Green IoT — Issues and Challenges

Ahmad, Rafeeq; Asim, Mohammed; Khan, Safwan Zubair; Singh, B.

doi:10.2139/ssrn.3350317

Cited by 23 publications

(5 citation statements)

References 7 publications

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“…The vast amount of data collected by IoT devices need a high-capacity [47] Network for storing and analysis to handle both in terms of the amount of information recorded and the rate at which it is produced. For optimal service provision, access to the Internet of Things Data from the egovernment must be made public to make educated decisions in real-time and on a wide scale.…”

Section: ▪ Infrastructurementioning

confidence: 99%

E-Government Privacy and Security Challenges in the Context of Internet of Things

Ujjan

Khan

Gaur

2022

Advances in Electronic Government, Digital Divide, and Regional Development

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The internet of things (IoT) is becoming more significant in everyday life as a mechanism for making major decisions in different fields as smart devices and data in real time are connected and updated. IoT is being used in a variety of ways to provide digital services to the public. Online payment, property purchase, and sailing are just a few examples. On the other hand, users' complaints about the safety and privacy of their personal information are growing. The internet of things (IoT) is becoming more popular and significantly enhances e-government. This chapter primarily focuses on how potential users can obtain information to use the internet of things and its related services within the e-government sectors. There are several technological, administrative, and political challenges to IoT adoption problems in e-government and legal problems that must be solved to develop effective and required applications. It's crucial to explore these problems and potential solutions.

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Section: ▪ Infrastructurementioning

confidence: 99%

E-Government Privacy and Security Challenges in the Context of Internet of Things

Ujjan

Khan

Gaur

2022

Advances in Electronic Government, Digital Divide, and Regional Development

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“…Attaining energy efficiency in any engineering system and low energy communications have become major research issues in recent times as they contribute to environmental benefits [121]- [123]. Green IoT (G-IoT) targets to implement environmental-friendly and energy-efficient features by adopting strategies at both hardware and software levels, and can be beneficial in reducing energy consumption from IoT-based applications [124]. Even small savings in energy in the operation of sensors and sensor networks will result in large energy conservation because of the sheer number of sensors to be deployed by IoT services in the near future.…”

Section: Open Research Issuesmentioning

confidence: 99%

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

et al. 2020

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The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. INDEX TERMS Association rules, behavioral patterns, data mining, frequent pattern, Internet of Things, knowledge discovery, wireless sensor networks.

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“…Contrary to the cloud computing, fog deploys SCN capabilities into the intermediary in-network devices as well as in the CDCs, making a cloud-to-edge continuum. As a result, FC reduces the overall service delay for the VANET applications [ 7 ]. Moreover, since the data are processed and stored in the edge devices, the volume of data traffic transmitted through the network is also reduced [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

Hussain

Azar

Ahmed

et al. 2023

Sensors

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With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

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Green IoT — Issues and Challenges

Cited by 23 publications

References 7 publications

E-Government Privacy and Security Challenges in the Context of Internet of Things

E-Government Privacy and Security Challenges in the Context of Internet of Things

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

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