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Stack4Things as a fog computing platform for Smart City applications

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... In literature, there exists a number of works implementing software frameworks for integrating IoT-enabled systems, Fog and Cloud infrastructures ( Rahmani et al., 2018;Dubey et al., 2017;Yangui et al., 2016;Bruneo et al., 2016 ). However, these frameworks barely support simultaneous execution of multiple applications and platform independence. ...
... In addition, security features from Cloud Foundry architecture are extended in the framework. Similarly, Bruneo et al. (2016) propose a Fog-centric PaaS framework named Stack4Things for deploying and executing multiple applications over computationally sound IoT devices. There, Fog infrastructure acts as a centralized programmable coordinator. ...
... To evaluate the framework characteristics of FogBus, we implement different features of Stack4Things ( Bruneo et al., 2016 ), Cloudlet-based PaaS ( Yi et al., 2015a ) and Indie Fog ( Chang et al., 2017 ) framework by following the given guidelines and compare their performance with FogBus. During experiments, the Blockchain feature of FogBus is kept enabled and the framework manages an integrated Fog-Cloud infrastructure. ...
... The sensor-cloud obviously provides many opportunities to develop sensing services [14][15][16][17]. In [14], Dinh and Younghan Kim propose to exploit the sensor-cloud for smart cities. ...
... In particular, a location-based sensor-cloud model is designed to support government officers on managing parking violation efficiently. In [15], Giovanni et al. also propose a framework, namely Stack4Things, for smart city applications. However, in Stack4Things, a device-oriented approach is used with fog computing, instead of the location-centric approach as used in [14]. ...
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This paper proposes an efficient interactive model for the sensor-cloud to enable the sensor-cloud to efficiently provide on-demand sensing services for multiple applications with different requirements at the same time. The interactive model is designed for both the cloud and sensor nodes to optimize the resource consumption of physical sensors, as well as the bandwidth consumption of sensing traffic. In the model, the sensor-cloud plays a key role in aggregating application requests to minimize the workloads required for constrained physical nodes while guaranteeing that the requirements of all applications are satisfied. Physical sensor nodes perform their sensing under the guidance of the sensor-cloud. Based on the interactions with the sensor-cloud, physical sensor nodes adapt their scheduling accordingly to minimize their energy consumption. Comprehensive experimental results show that our proposed system achieves a significant improvement in terms of the energy consumption of physical sensors, the bandwidth consumption from the sink node to the sensor-cloud, the packet delivery latency, reliability and scalability, compared to current approaches. Based on the obtained results, we discuss the economical benefits and how the proposed system enables a win-win model in the sensor-cloud.
... Over the last years, several middlewares and platforms for smart cities have been proposed [7], [8], [9], [10], [11], [12], [13], [14]. However, they mostly focus on one of these tiers, in some cases providing entry slots for connection to other layers. ...
... The Stack4Things [14] framework is a scalable system based on OpenStack [36]. The system has two layers. ...
Article
In this paper, we present and analyze MUSANet, a hierarchical, distributed, context-aware architecture for collecting, processing, and distributing data in smart cities. We discuss some use case examples related to monitoring and predicting bus arrivals in public transportation and weather conditions. We also present performance results in different scenarios that point to the feasibility of our goal: a scalable architecture with a fast response time to traffic events. MUSANet is based on a three-tier architecture distributed over cloud, fog, and edge, and supporting complex event processing (CEP) in all of them. Although the system is under development using the InterSCity platform in the cloud, the ContextNet middleware at the fog, and the Mobile-Hub platform at the edge, the MUSANet architecture can be deployed using other platforms, maintaining the concept of tiering responsibilities to minimize network bandwidth and delay, group communication, and broad mobile support.
... Fog computing offloads computing tasks from the cloud servers to fog nodes, which can relieve the pressure of processing multimedia data in mobile crowdsensing [8,11]. Meanwhile, since 21:2 H. Li et al. fog nodes are usually much nearer to mobile devices, the network overhead in transferring large multimedia data is reduced [48]. ...
... In the scheduling, an ϵ-greedy strategy is performed to generate the decision A n . Thus, the reward is calculated with (11), and all fog nodes are assigned with decision A n . The algorithm calculates the new state X n+1 with assigned fog nodes from time n + 1 to n + 1 + max (n a ). ...
Article
Mobile crowdsensing becomes a promising technology for the emerging Internet of Things (IoT) applications in smart environments. Fog computing is enabling a new breed of IoT services, which is also a new opportunity for mobile crowdsensing. Thus, in this article, we introduce a framework enabling mobile crowdsensing in fog environments with a hierarchical scheduling strategy. We first introduce the crowdsensing framework that has a hierarchical structure to organize different resources. Since different positions and performance of fog nodes influence the quality of service (QoS) of IoT applications, we formulate a scheduling problem in the hierarchical fog structure and solve it by using a deep reinforcement learning–based strategy. From extensive simulation results, our solution outperforms other scheduling solutions for mobile crowdsensing in the given fog computing environment.
... These entities may be connected to the Internet or each other. Many smart mobility scenarios such as the mobile and dynamic Internet of Vehicles (IoV) [24,25] in smart cities have encountered different challenges. Therefore, several solutions, including architectures, protocols, and distributed mobility management mechanisms for mobile devices used in IoT infrastructure, have attracted considerable attention and discussed smart city objects. ...
... In Bruneo et al. [25] have presented architecture, framework, and platform named Stack4Things based on fog computing for smart cities applications. A uniform representation of connected smart objects is provided using rewiring approach. ...
Article
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Nowadays, the smart city is a topic that has attracted the attention of many researchers, engineers and even the public because of to its pervasive and vast effect on everyday life. The technologies used to realize the smart cities are often based on cloud computing. As a result, they have carried the limitations of cloud computing, such as unreliable latency, lack of mobility support, and location awareness. Fog computing provides different solutions to these problems. Although efforts have been done in the area of fog computing applications in smart cities, it is still difficult to find a systematic reliable survey that covers this area. This article aims to provide a comprehensive overview based on a systematic literature review of current works that have been done in the area of fog computing applications in smart cities. In addition, a different analytical comparison of related works, the trends, and future research directions are pointed out in this article.
... Finally, in Reference [26], the authors propose Stack4Things as a FC platform for Smart City applications. They exploit Cloud-based network virtualization functionalities to implement a smart mobility use case in which smart cars can interact with Smart City objects to implement geolocalised services. ...
... Table 9 reports a comparison among such FC hardware platforms on the basis of those features that we believe are of particular interest in an IoT context. Specifically, besides some information about the hardware resources and the approximate price, we include details on: (i) the network connectivity; (ii) the additional interfaces that can be used to connect with external sensors and actuators (which represents the 26 See https://github.com/edgexfoundry. 27 See https://github.com/macchina-io/macchina.io. ...
Article
Research in the Internet of Things (IoT) conceives a world where everyday objects are connected to the Internet and exchange, store, process, and collect data from the surrounding environment. IoT devices are becoming essential for supporting the delivery of data to enable electronic services, but they are not sufficient in most cases to host application services directly due to their intrinsic resource constraints. Fog Computing (FC) can be a suitable paradigm to overcome these limitations, as it can coexist and cooperate with centralized Cloud systems and extends the latter toward the network edge. In this way, it is possible to distribute resources and services of computing, storage, and networking along the Cloud-to-Things continuum. As such, FC brings all the benefits of Cloud Computing (CC) closer to end (user) devices. This article presents a survey on the employment of FC to support IoT devices and services. The principles and literature characterizing FC are described, highlighting six IoT application domains that may benefit from the use of this paradigm. The extension of Cloud systems towards the network edge also creates new challenges and can have an impact on existing approaches employed in Cloud-based deployments. Research directions being adopted by the community are highlighted, with an indication of which of these are likely to have the greatest impact. An overview of existing FC software and hardware platforms for the IoT is also provided, along with the standardisation efforts in this area initiated by the OpenFog Consortium (OFC).
... Therefore, the cloud is provided with great capabilities related to large storage, high-speed communication, and powerful processing units. One of the essential features presented in cloud computing is that they can be accessed online at any time and from anywhere [16]- [20]. Many cloud-computing providers are now on the business side, offering cloud-computing services in the form of virtual machines. ...
... When a new task is generated at one IoT device, it can offload it directly to the fog network. The work in [16] focused on one task offloading for the IoT devices that are used in smart cities. Moreover, task offloading in mobile cloud computing was discussed in [17], where no fog computing exists in the system. ...
Article
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With the rapid development of the internet of things (IoT) devices and applications, the necessity to provide these devices with high processing capabilities appears to run the applications more quickly and smoothly. Though the manufacturing companies try to provide IoT devices with the best technologies, some drawbacks related to run some sophisticated applications like virtual reality and smart healthcare-based are still there. To overcome these drawbacks, a hybrid fog-cloud offloading (HFCO) is introduced, where the tasks associated with the complex applications are offloaded to the cloud servers to be executed and sent back the results to the corresponding applications. In the HFCO, when an IoT node generates a high-requirement processing task that cannot handle itself, it must decide to offload the task to the cloud server or to the nearby fog nodes. The decision depends on the conditions of the task requirements and the nearby fog nodes. Considering many fog nodes and many IoT nodes that need to offload their tasks, the problem is to select the best fog node to offload each task. In this paper, we propose a novel solution to the problem, where the IoT node has the choice to offload tasks to the best fog node or to the cloud based on the requirements of the applications and the conditions of the nearby fog nodes. In addition, fog nodes can offload tasks to each other or to the cloud to balance the load and improve the current conditions allowing the tasks to be executed more efficiently. The problem is formulated as a Markov Decision Process (MDP). Besides, a Q-learning-based algorithm is presented to solve the model and select the optimal offload policy. Numerical simulation results show that the proposed approach has superiority over other methods regarding reducing delay, executing more tasks, and balance the load.
... To enable deployment and execution of multiple location-1517 aware and low-latency applications leveraging CPS as smart 1518 city facility, a fog computing-based infrastructure is proposed 1519 in [151]. This infrastructure acts as a tunneling and forward-1520 ing agent enabling inter-object communication at data-link 1521 and network layer. ...
