scholarly journals Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1484
Author(s):  
Yunyoung Choi ◽  
Jaehyung Park ◽  
Jiwon Jung ◽  
Younggoo Kwon
Keyword(s):  
Energy Consumption ◽  
Collision Avoidance ◽  
Energy Efficient ◽  
Convergence Time ◽  
Location Information ◽  
Route Discovery ◽  
Localization Algorithm ◽  
Control Packet ◽  
Network Convergence ◽  
Routing Control

In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space.

scholarly journals Event Based Clustering Localized Energy Efficient Ant Colony Optimization (EBC_LEE-ACO) for Performance Enhancement of Wireless Sensor Network

2018 ◽  
Vol 8 (4) ◽  
pp. 3177-3183 ◽  
Author(s):  
S. D. Chavan ◽  
A. V. Kulkarni
Keyword(s):  
Energy Consumption ◽  
Ant Colony Optimization ◽  
Energy Efficient ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Location Information ◽  
Packet Delivery ◽  
Event Based ◽  
Packet Drop

The main challenge of a wireless sensor network (WSN) in disaster situations is to discover efficient routing, to improve quality of service (QoS) and to reduce energy consumption. Location awareness of nodes is also useful or even necessary. Without knowing the position of sensor nodes, collected data is insignificant. Ant colony optimization (ACO) is a unique form of optimization method, which is highly suitable for adaptive routing and guaranteed packet delivery. The primary drawbacks of ACO are data flooding, huge overhead of control messages and long convergence time. These drawbacks are overcome by considering location information of sensor nodes. An event-based clustering localized energy efficient ant colony optimization (EBC_LEE-ACO) algorithm is proposed to enhance the performance of WSN. The main focus of the proposed algorithm is to improve QoS and minimize the network energy consumption by cluster formation and selecting the optimal path based on the biological inspired routing-ACO and location information of nodes. In clustering, data is aggregated and sent to the sink (base station) through cluster head (CH) which reduces overheads. EBC_LEE-ACO is a scalable and energy efficient reactive routing algorithm which improves QoS, lifetime and minimizes energy consummation of WSN as compared to other routing algorithms like AODV, ACO, ACO using RSSI. The proposed algorithm reduces energy consumption by approximately 7%, in addition to improvement in throughput, packet delivery ratio and increase in packet drop which has been observed in comparison with other algorithms, i.e. autonomous localization based eligible energetic Path_with_Ant Colony optimization (ALEEP with ACO) of the network. Use of IEEE 802.11 standard in proposed work increased packet drop.

A Neuroevolution-Based Learning of Reciprocal Maneuver for Collision Avoidance in Quadcopters Under Pose Uncertainties

2019 ◽  
Author(s):  
Amir Behjat ◽  
Krushang Gabani ◽  
Souma Chowdhury
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Collision Avoidance ◽  
Energy Efficient ◽  
Evolutionary Process ◽  
The Other ◽  
Pd Controller ◽  
Prior Work ◽  
Optimal Action ◽  
Trained Neural Network

Abstract This paper focuses on the idea of energy efficient cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy they change their heading to avoid a collision. The avoidance actions are parameterized in terms of the time difference between detecting the collision and starting the maneuver and the amount of speed/heading change. These action parameters are used to generate intermediate way-points, subsequently translated into a minimum snap trajectory, to be executed by a PD controller. For realism, the relative pose of the other UAV, estimated by each UAV (at the point of detection), is considered to be uncertain — thereby presenting substantial challenges to undertaking reciprocal actions. Performing supervised learning based on optimization derived labels (as done in prior work) becomes computationally burden-some under these uncertainties. Instead, an (unsupervised) neuroevolution algorithm, called AGENT, is employed to learn a neural network (NN) model that takes the initial (uncertain) pose as state inputs and maps it to a robust optimal action. In neuroevolution, the NN topology and weights are simultaneously optimized using a special evolutionary process, where the fitness of candidate NNs are evaluated over a set of sample (in this case, various collision) scenarios. For further computational tractability, a surrogate model is used to estimate the energy consumption and a classifier is used to identify trajectories where the controller fails. The trained neural network shows encouraging performance for collision avoidance over a large variety of unseen scenarios.

