FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

Saleem, Ahsan; Khan, Abid; Malik, Saif Ur Rehman; Pervaiz, Haris; Malik, Hassan; Alam, Mansaf; Jindal, Anish

doi:10.1109/jiot.2019.2957314

Cited by 98 publications

(48 citation statements)

References 34 publications

(100 reference statements)

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“…Multiple authors have suggested the embedding of deep learning techniques to improve the security in IoT-based Localization applications [ 98 , 99 , 100 ]. Additionally, encryption algorithms and digital signatures using public and private keys can improve the resilience against external attacks [ 101 , 102 ]. However, the extent of security by using minimal resources is still a big challenge.…”

Section: Future Directionsmentioning

confidence: 99%

A Systematic Review of Location Aware Schemes in the Internet of Things

Khan

Saboor

Kim

et al. 2021

Sensors

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The rapid development in wireless technologies is positioning the Internet of Things (IoT) as an essential part of our daily lives. Localization is one of the most attractive applications related to IoT. In the past few years, localization has been gaining attention because of its applicability in safety, health monitoring, environment monitoring, and security. As a result, various localization-based wireless frameworks are being presented to improve such applications’ performances based on specific key performance indicators (KPIs). Therefore, this paper explores the recently proposed localization schemes in IoT. Initially, this paper explains the major KPIs of localization. After that, a thorough comparison of recently proposed localization schemes based on the KPIs is presented. The comparison includes an overview, architecture, network structure, performance parameters, and target KPIs. At the end, possible future directions are presented for the researchers working in this domain.

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Section: Future Directionsmentioning

confidence: 99%

A Systematic Review of Location Aware Schemes in the Internet of Things

Khan

Saboor

Kim

et al. 2021

Sensors

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“…Offloading by the vehicles to MEC/cloud servers is done through the RSU. The authors of [28,59,60] also use a similar system model in their work. In [33], authors consider a model which uses neither edge devices nor cloud servers but edge-cloud devices in their system model.…”

Section: Cloud Computingmentioning

confidence: 99%

A survey on computation resource allocation in IoT enabled vehicular edge computing

Naren

Gaurav

Sahu

et al. 2021

Complex Intell. Syst.

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The number of vehicles is increasing at a very high rate throughout the globe. It reached 1 billion in 2010, in 2020 it was around 1.5 billion and experts say this could reach up to 2–2.5 billion by 2050. A large part of these vehicles will be electrically driven and connected to a vehicular network. Rapid advancements in vehicular technology and communications have led to the evolution of vehicular edge computing (VEC). Computation resource allocation is a vehicular network’s primary operations as vehicles have limited onboard computation. Different resource allocation schemes in VEC operate in different environments such as cloud computing, artificial intelligence, blockchain, software defined networks and require specific network performance characteristics for their operations to achieve maximum efficiency. At present, researchers have proposed numerous computation resource allocation schemes which optimize parameters such as power consumption, network stability, quality of service (QoS), etc. These schemes are based on widely used optimization and mathematical models such as the Markov process, Shannon’s law, etc. So, there is a need to present an organized overview of these schemes to help in the future research of the same. In this paper, we classify state-of-the-art computation resource allocation schemes based on three criteria: (1) Their optimization goal, (2) Mathematical models/algorithms used, and (3) Major technologies involved. We also identify and discuss current issues in computation resource allocation in VEC and mention the future research directions.

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“…There is also trustworthiness issue related to security and privacy of data in cloud storage scenarios, there may arise a security concern due to continuous connectivity of IoT-based sensors with the edge entities [7,8], the vulnerabilities can arise specially in sectors like healthcare where a small change in values can be life threatening. Data aggregation is also termed as one big problem for smart grid IoT systems [9] which is somewhere related to data gathering and management issue. Some applications are delay sensitive due to challenges when processing a large amount of data at edge or the cloud level of devices, leading to latency, which is not acceptable in some critical applications like health scenarios, transport management, etc.…”

Section: Introductionmentioning

confidence: 99%

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Bajaj

Sharma

Singh

2021

Complex Intell. Syst.

102

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The Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

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FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

Cited by 98 publications

References 34 publications

A Systematic Review of Location Aware Schemes in the Internet of Things

A Systematic Review of Location Aware Schemes in the Internet of Things

A survey on computation resource allocation in IoT enabled vehicular edge computing

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

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