Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

Fiandrino, Claudio; Allio, Nicholas; Kliazovich, Dzmitry; Giaccone, Paolo; Bouvry, Pascal

doi:10.1109/access.2019.2892508

Cited by 36 publications

(26 citation statements)

References 29 publications

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“…This section includes challenges in wearable technologies and some future study suggestions. When wearable device technology is combined with fog computing, this integrated structure can be a solution for applications requiring low latency (Fiandrino et al, 2019). However, today there are a lot of challenges with wearable technologies.…”

Section: Challenges In Wearable Technologies and Future Workmentioning

confidence: 99%

A novel IoT-based health and tactical analysis model with fog computing

Karakaya

Akleylek

2021

PeerJ Computer Science

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In sports competitions, depending on the conditions such as excitement, stress, fatigue, etc. during the match, negative situations such as disability or loss of life may occur for players and spectators. Therefore, it is extremely important to constantly check their health. In addition, some strategic analyzes are made during the match. According to the results of these analyzes, the technical team affects the course of the match. Effects can have positive and sometimes negative results. In this article, fog computing and an Internet of Things (IoT) based architecture are proposed to produce new technical strategies and to avoid disabilities. Players and spectators are monitored with sensors such as blood pressure, body temperature, heart rate, location etc. The data obtained from the sensors are processed in the fog layer and the resulting information is sent to the devices of the technical team and club doctors. In the architecture based on fog computing and IoT, priority processes are computed with low latency. For this, a task management algorithm based on priority queue and list of fog nodes is modified in the fog layer. Authentication and data confidentiality are provided with the Federated Lightweight Authentication of Things (FLAT) method used in the proposed model. In addition, using the Software Defined Network controller based on blockchain technology ensures data integrity.

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Section: Challenges In Wearable Technologies and Future Workmentioning

confidence: 99%

A novel IoT-based health and tactical analysis model with fog computing

Karakaya

Akleylek

2021

PeerJ Computer Science

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“…A, N, SW [46], [111] Energy awareness regarding neighboring nodes to select the optimal route [50] Multi parameter cost function for the next hop selection [60] Selective data routing based on the data priority Securityrelated aspects HW, DP, SW [110] Content agnostic privacy and encryption protocol eliminating the need for asymmetric encryption [180], [181] Integration of lightweight cryptography solutions including more appropriate elliptic curve types or algorithm implementations [186] More efficient utilization of manufacturer-provide SoCs accelerated for cryptographic primitives execution [187] Finding trade-offs between the primitive and required level of the provided security Processing limitations HW, DP, SW [54] The use of heterogeneous multicore processor gateway as compared to little cores gateway working as a router [64], [86], [104], [105] Task offloading to leverage high computing resources of nearby devices for improved performance [106] Edge/fog/cloud computing techniques for optimal performance [107] Seamless resource sharing between heterogeneous mobile devices Storage limitations HW [47], [55] Data compression to reduce the size of the dataset for efficient data processing and storage [106] Edge/Fog/Cloud computing techniques for better performance [173] Data summarization and aggregation Lack of hardware acceleration HW, SW [47], [55] Data compression to reduce the size of the data set for more efficient data processing and storage [64], [86], [104], [105] Task offloading to leverage high computing resources of the nearby devices for the improved performance [186] Identifying and use of present hardware acceleration, which may not be accessible by the default Inefficient use of energy consuming modules HW, SW [62] Configurable data acquisition modules [88] Replacing high power consumption modules with low power alternates, e.g., using two accelerometers instead of a gyroscope as...…”

Section: Inefficient Routingmentioning

confidence: 99%

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

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Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged-therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables' energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.

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“…In [14], it proposed a service placement policy in IoT networks based on graph partitions to increase the service availability and QoS satisfaction. In [15], the authors discussed the application partitioning rationale of wearable devices in mobile cloud and fog computing for computation offloading. In order to solve the user association and resource allocation problem for broadband IoT applications in fog computing, a two-side matching game was formulated based on the determination of QoS requirements priorities in [16].…”

Section: A Related Workmentioning

confidence: 99%

“…5. Please note that all the OAS m and RA n will exchange according to formula (15) in the mutation stage.…”

Section: ) Mutationmentioning

confidence: 99%

Optimizing Resources Allocation for Fog Computing-Based Internet of Things Networks

Liu

et al. 2019

IEEE Access

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In the wireless Internet of Things (IoT) networks with resource-constrained devices, fog computing has been introduced to deal with the computation-intensive applications at the edges of the networks. While fog computing decreases the computation delay and fronthaul traffic data, it also brings the severe challenge on complex resource allocation of the available computation and communication resources under the stringent quality of service (QoS) requirements. In this paper, we investigate the problem of tasks scheduling and heterogeneous resource allocation for multiple devices in the wireless IoT networks. The IoT devices that collect a massive amount of data need to make proper offloading decision to transfer the data to the fog computing nodes (FNs). Moreover, to support a massive number of device connections and transfer a huge amount of data with low latency and limited resource, we consider the deployment of non-orthogonal multiple access (NOMA) in IoT networks, which enables multiple IoT devices to simultaneously transmit data to the same FN at the same time, frequency, and code domain. We jointly optimize the allocation of resource blocks and transmit power of multiple IoT devices, subject to the respective QoS requirements. Furthermore, the optimization problem is formulated as a mixed-integer nonlinear programming problem to minimize the system energy consumption. Since it is an NP-hard problem, we introduce an improved genetic algorithm (IGA) to solve it. The simulation results show that the proposed scheme achieves good performance in throughput, delay, outage probability, and energy consumption.INDEX TERMS Fog computing, Internet of things, non-orthogonal multiple access, resource allocation, energy consumption.

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Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

Cited by 36 publications

References 29 publications

A novel IoT-based health and tactical analysis model with fog computing

A novel IoT-based health and tactical analysis model with fog computing

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

Optimizing Resources Allocation for Fog Computing-Based Internet of Things Networks

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