A Probabilistic Model for the Deployment of Human-Enabled Edge Computing in Massive Sensing Scenarios

Belli, Dimitri; Chessa, Stefano; Girolami, Michele

doi:10.1109/jiot.2019.2957835

Cited by 16 publications

(9 citation statements)

References 45 publications

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“…They further extended their work in [20] by introducing the concept of social MEC (SMEC) proxies in the MCS environment, i.e., they add SMEC nodes between other devices and cloud, based on the incentives and centrality measures concerning other people in the group. In [21], the authors focused on the synergy between MCS and MEC and considered a possible extension of the HEC architecture by introducing mobile edge nodes operated by the users' devices that can be selected as substitutes for fixed edge servers. In particular, they proposed a probabilistic model to estimate the number of nodes that need to be promoted as mobile MEC nodes to assist the MCS data gathering.…”

Section: Edge-based Mcs Environmentsmentioning

confidence: 99%

Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario

Antonić

Antonić²,

Žarko

2022

Sensors

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Mobile crowdsensing (MCS) is a sensing paradigm that allows ordinary citizens to use mobile and wearable technologies and become active observers of their surroundings. MCS services generate a massive amount of data due to the vast number of devices engaging in MCS tasks, and the intrinsic mobility of users can quickly make information obsolete, requiring efficient data processing. Our previous work shows that the Bloom filter (BF) is a promising technique to reduce the quantity of redundant data in a hierarchical edge-based MCS ecosystem, allowing users engaging in MCS tasks to make autonomous informed decisions on whether or not to transmit data. This paper extends the proposed BF algorithm to accept multiple data readings of the same type at an exact location if the MCS task requires such functionality. In addition, we thoroughly evaluate the overall behavior of our approach by taking into account the overhead generated in communication between edge servers and end-user devices on a real-world dataset. Our results indicate that using the proposed algorithm makes it possible to significantly reduce the amount of transmitted data and achieve energy savings up to 62% compared to a baseline approach.

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Section: Edge-based Mcs Environmentsmentioning

confidence: 99%

Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario

Antonić

Antonić²,

Žarko

2022

Sensors

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“…One of the key issues of such layered architecture is how to select the end M2EC devices among all the devices of the MCS subscribers [ 62 ]. Clearly, the selection of M2ECs should be carried out in such a way to increase the opportunities of (short- range) communication between them and the other end-devices.…”

Section: Usage Scenarios and Beyondmentioning

confidence: 99%

“…Clearly, the selection of M2ECs should be carried out in such a way to increase the opportunities of (short- range) communication between them and the other end-devices. In this regard, a consolidated approach consists of exploiting the knowledge of MCS user communities to identify the leaders that may act as proxies for the members of the community they represent [ 62 ]. Since members of a community interact periodically, the resulting dynamic network can be analysed to measure the centrality of its members (e.g., betweenness, eigenvector, k-core scores).…”

Section: Usage Scenarios and Beyondmentioning

confidence: 99%

How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing

Girolami

Belli

Chessa

2021

Sensors

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The possibility of understanding the dynamics of human mobility and sociality creates the opportunity to re-design the way data are collected by exploiting the crowd. We survey the last decade of experimentation and research in the field of mobile CrowdSensing, a paradigm centred on users’ devices as the primary source for collecting data from urban areas. To this purpose, we report the methodologies aimed at building information about users’ mobility and sociality in the form of ties among users and communities of users. We present two methodologies to identify communities: spatial and co-location-based. We also discuss some perspectives about the future of mobile CrowdSensing and its impact on four investigation areas: contact tracing, edge-based MCS architectures, digitalization in Industry 5.0 and community detection algorithms.

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“…erefore, the problems associated with the massive computation and energy consumption of smart devices have been the focus in recent researches [9,10]. e mobile edge computing provides new approach to solve the aforementioned problems, which is also the most forward-looking solution [11,12]. It allows terminal users to offload missions to servers for implementation, which can reduce the energy consumption and time delay [13].…”

Section: Introductionmentioning

confidence: 99%

Computation Offloading Strategy for IoT Using Improved Particle Swarm Algorithm in Edge Computing

2022

Wireless Communications and Mobile Computing

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To address the problems of high energy consumption and time delay of the offloading strategies in traditional edge computing, a computation offloading strategy for the Internet of Things (IoT) using the improved Particle Swarm Optimization (PSO) in edge computing is proposed. First, a system model and an optimization objective function are constructed based on the communication model for the uplink transmission and the multiuser personalized computation task load model while considering constraints from multiple aspects. Then, the PSO is used to update the position of particles by encoding them and calculating the fitness values to find the optimal solution of the task offloading strategies, which greatly reduces the energy consumption during the task allocation process in the system. Finally, the simulations are conducted to compare the proposed method with two other algorithms in terms of the average time delay and energy consumption under different numbers of user mobile devices and data transmission rates. The simulation results showed that the average time delay and energy consumption of the proposed method are the smallest in different cases. And, the average delay and energy consumption are 0.205 s and 0.2 J, respectively, when the number of users’ mobile devices is 80, which are better than the other two comparison algorithms. Therefore, the proposed method can reduce the task execution delay with less energy consumption.

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A Probabilistic Model for the Deployment of Human-Enabled Edge Computing in Massive Sensing Scenarios

Cited by 16 publications

References 45 publications

Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario

Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario

How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing

Computation Offloading Strategy for IoT Using Improved Particle Swarm Algorithm in Edge Computing

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