@article{9820768,

title = {Joint Wireless Resource and Computation Offloading Optimization for Energy Efficient Internet of Vehicles},

author = {Dimitrios Pliatsios and Panagiotis Sarigiannidis and Thomas D Lagkas and Vasileios Argyriou and Alexandros-Apostolos A Boulogeorgos and Peristera Baziana},

url = {https://www.researchgate.net/publication/361864374_Joint_Wireless_Resource_and_Computation_Offloading_Optimization_for_Energy_Efficient_Internet_of_Vehicles},

doi = {10.1109/TGCN.2022.3189413},

issn = {2473-2400},

year  = {2022},

date = {2022-07-08},

journal = {IEEE Transactions on Green Communications and Networking},

volume = {6},

number = {3},

pages = {1468-1480},

abstract = {The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.},

keywords = {6G, B5G, block coordinate descent, Computation offloading},

pubstate = {published},

tppubtype = {article}

}

@article{Jaddoa2020,

title = {Dynamic decision support for resource offloading in heterogeneous Internet of Things environments},

author = { A. Jaddoa and G. Sakellari and E. Panaousis and G. Loukas and P.G. Sarigiannidis},

url = {https://www.researchgate.net/publication/337218323_Dynamic_decision_support_for_resource_offloading_in_heterogeneous_Internet_of_Things_environments},

doi = {10.1016/j.simpat.2019.102019},

year  = {2020},

date = {2020-01-01},

journal = {Simulation Modelling Practice and Theory},

volume = {101},

abstract = {Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices’ resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time condition of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. Here, we model the expected time and energy costs for the different options of offloading a task to the edge or the cloud, as well as of carrying out on the device itself. We use this model to allow the device to take the offloading decision dynamically as a new task arrives and based on the available information on the network connections and the states of the edge and the cloud. Having extended EdgeCloudSim to provide support for such dynamic decision making, we are able to compare this approach against IoT-first, edge-first, cloud-only, random and application-oriented probabilistic strategies. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, and the energy consumption not only of the individual IoT devices but also of the system as a whole. This paves the way for future IoT devices that optimise their application response times, as well as their own energy autonomy and overall energy efficiency, in a decentralised and autonomous manner. © 2019 Elsevier B.V.},

keywords = {Cloud Computing, Computation offloading, Decision support, Edge computing, EdgeCloudSim simulator, Internet of things, IoT offloading},

pubstate = {published},

tppubtype = {article}

}