Optimized 3D Deployment of UAV-Mounted Cloudlets to Support Latency-Sensitive Services in IoT Networks

Abstract

The paradigm of Internet of Things (IoT) is transforming physical environments into smart and interactive platforms to offer a wide range of innovative services supported by the evolution towards 5G networks. A major class of emerging services relies on highly intensive computations to make real-time decisions with ultra-low latency. Edge computing has been established as an effective approach to reduce the latency overhead of cloud computing and effectively augment the computational capabilities of IoT devices. In this work, we leverage the mobility and agility of Unmanned Aerial Vehicles (UAVs) as mobile edge servers or cloudlets to offer computation offloading opportunities to IoT devices. In particular, we consider the joint problem of optimizing the number and positions of deployed UAV cloudlets in 3D space and task offloading decisions with cooperation among UAVs, in order to provision IoT services with strict latency requirements. We formulate the problem as a mixed integer program, and propose an efficient meta-heuristic solution based on the ions motion optimization algorithm. The performance of the meta-heuristic solution is evaluated and compared to the optimal solution as a function of various system parameters and for different application use cases. It is shown to achieve near-optimal performance with low complexity and, thus, can efficiently scale up to large IoT network scenarios.INDEX TERMS Edge computing, unmanned aerial vehicles, 3D deployment, IoT network.

An Empirical Study on System Level Aspects of Internet of Things (IoT)

“…The authors in [246] have summarized the problems associated with the use of Unmanned Aerial Vehicles (UAVs) to provide the latency-sensitive services using IoT. In this, UAVs are used as cloudlets, which offers the computation offloading ability to resource constraint IoT devices.…”

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

“…Some work does not consider dividing to simplify the scheme [58]. The task generation speed [79] or distribution [82] have impact on the workload over the network, and may help prevent bottlenecks.…”

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

“…UAV-based cloudlets have been established in [73] as an efficient approach to reduce the latency and effectively enlarge the computational abilities of IoT devices. In particular, the problem of jointly optimizing the number and positions of deployed UAV cloudlets in 3D space in order to support IoT services with stringent latency requirements is discussed.…”

“…In particular, the problem of jointly optimizing the number and positions of deployed UAV cloudlets in 3D space in order to support IoT services with stringent latency requirements is discussed. The method of [73] exhibits good performance with low complexity, under some restrictive assumptions. For instance, it is assumed in that the uplink bandwidth is equally distributed among the nodes and, moreover, the average time services and QoS of the different users are equal.…”