Smart Cities IoT: Enablers and Technology Road Map

Suzuki, Larissa

doi:10.1007/978-3-319-61313-0_10

Cited by 31 publications

(20 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since 2010, interest has significantly shifted towards smart cities, whereby innovative information and communication technologies (ICTs) are utilised to improve the economic competitiveness, standard of living and operational efficiency of cities [21,55]. This is driven by the use of connected devices such as sensors, actuators and wearables that are able to store and transmit data through the Internet, i.e., the "Internet of Things" (IoT), which forms the backbone of smart infrastructure in smart cities as these devices can interact and synchronise their actions across multiple smart applications such as community development, grid distribution and transportation [4,56]. The concept of smart cities goes beyond earlier concepts of "information city", "digital city", "sustainable city" and "intelligent city" by emphasising the use of technology to serve the needs of people [52,55].…”

Section: Algorithmic Decision-making In Avs For Smart and Sustainablementioning

confidence: 99%

“…Smart mobility integrates intelligent transportation systems, which comprise of connected road infrastructure and smart vehicular technologies such as AVs, to improve transportation outcomes such as safety, access to mobility and traffic management, which are key to enhancing public well-being and economic efficiency [7,10,17,58]. In AVs, data collection conducted by on-board sensors directly influences the AV's situational awareness and the algorithms that make decisions such as route planning, obstacle avoidance and reverting control to humans [56,59]. Secondly, the highly autonomous nature and high computational power of the AV's algorithms significantly increases the efficiency, accuracy and timeliness of decision-making relative to human drivers [24].…”

Section: Algorithmic Decision-making In Avs For Smart and Sustainablementioning

confidence: 99%

See 1 more Smart Citation

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Lim

Taeihagh

2019

Sustainability

View full text Add to dashboard Cite

Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms' decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs' perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV's algorithms and of policies and regulations to fully realise AVs' benefits for smart and sustainable cities.Sustainability 2019, 11, 5791 2 of 28 surroundings and algorithms that process the data, make, and execute driving decisions through the vehicle's actuators. The quality of data retrieved by the AV's sensors is critical for decision-making [21], and the efficiency, precision and reliability of decision-making algorithms allows AVs to surpass the typical human driver in performing driving tasks [22]. A major component in AVs is machine-learning (ML) [23] algorithms that continuously learn and adapt to new information, which is essential for responding to unexpected situations and providing on-demand transportation services [24]. Algorithms' independence from human input and ML's data-driven nature allows AVs to significantly reduce or eliminate human errors that have been responsible for 90% of road fatalities, such as speeding, alcohol impairment, distractions and induced fear [25,26].However, an overemphasis on technological solutions alone for economic development could risk neglecting social and environmental considerations and thus hinder true "smartness" [27]. Scholars have cautioned against rushing to develop smart mobility solutions such as AVs without being prepared to manage their potential "negative externalities" [1,28]. In particular, issues in algorithmic decision-making in AVs can have undesirable effects on safety and equity. Firstly, data mining processes in AVs are susceptible to biases that lead algorithms to prioritise the safety of certain groups of road users over others and thus, perpetuate discrimination [29,30]. Secondly, many scholars stress the need to design algorithms with ethical considerations to ensure that AVs make ethical driving dec...

show abstract

Section: Algorithmic Decision-making In Avs For Smart and Sustainablementioning

confidence: 99%

Section: Algorithmic Decision-making In Avs For Smart and Sustainablementioning

confidence: 99%

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Lim

Taeihagh

2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…The cognitive network of smart grid performs various cognitive processes simultaneously to transmit RDF triples through multiple paths [18]. The same is the case of pheromone-aware RDF triple processing onto a cognitive network that also adopts various paths to transmit RDF triples towards semantic reservoir.…”

Section: ) Cognitive Node Load Analyticsmentioning

confidence: 99%

A Knowledge-Based Path Optimization Technique for Cognitive Nodes in Smart Grid

Qureshi

Siddiqui

Abbas

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

The cognitive network uses cognitive processes to record data transmission rate among nodes and applies selflearning methods to trace data load points for finding optimal transmission path in the distributed computing environment. Several industrial systems, e.g., data-centers, smart grids, etc., have adopted this cognitive paradigm and retrieved the least HOP count paths for processing huge datasets with minimum resource consumption. Therefore, this technique works well in transmitting structured data such as 'XML', however, if the data is in unstructured format i.e. 'RDF', the transmission technique wraps it with the same layout of payload and eventually returns inaccuracy in calculating traces of data load points due to the abnormal payload layout. In this paper, we propose a knowledgebased optimal routing path analyzer (RORP) that resolves the transmission wrapping issue of the payload by introducing a novel RDF-aware payload-layout. The proposed analyzer uses the enhanced payload layout to transmit unstructured RDF triples with an append pheromone (footsteps) value through cognitive nodes towards the semantic reservoir. The grid performs analytics and returns least HOP count path for processing huge RDF datasets in the cognitive network. The simulation results show that the proposed approach effectively returns the least HOP count path, enhances network performance by minimizing the resource consumption at each of the cognitive nodes and reduces traffic congestion through knowledge-based HOP count analytics technique in the cognitive environment of the smart grid.

show abstract

“…Several successful cities in Europe served as an example for the creation of the model of a smart city (Figure 4) based on the combination of the following characteristics: smart economy, smart mobility, smart environment, smart people, smart living conditions, smart governance. Such authors as Anthopouls andVakali (2011), Carvalho (2015), Hollands (2015), Kitchin (2015), Nam and Pardo (2011), Shelton, Zook, & Wiig (2015), Suzuki and Finkelstein (2013), Suzuki (2017) supported the idea of smart city creation and development. Applications of these approaches and models are visible in many successful cities in the world.…”

Section: Needs Of a Society And Marketmentioning

confidence: 99%

Smart management systems (Triple Helix model) in Waterloo, Canada

Mazurek¹

2018

JGBI

View full text Add to dashboard Cite

Smart Cities IoT: Enablers and Technology Road Map

Cited by 31 publications

References 50 publications

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

A Knowledge-Based Path Optimization Technique for Cognitive Nodes in Smart Grid

Smart management systems (Triple Helix model) in Waterloo, Canada

Contact Info

Product

Resources

About