Network Energy Consumption Models and Energy Efficient Algorithms

2012 ◽  
Vol 35 (3) ◽  
pp. 603-615 ◽  
Author(s):  
Fa ZHANG ◽  
Antonio Fernandez Anta ◽  
Lin WANG ◽  
Chen-Ying HOU ◽  
Zhi-Yong LIU
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Efficient Algorithms

Performance Analysis of Energy Efficient Opportunistic Routing Protocols in Wireless Sensor Network

2019 ◽  
Vol 09 ◽  
Author(s):  
Premkumar Chithaluru ◽  
Rajeev Tiwari ◽  
Kamal Kumar
Keyword(s):  
Energy Consumption ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Data Communication ◽  
Opportunistic Routing ◽  
Link Quality ◽  
Delivery Ratio ◽  
Performance Parameters ◽  
Wireless Medium ◽  
Reduce Energy Consumption

Background: Energy Efficient wireless routing has been an area of research particularly to mitigate challenges surrounding performance in category of Wireless Networks. Objectives: The Opportunistic Routing (OR) technique was explored in recent times and exhibits benefits over many existing protocols and can significantly reduce energy consumption during data communication with very limited compromise on performance. Methods : Using broadcasting nature of the wireless medium, OR practices to discourse two foremost issues of variable link quality and unpredictable node agility in constrained WSNs. OR has a potential to reduce delay in order to increase the consistency of data delivery in network. Results : Various OR based routing protocols have shown varying performances. In this paper, a detailed conceptual and experimental analysis is carried out on different protocols that uses OR technique for providing more clear and definitive view on performance parameters like Message Success Rate, Packet Delivery Ratio and Energy Consumption.

A Split Network based Routing Approach in Wireless Sensor Network to Enhance Network Stability

2019 ◽  
Vol 9 (4) ◽  
pp. 480-487 ◽  
Author(s):  
Hardeep S. Saini ◽  
Dinesh Arora
Keyword(s):  
Energy Consumption ◽  
Sensor Network ◽  
Energy Efficient ◽  
Cluster Head ◽  
Base Station ◽  
Operating Efficiency ◽  
Network Stability ◽  
Major Factors ◽  
Ch Selection

Background & Objective: The operating efficiency of a sensor network totally relies upon the energy that is consumed by the nodes to perform various tasks like data transmission etc. Thus, it becomes mandatory to consume the energy in an intelligent way so that the network can run for a long period. This paper proposed an energy efficient Cluster Head (CH) selection mechanism by considering the distance to Base Station (BS), distance to node and energy as major factors. The concept of volunteer node is also introduced with an objective to reduce the energy consumption of the CH to transmit data from source to BS. The role of the volunteer node is to transmit the data successfully from source to destination or BS. Conclusion: The results are observed with respect to the Alive nodes, dead nodes and energy consumption of the network. The outcome of the proposed work proves that it outperforms the traditional mechanisms.

Energy Efficient Clustering and Routing Algorithm for WSN

2019 ◽  
Vol 12 ◽  
Author(s):  
Mohit Kumar ◽  
Sonu Mittal ◽  
Md. Amir Khusru Akhtar
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Hot Spot ◽  
Routing Algorithm ◽  
Relay Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering ◽  
Hot Spot Problem

Background: This paper presents a novel Energy Efficient Clustering and Routing Algorithm (EECRA) for WSN. It is a clustering-based algorithm that minimizes energy dissipation in wireless sensor networks. The proposed algorithm takes into consideration energy conservation of the nodes through its inherent architecture and load balancing technique. In the proposed algorithm the role of inter-cluster transmission is not performed by gateways instead a chosen member node of respective cluster is responsible for data forwarding to another cluster or directly to the sink. Our algorithm eases out the load of the gateways by distributing the transmission load among chosen sensor node which acts as a relay node for inter-cluster communication for that round. Grievous simulations show that EECRA is better than PBCA and other algorithms in terms of energy consumption per round and network lifetime. Objective: The objective of this research lies in its inherent architecture and load balancing technique. The sole purpose of this clustering-based algorithm is that it minimizes energy dissipation in wireless sensor networks. Method: This algorithm is tested with 100 sensor nodes and 10 gateways deployed in the target area of 300m × 300m. The round assumed in this simulation is same as in LEACH. The performance metrics used for comparisons are (a) network lifetime of gateways and (b) energy consumption per round by gateways. Our algorithm gives superior result compared to LBC, EELBCA and PBCA. Fig 6 and Fig 7 shows the comparison between the algorithms. Results: The simulation was performed on MATLAB version R2012b. The performance of EECRA is compared with some existing algorithms like PBCA, EELBCA and LBCA. The comparative analysis shows that the proposed algorithm outperforms the other existing algorithms in terms of network lifetime and energy consumption. Conclusion: The novelty of this algorithm lies in the fact that the gateways are not responsible for inter-cluster forwarding, instead some sensor nodes are chosen in every cluster based on some cost function and they act as a relay node for data forwarding. Note the algorithm does not address the hot-spot problem. Our next endeavor will be to design an algorithm with consideration of hot-spot problem.

