An IoT service ecosystem for Smart Cities: The #SmartME project

Bruneo, Dario; Distefano, Salvatore; Giacobbe, Maurizio; Minnolo, Antonino Longo; Longo, Francesco; Merlino, Giovanni; Mulfari, Davide; Panarello, Alfonso; Patanè, Giuseppe; Puliafito, Antonio; Puliafito, Carlo; Tapas, Nachiket

doi:10.1016/j.iot.2018.11.004

Cited by 81 publications

(32 citation statements)

References 7 publications

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“…Apart from waste management, edge computing could generally find a much broader employment within smart cities where multiple facilities (e.g., public lighting, parking structures, energy consumption in buildings, waste management, carpooling, tourism and taxi cabs) may be created, run, maintained and enhanced. This was the scope that motivated [ 37 , 38 ]: the former dealt with the problem by leveraging on Single Board Computers (SBCs), while the latter performed the aforesaid tasks by setting up a comprehensive infrastructure which was developed and tested in the city of Messina, Italy.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

Baldo

Mecocci

Parrino

et al. 2021

Sensors

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Long Range Wide Area Network (LoRaWAN) has rapidly become one of the key enabling technologies for the development of Internet of Things (IoT) architectures. A wide range of different solutions relying on this communication technology can be found in the literature: nevertheless, the most part of these architectures focus on single task systems. Conversely, the aim of this paper is to present the architecture of a LoRaWAN infrastructure gathering under the same network different typologies of services within one of the most significant sub-systems of the Smart City ecosystem (i.e., the Smart Waste Management). The proposed architecture exploits the whole range of different LoRaWAN classes, integrating nodes of growing complexity according to the different functions. The lowest level of this architecture is occupied by smart bins that simply collect data about their status. Moving on to upper levels, smart drop-off containers allow the interaction with users as well as the implementation of asynchronous downlink queries. At the top level, Video Surveillance Units (VSUs) are provided with machine learning capabilities for the detection of the presence of fire nearby bins or drop-off containers, thus fully implementing the Edge Computing paradigm. The proposed network infrastructure and its subsystems have been tested in a laboratory and in the field. This study has enhanced the readiness level of the proposed technology to Technology Readiness Level (TRL) 3.

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Section: Related Workmentioning

confidence: 99%

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

Baldo

Mecocci

Parrino

et al. 2021

Sensors

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“…To enhance the QoS of IoT devices, the interactions between IoT devices are linked to the cloud. In this regard, Dario et al 21 presented 2 years #SmartMe project. The project enables heterogeneous IoT devices to interact with one another via the cloud.…”

Section: Related Workmentioning

confidence: 99%

A secure blockchain‐based demurrage mechanism for energy trading in smart communities

Dahunsi

Javaid

2020

Int J Energy Res

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This paper combines additive homomorphic encryption and consortium blockchain technology to provide privacy and trust. Additionally, a dynamic energy pricing model is formulated based on the demand response ratio (DRR) of the load demand of prosumers to address fixed energy pricing problems. The proposed dynamic pricing model includes demurrage fees, which is a monetary penalty imposed on a prosumer, if it failed to deliver energy within the agreed duration. Furthermore, this paper also designs and analyzes a threat model of the proposed system. Experimental evaluations show the effectiveness of the proposed model with regards to low transaction cost, the minimum execution time for block creation, the privacy of prosumers and dispute resolution of demurrage fees. Moreover, the proposed scheme reduces the average system overhead cost up to 66.67% as compared to 33.43% for an existing scheme. Additionally, the proposed blockchain proof-of-authority consensus average hash power is minimized up to 82.75% as compared to 60.34% for proof-of-stake and 56.89% for proof-of-work consensus mechanisms.

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“…The mathematical model can change significantly on a computer when other design priorities are mathematically analyzed, hence, based on the solution strategy the exception of the linear system that produces the estimated front of Pareto can adjust the genetic algorithm according to the sort of programming goal. In [21] the author proposed the use of the opportunities offered by global charge equilibrium, the ability to such plan workloads for delay tolerance and the control of thermal storage in data centers, can promote the improvement of renewable energy. In fact, differences in bandwidth rates are recognized between customers and data centers [22].…”

Section: Background Study On Workload Management In Data Centers and mentioning

confidence: 99%

Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms

et al. 2020

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Presently, energy is considered a significant resource that grows scarce with high demand and population in the global market. Therefore, a survey suggested that renewable energy sources are required to avoid scarcity. Hence, in this paper, a smart, sustainable probability distribution hybridized genetic approach (SSPD-HG) has been proposed to decrease energy consumption and minimize the total completion time for a single machine in smart city machine interface platforms. Further, the estimated set of non-dominated alternative using a multi-objective genetic algorithm has been hybridized to address the problem, which is mathematically computed in this research. This paper discusses the need to promote the integration of green energy to reduce energy use costs by balancing regional loads. Further, the timely production of delay-tolerant working loads and the management of thermal storage at data centers has been analyzed in this research. In addition, differences in bandwidth rates between users and data centers are taken into account and analyzed at a lab scale using SSPD-HG for energy-saving costs and managing a balanced workload.

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An IoT service ecosystem for Smart Cities: The #SmartME project

Cited by 81 publications

References 7 publications

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

A secure blockchain‐based demurrage mechanism for energy trading in smart communities

Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms

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