A Systematic Approach for Scheduling IoT Devices for Effective Load Balancing Based on Deep Sleep

2021 ◽  
Author(s):  
Lahiru J. Ekanayake ◽  
Ruwan D. Nawarathna ◽  
Saluka R. Kodituwakku ◽  
Roshan D. Yapa ◽  
Amalka J. Pinidiyaarachchi
Keyword(s):  
Load Balancing ◽  
Systematic Approach ◽  
Deep Sleep ◽  
Iot Devices

scholarly journals Optimized Resource Allocation in IoT using Fuzzy Logic and Bio-Inspired Algorithms

2021 ◽  
Author(s):  
Deepak Kumar Sharma ◽  
Jahanavi Mishra ◽  
Aeshit Singh ◽  
Raghav Govil ◽  
Krishna Kant Singh ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Fuzzy Logic ◽  
Load Balancing ◽  
Network Performance ◽  
Global Network ◽  
Smart Devices ◽  
Delivery Ratio ◽  
Iot Devices ◽  
Nature Inspired Algorithms ◽  
Brute Force Approach

Abstract IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks.Low and discrete power and limited amount of memory and finite amount of resources form some major bottlenecks in the workflow.Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility.Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms - grey wolf algorithm and firefly algorithm in order to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach on the basis of energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio.

scholarly journals Load Balancing at Fog Nodes using Scheduling Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 4129-4134
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Fog Computing ◽  
Scheduling Algorithms ◽  
Small Scale ◽  
Location Awareness ◽  
Time Cost ◽  
Cloud Server ◽  
Iot Devices ◽  
Cloud Servers

Cloud Computing proves to be most predominant innovative field in the area of Information technology. Cloud is best suited for small scale to large scale businesses and personal purposes such as storing, computing, managing data & resources, running applications and many more. Due to increasing large volumes of data over cloud servers created subsequent specific issues like data maintainability, network elasticity, managing Internet of Things (I.o.T’s) devices and many more. Recent progresses in Technology are given rise to fog computing or decentralized cloud to overcome cloud server issues called fog nodes. In this paper we present a brief note on how cloud issues can overcome using fog nodes benefits along with elaboration of load balancing factor. To maintain load balancing of fog nodes no much appreciable work took place in the field of fog computing. This paper proposes a scheduler which receives the devices in to a Job Queue to be connected over cloud. To apply scheduling algorithms like F.C.F.S, S.J.F, P.S, R.R and W.R.R. over fog nodes will be discussed along with their merits & demerits. At last we try to compare the various parameters of load balancing among various scheduling algorithms. In this paper we focus on how fog nodes perform functions like considerable storages, low latency, heterogeneity, allocation & interaction with limited IoT devices and Security along with architecture cloud to fog. During allocation of IoT devices to various fog nodes we will come across a serious issues i.e load balancing on fog nodes. Our detailed study presents the comparison of above mentioned scheduling algorithms load balancing factors such as rich resources allocations & Balancing among fog nodes, Identification of devices, Authentication of fog nodes, bandwidth consumption, location awareness, response time, cost maintenances, Intrusion detection, fault forbearances and maintainability.

scholarly journals A Systematic Approach for a Secure Authentication System

2021 ◽  
Vol 10 (6) ◽  
pp. 7-11
Author(s):  
H A Gautham ◽  
◽  
Dr. Ramakanth Kumar P ◽  
Keyword(s):  
Systematic Approach ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
The Internet ◽  
Single Factor ◽  
Internet Users ◽  
Biometric Systems ◽  
Authentication System ◽  
Iot Devices ◽  
Secure Authentication

Authentication is a process of verifying the credibility of a user who is trying to access classified or confidential information. There is a vast unfold in the number of internet users, and the demand for IoT devices, cloud services has been increasing; it is now essential more than ever to protect the data hosted on the internet. So, the authentication process cannot be relied on single-factor static authentication methods to verify the user credentials. All devices in the market are not equipped with biometric systems, so a form of multi-factor authentication which is independent of biometrics needs to be adopted for a secure authentication system. This paper portraits a systematic architecture to verify user credentials using specific parameters, trying to unfold patterns using machine learning algorithms based on user's past login records, thus trying to provide a safer and secure authentication process for the users.

