With the rapid development of the Web of Things, there have been a lot of sensors deployed. Advanced knowledge can be achieved by deep learning method and easier integration with open Web standards. A large number of ...
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ISBN:
(纸本)9781450383134
With the rapid development of the Web of Things, there have been a lot of sensors deployed. Advanced knowledge can be achieved by deep learning method and easier integration with open Web standards. A large number of the data generated by sensors required extra processing resources due to the limited resources of the sensors. Due to the limitation of bandwidth or requirement of low latency, it is impossible to transfer such large amounts of data to cloud servers for processing. Thus, the concept of distributed fog computing has been proposed to process such big data into knowledge in real-time. Large scale fogcomputing system is built using cheap devices, denotes as fog nodes. Therefore, the resiliency to fog node failures should be considered in design of distributed fog computing. LT codes (LTC) have important applications in the design of modern distributedcomputing, which can reduce the latency of the computing tasks, such as matrix multiplication in deep learning methods. In this paper, we consider that fog nodes may be failure, and an improved LT codes are applied to matrix multiplication of distributed fog computing process to reduce latency. Numerical results show that the improved LTC based scheme can reduce average overhead and degree simultaneously, which reduce the latency and computation complexity of distributed fog computing.
Swarm of drones, as an intensely significant category of swarm robots, is widely used in various fields, e.g., search and rescue, detection missions, military, etc. Because of the limitation of computing resource of d...
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Swarm of drones, as an intensely significant category of swarm robots, is widely used in various fields, e.g., search and rescue, detection missions, military, etc. Because of the limitation of computing resource of drones, dealing with computation-intensive tasks locally is difficult. Hence, the cloud-based computation offloading is widely adopted, nevertheless, for some latency-sensitive tasks, e.g., object recognition, path planning, etc., the cloud-based manner is inappropriate due to the excessive delay. Even in some harsh environments, e.g., disaster area, battlefield, etc., there is no wireless infrastructure existed to combine the drones and cloud center. Thus, to solve the problem encountered by cloud-based computation offloading, in this paper, fogcomputing aided Swarm of Drones (FCSD) architecture is proposed. Considering the uncertainty factors in harsh environments which may threaten the success of FCSD processing tasks, not only the latency model, but also the reliability model of FCSD is constructed to guarantee the high reliability of task completion. Moreover, in view of the limited battery life of the drone, we formulated the problem as the task allocation problem which minimized the energy consumption of FCSD under the constraints of latency and reliability. Furthermore, to speed up the process of the optimization problem solving to improve the practicality, relying on the recent advances in distributed convex optimization, we develop a fast Proximal Jacobi Alternating Direction Method of Multipliers (ADMM) based distributed algorithm. Finally, simulation results validate the effectiveness of our proposed scheme.
Internet of Mobile Things (IoMTs) refers to the interconnection of mobile devices, for example, mobile phones, vehicles, robots, etc. For mobile data, strong extra processing resources are normally required due to the...
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Internet of Mobile Things (IoMTs) refers to the interconnection of mobile devices, for example, mobile phones, vehicles, robots, etc. For mobile data, strong extra processing resources are normally required due to the limited physical resources of the mobile devices in IoMTs. Due to latency or bandwidth limitations, it may be infeasible to transfer a large amounts of mobile data to remote server for processing. Thus, distributedcomputing is one of the potential solutions to overcome these limitations. We consider the device mobility in IoMTs. Two situations of the movement position of the mobile devices, i.e., unpredictable and predictable, are considered. In addition, three possible relative positions between the two server sets which respectively correspond to the positions of a mobile device for computation tasks offloading and for output results receiving, i.e., within the same server sets, with two different server sets and with two adjacent server sets, are studied. Coded schemes with high flexibility and low complexity are proposed based on Fountain codes to reduce the total processing time and latency of the distributed fog computing process in IoMTs for the above different situations. The latency related performance, i.e., the computation, the communication and the transmission loads, is analyzed. We also compare of the Fountain code-based and the uncoded schemes and numerical results demonstrate that shorter total processing time and lower latency can be achieved by the Fountain code-based schemes.
