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

Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.Comment: To appear in Journal of Computer and System Science

A Review of Various Swarm Intelligence Based Routing Protocols for Iot

The paper provides insight into various swarm intelligence based routing protocols for Internet of Things (IoT), which are currently available for the Mobile Ad-hoc networks (MANETs) and wireless sensor networks (WSNs). There are several issues which are limiting the growth of Internet of Things. These include the reliability, link failures, routing, heterogeneity etc. The MANETs and WSNs routing issues impose almost same requirements for IoT routing mechanism. The recent work of the worldwide researchers is focused on this area. protocols are based on the principles of swarm intelligence. The swarm intelligence is applied to achieve the optimality and the efficiency in solving the complex, multi-hop and dynamic requirements of the wireless networks. The application of the ACO technique tries to provide answers to many routing issues. Using the swarm intelligence and ant colony optimization principles, it has been seen that, the protocols’ efficiency definitely increases and also provides more scope for the development of more robust, reliable and efficient routing protocols for the IoT. As the various standard protocols available for MANETs and WSNs are not reliable enough, the paper finds the need of some efficient routing algorithms for IoT

A Routing Algorithm Based on Ant Colony, Local Search and Fuzzy Inference to Improve Energy Consumption in Wireless Sensor Networks

Wireless sensor network is a new generation of networks in which the main aim is to collect data from the surrounding environment of network sensors. The major differences of wireless sensor networks with other networks are limited energy resources and relatively low processing capabilities. Therefore, managing power and reducing energy consumption are of great importance in these networks. In this paper, there was presented a mechanism for Wireless sensor network routing which can be more effective regarding the criteria of route length, end–to–end delay and network node energy for the quality of mechanism service. The proposed method used ant colony–based routing algorithm and local enquiry to find optimal routes. Also, a fuzzy inference system was used to determine the route quality which showed better performance compared with equation of route quality. The results of simulating mechanism showed that energy consumption and network efficiency had improved compared with those of previous methods.DOI:http://dx.doi.org/10.11591/ijece.v3i5.362

A Hybrid Cluster and Chain-based Routing Protocol for Lifetime Improvement

International audienceThe main challenge in the field of Wireless Sensor Networks (WSNs) is the energy conservation as long as possible. Clustering paradigm has proven its ability to prolong the network lifetime. The present paper proposes two algorithms using an approach that combines fuzzy c-means and ant colony optimization to form the clusters and manage the transmission of data in the network. First, fuzzy c-means is used to construct a predefined number of clusters. Second, we apply Ant Colony Optimization (ACO) algorithm to form a local shortest chain in each cluster. A leader node is randomly chosen at the beginning since all cluster nodes have the same amount of energy. In the next transmission, a remaining energy parameter is employed to select leader node. In the first algorithm, leader nodes transmit data in single hop to the distant base station (BS) while in the second the ACO algorithm is applied again to form a global chain between leader nodes and the BS. Simulation results show that the second proposed algorithm consumes less energy and effectively prolongs the network lifetime compared respectively with the first proposed and the LEACH algorithms

Design and Analysis of Soft Computing Based Improved Routing Protocol in WSN for Energy Efficiency and Lifetime Enhancement

Mobile wireless sensor networks have been developed as a result of recent advancements in wireless technologies. Sensors in the network are low-cost and have a short battery life, in addition to their mobility. They are more applicable in terms of the essential properties of these networks. These networks have a variety of uses, including search and rescue operations, health and environmental monitoring, and intelligent traffic management systems, among others. According to the application requirements, mobile wireless sensor nodes are energy limited equipment, so energy conservation is one of the most significant considerations in the design of these networks. Aside from the issues posed by sensor node mobility, we should also consider routing and dynamic clustering. According to studies, cluster models with configurable parameters have a substantial impact on reducing energy usage and extending the network's lifetime. As a result, the primary goal of this study is to describe and select a smart method for clustering in mobile wireless sensor networks utilizing evolutionary algorithms in order to extend the network's lifetime and ensure packet delivery accuracy. For grouping sensor nodes in this work, the Genetic Algorithm is applied initially, followed by Bacterial Conjugation. The simulation's results show a significant increase in clustering speed acceleration. The speed of the nodes is taken into account in the suggested approach for calibrating mobile wireless sensor nodes

A Comparative Analysis of Optimization Algorithms for Wireless Sensor Network

As we know that next era will be of IoT (Internet of things). According to cisco, one of the giant in Networking filed, in 2008 the n umber of thing connected to the internet was greater than the number of people living on the Earth. By 2020 that number will increase up to 50 billion. That clearly indicates that we all are surrounded by the sensors. So, the Wireless Sensor Network (WSN) will be the important part of our life in future. The sensor node is equipped with very small power supply. Failure of WSN or a single node of that network will cause for a serious effect on an operation, especially when WSN is used in critical application like military, healthcare monitoring etc. So power conservation is the biggest issue in WSN. Different kind of optimization algorithm used to get optimal output at each and every possible phenomenon where we can save the power. Routing and sensor deployment are two main issues where we can get fruitful output using optimization algorithm. This paper gives the brief detail of optimizing in WSN at different phenomenon

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions