Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks

Zeng, Feng; Ren, Yongzheng; Deng, Xiaoheng; Li, Wenjia

doi:10.3390/s19010032

Cited by 68 publications

(59 citation statements)

References 38 publications

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“…Although the main motivation scenario of the continuous data flow problem in this paper is E-health monitoring system, the continuous data flow problem can also be extended to more application scenarios, such as mobile social network [40], intelligent industrial monitoring system [41], etc. We will further consider the location of fog nodes and MEC nodes in continuous data flow problem [42]. Furthermore, we will conduct more simulations in an event-driven simulator [43], such as the YAFS [44], in our future work.…”

Section: Discussionmentioning

confidence: 99%

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Luo

Qiu

2019

Sensors

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The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy.Sensors 2020, 20, 122 2 of 20 aggregation site, etc. [5]. On the other side, the fog computing nodes are not necessarily deployed around these access points. To distinguish these two concepts, in this paper, nodes with sufficient communication and computing resources scattered among IoT devices are referred to as fog nodes. The nano-server clusters deployed at the access points are referred to as MEC nodes, which usually have more resources than fog nodes. The collaborative computing of the IoT-end nodes, the fog nodes, the MEC nodes and the data center are referred to as IoT-Fog-Edge computing in this context.The studies on collaboration of Fog, MEC, and Cloud computing usually focus on finding an optimal solution to allocate the IoT tasks to appropriate virtual machines which are hosted on fog, MEC or cloud nodes. In [10], the authors propose to allocate the workload among local MEC servers, neighborhood MEC servers or cloud servers to minimize the energy consumption of MEC nodes subject to delay constraints. Lyapunov drift-plus-penalty-based dynamic queue evaluation is used for the online allocation algorithm. In [11], an optimal algorithm is put forward to determine that the tasks should be allocated to clouds near the end devices or to the one far from the end devices for the energy efficient big data processing. The delay constraints to tasks in near clouds and far cloud are taken into account. In [12], an optimal algorithm for joint task allocation among mobile devices, the computing access point and the remote cloud is proposed, where computing access point can be treated as an MEC node. The studies introduced above are not correlated with the continuous data-fl...

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Section: Discussionmentioning

confidence: 99%

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Luo

Qiu

2019

Sensors

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“…Similarly, knowledge-sharing strategies to take advantage of self-taught knowledge between EC devices, such as pertaining to home IoT, have been considered [80]. Undoubtedly, EC-specialized or related optimization problems formulation and solution will be valuable [72,81,82,83], as discussed in the case of optimization consideration for platooning for automatic driving [84], evolutionary game theoretical proposals for mobile devices and security [85,86], better estimation of interference in EC devices [87], cost-effective placement of EC servers [88], computational power allocation for blockchains [89], and incorporation of computer vision [90].…”

Section: Edge Computingmentioning

confidence: 99%

“…To avoid wasting cache space when the content is not popular enough, the proposed method first caches initial “chunks” of the content at the edge node and then progressively continues caching subsequent chunks at upstream according to the content popularity and each content node position [98]. Also, the cost-effective placement of edge servers has been proposed for metropolitan area network [88].…”

Section: Edge Computingmentioning

confidence: 99%

Nanosystems, Edge Computing, and the Next Generation Computing Systems

Passian

Imam

2019

Sensors

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It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.

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“…Based on (11), (23) and (24), we compute the first and second derivative of the P(m i ) with respect to d m i as (25) and (26) respectively.…”

Section: ) F-st Stackelberg Gamementioning

confidence: 99%

“…Notably, as far as the speed of establishing a contributor group is concerned, WoM can do much faster than DM, since the contributors with WoM can communicate directly with each other and flood the task invitations to everybody nearby rapidly. With the development of communication network [8]- [11], it is efficient to invite other contributors with WoM. It should be pointed out that WoM has good flexibility in satisfying the requirements of different crowdsourcing tasks with different invitation channels.…”

Section: Introductionmentioning

confidence: 99%

How Mobile Contributors Will Interact With Each Other in Mobile Crowdsourcing With Word of Mouth Mode

Zeng

Wang

2019

IEEE Access

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Mobile crowdsourcing is a promising paradigm for collecting sensing data by leveraging contributions of numerous mobile smart phones. It works efficiently with Word of Mouth Mode (WoM), especially for sensing tasks with time and location constraints, since the sensing task can be spread quickly among mobile contributors in the WoM mode. In this paper, we first investigate the behaviors of contributors, categorize all contributors into four types according to their different behaviors, and propose an inviting algorithm for contributors to recruit cooperators through social closeness. Then, we design a reward mechanism for crowdsourcing platform to evaluate the budget and pay the reward to contributors, meanwhile stimulate contributors to make the maximum contribution. Furthermore, considering two different scenarios, we model the interactions among contributors as two Stackelberg games, and backward induction approach is used to analyze each game. We propose an algorithm to compute the best response of every contributor, and we theoretically prove that this proposed algorithm may converge a unique Stackelberg equilibrium. The proposed approach can be applied to task formulation and task budget evaluations for crowdsourcing platforms. INDEX TERMS Mobile crowdsourcing, word of mouth, game theory, Stackelberg game.

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Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks

Cited by 68 publications

References 38 publications

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Nanosystems, Edge Computing, and the Next Generation Computing Systems

How Mobile Contributors Will Interact With Each Other in Mobile Crowdsourcing With Word of Mouth Mode

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