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Fog computing is an emerging paradigm that extends computation, communication, and storage facilities towards the edge of a network. Compared to traditional cloud computing, fog computing can support delay-sensitive service requests from End-Users (EUs) with reduced energy consumption and low traffic congestion. Basically, fog networks are viewed as offloading to core computation and storage. Fog nodes in fog computing decide to either process the services using its available resource or send to the cloud server. Thus, fog computing helps to achieve efficient resource utilization and higher performance regarding the delay, bandwidth, and energy consumption. This survey starts by providing an overview and fundamental of fog computing architecture. Furthermore, service and resource allocation approaches are summarized to address several critical issues such as latency, and bandwidth, and energy consumption in fog computing. Afterward, compared to other surveys, this paper provides an extensive overview of state-of-the-art network applications and major research aspects to design these networks. In addition, this study highlights ongoing research effort, open challenges, and research trends in fog computing.
... This is also applicable in the context of smart city based IoT applications where thousands of smart objects, vehicles etc. interact in order to provide effective services. In [268], the authors have proposed Stack4Things, an Open Stack based framework which is capable of managing the IoT infrastructure. This framework involves the Infrastructure-asa-Service and Platform-as-a-Service layers. ...
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In this survey, we discuss the evolution of distributed computing from the utility computing to the fog computing, various research challenges for the development of fog computing environments, the current status on fog computing research along with a taxonomy of various existing works in this direction. Then, we focus on the architectures of fog computing systems, technologies for enabling fog, fog computing features, security and privacy of fog, the QoS parameters, applications of fog, and give critical insights of various works done on this domain. Lastly, we briefly discuss about different fog computing associations that closely work on the development of fog based platforms and services, and give a summary of various types of overheads associated with fog computing platforms. Finally, we provide a thorough discussion on the future scopes and open research areas in fog computing as an enabler for the next generation computing paradigm.
... Distributed processing approaches based on Fog Computing represent a much better suited and more promising approach [14]. Fog Computing is a relatively recent paradigm and there is still no universal definition for it [15] [16] [17]. ...
... Bruneo et al. designed Stack4Things 12 , a framework based on OpenStack IaaS middleware that adopts a cloud-oriented model for IoT resource provisioning [381]. Their framework allows injected code at runtime through the cloud, which they define as "contextualization. ...
Article
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With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.
... The proposed mechanism aims to minimize the overall cost of all users using a centralized graph matching technique. A framework to enable developers and users to manage an IoT infrastructure is developed in [13]. This framework realizes the applications of IoT in smart cities. ...
Article
Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. In this paper, we study the allocation of fog computing resources to the IoT users in a hierarchical computing paradigm including fog and remote cloud computing services. We formulate a computation offloading game to model the competition between IoT users and allocate the limited processing power of fog nodes efficiently. Each user aims to maximize its own quality of experience (QoE), which reflects its satisfaction of using computing services in terms of the reduction in computation energy and delay. Utilizing a potential game approach, we prove the existence of a pure Nash equilibrium and provide an upper bound for the price of anarchy. Since the time complexity to reach the equilibrium increases exponentially in the number of users, we further propose a near-optimal resource allocation mechanism and prove that in a system with $N$ IoT users, it can achieve an $\epsilon$-Nash equilibrium in $O(N/\epsilon)$ time. Through numerical studies, we evaluate the users' QoE as well as the equilibrium efficiency. Our results reveal that by utilizing the proposed mechanism, more users benefit from computing services in comparison to an existing offloading mechanism. We further show that our proposed mechanism significantly reduces the computation delay and enables low-latency fog computing services for delay-sensitive IoT applications.
... The research and development on fog computing has focused on adapting infrastructures conceived for cloud computing, such as the OpenStack, to fog computing. For instance, in [4] an OpenStack based infrastructure is proposed to serve as a fog layer for smart cities. In [8], the authors identify some of the limitations induced by OpenStack's centralized architecture and propose the use of a distributed key value store to eliminate the dependency on a centralized database. ...
Conference Paper
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Fog computing aims at providing horizontal, system-level, abstractions to distribute computing, storage, control and networking functions closer to the user along a cloud-to-thing continuum. Whilst fog computing is increasingly recognized as the key paradigm at the foundation of Consumer and Industrial Internet of Things (IoT), most of the initiatives on fog computing focus on extending cloud infrastructure. As a consequence, these infrastructure fall short in addressing heterogeneity and resource constraints characteristics of fog computing environments. In this paper, we (1) explain the requirements of fog computing infrastructure and how they extend well beyond those traditionally addressed by Cloud Computing infrastructures; (2) introduce fog?5, a fog Infrastructure that unifies computing, networking and storage fabrics end-to-end, while addressing the challenges imposed by resource heterogeneity, (3) explain the novel architectural approach adopted by fog?5 to have a server-less data-centric architecture that is scalable, secure, and highly resilient to failures, (4) demonstrate the use of fog?5 in some real-world use cases and (5) conclude and reports on future works.
