Internet-Of-Things in Motion: A UAV Coalition Model for Remote Sensing in Smart Cities

Ismail, Anis; Bagula, Antoine; Tuyishimire, Emmanuel

doi:10.3390/s18072184

Cited by 41 publications

(19 citation statements)

References 40 publications

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“…This work has been proposed as part of the Internet-of-Things in Motion, a project that targets both data muling/ferrying using a team of UAVs working in a coalesced manner such as in [33,34], or independently based on a competitive model as suggested in [35], or a collaborative model as proposed by [36,37]. The integration of the proposed networking engineering model to enhance service differentiation in complex sensor networking scenarios with mixed devices as suggested in [38] by balancing sensor roles and UAV proximity is another avenue for future work.…”

Section: Discussionmentioning

confidence: 99%

Clustered Data Muling in the Internet of Things in Motion

Tuyishimire

Bagula

Ismail

2019

Sensors

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This paper considers a case where an Unmanned Aerial Vehicle (UAV) is used to monitor an area of interest. The UAV is assisted by a Sensor Network (SN), which is deployed in the area such as a smart city or smart village. The area being monitored has a reasonable size and hence may contain many sensors for efficient and accurate data collection. In this case, it would be expensive for one UAV to visit all the sensors; hence the need to partition the ground network into an optimum number of clusters with the objective of having the UAV visit only cluster heads (fewer sensors). In such a setting, the sensor readings (sensor data) would be sent to cluster heads where they are collected by the UAV upon its arrival. This paper proposes a clustering scheme that optimizes not only the sensor network energy usage, but also the energy used by the UAV to cover the area of interest. The computation of the number of optimal clusters in a dense and uniformly-distributed sensor network is proposed to complement the k-means clustering algorithm when used as a network engineering technique in hybrid UAV/terrestrial networks. Furthermore, for general networks, an efficient clustering model that caters for both orphan nodes and multi-layer optimization is proposed and analyzed through simulations using the city of Cape Town in South Africa as a smart city hybrid network engineering use-case.

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Section: Discussionmentioning

confidence: 99%

Clustered Data Muling in the Internet of Things in Motion

Tuyishimire

Bagula

Ismail

2019

Sensors

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“…They were initially used for a defined purpose by the US Department of Defense. They are currently being implemented into Smart Cities variable area concepts, thus contributing to the fulfillment of set goals [32,33]. UAVs were primarily used in the military field, and currently monitor and perform services for healthcare, mobility, supply of goods and security.…”

Section: Unmanned Aerial Vehicle (Uav)mentioning

confidence: 99%

Advanced Technologies and Their Use in Smart City Management

2021

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Building Smart City management concepts is based on the implementation and use of advanced technologies. The primary impulse for writing the article was the ambition to identify the current advanced technologies of Smart City management. The aim of the article is to propose a general model for the implementation of advanced technologies for Smart City management, based on the knowledge gained from the analysis of literature and case studies. In order to fulfill the set goal, it is necessary to obtain answers to two research questions. The findings were obtained through a secondary analysis of the literature, i.e., relevant articles from the scientific databases Web of Science and Scopus analysis of case studies of the best Smart Cities practices. According to the Smart City Index 2020 and IESE Cities in Motion, the leaders among the Smart Cities are Singapore and London, followed by Helsinki. In addition to the analyses, the article also uses methods of summarization, comparison, creativity, logic, induction and deduction. Smart Cities use 12 identified advanced technologies in their practice. Strategic management in Singapore, London and Helsinki adapts technology to the needs and requirements of its citizens, thus connecting the technological aspect with the managerial and social aspects. The contributions of the work include results for fellow researchers and a model for strategic management of new Smart Cities. The results of the article provide fellow researchers with the findings of a secondary analysis of relevant articles, from which they can draw when writing their own publications without the need for time-consuming search of the articles about this topic in databases. The general model of implementation of advanced technologies serves as a basis for strategic management of new Smart Cities that want to implement a technological base and at the same time do not want to forget the managerial and social aspects. Testing the model in practice with a new Slovak Smart City is part of future research activities.

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“…where means to remove the task from the set. Then merge the new target T new into the set T according to Equation (27): (27) Finally, the tasks generated by the targets in the set T are treated as new tasks, and the offline task allocation algorithm is used for allocation.…”

Section: Set B Aamentioning

confidence: 99%

UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm

Yao-zhong

Wen-cheng

Shi

et al. 2020

Sensors

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To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.

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Internet-Of-Things in Motion: A UAV Coalition Model for Remote Sensing in Smart Cities

Cited by 41 publications

References 40 publications

Clustered Data Muling in the Internet of Things in Motion

Clustered Data Muling in the Internet of Things in Motion

Advanced Technologies and Their Use in Smart City Management

UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm

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