Energy Efficiency In Internet Of Things Research Articles

Accurate Anomaly Detection With Energy Efficiency in IoT–Edge–Cloud Collaborative Networks

With the applicability of edge intelligence in various domains, anomaly detection, which aims to identify unusual and infrequent circumstances, is regarded as a regularly performed task to guarantee the health of the Internet of Things (IoT) applications. Generally, sensory data are gathered at the network edge and completely transmitted to the cloud, where computational-heavy algorithms are mostly adopted to determine the locations of anomaly. Considering the occurrence infrequency of anomalies, this strategy may transmit relatively huge-volume of sensory data, which may reflect a healthy situation indeed, to the cloud. To mitigate this problem, this paper proposes an accurate anomaly detection mechanism with energy efficiency in three-Tier IoT-Edge-Cloud collaborative networks. Specifically, after gathering sensory data provided by IoT nodes in certain edge networks, the edge node applies the Marching Squares algorithm to generate isopleths, where an isopleth may capture the boundary of anomaly. A sensory data filtering mechanism is conducted at the edge tier, such that anomaly-relevant sensory data are transmitted to the cloud, and thus, the network traffic is decreased significantly. Thereafter, the boundary of anomaly is obtained, and the locations of candidate boundary nodes are determined by adopting the Kriging spatial interpolation algorithm at the cloud tier. These locations are traversed by mobile sensing nodes at edge networks, and their sensory data are gathered for boundary refinement. Extensive experiments are conducted on an air quality hazardous gas dataset from Towards Data Science, and evaluation results show that our technique outperforms the state-of-the-art counterparts in boundary accuracy and energy consumption.

Semantic-Based Multi-Objective Optimization for QoS and Energy Efficiency in IoT, Fog, and Cloud ERP Using Dynamic Cooperative NSGA-II

Regarding enterprise service management, optimizing business processes must achieve a balance between several service quality factors such as speed, flexibility, and cost. Recent advances in industrial wireless technology and the Internet of Things (IoT) have brought about a paradigm shift in smart applications, such as manufacturing, predictive maintenance, smart logistics, and energy networks. This has been assisted by smart devices and intelligent machines that aim to leverage flexible smart Enterprise Resource Planning (ERP) regarding all the needs of the company. Many emerging research approaches are still in progress with the view to composing IoT and Cloud services for meeting the expectation of companies. Many of these approaches use ontologies and metaheuristics to optimize service quality of composite IoT and Cloud services. These approaches lack responsiveness to changing customer needs as well as changes in the power capacity of IoT devices. This means that optimization approaches need an effective adaptive strategy that replaces one or more services with another at runtime, which improves system performance and reduces energy consumption. The idea is to have a system that optimizes the selection and composition of services to meet both service quality and energy saving by constantly reacting to context changes. In this paper, we present a semantic dynamic cooperative service selection and composition approach while maximizing customer non-functional needs and quickly selecting the relevant service drive with energy saving. Particularly, we introduce a new QoS energy violation degree with a cooperative energy-saving mechanism to ensure application durability while different IoT devices are run-out of energy. We conduct experiments on a real business process of the company SETIF IRIS using different cooperative strategies. Experimental results showed that the smart ERP system with the proposed approach achieved optimized ERP performance in terms of average service quality and average energy consumption ratio equal to 0.985 and 0.057, respectively, in all simulated configurations compared to ring and maser/slave methods.

A neuro evolutionary scheme for improved IoT energy efficiency in smart cities

With the emergence of Internet of Things (IoT) and allied applications for smart cities, sustainability goals have seen a prominent emphasis. This paper focuses on the energy efficiency aspect of such sustainable smart city goals. Although energy efficiency has been studied at different levels of a smart city’s Information and Communication Technology (ICT) infrastructure, this paper specially focuses on device level energy minimization strategy by means of modelling the energy consumption while accounting for the Clusterheads (CluH) and duty cycling and thereby using evolutionary algorithms. In this paper, a Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO) has been used to solve the energy minimization problem. Simulation experiments carried out for different scenarios with varying configuration demonstrate the efficacy of the hybrid neuro evolutionary scheme.

Energy-efficient and Secure Wireless Communication for Telemedicine in IoT

The Internet of Things (IoT) represents a radical shifting paradigm for technological innovations as it can play critical roles in cyberspace applications in various sectors, such as security, monitoring, medical, and environmental sectors, and also in control and industrial applications. The IoT in E-medicine unleashed the design space for new technologies to give instant treatment to patients while also monitoring and tracking health conditions. This research presents a system-level architecture approach for IoT energy efficiency and security. The proposed architecture includes functional components that provide privacy management and system security. Components in the security function group provide secure communications through Multi-Authority Ciphertext-Policy Attributes-Based Encryption (MA-CPABE). Because MA-CPABE is assigned to unlimited devices, presuming that the devices are reliable, the user encodes data with Advanced Encryption Standard (AES) and protects the ABE approach using the solutions of symmetric key. The Johnson’s algorithm with a new computation measure is used to increase network lifetime since an individual sensor node with limited energy represents the inevitable constraints for the broad usage of wireless sensor networks. The optimal route from a source to destination turns out as the cornerstone for longevity of network and its sustainability. To reduce the energy consumption of networks, the evaluation measures consider the node’s residual energy, the number of neighbors, their distance, and the link dependability. The experiment results demonstrate that the proposed model increases network life by about 12.25% (27.73%) compared to Floyd–Warshall’s, Bellman–Ford’s, and Dijkstra’s algorithms, lowering consumption of energy by eliminating the necessity for re-routing the message as a result of connection failure.