... Nevertheless, some related work in the remainder of this section will be discussed. Very recently, [10] designed an OpenStack platform using FC, Stack4Things, to enable smart cities to meet scalability and low latency requirements. Also, the work [11] proposes a solution to utilize resource pooling, content storing, node locating and other related situations with Smart Collaborative Caching (SCC) scheme that is established by leveraging high-level Information-centric networking principles for IoT within fog computing paradigm. ...
Article
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Smart city vision brings emerging heterogeneous communication technologies such as Fog Computing (FC) together to substantially reduce the latency and energy consumption of Internet of Everything (IoE) devices running various applications. The key feature that distinguishes the FC paradigm for smart cities is that it spreads communication and computing resources over the wired/wireless access network (e.g., proximate access points and base stations) to provide resource augmentation (e.g., cyberforaging) for resource and energy-limited wired/wireless (possibly mobile) things. Moreover, smart city applications are developed with the goal of improving the management of urban flows and allowing real-time responses to challenges that can arise in users' transactional relationships. This article presents a Fog-supported smart city network architecture called Fog Computing Architecture Network (FOCAN), a multi-tier structure in which the applications running on things jointly compute, route, and communicate with one another through the smart city environment to decrease latency and improve energy provisioning and the efficiency of services among things with different capabilities. An important concern that arises with the introduction of FOCAN is the need to avoid transferring data to/from distant things and instead to cover the nearest region for an IoT application. We define three types of communications between FOCAN devices (e.g., interprimary, primary, and secondary communication) to manage applications in a way that meets the quality of service standards for the IoE. One of the main advantages of FOCAN is that the devices can provide the services with low energy usage and in an efficient manner. Simulation results for a selected case study demonstrate the tremendous impact of the FOCAN energy-efficient solution on the communication performance of various types of things in smart cities.
... Bruneo et al. designed Stack4Things 12 , a framework based on OpenStack IaaS middleware that adopts a cloud-oriented model for IoT resource provisioning [381]. Their framework allows injected code at runtime through the cloud, which they define as "contextualization. ...
Article
Full-text available
48 pages, 7 tables, 11 figures, Open Access ***************************************************************************************** The data (categories and features/objectives of the papers) of this survey are available at https://github.com/ashkan-software/fog-survey-data ***************************************************************************************** Complete list of conferences, journals, and magazines that publish state-of-the-art research papers on fog computing and its related edge computing paradigms is compiled along with this article and is available at https://anrlutdallas.github.io/resource/projects/fog-computing-conferences.html ***************************************************************************************** With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.
... Bruneo et al. designed Stack4Things 10 , a framework based on OpenStack IaaS middleware that adopts a cloud-oriented model for IoT resource provisioning [328]. Their framework allows injected code at runtime through the cloud, which they define as "contextualization. ...
Preprint
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48 pages, 7 tables, 11 figures, Open Access ***************************************************************************************** The data (categories and features/objectives of the papers) of this survey are available at https://github.com/ashkan-software/fog-survey-data ***************************************************************************************** Complete list of conferences, journals, and magazines that publish state-of-the-art research papers on fog computing and its related edge computing paradigms is compiled along with this article and is available at https://anrlutdallas.github.io/resource/projects/fog-computing-conferences.html ***************************************************************************************** With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.
... Hong Kong is the leader in the use of smart cards, which millions of citizens cover the cost of public transportation, shopping and car parking. Barcelona was the first to introduce solar thermal collectors for hot sanitary water, and now it is working on a project for the introduction of electric vehicles and the construction of filling stations for electric vehicles [21]. In Stockholm, it seeks to reduce the amount of greenhouse effect gases by lowering energy consumption for heating apartments enhanced by isolation of apartments, urban traffic control and the development of electronic communications in order to reduce the use of paper. ...
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The Internet of Things (IoT) has started to appear everywhere in many shapes and forms. But security is one of the crucial topics that could trip up the growth of the IoT. Following security principles used in enterprise computing can help clear that issue. Already there are more connected devices than people on the planet, according to leading researchers in this area. By 2020, there will be 50 billion connected devices, outnumbering people by more than 6 to 1. Most of these devices will be controllable over the Internet, and they will increasingly be responsible for collecting and transmitting sensitive data. Today consumers might own an app that collects information on their exercise routine. In a few years, those same people might have an Internet-enabled medical device that continually delivers data to their doctor. In the wrong hands, data from home management systems could be used to assess user’s whereabouts. Likewise, businesses could be vulnerable when they connect things like HVAC, irrigation, or commercial appliances.
... Fixed devices concern smart meters, sensors and actuators, cameras, and Radiofrequency Identity (RFID) readers. They can all be used to sense, measure, and record data with regard to mobility, environment, and living SC dimensions [79][80][81][82][83][84][85][86][87][88][89]. ...
