2,668 research outputs found
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Discrete Artificial Bee Colony for Computationally Efficient Symbol Detection in Multidevice STBC MIMO Systems
A Discrete Artificial Bee Colony (DABC) is presented for joint symbol detection at the receiver in a multidevice Space-Time Block Code (STBC) Mutli-Input Multi-Output (MIMO) communication system. Exhaustive search (maximum likelihood detection) for finding an optimal detection has a computational complexity that increases exponentially with the number of mobile devices, transmit antennas per mobile device, and the number of bits per symbol. ABC is a new population-based, swarm-based Evolutionary Algorithms (EA) presented for multivariable numerical functions and has shown good performance compared to other mainstream EAs for problems in continuous domain. This algorithm simulates the intelligent foraging behavior of honeybee swarms. An enhanced discrete version of the ABC algorithm is presented and applied to the joint symbol detection problem to find a nearly optimal solution in real time. The results of multiple independent simulation runs indicate the effectiveness of DABC with other well-known algorithms previously proposed for joint symbol detection such as the near-optimal sphere decoding, minimum mean square error, zero forcing, and semidefinite relaxation, along with other EAs such as genetic algorithm, estimation of distributions algorithm, and the more novel biogeography-based optimization algorithm
Ant with Artificial Bee Colony Techniques in Vehicular Ad-hoc Networks
A VANET faces many problems due to dynamic changing of networks with certain requirements such as low delay, high (PDR) packet delivery ratio, low routing overhead and throughput. However, numerous routing protocols have been suggested to meet the demands of Quality of Service (QoS), but none of them can consistently maintain the highest level of QoS simultaneously. The proposed method Ant with Artificial Bee Colony Techniques provides better performance when compared to the existing techniques. This work is compared with latest developed Techniques in VANET to find the best path and different performance metrics are used to check the performance. This work premeditated the comparative analysis of Quality of services made by the performance of latest emerging techniques in VANET and will provide the best solution for the recognition problem in finding the best path based on the evaluation of performance of Quality of Service. Simulation results imply the benefits of the proposed Ant with Artificial Bee Colony Techniques (AABC) produces better result when compare to the other conventional method and Ant Colony Techniques(ACT)in terms of high packet delivery ratio, less end-to-end delay and less energy consumption level. The performance is evaluated by using Ns2 simulator and results shows that the AABC successfully achieve the optimal routes
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