scholarly journals Lightweight Caching and Load Balancing for Efficient Content Delivery in Information-Centric IoT

2021 ◽  
Author(s):  
◽  
Jakob Pfender
Keyword(s):  
Load Balancing ◽  
Network Topology ◽  
Future Internet ◽  
Content Delivery ◽  
Future Internet Architecture ◽  
Cache Placement ◽  
Iot Devices ◽  
Key Aspects

<p>In recent years, Information-Centric Networking (ICN) has emerged as a promising candidate for a future Internet architecture. While originally designed with the traditional Internet in mind, it has also been identified as a potential replacement for current Internet of Things (IoT) networking solutions. However, applications in the IoT face a number of unique challenges due to the constrained nature of the hardware. One of these challenges is that available memory is often extremely limited.  This thesis aims to evaluate the feasibility of using ICN in-network caching on IoT devices in order to achieve efficient content delivery. It evaluates the performance of existing approaches on constrained hardware and explores how the technology can be improved and tailored towards that environment. Existing strategies are found to be lacking in key aspects, particularly the fact that the effects of network topology are often not considered when making caching decisions. It is shown that approaches based on network centrality are promising, but existing implementations are not suited for constrained hardware. Therefore, a lightweight in-network caching strategy called Approximate Betweenness Centrality (ABC) is proposed that takes the specific requirements of IoT into consideration and allows for efficient cache placement regardless of network topology. Then, a modular solution for load balancing through off-path caching is presented to address potential shortcomings of the centrality-based caching approach. It allows the network to make more efficient use of available caching resources without introducing additional overhead. Furthermore, solutions for ensuring Quality of Service (QoS) are discussed. The expanded role of caching strategies under such QoS constraints is explored and their performance is evaluated.  This thesis shows that it is possible to design and deploy lightweight, low-overhead solutions on constrained hardware. Using a realistic deployment of physical IoT devices, it is demonstrated that these approaches can reach satisfactory levels of performance.</p>

scholarly journals Load balancing algorithm based-SDN for the IoT applications

2021 ◽  
Vol 21 (2) ◽  
pp. 1209
Author(s):  
Hind Sounni ◽  
Najib El kamoun ◽  
Fatima Lakrami
Keyword(s):  
Load Balancing ◽  
Network Information ◽  
Network Load ◽  
Global View ◽  
Communication Devices ◽  
Network Load Balancing ◽  
Iot Devices ◽  
Load Balancing Algorithm ◽  
Unbalanced Network ◽  
Communication Habits

Nowadays, the emergence of IoT devices has wholly revolutionized the customer's communication habits. The information can be collected at anytime and anywhere. However, the mobility of communication devices in a dense network results in an unbalanced network load and an increase in bandwidth demands. To address these issues, this study proposed a load balancing algorithm based on SDN for enhancing the performance of mobile IoT devices communication over a Wi-Fi network. The use of the SDN makes possible the automatic configuration of the network through a centralized controller, it provides programmability, a global view of the network, it also optimizes resource allocation based on real-time network information that helps implement our algorithm. The proposed algorithm is evaluated through simulation using mininet. The results indicate that our proposed method provides an efficient network load balancing and improves the throughput of associated devices.

Research on fog Resource Scheduling based on Cloud-fog Collaboration Technology in the Electric Internet of Things

2021 ◽  
Vol 14 ◽  
Author(s):  
Youchan Zhu ◽  
Yingzi Wang ◽  
Weixuan Liang
Keyword(s):  
Internet Of Things ◽  
Load Balancing ◽  
Response Time ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Resource Scheduling ◽  
Previous Method ◽  
Collaboration Technology ◽  
Iot Devices ◽  
Load Balancing Algorithm