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Smart cities (SCs) are becoming highly sophisticated ecosystems at which innovative solutions and smart services are being deployed. These ecosystems consider SCs as data production and sharing engines, setting new challenges for building effective SC architectures and novel services. The aim of this article is to “connect the pieces” among Data Science and SC domains, with a systematic literature review which identifies the core topics, services, and methods applied in SC data monitoring. The survey focuses on data harvesting and data mining processes over repeated SC data cycles. A survey protocol is followed to reach both quantitative and semantically important entities. The review results generate useful taxonomies for data scientists in the SC context, which offers clear guidelines for corresponding future works. In particular, a taxonomy is proposed for each of the main SC data entities, namely, the “D Taxonomy” for the data production, the “M Taxonomy” for data analytics methods, and the “S Taxonomy” for smart services. Each of these taxonomies clearly places entities in a classification which is beneficial for multiple stakeholders and for multiple domains in urban smartness targeting. Such indicative scenarios are outlined and conclusions are quite promising for systemizing.
... Even with the present communication technology, social and political revolutions have sped up tremendously [37]. The new internet of everything with the power of intelligent assistants, connected devices, robots, and abundant computational power available through clouds and on the edge [38] will change the way individuals perceive, interact, think, and feel-not only as individuals within smart infrastructure and the new society, but as societies itself. This will have certain implications for public service, law enforcement, and many other facets of the civil sphere. ...
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Considering the current advancements in biometric sensors and other related technologies, and using bio-inspired models for AI improvements, we can infer that swarm intelligence paradigm can be implemented in human daily spheres through the connectivity between user gadgets connected to the Internet of Things. This is a first step towards the a real Ambient intelligence, but also of a Global Intelligence. This unconscious, by the user, connectivity may alter the way by which we feel the world. Besides, with the arrival of new augmented ways of capturing and providing information or radical new ways of expanding our bodies (through synthetic biology or artificial prosthesis like brain-computer connections), we can be very close to a change which may affect radically our experience of ourselves and of the feeling of collectivity. We call it the Techno-phenomenological turn. We show social implications, present challenges and and open question for the new kind of swarm intelligence enhanced society, as well as provide the taxonomy of the field of study. We will also explore the possible roadmaps of this next possible situation
... Evaluation of Existing Research EffortsDDIO DCDM MPS-C MPS-A Mob CDA SA DA SP CCS[Farris et al. 2016][Tang et al. 2015][Belli et al. 2015] [Gazis et al. 2015] [Abdullahi et al. 2015] [Gu et al. 2015] [Truong et al. 2015] [Stolfo et al. 2012] [Jiang Zhu et al. 2013] [Zao et al. 2014] [Aazam and Huh 2014] [Kulkarni et al. 2012] [Dsouza et al. 2014] [Aazam and Huh 2015b] [Preden et al. 2015] [Oueis et al. 2015] [Cao et al. 2015] [Su et al. 2015] [Al Faruque and Vatanparvar 2016] [Giang et al. 2015] [Hassan et al. 2015] [Ismail et al. 2015] [Gia et al. 2015] [Li et al. 2015] [Farris et al. 2015] [Dubey et al. 2015] [Nishio et al. 2013] [Stantchev et al. 2015] [Sehgal et al. 2015] [Aazam and Huh 2015a][Suciu et al. 2013][Bruneo et al. 2016] ...
Article
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The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities.
... This approach allows for negotiation and conflict resolution for optimized energy management in smart buildings. Stack4Things [42] offers a framework for smart city CPS that migrates some of the control and processing from the cloud to fog nodes to allow developers to manage sCPS applications and scatter application logic closer to the CPS components. Another area where CPS becomes useful is the food industry as illustrated in [43], where CPS and fog computing are used together to facilitate value stream-based food traceability sCPS. ...
Article
Smart Cyber-Physical Systems (sCPS) extend the traditional CPS by introducing intelligent and autonomous capabilities to these systems. sCPS provide smart interactions, smart controls, and smart enhancements for the physical world. These smart features can enhance the operations, efficiency, safety, utilization, reliability, quality, and cost-effectiveness of the physical world. These systems are usually highly distributed, real-time, deal with huge data sets, implement intelligent algorithms, and need powerful computation power and large-scale storage capacity. Some of the promising approaches to achieve the sCPS objectives include the use of a combination of cloud computing and fog computing to enable developing and operating them. Cloud computing can provide scalable and powerful computation platforms, large storage capacities, and advanced and intelligent software services, while fog computing can provide more optimized real-time controls for sCPS. Although cloud and fog computing can provide many advantages for sCPS, developing and integrating all these systems is challenging. This is due to the strict requirements of sCPS on one hand and the types of distributed and heterogeneous environments these systems support on the other. This paper proposes a distributed platform for cloud and fog integrated sCPS, named PsCPS. This platform can be distributed among multiple clouds, multiple fog nodes, and sCPS subsystems to provide services to relax many challenges of such integration. The proposed platform includes system and application agents that can be deployed on participating nodes to provide different services for cloud and fog integrated sCPS. These agents can be developed, implemented, controlled, and managed as a set of single agents, as multi-agent systems, or as hierarchical multi-agent systems. A prototype of the proposed platform is implemented and evaluated as well.
