scholarly journals A metaheuristic optimization approach for energy efficiency in the IoT networks

Author(s):  
Celestine Iwendi ◽  
Praveen Kumar Reddy Maddikunta ◽  
Thippa Reddy Gadekallu ◽  
Kuruva Lakshmanna ◽  
Ali Kashif Bashir ◽  
...  
Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4300 ◽  
Author(s):  
Hoon Lee ◽  
Han Seung Jang ◽  
Bang Chul Jung

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.


Materials ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 2489 ◽  
Author(s):  
Gonçalo Pina Cipriano ◽  
Lucian Blaga ◽  
Jorge dos Santos ◽  
Pedro Vilaça ◽  
Sergio Amancio-Filho

The present work investigates the correlation between energy efficiency and global mechanical performance of hybrid aluminum alloy AA2024 (polyetherimide joints), produced by force-controlled friction riveting. The combinations of parameters followed a central composite design of experiments. Joint formation was correlated with mechanical performance via a volumetric ratio (0.28–0.66 a.u.), with a proposed improvement yielding higher accuracy. Global mechanical performance and ultimate tensile force varied considerably across the range of parameters (1096–9668 N). An energy efficiency threshold was established at 90 J, until which, energy input displayed good linear correlations with volumetric ratio and mechanical performance (R-sq of 0.87 and 0.86, respectively). Additional energy did not significantly contribute toward increasing mechanical performance. Friction parameters (i.e., force and time) displayed the most significant contributions to mechanical performance (32.0% and 21.4%, respectively), given their effects on heat development. For the investigated ranges, forging parameters did not have a significant contribution. A correlation between friction parameters was established to maximize mechanical response while minimizing energy usage. The knowledge from Parts I and II of this investigation allows the production of friction riveted connections in an energy efficient manner and control optimization approach, introduced for the first time in friction riveting.


Author(s):  
A. Kamenders ◽  
A. Blumberga

Multi-Objective Optimization Approach for Improving Performance of Building Energy efficiency measures are different from energy efficiency and cost effectiveness perspective. For decision maker it is hard to make right decision about different energy efficiency measure combinations in building. It is a complex problem to choose the best energy efficiency measure combination as decision involves many different factors that should be taken in account. Decision on implementation of energy efficiency measure implementation usually depends on investment costs and pay back time. Standards like Latvian Building Code LBN 002-01 can't be used to achieve reasonable expenses in renovation of buildings. Therefore, in order to find the optimal energy-efficiency measures, it is necessary to carry out optimization taking all the variable parameters into account. In the paper target function was presented that gives ability of the multi-objective optimization approach to handle the problem of improving energy efficiency in buildings. Case study is used to demonstrate the feasibility of the approach. 104. series soviet type dwellings was analysed to optimized insulation thickness for external walls. Even if accord with the LBN 002-01 it is enough to use 7 cm thick isolation (λ-0,039 W/(m2K)) layers optimal insulation layer is 12 cm (λ-0,039 W/(m2K)).


Author(s):  
Michele Trancossi ◽  
Jose C. Pascoa

Modular Design has made an important contribution to the industrial evolution, increase of quality of products and goods and to economic development. It has produced an important evolution in design (technical modularity), in the organization of production and of companies. It allowed going beyond vertical integration, by fostering vertical specialization in both manufacturing and innovation. Several authors are appointing important question on the modular approach. They move observations of different nature concluding that the enthusiasm for modularity has gone too far. One of the critical positions sustains that modular design has imposed technical choices that conflicts with energy efficiency in vehicle design such as a gradual increase of weight over time and the consequent reduction of potential gains in terms of energy consumption and environmental footprint of vehicles. This paper agrees with some arguments of the revisionist literature in cautioning against errors that can be produced by a pervasive modularity. But it moves from an energetic analysis and has not the objective of defining an alternative theory. More modestly, it aims to present a possible way for coupling modular design with energy optimization in the case of an electric vehicle. The initial inspiration can be of this case study is Bejan’s preliminary modular definition of constructal optimization, which can fit perfectly with industrial modular design. Even if this modular optimization does not have the ambition of defining the best possible solution to a complex design problem, such as Multidisciplinary Design Optimization has, it allows defining configuration that can simply evolve over time by mean of a step by step optimization of the critical components that influences the behavior of a complex industrial system. It reveals then to be applicable to the concept of vehicle platform that is today widely in use. The specific test case is the design of an electric city vehicle which has been optimized by a step applying this modular optimization approach. This paper has also a romantic value because it ha taken the move from the emotion that has been caused by the stop to the production of an extraordinary myth, such as Land Rover Defender. 70 years of production without important changes means that Defender has been not only the most successful British vehicle, but also that it has been a fundamental part of our way of living. This extraordinary longevity is an extraordinary technical and cultural heritage to our time. This decision forces the authors to try to analyze the conceptual modular design of a vehicle that can compete with Defender in terms of use and performances. Results have been surprising demonstrating that the use of industrial grade components and their accurate choice will allow defining new vehicle platforms that can radically improve energy efficiency of vehicles.


2008 ◽  
Vol 40 (9) ◽  
pp. 1747-1754 ◽  
Author(s):  
Christina Diakaki ◽  
Evangelos Grigoroudis ◽  
Dionyssia Kolokotsa

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