A Secure Energy Aware Meta-Heuristic Routing Protocol (SEAMHR) for sustainable IoT-Wireless Sensor Network (WSN)

G, Girija Vani; Shariff, Noorullah C; Biradar, Rajkumar L.

doi:10.1016/j.tcs.2022.07.011

Cited by 27 publications

(18 citation statements)

References 32 publications

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“…Sensor-based waste monitoring is a technology that utilizes sensors to track the amount of waste generated, identify the sources of waste, and measure the effectiveness of waste management strategies in a specific area. Wireless sensor network is a network composed of many self-organized wireless sensors installed in the network to monitor the physical or environmental parameters of the system (Gurram et al 2022 ). As illustrated in Fig.…”

Section: Artificial Intelligence In Waste Managementmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

et al. 2023

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The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

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Section: Artificial Intelligence In Waste Managementmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

et al. 2023

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“…This parameter is about adopting trustworthy systems to enhance the energy efficiency, lifetime, and performance of sensor networks (SNs) and WSNs for smart grids. It captures various dimensions of "Senser Networks," including task off-loading at smart grids' edge for trustworthiness (Gunaratne et al, 2022), enhancing the effectiveness of IoT-WSN by using the Secure Energy-Aware Meta-Heuristic Routing (SEAMHR) protocol (Gurram et al, 2022), and reliable and trustworthy DL method to detect black-hole attacks in wireless sensors and maximize energy efficiency (Kolangiappan and Kumar, 2022). Further dimensions include temperature-aware trustworthy routing in SNs to optimize energy efficiency (Khan et al, 2022) and DL techniques to improve energy usage fairness across the cluster members in Cognitive Radio Sensor Networks (CRSN) (Stephan et al, 2021).…”

Section: Sensor Networkmentioning

confidence: 99%

AI explainability and governance in smart energy systems: A review

2023

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Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on “deep journalism,” our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.

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“…where ATC m t ð Þ embodies the anticipated transmission amount (ATC) of m th node at time t,χ depicts the communication delay of WSN, and δ m depicts disseminated delay of m th node. ATC is established in transferred and attained PDR of the sensor node in t time, and it is articulated based on the following Equation (10):…”

Section: Sensor Nodes Delaymentioning

confidence: 99%

“…In the future, homes and offices are going to develop into smart office and smart homes 9 . Wireless sensor network is combined with many business activities for smart homes, virtual reality, infrastructure, artificial intelligence, machine learning, health care, monitoring, surveillance, decision‐making, and so on 10 . The efficiency of decision‐making is solely dependent on sensor sensitivity 28 .…”

Section: Introductionmentioning

confidence: 99%

“…9 Wireless sensor network is combined with many business activities for smart homes, virtual reality, infrastructure, artificial intelligence, machine learning, health care, monitoring, surveillance, decision-making, and so on. 10 The efficiency of decision-making is solely dependent on sensor sensitivity. 28 Stakeholders can utilise this network for observations, data gathering, storage, forecasting, forecasting forecasts, and corporate policy making.…”

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confidence: 99%

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Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

Soundararajan,

Tapas Bapu,

Sargunavathi

et al. 2023

Int J Communication

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SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

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A Secure Energy Aware Meta-Heuristic Routing Protocol (SEAMHR) for sustainable IoT-Wireless Sensor Network (WSN)

Cited by 27 publications

References 32 publications

Artificial intelligence for waste management in smart cities: a review

Artificial intelligence for waste management in smart cities: a review

AI explainability and governance in smart energy systems: A review

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

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