... We can now use mathematical calculations for presenting communication skills to the public and evaluating revolutions in government that may be greatly sped up with new ideas [54]. The innovative ideas can be implemented using the internet with the interconnected devices and equipment's, robotics, and rich source of computational power that are mainly available throughout the internet and exceed the edge [55] up to the certain boundary conditions that will alter the way to identify the people's, network, imagine, and feel the difference that not only contributes to the smart infrastructure and but also contribute to the new society. This will have certain implications for the welfare of the public services and their needs, implementing orders under the law, and many other aspects of the social arena. ...
Chapter
Interesting behaviors are utilized in nature to solve different challenges and they provide an attractive source of ideas for solving actual global problems. The SI method is based on calculations that concentrate on the combined conduct of centralized and self-structured systems to maintain artificial intelligence systems [1]. The following points should be kept in mind for the SI method: l This method simulates behaviors seen in nature in the activities of some animals and insects, including termites, ants, fish, and birds. The SI method is described through developing local interactions between different individuals , and thus produces intelligent behavior of individuals that relates to different groups [2]. l There are various different SI-based algorithms that have been projected for the management of different problems and have been applied successfully. New problems have been raised requiring increases in efficiency, and this chapter is based on the analysis of different applications and provides brief solutions for problems in a shorter time. This paper helps the reader to immediately focus on their research by offering them easy access to the associated text. In addition, this paper can be used as a preliminary reading point for examining several SI utilized methods and associated internet-utilized applications. IoT is the network planned to set the standard according to which each thing around us, including traffic lights or water supply pumps, operates; IoT System Assurances. https://doi.
... Management of all the collected data from end devices is an important concern to be addressed, as data is collected from sensory units continuously [9]. Efficient data analytics mechanism are required to extract meaningful and useful information from the massive data collected. ...
Article
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Fog computing is a distributed technology which performs computations at the edge of the network. IoT devices are generating large amount of data that too Heterogeneous in nature. In order to handle applications which, require time sensitive data and quick decision making, a reliable platform is required which can handle all the tasks in a robust manner considering fault tolerance and state migration. To process request originating from end user, an efficacious and pragmatic approach is to get the request served at the edge of the network rather than cloud envisioned with the aim to minimize the latency. Quick decision-making characteristic of fog computing makes it a smart choice for smoother processing and execution of smart city applications. As utilization of internet and IoT devices has boomed the industry, it is important to have reliable applications for day to day activities like payment of bills, traffic light management, health support system etc. The research study conducted explores multi-dimensional abstractions that can extend technological contributions in current epidemic situation.
... Generally, these types of services for security solutions are not adequate to address the new challenges in emerging technology [23,24]. In [25], the authors proposed a new FC paradigm that allowed various security functions (distributed monitoring of malware) for the diverse IoT devices to recompense the limited security of these devices. They benefited from the local information collection to deal with attacks and threats promptly. ...
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With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of object) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT infrastructure for smart cities. Then, spanning-tree routing (STP) protocol is used for data collection and routing. Further, 5G networks are proposed to establish fast transmission and communication between users. Finally, the performance of our proposed system is evaluated in terms of response time, latency, and amount of data used.
... Regarding the projects that used this architecture, Bruneo et al. 31 present a smart mobility use case using fog computing paradigm. Chen et al. 32 developed a dynamic video stream processing scheme based in fog computing architecture to offer real-time information processing and decision making. ...
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The introduction of the Information and Communication Technologies throughout the last decades has created a trend of providing daily objects with smartness, aiming to make human life more comfortable. The paradigm of Smart Cities arises as a response to the goal of creating the city of the future, where (1) the well-being and rights of their citizens are guaranteed, (2) industry and (3) urban planning is assessed from an environmental and sustainable viewpoint. Smart Cities still face some challenges in their implementation, but gradually more research projects of Smart Cities are funded and executed. Moreover, cities from all around the globe are implementing Smart City features to improve services or the quality of life of their citizens. Through this article, (1) we go through various definitions of Smart Cities in the literature, (2) we review the technologies and methodologies used nowadays, (3) we summarise the different domains of applications where these technologies and methodologies are applied (e.g. health and education), (4) we show the cities that have integrated the Smart City paradigm in their daily functioning and (5) we provide a review of the open research challenges. Finally, we discuss about the future opportunities for Smart Cities and the issues that must be tackled in order to move towards the cities of the future.
... The real time fall detection algorithm [8] based on accelerometer data, uses the edge devices and the server to execute the task. This [9] offers Stack4Things, a framework spanning the IaaS and PaaS layers, for Smart City Cyber Physical Systems solutions. The algorithm [10] utilizes the available fog resources and the cloud resources to offer a resource management and task scheduling scheme, to the large scale offloading applications. ...
Chapter
In some applications of IoT, it is Banerjee Sumanta to hasten the processing of data in order to get real-time conclusions. With this objective, CISCO devised fog computing, a next-generation networking technology. It resembles to the Mukherjee Shyamapada but in a miniature form, which serves the edge of the network from a closer geographic location. It has the ability to enhance the yields of ubiquitous smart networks and is a step Purkayastha Biswajit toward a smarter world. In this paper, an effort has been made to expound the underlying concept of fog computing technology with a comparison with other similar technologies. The challenges of this technology highlighted along with recent work progress to address them are also discussed.
