Enabling distributed intelligence for the Internet of Things with IOTA and mobile agents

Alsboui, Tariq; Qin, Yongrui; Hill, Richard; Al-Aqrabi, Hussain

doi:10.1007/s00607-020-00806-9

Cited by 37 publications

(27 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-Challenges: Connected IoT devices in the coming future lead to a number of fundamental challenges, e.g., resource constraints, scalability, energy efficiency, security, privacy, offline capability, and Interoperability [5,25,26] as well as the massive amount of data produced by IoT.…”

Section: Distributed Intelligence In the Iotmentioning

confidence: 99%

“…Most recently, the authors in [5] propose a novel approach in support of distributed intelligence. The approach consists of (1) IoT nodes; Tangle to manage transactions; (3) PoW Server, and mobile agents to gather transactions data.…”

Section: Hybrid DImentioning

confidence: 99%

“…Both of the high-level and low-level intelligence are supported in all levels of the IoT system to ensure minimum resource usage. For example, the DI approaches proposed in [5,15,65,69,70,74] focuses on enabling high-level and low-level intelligence. In these approaches, low-level intelligence is supported by enabling data to be shared among various IoT devices and perform computation to provide usefulness insight out of the data.…”

Section: Intelligence Levelsmentioning

confidence: 99%

“…By 2030, the study predicts that IoT will rise exponentially, for example, by about 125 billion connected devices to the internet [1][2][3]. As a result, this poses several challenges in terms of providing timely delivery, data volume, speed, confidentiality and scalability [4,5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

et al. 2021

Self Cite

View full text Add to dashboard Cite

Widespread adoption of smart IoT devices is accelerating research for new techniques to make IoT applications secure, scalable, energy-efficient, and capable of working in mission-critical use cases, which require an ability to function offline. In this context, the novel combination of distributed ledger technology (DLT) and distributed intelligence (DI) is seen as a practical route towards the decentralisation of IoT architectures. This paper surveys DI techniques in IoT and commences by briefly explaining the need for DI, by proposing a comprehensive taxonomy of DI in IoT. This taxonomy is then used to review existing techniques and to investigate current challenges that require careful attention and consideration. Based on the taxonomy, IoT DI techniques can be classified into five categories based on the factors that support distributed functionality and data acquisition: cloud-computing, mist-computing, distributed-ledger-technology, service-oriented-computing and hybrid. Existing techniques are compared and categorized mainly based on related challenges, and the level of intelligence supported. We evaluate more than thirty current research efforts in this area. We define many significant functionalities that should be supported by DI frameworks and solutions. Our work assists system architects and developers to select the correct low-level communication techniques in an integrated IoT-to-DLT-to-cloud system architecture. The benefits and shortcomings of different DI approaches are presented, which will inspire future work into automatic hybridization and adaptation of DI mechanisms. Finally, open research issues for distributed intelligence in IoT are discussed.

show abstract

Section: Distributed Intelligence In the Iotmentioning

confidence: 99%

Section: Hybrid DImentioning

confidence: 99%

Section: Intelligence Levelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…This SI also has "tasty dishes of generalist work. Tariq Alsboui et al [4] present a Distributed Intelligent approach that could be useful in different IoT scenarios. The authors propose a Mobile-Agent Distributed Intelligence Tangle-Based approach (MADIT), where Tangle IOTA is a distributed ledger platform to data exchange in large P2P networks, and MADIT enables distributed intelligence at two levels.…”

mentioning

confidence: 99%

Emerging applications of internet of things, big data, security, and complexity: special issue on collaboration opportunity for IoTBDS and COMPLEXIS

2020

View full text Add to dashboard Cite

Today, Big Data, IoT and Analytics are driving and making the differences in key performing top organizations. The interplay of these three areas can be instrumental for the future development of research, complex systems and enterprises. IoT will be estimated to rise to billions of devices connected by 2020 [1]. This has huge implications for research, businesses and future activities for mankind. The development of this vision is pivotal to support and foster IoTBDS (iotbds.org). Sensors are used to extract an unprecedented amount of data, which can be filtered and processed in the IoT by machine networks, automated analysis, tools and systems. This can become a Big Data layer abstraction from the captured data if the process is well-organized. Hereby, complex information systems can obtain the benefits of collecting, processing and analyzing highly valuable data. This is one of the most important scopes of COM-PLEXIS (complexis.org). Therefore, starting from the pillars of IoT, Analytics and Big Data, we can build complex information architectures by using tools like social media, predictive modeling, insight analysis and sentiment analysis. Eventually, we can build three additional layers: complex data, information and knowledge, and offer service related to these three valued-added layers. The application fields are widespread in fields like social networking, financial services, or biological research.This special issue (SI) call is focused on contributions that explore and demonstrate related areas, approaches and recommendations for both IoTBDS and COMPLEXIS. In particular, research work and projects that offer solutions to the underlying prob-B Victor Chang

show abstract

A distributed reliable collusion‐free algorithm for selecting multiple coordinators in IOTA using fog computing

Alavizadeh,

Erfani,

Mirabi

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryIOTA is a distributed ledger technology with a new structure called Tangle, which offers high scalability, no fees, and near‐instant transfers for the internet‐of‐things (IoT) networks. The most important issue of IOTA is consensus achievement, which is handled by a single node acting as a coordinator. A single and default coordinator node exposes IOTA to the single point of failure and incomplete distribution issues. In this paper, a novel algorithm for selecting multiple coordinators to participate in the consensus process at milestones is proposed (i.e., MCS algorithm) to overcome the problem of a single coordinator in IOTA network. The MCS algorithm is applied in a two‐layered architecture including IoT devices (i.e., Layer 1) and fog nodes (i.e., Layer 2). We define and formulate four different properties as metrics to select multiple coordinators in this architecture. Moreover, a collusion list is defined to decrease the risk of collusion between fog nodes in the network. Experimental results show that using multiple fog nodes as coordinators in IOTA network can improve the average response time and average throughput while does not considerably sacrifice the total cost and system utilization in comparison with the use of a single default coordinator in IOTA network.

show abstract

Enabling distributed intelligence for the Internet of Things with IOTA and mobile agents

Cited by 37 publications

References 33 publications

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

Emerging applications of internet of things, big data, security, and complexity: special issue on collaboration opportunity for IoTBDS and COMPLEXIS

A distributed reliable collusion‐free algorithm for selecting multiple coordinators in IOTA using fog computing

Contact Info

Product

Resources

About