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In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote Cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices cont...
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... The primary function of CDMA is to employ distinct codes for encoding and decoding user data, facilitating concurrent communication. MWC aims to improve network capacity and efficiency by allowing multiple devices to transmit and receive data simultaneously using different access methods [34]. This approach is instrumental in environments with many wireless devices, such as IoT applications. ...
... For instance, when the maximum data size of tasks is 10 MB, more than 95% of operations are offloaded in our proposed algorithm, in which 3.5% of functions are offloaded to the cloud. These ratios in the Q-learning and block successive upper-bound minimization benchmark is 59%, and 98%, respectively [34]. Table 11 shows a gained energy in terms of mJ for existing Q-learning, block successive upper-bound minimization with the proposed optimized, flexible network based on the increasing user count. ...
... The primary function of CDMA is to employ distinct codes for encoding and decoding user data, facilitating concurrent communication. MWC aims to improve network capacity and efficiency by allowing multiple devices to transmit and receive data simultaneously using different access methods [34]. This approach is instrumental in environments with many wireless devices, such as IoT applications. ...
... For instance, when the maximum data size of tasks is 10 MB, more than 95% of operations are offloaded in our proposed algorithm, in which 3.5% of functions are offloaded to the cloud. These ratios in the Q-learning and block successive upper-bound minimization benchmark is 59%, and 98%, respectively [34]. Table 11 shows a gained energy in terms of mJ for existing Q-learning, block successive upper-bound minimization with the proposed optimized, flexible network based on the increasing user count. ...
Multi-access wireless computing is an excellent catalyst for the changes in wireless computing in today’s scenario. The network architecture used here should suit multi-access wireless and robust wireless computing. But in many cases, performing the above two types of wireless computing is infrequent. Keeping this in mind, this research has taken advantage of multi-access wireless computing and solved the changes by creating an optimized, flexible network architecture. Because generally, wireless computing has two types of computing methods. They are information-centric wireless computing and data-centric wireless computing. From these two types of computing, multi-access wireless computing (MWC) and information-centric wireless computing (ICWC), this research focuses on how these enable the IoT with the 5G Platform and explains the following four terms. Based on the study mentioned above processes, we have introduced a proposed system called MWC and ICWC for 5G communication. First, we explain all the problems occurring in 5G communication-based IoT. Based on this, the following processes are performed. Their most important explain why we need flexible network architecture concerning “multi-access wireless computing (MWC).” Because “multi-access wireless computing (MWC)” is also called flexible network architecture because it has multi-access authentication. Secondly, explain the “IoT” in “5G communication” by introducing and applying “information-centric wireless computing.” Finally, explained factors for improving the 5G communication by applying “multi-access wireless computing (MWC)” for improving 5G communication. Finally, this research compare (MWC) and (ICWC) approach by justifying which factor is giving more impact on improving 5G communication.
... In results section, it is evident that the latency remains lower when the cloud-fog combination is used compared to the traditional cloud model. Different from cloud-fog based approach presented in [53], a fog-to-fog strategy is delineated in [55]. The name fog-to-fog comes from the fact that there are multiple horizontal layers of the fog handling most of the sensory data processing and storage. ...
Energy plays a pivotal role for economic development of a country. A reliable energy source is needed to improve the living standards of people. To achieve such a goal, governments and industries are trying to install a new energy infrastructure called the “Smart Grid”. This helps to manage the electricity generation and distribution in an efficient manner. Buildings and other structures are the biggest consumers of electricity. There is a need to reduce the energy consumption so that the resources can be utilized efficiently. Therefore, in this paper, we give a comprehensive state-of-the-art on various recent techniques and solutions which provide energy savings in smart homes and buildings. This includes statistical models, cloud computing based solutions, fog computing and smart metering based architectures, and several other IoT (internet of things) inspired solutions. We also present a hypothetical model that treats energy supply and usage in buildings as a self-managing energy system (SES). This paper is concluded by highlighting several open issues and challenges related to energy management in buildings.
... Source: [34] [35]. ...
The growing concerns over the adverse effects of buildings on the environment and the need to achieve users’ thermal, visual, acoustic, spatial and indoor environmental air quality comfort have given rise to the demand for energy efficient buildings. This paper relied on a review of 36 articles published between 2007 and 2019 to identify and categorise energy efficiency design, planning and construction applicable to office buildings. The data were randomly selected using Science Direct and Google Scholar search engines on the internet and analysed by thematic textual analysis. The results revealed 29 energy efficiency design strategies applicable for office buildings which were categorised into the three distinct phases of building projects: pre-construction (design and planning), construction and post construction stages. The strategies were further categorised into energy efficient landscape designs, site selection, building orientation, building plan and appropriate space organisation. The study also established that building envelope systems, building orientation, integration of renewable energy sources and day lighting design strategies were the most implemented in office buildings. The review concluded by highlighting the key areas of focus in energy efficiency design strategies in office buildings, the extent research has progressed on the subject and future possible directions on the topic.