The large volumes of Internet of Things (IoT) data transmission to and from the cloud leads to one of cloud -centric processing's major drawbacks: latency. fogcomputing gives a solution to this by bringing the pr...
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The large volumes of Internet of Things (IoT) data transmission to and from the cloud leads to one of cloud -centric processing's major drawbacks: latency. fogcomputing gives a solution to this by bringing the processing closer to the edge devices. Although a lot of work has been done which makes use of fog nodes like aggregators and offloaders, data intensive tasks like machine learning are still, for the most part, being performed on cloud. In a fog network, there are multiple fog devices, albeit resource-constrained ones. We can make use of distributed processing to collectively utilize these resources. So, we propose CANTO, a general distributed framework which uses the actor model to train neural networks on a network of fog devices in an IoT setting. Since the actors communicate using messages and act according to the type of message they receive, the framework provides a new message type wherein we can specify parameters like the dataset, the size of each dataset part, the activation function, learning rate, etc. The framework is containerized and deployed in a docker swarm. The framework is demonstrated with the use case on IoT-based forest fire prediction. In addition, the effectiveness of the framework is demonstrated with respect to accuracy, latency and load distribution.
By performing fogcomputing, a device can offload delay-tolerant computationally demanding tasks to its peers for processing, and the results can be returned and aggregated. In distributed wireless networks, the chall...
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By performing fogcomputing, a device can offload delay-tolerant computationally demanding tasks to its peers for processing, and the results can be returned and aggregated. In distributed wireless networks, the challenges of fogcomputing include lack of central coordination, selfish behaviors of devices, and multi-hop signaling delays, which can result in outdated network knowledge and prevent effective cooperations beyond one hop. This paper presents a new approach to enable cooperations of N selfish devices over multiple hops, where selfish behaviors are discouraged by a tit-for-tat mechanism. The titfor-tat incentive of a device is designed to be the gap between the helps (in terms of energy) the device has received and offered;and indicates how much help the device can offer at the next time slot. The tit-for-tat incentives can be evaluated at every device by having all devices broadcast how much help they offered in the past time slot, and used by all devices to schedule task offloading and processing. The approach achieves asymptotic optimality in a fully distributed fashion with a time-complexity of less than O(N-2). The optimality lass resulting from multi-hop signaling delays and consequently outdated titfor-tat incentives is proved to asymptotically diminish. Simulation results show that our approach substantially reduces the time-average energy consumption of the state of the art by 50% and accommodates more tasks, by engaging devices hops away under multi-hop delays.
Nonvolatile processors have emerged as one of the promising solutions for energy harvesting scenarios, among which Wireless Sensor Networks (WSN) provide some of the most important applications. In a typical distribut...
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ISBN:
(纸本)9781450349116
Nonvolatile processors have emerged as one of the promising solutions for energy harvesting scenarios, among which Wireless Sensor Networks (WSN) provide some of the most important applications. In a typical distributed sensing system, due to difference in location, energy harvester angles, power sources, etc. different nodes may have different amount of energy ready for use. While prior approaches have examined these challenges, they have not done so in the context of the features offered by nonvolatile computing approaches, which disrupt certain foundational assumptions. We propose a new set of nonvolatility-exploiting optimizations and embody them in the NEOfog system architecture. We discuss shifts in the tradeoffs in data and program distribution for nonvolatile processing-based WSNs, showing how nonvolatile processing and non-volatile RF support alter the benefits of computation and communication-centric approaches. We also propose a new algorithm specific to nonvolatile sensing systems for load balancing both computation and communication demands. Collectively, the NV-aware optimizations in NEOfog increase the ability to perform in-fog processing by 4.2X and can increase this to 8X if virtualized nodes are 3X multiplexed.
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