Analysis of Green IoT

Internet of Things (IoT) is an idea and theory of connecting billions of devices and enable them to exchange a massive amount of information. Green IoT visualize the theory of IoT with improved energy efficiency. The overview of Green IoT and the challenges that were faced due to the excessive usage of energy hungry IoT devices were given initially. The research of energy efficiency in IoT had been presented extensively in this paper. Authors presented some challenges, existing works, opportunities, and future directions of green IoT.

Optimization-assisted water supplement mechanism with energy efficiency in IoT based greenhouse

Remarkable resource management and energy efficiency improvements can be achieved in greenhouses using innovative technological advancements and modern agricultural methods. Deployment of Internet of Things (IoT) and optimization algorithms in greenhouse farming is highly desirable for real-time monitoring and controlling various parameters with optimal solutions. However, IoT based greenhouses require more energy as compared to traditional farming. This paper proposes an optimal greenhouse water supplement mechanism with efficient energy consumption based on IoT and optimization techniques. The first contribution of this study is to gather the actual water and soil moisture levels from the greenhouse and tank using IoT devices. Secondly, the formulation and deployment of an objective function to compute the optimal water and soil moisture levels for greenhouse and tank based on user-desired settings, the system constraints and actual sensing values. We applied a rule-based expert system to activate water pumps with the required flow rate and operational duration to achieve efficient energy consumption. To prove the effectiveness of the proposed concept, embedded IoT devices and objective function for optimization are deployed as well as, a number of experiments are conducted to provide the optimal water and soil moisture levels in a real greenhouse and water tank environment.

A systematic survey on internet of things: Energy efficiency and interoperability perspective

AbstractWith the provision of rapid advancement in smart devices used in various technological fields exponentially increases the heterogeneity and energy consumption in internet of things (IoT). The technological leap in information and communication technology instigates the heterogeneity in smart devices, frameworks, architectures, communication technologies, and various industrial and nonindustrial applications. Therefore, a detailed taxonomy of IoT is proposed covering the diverse aspect of IoT lacking interoperability and energy efficiency. Existing research lacks the root causes of heterogeneity and energy consumption at the industrial and technological level. Keeping this in view, our research identified industrial integration and technological challenges. Moreover, we explore the effect on IoT devices when different types of energy harvesters are connected with IoT devices. Our comprehensive research addresses the various issues such as resource management, fog data analytics, energy consumption, heterogeneity, scalability, and the role of quality of service, data science, machine learning to accomplish interoperability and energy efficiency in IoT.

Smart and Fast Data Processing for Deep Learning in Internet of Things: Less is More

Recent advances of deep learning have produced encouraging results comparable to and in some cases superior to human experts. However, the large amount of data input has been a daunting task for deep learning to be widely applied in Internet of Things (IoT) with real-time processing. The goal of this paper is to develop smart and fast data processing scheme for more computational efficient deep learning to support adaptive and real-time applications, which will be able to increase the spectrum and energy efficiency in IoT. We propose to apply singular-value decomposition (SVD)-QR algorithm to preprocessing of deep learning for large scale data input. For the mass data input, we apply limited memory subspace optimization for SVD (LMSVD)-QR algorithm to increase the data processing speed. Simulation results in automated handwritten digit recognition show that SVD-QR and LMSVD-QR can tremendously reduce the number of input to deep learning neural network without losing its performance, and both can tremendously increase the data processing speed for deep learning in IoT.

A Sleep-Awake Scheme Based on CoAP for Energy-Efficiency in Internet of Things

Internet Engineering Task Force (IETF) have developed Constrained Application Protocol (CoAP) to enable communication between sensor or actuator nodes in constrained environments, such as small amount of memory, and low power. IETF CoAP and HTTP are used to monitor or control environments in Internet of Things (IoT) and Machine-to-Machine (M2M). In this paper, we present a sleep-awake scheme based on CoAP for energy efficiency in Internet of Things. This scheme supports to increase energy efficiency of IoT nodes using CoAP protocol. We have slightly modified the IoT middleware to improve CoAP protocol to conserve energy in the IoT nodes. Also, the IoT middleware includes some functionality of the CoRE Resource Directory (RD) and the Message Queue (MQ) broker with IoT nodes to synchronize sleepy status.

Self-Organized Connected Objects: Rethinking QoS Provisioning for IoT Services

The proliferation of connected objects has revolutionized the traditional Internet, giving rise to the emerging Internet of Things (IoT). The IoT ecosystem is very large, and it includes smart interconnections among sensors and devices with applications in both the industrial world and customers' daily lives. As a unified standard for IoT is still under development, many challenges related to IoT must be discussed and addressed, especially those related to energy efficiency. This article tackles the challenge of energy efficiency in IoT from a novel perspective. It shows that instead of maximizing the QoS, which is generally energy costly, better energy efficiency can be achieved by targeting satisfactory QoS levels only. The approach aims to enhance energy efficiency while ensuring a desired QoS threshold. This is supported by a game theoretical solution concept referred to as the satisfaction equilibrium. Moreover, as IoT objects require self-configuring techniques to maintain the network scalability and flexibility, this article introduces fully distributed schemes in order to reach efficient satisfaction equilibria in both slow- and fast-fading channel contexts. The proposed schemes can also be adapted to achieve the maximum performance of IoT applications that desire the highest QoS levels. The performance of these algorithms is illustrated through a smart home use case scenario.