... According to Bonomi [7], a possible solution to these problems is Fog computing, which uses computational resources at the edges of networks with low-latency and high-speed communication links [8]. The Fog computing approach has advantages in terms of latency, local proximity, and battery life extension [9]. However, it is also associated with several challenges due to the heterogeneity of the devices involved as well as the transience of their permanence in the network. ...
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Internet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (Cloud of Things). However, the use of CoT in latency-sensitive applications has been shown to be unfeasible due to the inherent latency of cloud computing services. One proposal to solve this problem is the use of the computational resources available at the edge of the network, which is called Fog computing. Fog computing solves the problem of latency but adds complexity to the use of these resources due to the dynamism and heterogeneity of the IoT. An even more accentuated form of fog computing is Mist computing, where the use of the computational resources is limited to the close neighborhood of the client device. The decision of what computing infrastructure (Fog, Mist, or Cloud computing) is the best to provide computational resources is not always simple, especially in cases where latency requirements should be met by CoT. This work proposes an algorithm for selecting the best physical infrastructure to use the computational resource (Fog, Mist, or Cloud computing) based on cost, bandwidth, and latency criteria defined by the client device, resource availability, and topology of the network. The article also introduces the concept of feasible Fog that limits the growth of device search time in the neighborhood of the client device. Simulation results suggest the algorithm's choice adequately attends the client's device requirements and that the proposed method can be used in IoT environment located on the edge of the network.
... The FC with Stack4Things and OpenStacks platform is presented to improve the latency and scalability [12]. FC architecture is designed as being service-oriented for the application of telehealth, using on-site processing of data [13]. ...
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Fog computing (FC) is used to reduce the energy consumption and latency for the heterogeneous communication approaches in the smart cities' applications of the Internet of Everything (IoE). Fog computing nodes are connected through wired or wireless medium. The goal of smart city applications is to develop the transaction relationship of real-time response applications. There are various frameworks in real-world to support the IoE in smart-cities but they face the issues like security, platform Independence, multi-application assistance, and resource management. This article is motivated from the Blockchain and Fog computing technologies and presents a secured architecture Blockchain and Fog-based Architecture Network (BFAN) for IoE applications in the smart cities. The proposed architecture secures sensitive data with encryption, authentication, and Blockchain. It assists the System-developers and Architects to deploy the applications in smart city paradigm. The goal of the proposed architecture is to reduce the latency and energy, and ensure improved security features through Blockchain technology. The simulation results demonstrate that the proposed architecture performs better than the existing frameworks for smart-cities.
... In general, the fog computing architecture is a service-oriented structure for telehealth application processing in real time, collecting raw data from wearable sensors and smart devices. To optimize these services, a three-tier architecture was proposed [14,15]. ...
... In the literature, there are also some edge computing architectures/ frameworks designed for specific applications. The Stack4Things EC platform (Bruneo et al., 2016) provides users with cloud-based virtualized networking, contextualization, and complex event processing for smart city applications. Al Faruque and Vatanparvar (2016) built an energy management platform over edge computing to provide services for home energy management or microgrid-level energy management. ...
Article
As a promising technology, edge computing extends computation, communication, and storage facilities toward the edge of a network. This new computing paradigm opens up new challenges, among which computation offloading is considered to be the most important one. Computation offloading enables end devices to offload computation tasks to edge servers and receive the results after the servers' execution of the tasks. In computation offloading, offloading modeling plays a crucial role in determining the overall edge computing performance. We present a comprehensive overview on the past development as well as the recent advances in research areas related to offloading modeling in edge computing. First, we present some important edge computing architectures and classify the previous works on computation offloading into different categories. Second, we discuss some basic models such as channel model, computation and communication model, and energy harvesting model that have been proposed in offloading modeling. Next, we elaborate on different offloading modeling methods which are based on (non-)convex optimization, Markov decision process, game theory, Lyapunov optimization, or machine learning. Finally, we highlight and discuss some research directions and challenges in the area of offloading modeling in edge computing.
... IoT can be used to build smart cities [25,26]. This includes smart transportation, smart hospitals, smart schools, smart traffic control, smart banking, smart vehicles, smart parking, smart environment (waste management, population management, weather monitoring), and smart police. ...
Chapter
Internet of Things (IoT) is a growing industry. Analysts predict that (IoT) products and services will grow exponentially in next years. It is a confluence of different sectors: embedded systems, communication systems, sensors/actuators, WWW, and mobile applications. Use Internet of Things Technology to solve all problems in different life sectors: healthcare, museums, libraries, inventory management, advertisement, real-estate identification, food tracking, maintenance, radiation/pollution monitoring, and security [1].
