mitigating Techniques in Multi-Hop Many to One Communication and its Significance in IoT Oriented Smart City Infrastructure" [7]. Their study provides valuable insights into energy management in IoT communication, particularly in the context of smart cities.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

Energy-Efficient Machine Learning for IoT Edge Devices: A Federated Learning Approach

Rosemaro,

Pandit

2023

RJCSE

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In the realm of modern-day IoT (Internet of Things) deployments, the quest for energy-efficient solutions stands paramount for different use cases. As the IoT ecosystem continues to burgeon, there arises an exigency for resource-conscious algorithms that can effectively navigate the intricacies of data routing while minimizing energy consumption levels. This paper illuminates a pivotal advancement in this domain, introducing a novel paradigm for different scenarios. Existing methodologies have long grappled with the challenge of balancing the ever-increasing computational demands of IoT devices with their constrained power resources. Conventional routing strategies often lack the depth required to optimize energy consumption, resulting in inefficiencies that hamper overall system performance. Prior research has primarily focused on rudimentary routing techniques that fall short in addressing the multifaceted demands of contemporary IoT ecosystems. In response to these limitations, this paper propounds a breakthrough model that leverages the power of Deep Dyna Q integrated with a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN). This fusion of cutting-edge technologies not only enhances energy efficiency but also ushers in a new era of intelligent data routing within IoT deployments. The chosen methodology is deliberate; Deep Dyna Q harnesses the power of reinforcement learning to dynamically adapt routing decisions, while the LSTM-based RNN augments the model's ability to capture contextual dependencies crucial in IoT scenarios. This synergy engenders superior energy optimization, transforming the way data is routed in IoT edge devices & samples. The results are resoundingly positive, with empirical evidence showcasing the model's prowess. Tested across multiple contextual datasets, our approach exhibits a remarkable 10.5% improvement in energy efficiency, a commendable 8.5% boost in speed, a 3.9% uptick in throughput, a 4.5% surge in packet delivery ratio, and a notable 4.9% enhancement in consistency when compared against existing methods. Such substantial improvements underscore the transformative impact of our work, setting new standards for energy-efficient data routing in IoT deployments.

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“…Their research focuses on the quality of service in Smart Cities, leveraging AI to optimize service placement for enhanced urban functionality. Shafique et al[8] review energy hole mitigating techniques in multi-hop many-to-one communication and its significance in IoT-oriented Smart City infrastructure. Their study addresses the energy efficiency challenges faced by wireless sensor networks in urban environments.…”

mentioning

confidence: 99%

Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

Jabakumar,

Dhablia

2022

RJCSE

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This work delves into the pressing need for more efficient Predictive Maintenance solutions in the context of IoT-enabled Smart Cities. Existing methodologies often fall short, lacking the precision and accuracy required to keep these rapidly evolving urban environments running smoothly for different use cases. The limitations of current approaches become apparent when considering their inability to cope with the intricacies of IoT data. They struggle to harness the wealth of information generated by countless interconnected devices and systems, resulting in suboptimal performance. This issue is further compounded by their relatively sluggish response times, hindering the timely detection of critical maintenance needs. In response to these challenges, this paper presents an innovative approach that leverages the fusion of Bidirectional Long Short-Term Memory (BiLSTM) with Autoencoders in conjunction with Recurrent Neural Networks (RNNs). This combination of advanced techniques brings forth a powerful predictive maintenance model process. The utilization of BiLSTM and Autoencoders adds a layer of sophistication, allowing for a deeper understanding of the underlying data patterns. BiLSTM's ability to capture contextual information from both past and future data points enriches the model's predictive capabilities. The inclusion of Autoencoders aids in feature extraction and reconstruction, enhancing the model's ability to discern relevant information sets. The advantages of this proposed model are profound. It exhibits 4.9% higher precision, ensuring that maintenance actions are precisely targeted, thus reducing unnecessary interventions. Moreover, the model achieves a remarkable 5.5% increase in accuracy, guaranteeing more reliable predictions. Its 4.5% boost in recall ensures that potential issues are identified promptly. The model's 3.9% increase in speed means that maintenance responses are faster and more effective, minimizing downtemporal instance sets. Lastly, an 8.5% improvement in the Area Under the Curve (AUC) underscores its superior performance compared to existing methodologies. The impacts of this work extend far beyond the realm of predictive maintenance levels. It contributes significantly to the realization of truly smart and efficient cities, where resources are optimized, disruptions are minimized, and the quality of life for citizens is enhanced for different use cases. This research marks a pivotal step towards the seamless integration of IoT technologies into urban environments, with the potential to revolutionize the way we manage and maintain our cities in the future scenarios.

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A Review of Energy Hole Mitigating Techniques in Multi-Hop Many to One Communication and its Significance in IoT Oriented Smart City Infrastructure

Cited by 6 publications

References 127 publications

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Energy-Efficient Machine Learning for IoT Edge Devices: A Federated Learning Approach

Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

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