... Yin et al. [27] used fog computing to build a new task scheduling resource allocation model based on containers for smart manufacture. In the building industry, fog computing can enhance the data processing performance of energy management systems of buildings [28]. Celaya-Echarri et al. [29] proposed fog computing-based communications architecture to provide smart parking services. ...
Die casting is a complex process performed in harsh working environments. Driven by cost and environmental pressure, die casting, as one of the most energy-intensive manufacturing processes, has received increasing attention on enhancing energy efficiency toward greener and more sustainable manufacturing. Energy efficiency evaluation is a starting point for energy audits and analysis of energy-saving scenarios, while complex production conditions in the die casting workshop (e.g. product changeover, technology improvements, and degradation of equipment performance) require even higher real-time and dynamic performance of energy efficiency evaluation. To this end, this paper proposes a multi-level energy efficiency evaluation framework based on fog-cloud computing. Accordingly, real-time parameter identification models and dynamic energy efficiency evaluation method are proposed. An industrial case study of die casting workshop has demonstrated the feasibility and effectiveness of the proposed approach. The results reported that the overall equipment effectiveness and energy utilization ratio of die casting units increased by 3% and 7%, respectively, and energy consumption per kilogram of the die casting workshop was reduced by 7.9%, showing its great potential in identifying energy efficiency improvement opportunities.
... This is important to favor the collaboration process among virtual nodes. Shen et al. (2019) propose an information-centric collaborative Fog (ICCF) platform. In their work, a new in-network self-learning algorithm to run on Fog nodes is presented. ...
Edge Computing is a novel paradigm that allows moving the computation closer to the end-users and/or data sources. In this paper, we present a three-tier architecture for virtualization and collaboration of Virtual Nodes that leverages the Edge tier to meet those emerging IoT applications that demand requirements such as low latency, geo-localization, and energy efficiency. Besides the Edge tier, our implementation is based on the mix of lightweight virtualization and microservices using the building blocks from the FIWARE platform to interact with the physical environment. Furthermore, we presented two experiments to assess our architecture under severe and realistic conditions, regarding the network latency and fault-tolerance.
... 2 temperature level (Shen et al., 2019a). More examples regarding area localization for WSN applications are summarized in Section 2. For WSN applications such as BEMSs, it is important to accurately identify areas in which a respective anomalous moving object or event is detected. ...
... Definition of area Why area localization is needed When area localization is needed ICCF (Shen et al., 2019a) Physical environment Anomaly detection A hotspot is detected Precooling for BEMS (Vishwanath et al., 2019) Physical environment, sensing coverage Anomaly detection Temperature anomaly is detected KRIPIS Smart Home (Tegou et al., 2019) Sensing coverage Monitoring the behaviour of users User enters the room Smart Syndesi (Zhao et al., 2017) Physical environment, sensing coverage (a) User tracking, (b) improve sensing accuracy (a) User enters the room, (b) a sensor is moved Strobe (Ding and Chandra, 2019) Transmission coverage, sensing coverage Improve sensing accuracy A sensor is moved Precision Agriculture (Abouzar et al., 2016) Transmission coverage, physical environment WSN maintenance After sensor replacement Badgers monitoring (Dyo et al., 2010) Sensing coverage WSN maintenance After sensor replacement Bird monitoring (Cerpa et al., 2001) Transmission coverage Bird tracking A bird enters the sensing area Rabbit tracking (Garcia-Sanchez et al., 2010) Transmission coverage, sensing coverage Improve sensing accuracy A sensor is moved 2.1.1. Smart home A typical smart home includes multiple sensors installed in rooms to detect anomalous events or objects. ...
... Smart home A typical smart home includes multiple sensors installed in rooms to detect anomalous events or objects. BEMS (Shen et al., 2019a;Vishwanath et al., 2019) is designed to detect anomalies in temperature or humidity and control the respective air conditioner closest to the anomaly area, such as in a given thermal zone (Vishwanath et al., 2019). The WSNs for smart home consist of several areas that depend on several factors, such as the room layout and the placement of variable air volume (VAV) boxes. ...
