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Comparison of running time of IPS and DPS architectures versus the number of subcarriers N with R located at the midpoint from S to D and PS=10dBm.
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The mobile fog computing-assisted resource allocation (RA) is studied for simultaneous wireless information and power transfer (SWIPT) two-hop orthogonal frequency division multiplexing (OFDM) networks, where a decode-and-forward (DF) relay first harvests energy from signals emitted by a source and then helps the source to forward information to it...
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Citations
... MCC can offer rich computational resources to scarce-resource underwater applications. However, its relevance to IoUT is low because of the long propagation distance from the distributed BMD sources to the remote cloud servers [117], as well as the narrow underwater bandwidth and limited access to energy. Another drawback of MCC is its service accessibility, which is via Internet connection only. ...
The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.
... MCC can offer rich computational resources to scarce-resource underwater applications. However, its relevance to IoUT is low because of the long propagation distance from the distributed BMD sources to the remote cloud servers [117], as well as the narrow underwater bandwidth and limited access to energy. Another drawback of MCC is its service accessibility, which is via Internet connection only. ...
The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine trans-portations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a mid-sized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-the-art applications, tools, and techniques. Finally, we analyze of the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.
... Although in [40]- [43], the FC systems with RF-based EH were studied, where however, only wireless power transfer (WPT) was adopted rather than SWIPT. Most recently, a few works studied fog computing-assisted SWIPT networks see e.g., [44]- [48]. However, they did not jointly design the task offloading or only considered the single user scenario or only involved the TS SWIPT receiver. ...
... However, they did not jointly design the task offloading or only considered the single user scenario or only involved the TS SWIPT receiver. Specifically, in [44], the authors studied the resource allocation for twohop fog-assisted SWIPT OFDM networks, where however, the computation offloading was not involved. In [45], the power minimization problem was studied in a SWIPT-aided fog computing networks with dog offloading, where however, the fog server was just used to assign tasks rather than participate in computing. ...
... Assuming that perfect channel state information (CSI) is known by MU m, which can be realized by channel estimation and fed back to HAP and FS. Such assumptions have been widely adopted for the optimal design and performance limit analysis of wireless communication systems, see e.g., [44]- [48]. The received signal at MU m is given by ...
This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors' transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closedform solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.
... Owing to the advantages of fog computing and SWIPT, inheriting their benefits is expected to provide an efficient way to simultaneously enhance the computing capacity and prolong the lifetime of energy constrained networks. So far, some works have studied the SWIPT-aware fog/MEC systems [30][31][32]. In Janatian et al. [30], the authors studied the optimal resource allocation in ultra-low power fog-computing SWIPT-based networks, where, however, only the TS receiver architecture was adopted. ...
... In Janatian et al. [30], the authors studied the optimal resource allocation in ultra-low power fog-computing SWIPT-based networks, where, however, only the TS receiver architecture was adopted. In Di et al. [31], the authors studied the fog-assisted resource allocation for two-hop SWIPT orthogonal frequency division multiplexing (OFDM) networks. Although the PS receiver architecture was considered in Di et al. [31], the computing task offoading was not involved. ...
... In Di et al. [31], the authors studied the fog-assisted resource allocation for two-hop SWIPT orthogonal frequency division multiplexing (OFDM) networks. Although the PS receiver architecture was considered in Di et al. [31], the computing task offoading was not involved. In Chai et al. [32], the power minimization problem was studied in a SWIPT-aided fog computing networks is considered. ...
This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor’s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed.
One of the most promising frameworks is the fog computing paradigm for time-sensitive applications such as IoT (Internet of Things). Though it is an extended type of computing paradigm, which is mainly used to support cloud computing for executing deadline-based user requirements in IoT applications. However, there are certain challenges related to the hybrid IoT -cloud environment such as poor latency, increased execution time, computational burden and overload on the computing nodes. This paper offers A Layer & Request priority-based framework for Dynamic Resource Allocation Method (LP-DRAM), a new approach based on layer priority for ensuring effective resource allocation in a fog-cloud architecture. By performing load balancing across the computer nodes, the suggested method achieves an effective resource allocation. Unlike conventional resource allocation techniques, the proposed work assumes that the node type and the location are not fixed. The tasks are allocated based on two constrain, duration and layer priority basis i.e, the tasks are initially assigned to edge computing nodes and based on the resource availability in edge nodes, the tasks are further allocated to fog and cloud computing nodes. The proposed approach's performance was analyzed by comparing it to existing methodologies such as First Fit (FF), Best Fit (BF), First Fit Decreasing (FFD), Best Fit Decreasing (BFD), and DRAM techniques to validate the effectiveness of the proposed LP-DRAM.
This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors’ required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors’ transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closed-form solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.
