Internet of Things for Buildings Monitoring

Coates, Adam; Hammoudeh, Mohammad; Holmes, K

doi:10.1145/3102304.3102342

Cited by 23 publications

(6 citation statements)

References 9 publications

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“…In future work, we intend to address the problem of offloading to air and ground devices, e.g., Internet of Thing (IoT)-enabled spaces [35] or city infrastructure [36]. Another avenue of research is to investigate the security of the UAV network especially in regards to authentication.…”

Section: Discussionmentioning

confidence: 99%

Energy- and Latency-Aware Hybrid Offloading Algorithm for UAVs

et al. 2019

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The publication has been prepared with the support of the ''RUDN University Program 5-100.'' ABSTRACT One of the most promising use cases of 5G/IMT2020 is the unmanned aerial vehicle (UAV). Due to their small size, the UAVs are resource constraint devices. To this end, this paper proposes an offloading algorithm for UAVs to assist in the execution of computationally intensive tasks. The proposed algorithm provides two UAV offloading methods. The first offloading method is the air-offloading, where a UAV can offload its computing tasks to nearby UAVs that have available computing and energy resources. The second offloading method is the ground-offloading, which enables the offloading of tasks to an edge cloud server from the multi-level edge cloud units connected to ground stations. The proposed algorithm is energy-and latency-aware, i.e., it selects the execution device and the offloading method based on the latency and energy constraints. The intensive algorithm simulation over reliable conditions for various scenarios with different cases for each scenario is conducted and results are presented. INDEX TERMS UAV, offloading, latency, energy, 5G, MEC. I. INTRODUCTION Unmanned Aerial Vehicles (UAVs), e.g., drones, have gained increasing interest in recent years [1], [2]. With the near release of fifth generation cellular system (5G), UAVs are expected to have many applications. These applications vary from simple environmental monitoring to the complex high security military applications [3]-[5]. There are many challenges associated with the development of UAV networks and applications. These challenges include [6]-[8]: Trajectory or path planning, collision avoidance, mobility control, cost, security, data offloading, energy consumption, latency and compatibility with existing systems and cellular networks. Part of these challenges is associated with the limited capabilities of UAVs, due to their small size, e.g., microdrones, required for many applications [3]. Applications with computationally intensive tasks and applications, e.g., imageor video-based, require high processing and energy resources, which affect real-time operation and lifetime of an UAV system or even cause task blocking. In order to prolong the The associate editor coordinating the review of this manuscript and approving it for publication was Muhammad Imran.

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Section: Discussionmentioning

confidence: 99%

Energy- and Latency-Aware Hybrid Offloading Algorithm for UAVs

et al. 2019

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“…WSN innovations promise to integrate and optimise smart buildings, autonomous vehicles, power grids, etc., to enable a successful transition towards smart, user-driven and demand-focused city infrastructures and services [1,2]. There is a wide range of current smart cities applications, which make our lives easier and more efficient, e.g., a smart phone application that let users find free parking spaces in the centre of town.…”

Section: Scope and Aimmentioning

confidence: 99%

Sensors and Actuators in Smart Cities

Hammoudeh

Arioua

2018

JSAN

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“…l represents the number of all participating IoT devices, k is randomly selected by d s and represents the number of IoT devices that are assigned genuine access predicates such that they have to encipher and sign a neutral group element 1 G , while the remaining l − k devices are assigned dummy access predicates such that they encipher and sign their collected data contents 5 . This phase consists of two randomized algorithms, performed by each involved IoT device, detailed hereafter:…”

Section: Data Signcrypt Phasementioning

confidence: 99%

“…The Internet of Things (IoT) applications are deployed in several fields such as health care, smart cities, smart monitoring [1,5]. IoT systems connect loosely defined objects, gateways and services that may exchange data about peoples' body state, life events, habits, location or professional information.…”

Section: Introductionmentioning

confidence: 99%

Coop-DAAB: Cooperative Attribute Based Data Aggregation for Internet of Things Applications

Belguith

Kaaniche

Mohamed

et al. 2018

Lecture Notes in Computer Science

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The deployment of IoT devices is gaining an expanding interest in our daily life. Indeed, IoT networks consist in interconnecting several smart and resource constrained devices to enable advanced services. Security management in IoT is a big challenge as personal data are shared by a huge number of distributed services and devices. In this paper, we propose a Cooperative Data Aggregation solution based on a novel use of Attribute Based signcryption scheme (Coop-DAAB). Coop-DAAB consists in distributing data signcryption operation between different participating entities (i.e., IoT devices). Indeed, each IoT device encrypts and signs in only one step the collected data with respect to a selected sub-predicate of a general access predicate before forwarding to an aggregating entity. This latter is able to aggregate and decrypt collected data if a sufficient number of IoT devices cooperates without learning any personal information about each participating device. Thanks to the use of an attribute based signcryption scheme, authenticity of data collected by IoT devices is proved while protecting them from any unauthorized access.

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Internet of Things for Buildings Monitoring

Cited by 23 publications

References 9 publications

Energy- and Latency-Aware Hybrid Offloading Algorithm for UAVs

Energy- and Latency-Aware Hybrid Offloading Algorithm for UAVs

Sensors and Actuators in Smart Cities

Coop-DAAB: Cooperative Attribute Based Data Aggregation for Internet of Things Applications

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