Localization has long been considered as a crucial research problem for pervasive sensing systems, especially with the arrival of big data era. Various techniques have been proposed to improve the localization accuracy by leveraging common wireless signals, such as radio signal strength indication (RSSI), collected from sensors placed in pervasive environments. However, the measured signal value can be easily affected by noise caused by physical obstacles in such sensing environment, which in turn compromises the localization performance. Hence, we present a novel RSSI-based area localization scheme using deep neural network (DNN) to explore the underlying correlation between the RSSI data and the respective sensor placement to achieve a superior localization performance. Moreover, to cope with the sensor data loss issue that commonly occurs during wireless sensor network (WSN) operation, an algorithm is designed to reconstruct the missing data for respective sensors in order to preserve the performance of DNN localization model. The effectiveness of the proposed scheme is verified with a real-world WSN testbed deployed inside an office building. The results demonstrate that the proposed scheme provides satisfactory prediction accuracy in area localization for pervasive sensing systems, regardless of the data loss issue that occurs with the respective sensors.
... Al Ridhawi et al. [13] Enhance QoS and load balancing, reduce energy consumption SLA configuration process based on reinforced learning Real-time IoT services Yes Yousefpour et al. [14] Reduce service delay Delay-minimizing fog offloading policy Real-time IoT services Yes Al-khafajiy et al. [15] Minimize delay for IoT services Fog resource management scheme Real-time IoT services Yes for indoor localization in which the experimental results indicated that energy saving can be achieved by offloading data over to the fog. Meanwhile, fog-to-fog horizontal data communication can further enhance the distributed processing capability at fog nodes while reducing dependency on the cloud [17]. For this reason, the impact of utilizing fog-to-fog collaboration is also widely discussed with the overarching goal of either service delay reduction [14], [15] or energy consumption reduction [13]. ...
... Similar approach was adopted in [20] for an RNN-based IoT device's intrusion detection application in which data training and detection function are performed locally on each fog node. Interestingly, Shen et al. [17] designed an in-network self-learning algorithm for anomaly detection for building energy management system (BEMS) so that fog node could communicate with each other horizontally via the information-centric networking. In their work, the detection function was fed with the data from not only the target fog node itself but also its neighboring ones, leading to a significant improvement of detection accuracy. ...
The emergence of fog computing has brought unprecedented opportunities to the Internet of Things (IoT) field, and it is now feasible to incorporate deep learning at the edge of the IoT network to provide a wide range of highly tailored services. In this paper, we present a fog-based democratically collaborative learning scheme in which fog nodes collaborate on the model training process even without the support of the cloud, contributing to the advances of IoT in terms of realizing a more intelligent edge. To achieve that, we design a voting strategy so that a fog node could be elected as the coordinator node based on both distance and computational power metrics to coordinate the training process. Also, a collaborative learning algorithm is proposed to generalize the training of different deep learning models in the fog-enabled IoT environment. We then implement two popular use cases, including a user trajectory prediction and a distributed image recognition, to demonstrate the feasibility, practicality and effectiveness of the scheme. More importantly, the experiments on both use cases are conducted through a real-world, in-door fog deployment. The result shows that the scheme can utilize fog to obtain a well-performing deep learning model in the cloudless IoT environment while mitigating the data locality issue for each fog node.
... Similar approach is adopted in [4] for an RNNbased intrusion detection application in which data training and the detection function are performed locally on each fog node. Interestingly, the authors in [5] designed an in-network self-learning algorithm for anomaly detection for building energy management system (BEMS) so that fog node could communicate with each other horizontally via the informationcentric networking. In their work, the detection function is fed with the data from not only the target fog node itself but also its neighboring ones. ...
... Debrito et al. [12] introduced fog computing and utilized programmable fog nodes to form a point-to-point network to enhance the computing capability of fog nodes and provide momentum for building a distributed fog framework. Shen et al. [13] integrated an information-centric collaborative fog (ICCF) platform to enhance data processing performance and reduce data connection overhead by a novel horizontal fog-to-fog layer with distributed nodes. These reasonable fog architectures can offload extensive data generated by the end devices to the fog network so as to dramatically reduce the latency and data size. ...
Nowadays, critical information services such as emergency management and in-situation navigation rely heavily on the assumption of a reliable networking infrastructure and stable cloud processing, which is ineffective in infrastructure-less environments where disrupted or even no telecommunication connectivity is the norm. On the other hand, fog computing is an extension of cloud computing that is at the physical proximity of end-users to enable local storage, computing, and various forms of communication between devices and users. To this end, we design a fog system spanning hardware, software and networking, thus meeting the requirements of various stakeholders in difficult surroundings. As a complement to a cloud-centred solution, this system is geo-distributed, self-powered, self-managed, location-aware, highly efficient and able to provide situational information services without Internet connectivity. The proposed system has been implemented in national parklands of Australia to achieve a personalized information service, emergency management and in-park navigation for all types of parkland users.
