@article{Diamantoulakis2021,

title = {Optimal Design and Orchestration of Mobile Edge Computing with Energy Awareness},

author = {P. Diamantoulakis and P. Bouzinis and P. Sarigiannidis and Z. Ding and G. Karagiannidis},

url = {https://www.researchgate.net/publication/352908814_Optimal_Design_and_Orchestration_of_Mobile_Edge_Computing_with_Energy_Awareness},

doi = {10.1109/TSUSC.2021.3103476},

year  = {2021},

date = {2021-08-13},

journal = {IEEE Transactions on Sustainable Computing },

volume = {1},

number = {1},

abstract = {In this work, we propose the use of an advanced multiple access technique and its joint design with adaptive task offloading, in order to reduce delay and energy consumption. More specifically, the use of generalized hybrid orthogonal/non-orthogonal multiple access (OMA/NOMA) for MEC is introduced, which is theoretically superior to other alternatives from the existing literature. In more detail, the proposed scheme is based on the joint utilization of dynamic user scheduling among OMA/NOMA phases and variable decoding order during the successive interference cancellation in NOMA phase. Also, the systems orchestration is optimized for both full and partial task offloading. Specifically, in full offloading scenario, the user scheduling, time allocation, and power control are jointly optimized. Regarding partial offloading, the computational resources, i.e., the clock speed of the local processors and the number of offloaded bits, are jointly optimized with the communication resources, taking into account the constraint of the energy that is consumed for both local processing and task offloading, which is particularly challenging due to the non-convex nature of the corresponding optimization problem. All optimization problems are efficiently solved by either using closed-form solutions that provide useful insights or low-complexity algorithms.},

keywords = {energy consumption, mobile edge computing, multiple access, task offloading},

pubstate = {published},

tppubtype = {article}

}

@article{Moysiadis2021b,

title = {Extending ADR mechanism for LoRa enabled mobile end-devices},

author = {V. Moysiadis and T. Lagkas and V. Argyriou and A. Sarigiannidis and I. Moscholios and P. Sarigiannidis},

url = {https://www.researchgate.net/publication/353860976_Extending_ADR_mechanism_for_LoRa_enabled_mobile_end-devices},

doi = {10.1016/j.simpat.2021.102388},

year  = {2021},

date = {2021-08-08},

journal = {Simulation Modelling Practice and Theory},

volume = {102388},

abstract = {A considerable percentage of Internet of Things end-devices are characterised by mobility, a feature that adds extra complexity to protocols used in Wireless Sensor Networks. LoRa is one of the newly introduced wireless sensor protocols, capable of delivering messages in long distances and consuming low energy, features that make it proper for low cost devices. Although LoRa was introduced as a technology for stationary devices, it can also be used for mobile devices of low speed. In this paper, we introduce an enhancement to Adaptive Data Rate (ADR) mechanism to enable mobile LoRa, by improving the connection reliability of mobile end-devices, while keeping energy consumption at low levels. Firstly, we propose the Linear Regression - ADR (LR-ADR) mechanism for the Network Server side to smooth the Signal to Noise Ratio (SNR) estimates per gateway and predict the SNR of the next transmission. Secondly, we propose the Linear Regression + ADR (LR+ADR) mechanism, an adaptive method for the end-device side to regain the connectivity faster with the Network Server. We conducted simulation modelling to evaluate the performance of our implementation while we compared our results with four alternative solutions ADR, ADR+, EMA-ADR, G-ADR. The results prove that our first approach (LR-ADR) performs better than the best competitor, and our second approach (LR+ADR) brings an additional improvement in terms of Packet Delivery Ratio (PDR), while they retain the Energy Consumption per Packet Delivered (ECPD) at low levels. In particular, in a scenario that mimics real world conditions, LR+ADR presents an increase of up to 520% for PDR compared to the original ADR and an improvement of up to 38% compared to the best competitor (G-ADR). Moreover, it reduces ECPD up to 74% compared to the original ADR, while keeping it at the same level with the best competitor (G-ADR).},

keywords = {adaptive data rate, energy consumption, LoRa, LoRaWAN, mobile devices},

pubstate = {published},

tppubtype = {article}

}