Background: With the further development of electric Internet of things (eIoT), IoT devices in the distributed network generate data with different frequencies and types. Objective: Fog platform is located between the smart collected terminal and cloud platform, and the resources of fog computing are limited, which affects the delay of service processing time and response time. Methods: In this paper, an algorithm of fog resource scheduling and load balancing is proposed. First, the fog devices divide the tasks into high or low priority. Then, the fog management nodes cluster the fog nodes through K-mean+ algorithm and implement the earliest deadline first dynamic (EDFD) task scheduling algorithm and De-REF neural network load balancing algorithm. Results: We use tools to simulate the environment, and the results show that this method has strong advantages in -30% response time, -50% scheduling time, delay, -50% load balancing rate and energy consumption, which provides a better guarantee for eIoT. Conclusion: Resource scheduling is important factor affecting system performance. This article mainly addresses the needs of eIoT in terminal network communication delay, connection failure, and resource shortage. And the new method of resource scheduling and load balancing is proposed, The evaluation was performed and proved that our proposed algorithm has better performance than the previous method, which brings new opportunities for the realization of eIoT.

scholarly journals Lightweight Caching and Load Balancing for Efficient Content Delivery in Information-Centric IoT

2021 ◽  
Author(s):  
◽  
Jakob Pfender
Keyword(s):  
Load Balancing ◽  
Network Topology ◽  
Future Internet ◽  
Content Delivery ◽  
Future Internet Architecture ◽  
Cache Placement ◽  
Iot Devices ◽  
Key Aspects

<p>In recent years, Information-Centric Networking (ICN) has emerged as a promising candidate for a future Internet architecture. While originally designed with the traditional Internet in mind, it has also been identified as a potential replacement for current Internet of Things (IoT) networking solutions. However, applications in the IoT face a number of unique challenges due to the constrained nature of the hardware. One of these challenges is that available memory is often extremely limited.  This thesis aims to evaluate the feasibility of using ICN in-network caching on IoT devices in order to achieve efficient content delivery. It evaluates the performance of existing approaches on constrained hardware and explores how the technology can be improved and tailored towards that environment. Existing strategies are found to be lacking in key aspects, particularly the fact that the effects of network topology are often not considered when making caching decisions. It is shown that approaches based on network centrality are promising, but existing implementations are not suited for constrained hardware. Therefore, a lightweight in-network caching strategy called Approximate Betweenness Centrality (ABC) is proposed that takes the specific requirements of IoT into consideration and allows for efficient cache placement regardless of network topology. Then, a modular solution for load balancing through off-path caching is presented to address potential shortcomings of the centrality-based caching approach. It allows the network to make more efficient use of available caching resources without introducing additional overhead. Furthermore, solutions for ensuring Quality of Service (QoS) are discussed. The expanded role of caching strategies under such QoS constraints is explored and their performance is evaluated.  This thesis shows that it is possible to design and deploy lightweight, low-overhead solutions on constrained hardware. Using a realistic deployment of physical IoT devices, it is demonstrated that these approaches can reach satisfactory levels of performance.</p>

scholarly journals Developing Load Balancing for IoT - Cloud Computing Based on Advanced Firefly and Weighted Round Robin Algorithms

2019 ◽  
Vol 16 (1) ◽  
pp. 0130 ◽  
Author(s):  
Abed Et al.
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
High Performance ◽  
Human Life ◽  
Round Robin ◽  
Use Of Resources ◽  
Huge Data ◽  
Hypertext Transfer Protocol ◽  
Iot Devices

The evolution of the Internet of things (IoT) led to connect billions of heterogeneous physical devices together to improve the quality of human life by collecting data from their environment. However, there is a need to store huge data in big storage and high computational capabilities.   Cloud computing can be used to store big data.  The data of IoT devices is transferred using two types of protocols: Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP). This paper aims to make a high performance and more reliable system through efficient use of resources. Thus, load balancing in cloud computing is used to dynamically distribute the workload across nodes to avoid overloading any individual resource, by combining two types of algorithms: dynamic algorithm (adaptive firefly) and static algorithm (weighted round robin). The results show improvement in resource utilization, increased productivity, and reduced response time.

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