intel-LEACH: An optimal framework for node selection using dynamic clustering for wireless sensor networks

Siddavaatam, Prathap; Sedaghat, Reza; Sharma, Aakriti

doi:10.23919/icitst.2017.8356419

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…A custom cross-layer protocol is utilized [49] to implement the cross-layer optimization framework in practical scenarios. We provide extensive simulation results which show that our proposed solution outperforms existing layered protocols.…”

Section: Contributionmentioning

confidence: 99%

“…The three most predominant modulations, namely, BPSK, QPSK and 16-QAM are utilized for simulation purposes using intel-LEACH protocol [49], but any other modulation can be easily included in the framework. These modulations are distinguishable in terms of achievable BER for a given SNR, i.e., S ψ in Eq.…”

Section: Power Modulation and Be For Transmissionmentioning

confidence: 99%

“…The intel-LEACH protocol [49] was incorporated to test our framework from synthesis to optimization steps in practical point-to-point IoT scenarios. The operation is briefly listed in following phases.…”

Section: Real-time Protocol Testingmentioning

confidence: 99%

“…The parameter paths for optimizing the layers is provided by a novel approach in form of delta diagram defined in Figures 3.2, 3.3 and 3.4. Moreover, a cross-layer communication protocol called intel -LEACH[49] is devised to implement and test this optimization framework under practical scenarios. The results show that the proposed solution can achieve a global communication optimum and outperforms existing layered solutions and highly flexible in nature, so that the concept can be adopted to synthesize any given communication protocol.…”

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confidence: 99%

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A Delta diagram synthesis for IoT optimization with grey wolf driven multi-objective automation

Siddavaatam¹

2021

Preprint

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Today, Internet of Things (IoT) is a major paradigm shift that will mark an epoch in communication technology such that every physical object can be connected to the Internet. With the advent of 5G communications, IoT is in urgent need of optimized architectures that can efficiently support wide ranging heterogeneous multi-objective requirements of communication, hardware and security aspects. The optimization challenges are rooted in the technology and how the information is acquired and manipulated by this technology. My research in this thesis provides a description of compelling challenges faced by IoT and how to mitigate these challenges by designing resource-aware communication protocols, resource- constrained device hardware with low computing power and low-powered computational security enhancements. This thesis lays the foundation for optimizing these challenging IoT paradigms by introducing a novel Delta-Diagram based synthesizing model. The Delta- Diagram provides a road-map linking the behavioral and structural domains of a given IoT paradigm to generate respective optimizer domain parameters, which can be utilized by any optimizer framework. The fundamental part of the communication synthesizer is a mathematical model, developed to obtain the best possible routing paths and communication parameters among things. The ultimate aim of the entire synthesis process is to devise a design automation tool for IoT, which exploits the interrelations between different layer functionalities. This thesis also proposes a novel cross-layer Grey wolf optimizer for IoT, which outperforms some of the contemporary optimizer algorithms such as Particle Swarm, Genetic Algorithm, Differential Evolution optimizers in solving unimodal, multi-modal and composition benchmark problems. The purpose of this optimizer is to accurately capture both the high heterogeneity of the IoT and the impact of the Internet as part of delta diagram synthesis enabled network architecture. In addition, the Grey wolf optimizer for IoT plays a crucial role in design exploration of system on chip architecture for IoT device hardware. The results generated by the optimizer yielded the most optimum feasible solutions in the design space exploration process of the IoT.

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Section: Contributionmentioning

confidence: 99%

Section: Power Modulation and Be For Transmissionmentioning

confidence: 99%

Section: Real-time Protocol Testingmentioning

confidence: 99%

mentioning

confidence: 99%

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A Delta diagram synthesis for IoT optimization with grey wolf driven multi-objective automation

Siddavaatam¹

2021

Preprint

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“…However, the drawback is the additional overhead to construct and maintain the clusters. One of the first works that developed the concept of clusters was LEACH [97]. From that moment on, other authors started to work on clustering schemes based on LEACH.…”

Section: Clustersmentioning

confidence: 99%

A self-healing framework for WSNs : detection and recovery of faulty sensor nodes and unreliable wireless links

Salas¹

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Proponemos un marco conceptual para acoplar técnicas de auto-organización y técnicas de autocuración. A este marco se le llama de auto-curación y es capaz de hacer frente a enlaces inalámbricos inestables y nodos defectuosos. Dividimos el marco en dos componentes principales: la auto-organización y auto-curación. En el componente de auto-organización, nosotros construimos una topología de árbol que determine las rutas hacia el sumidero. En el componente de auto-curación, la topología del árbol se adapta a ambos tipos de fallas siguiendo tres pasos: recopilación de información, detección de fallas, y la recuperación de fallos. En el paso de recopilación de información, los nodos determinan el estado actual de la red mediante la recopilación de información de la capa MAC. En el paso de detección de fallas, los nodos analizan la información recopilada y detectan nodos/enlaces defectuosos. En el paso de recuperación de fallos, los nodos recuperan la topología del árbol mediante la sustitución de componentes defectuosos con redundantes (es decir, componentes de respaldo). Este marco permite una red con resiliencia que se recupera sin agotar los recursos de la red.

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A Delta-Diagram Based Synthesis for Cross Layer Optimization Modeling of IoT

Siddavaatam

Sedaghat

2018

Lecture Notes in Computer Science

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intel-LEACH: An optimal framework for node selection using dynamic clustering for wireless sensor networks

Cited by 5 publications

References 17 publications

A Delta diagram synthesis for IoT optimization with grey wolf driven multi-objective automation

A Delta diagram synthesis for IoT optimization with grey wolf driven multi-objective automation

A self-healing framework for WSNs : detection and recovery of faulty sensor nodes and unreliable wireless links

A Delta-Diagram Based Synthesis for Cross Layer Optimization Modeling of IoT

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