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Detecting anomalies in computer-based systems, including Cyber-Physical Systems (CPS), has attracted a large interest recently. Behavioral anomalies represent deviations from what is regarded to as the nominal expected behavior of the system. Both Process Science and Data Science can yield satisfactory results in detecting behavioral anomalies. Within Process Mining, Conformance Checking addresses data retrieval and the connection of data to behavioral models with the aim to detect behavioral anomalies. Nowadays, computer-based systems are increasingly complex and require appropriate validation, monitoring and maintenance techniques. Within complex computer-based systems, European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) represents the specification of a standard Railway System integrating heterogeneous hardware and software components, with the aim of providing international interoperability with trains seemingly interacting within standardized infrastructures. Compliance to the standard as well as expected behavior is essential, considering the criticality of the system in terms of performance, avail-ii Run-time Anomaly Detection with Process Mining ability and safety. To that aim, a Process Mining Conformance Checking process can be employed to validate the requirements through run-time model-checking techniques against design-time process models. A Process Mining Conformance Checking methodology has been developed and applied with the goal of validating the behavior exposed by an ERTMS/ETCS system during execution of specific scenarios. The methodology has been tested and demonstrated correct classification of valid behaviors exposed by the ERTMS/ETCS system prototype. Results also showed that the Fitness metric developed in the methodology allows the classification of the anomalies based on their severity and physical localization.
Conference Paper
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Engineering Internet of Things (IoT) and cloud services to provide a coherent software layer for continuous deployment, provision, and execution of applications for various domains is complex. The authors consider whether IoT cloud systems could provide a uniform layer to enable continuous execution of complex applications consisting of diverse types of software components, although these systems are built by integrating and blending IoT infrastructures with cloud-based datacenters. The authors analyze requirements and perspectives for engineering such IoT cloud systems and discuss the main engineering principles that should be supported. They then highlight seven main principles, covering different development and operation phases of IoT cloud systems. To show the importance and feasibility of these principles, they present some of their recent work in providing concepts and tools for IoT cloud systems.
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The current global emphasis on “Internet of Things (IoT)” have highlighted the extreme importance of sensor-based intelligent and ubiquitous systems which are more commonly known as “cyber-physical systems.” The technology has the potential to create a network of smart devices and things to an extent that has never been envisaged before, far outnumbering the number of devices connected in the Internet as we know today. The sheer number of such connected ubiquitous devices is likely to give rise to a hitherto unforeseen volume of data of different types with a demand for execution of analytical algorithms over the data. On the success of these analytic processes will depend the actual “smartness” of the “Intelligent Infrastructures” which now form the crux of the IoT paradigm. We have seen the advent of cloud-based paradigms to analyse the data in a data-parallel fashion within large data centres which now form the basis of the “big-data” problem. But apart from the servers in the data centres, we potentially have a huge pool of compute resources if we think about the smart devices in and around our homes collectively, which remain relatively idle. In this paper, we present a proposal with some emulated experimental results where we claim that in an IoT framework, the smart devices such as mobile phones, home gateways etc. can be utilised for execution of dataparallel analytic jobs. This is effectively a work-in-progress and it is acknowledged that there will be further challenges for real devices. Future research will attempt to consider these challenges.
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The transformation to smarter cities will require innovation in planning, management, and operations. Several ongoing projects around the world illustrate the opportunities and challenges of this transformation. Cities must get smarter to address an array of emerging urbanization challenges, and as the projects highlighted in this article show, several distinct paths are available. The number of cities worldwide pursuing smarter transformation is growing rapidly. However, these efforts face many political, socioeconomic, and technical hurdles. Changing the status quo is always difficult for city administrators, and smarter city initiatives often require extensive coordination, sponsorship, and support across multiple functional silos. The need to visibly demonstrate a continuous return on investment also presents a challenge. The technical obstacles will center on achieving system interoperability, ensuring security and privacy, accommodating a proliferation of sensors and devices, and adopting a new closed-loop human-computer interaction paradigm.
Fog computing and its role in the internet of things
  • F Bonomi
  • R Milito
  • J Zhu
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F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the internet of things," in Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. ACM, 2012, pp. 13-16.
Angels for distributed analytics in iot
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A. Mukherjee, H. Paul, S. Dey, and A. Banerjee, "Angels for distributed analytics in iot," in Internet of Things (WF-IoT), 2014 IEEE World Forum on, March 2014, pp. 565-570.
Uniminer: Towards a unified framework for data mining
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M. Habib ur Rehman, C. S. Liew, and T. Y. Wah, "Uniminer: Towards a unified framework for data mining," in Information and Communication Technologies (WICT), 2014 Fourth World Congress on, Dec 2014, pp. 134-139.
Data to decision: pushing situational information needs to the edge of the network," in Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)
  • J Preden
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J. Preden, J. Kaugerand, E. Suurjaak, S. Astapov, L. Motus, and R. Pahtma, "Data to decision: pushing situational information needs to the edge of the network," in Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2015 IEEE International Inter-Disciplinary Conference on, March 2015, pp. 158-164.
Stack4things: Integrating IoT with OpenStack in a Smart City context
  • G Merlino
  • D Bruneo
  • S Distefano
  • F Longo
  • A Puliafito
G. Merlino, D. Bruneo, S. Distefano, F. Longo, and A. Puliafito, "Stack4things: Integrating IoT with OpenStack in a Smart City context," in Proceedings of the IEEE First International Workshop on Sensors and Smart Cities, 2014.