scholarly journals LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

2021 ◽  
Vol 69 (1) ◽  
pp. 351-371
Author(s):  
Sankar Sennan ◽  
Somula Ramasubbareddy ◽  
Anand Nayyar ◽  
Yunyoung Nam ◽  
Mohamed Abouhawwash
Keyword(s):  
Internet Of Things ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Optimization Algorithm ◽  
Energy Efficient ◽  
The Internet ◽  
Energy Efficient Routing ◽  
Lion Optimization Algorithm ◽  
The Internet Of Things

scholarly journals A Survey on Enhanced Energy Efficient Routing Protocol of WSN for Internet of Things

2021 ◽  
Vol 23 (07) ◽  
pp. 1499-1508
Author(s):  
Bhukya Suresh ◽  
◽  
G Shyama Chandra Prasad ◽  
Keyword(s):  
Internet Of Things ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Fog Computing ◽  
Routing Algorithms ◽  
Wireless Sensor ◽  
The Internet ◽  
Energy Efficient Routing ◽  
Real World Applications ◽  
The Internet Of Things

Wireless Sensor Networks (WSNs) are a resource-constrained network class recognized as a major energy consumer. Wireless sensor technologies are used in many commercialized industrial automation processes and other real-world applications. The WSN protocol is well-suited to harsh situations where deployment is difficult or impossible, such as the battlefield, a toxic chemical plant, the cloud, fog computing, and the Internet of Things, but not in a high-temperature network infrastructure environment. WSNs have introduced various Energy-Efficient Routing Protocols based on network (NW) organization and protocols in recent years. Various WSN routing options for energy efficiency are explored in this work. The WSN Energy Efficient Routing Protocol is compared to other routing systems. We also compare and investigate better WSN routing algorithms for cloud computing, fog computing, and the Internet of Things.

A Location Based Energy Proficient Path Routing for Internet of Things and Its Applications

2020 ◽  
pp. 6-10
Author(s):  
Arulanantham D ◽  
Pradeepkumar G ◽  
Palanisamy C ◽  
Dineshkumar Ponnusamy
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Network Architecture ◽  
Base Station ◽  
Consumer Electronics ◽  
The Internet ◽  
Energy Efficient Routing ◽  
The Internet Of Things

The Internet of Things (IoT) is an establishment with sensors, base station, gateway, and network servers. IoT is an efficient and intellectual system that minimizes human exertion as well as right to use to real devices. This method also has an autonomous control property by which any device can control without any human collaboration. IoT-based automation has become very reasonable and it has been applied in several sectors such as manufacturing, transport, health care, consumer electronics, etc. In WSN’s smaller energy consumption sensors are expected to run independently for long phases. So much ongoing researches on implementing routing protocols for IoTbased WSNs.Energy consciousness is an essential part of IoT based WSN design issue. Minimalizing Energy consumption is well-thought-out as one of the key principles in the Expansion of routing protocols for the Internet of things. In this paper, we propose a Location based Energy efficient path routing for Internet of things and its applications its sensor position and clustering based finding the shortest path and real time implementation of Arduino based wireless sensor network architecture with the ESP8266 module. Finally, analyze the principles of Location-based energy-efficient routing and performance of QoS parameters, and then implemented automatic gas leakage detection and managing system.

scholarly journals EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Vially Kazadi Mutombo ◽  
Seungyeon Lee ◽  
Jusuk Lee ◽  
Jiman Hong
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Routing Protocol ◽  
Energy Efficient ◽  
The Internet ◽  
Human Beings ◽  
Energy Efficient Routing ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Internet Of Things

Wireless sensor devices are the backbone of the Internet of things (IoT), enabling real-world objects and human beings to be connected to the Internet and interact with each other to improve citizens’ living conditions. However, IoT devices are memory and power-constrained and do not allow high computational applications, whereas the routing task is what makes an object to be part of an IoT network despite of being a high power-consuming task. Therefore, energy efficiency is a crucial factor to consider when designing a routing protocol for IoT wireless networks. In this paper, we propose EER-RL, an energy-efficient routing protocol based on reinforcement learning. Reinforcement learning (RL) allows devices to adapt to network changes, such as mobility and energy level, and improve routing decisions. The performance of the proposed protocol is compared with other existing energy-efficient routing protocols, and the results show that the proposed protocol performs better in terms of energy efficiency and network lifetime and scalability.

scholarly journals Energy Efficiency in Short and Wide-Area IoT Technologies—A Survey

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 22
Author(s):  
Eljona Zanaj ◽  
Giuseppe Caso ◽  
Luca De Nardis ◽  
Alireza Mohammadpour ◽  
Özgü Alay ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Low Power ◽  
Energy Efficient ◽  
Wide Area ◽  
Wide Area Networks ◽  
The Internet ◽  
Extensive Review ◽  
Research Directions ◽  
The Internet Of Things

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions.

scholarly journals A Novel Bioinspired Multiobjective Optimization Algorithm for Designing Wireless Sensor Networks in the Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jun Huang ◽  
Liqian Xu ◽  
Cong-cong Xing ◽  
Qiang Duan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Chaotic Behavior ◽  
Ant Colony ◽  
Wireless Sensor ◽  
The Internet ◽  
The Internet Of Things

The design of wireless sensor networks (WSNs) in the Internet of Things (IoT) faces many new challenges that must be addressed through an optimization of multiple design objectives. Therefore, multiobjective optimization is an important research topic in this field. In this paper, we develop a new efficient multiobjective optimization algorithm based on the chaotic ant swarm (CAS). Unlike the ant colony optimization (ACO) algorithm, CAS takes advantage of both the chaotic behavior of a single ant and the self-organization behavior of the ant colony. We first describe the CAS and its nonlinear dynamic model and then extend it to a multiobjective optimizer. Specifically, we first adopt the concepts of “nondominated sorting” and “crowding distance” to allow the algorithm to obtain the true or near optimum. Next, we redefine the rule of “neighbor” selection for each individual (ant) to enable the algorithm to converge and to distribute the solutions evenly. Also, we collect the current best individuals within each generation and employ the “archive-based” approach to expedite the convergence of the algorithm. The numerical experiments show that the proposed algorithm outperforms two leading algorithms on most well-known test instances in terms of Generational Distance, Error Ratio, and Spacing.

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