scholarly journals Energy-Efficient Packet Forwarding Scheme Based on Fuzzy Decision-Making in Underwater Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4368
Author(s):  
Jitander Kumar Pabani ◽  
Miguel-Ángel Luque-Nieto ◽  
Waheeduddin Hyder ◽  
Pablo Otero
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Real Life ◽  
Base Station ◽  
Fuzzy Decision Making ◽  
Underwater Sensor Networks ◽  
Packet Forwarding ◽  
Node Number ◽  
The Impact

Underwater Wireless Sensor Networks (UWSNs) are subjected to a multitude of real-life challenges. Maintaining adequate power consumption is one of the critical ones, for obvious reasons. This includes proper energy consumption due to nodes close to and far from the sink node (gateway), which affect the overall energy efficiency of the system. These wireless sensors gather and route the data to the onshore base station through the gateway at the sea surface. However, finding an optimum and efficient path from the source node to the gateway is a challenging task. The common reasons for the loss of energy in existing routing protocols for underwater are (1) a node shut down due to battery drainage, (2) packet loss or packet collision which causes re-transmission and hence affects the performance of the system, and (3) inappropriate selection of sensor node for forwarding data. To address these issues, an energy efficient packet forwarding scheme using fuzzy logic is proposed in this work. The proposed protocol uses three metrics: number of hops to reach the gateway node, number of neighbors (in the transmission range of a node) and the distance (or its equivalent received signal strength indicator, RSSI) in a 3D UWSN architecture. In addition, the performance of the system is also tested with adaptive and non-adaptive transmission ranges and scalable number of nodes to see the impact on energy consumption and number of hops. Simulation results show that the proposed protocol performs better than other existing techniques or in terms of parameters used in this scheme.

Wearable energy efficient fitness tracker for sports person health monitoring application

2021 ◽  
pp. 1-10
Author(s):  
Yongyue Huang ◽  
Min Hu ◽  
BalaAnand Muthu ◽  
R. Gayathri
Keyword(s):  
Energy Consumption ◽  
Data Storage ◽  
Energy Efficient ◽  
Consumer Satisfaction ◽  
Form Factors ◽  
Recognition System ◽  
Energy Utilization ◽  
Biologically Inspired Algorithms ◽  
Enhance Efficiency ◽  
Fitness Tracker

Continuous evaluation of biological and physiological metrics of sports personalities, evaluating general health status, and alerting for life-saving treatments, is supposed to enhance efficiency and healthy performance. Wearable devices with acceptable form factors compact, flexibility, minimal power consumption, etc., are needed for continuous monitoring to avoid affecting everyday operations, thereby retaining functional effectiveness and consumer satisfaction. This research focuses on the acceleration tracker for particularizing the work. Acceleration data is typically collected on battery-powered sensors for activity detection, referring to an exchange between high-precision detection and energy-efficient processing. From a feature selection perspective, the paper explores this trade-off. It suggests an Energy-Efficient Behavior Recognition System with a comprehensive energy utilization model and the Multi-objective Algorithm of Particle Swarm Optimization (EEBRS-MPSO). Therefore, using Random Forest (RF) classifiers, the model and algorithm are tested to measure the precision of identification and obtain the task’s best performance with the lowest energy consumption, among other biologically-inspired algorithms. The findings indicate that energy consumption for data storage and data processing is minimized with magnitude relative to the raw data method by choosing suitable groups of attributes. Thus, the platform allows a scalable range of feature clusters that require the authors to provide an adequate power adjustment for given target use.

scholarly journals Energy efficient cutting parameter optimization

2021 ◽  
Author(s):  
Xingzheng Chen ◽  
Congbo Li ◽  
Ying Tang ◽  
Li Li ◽  
Hongcheng Li
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Parameter Optimization ◽  
Energy Efficient ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Current Status ◽  
Cutting Parameter ◽  
Cutting Parameter Optimization

AbstractMechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

scholarly journals Assessment Methodology for Efficiency, CO2-Emissions and Primary Energy Consumption for Refrigeration Technologies in the Industry

2018 ◽  
Vol 882 ◽  
pp. 215-220
Author(s):  
Matthias Koppmann ◽  
Raphael Lechner ◽  
Tom Goßner ◽  
Markus Brautsch
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Air Conditioning ◽  
Primary Energy ◽  
Primary Energy Consumption ◽  
Assessment Methodology ◽  
Alternative Technologies ◽  
Overall Efficiency ◽  
Chp System ◽  
The Individual

Process cooling and air conditioning are becoming increasingly important in the industry. Refrigeration is still mostly accomplished with compression chillers, although alternative technologies are available on the market that can be more efficient for specific applications. Within the scope of the project “EffiCool” a technology toolbox is currently being developed, which is intended to assist industrials users in selecting energy efficient and eco-friendly cooling solutions. In order to assess different refrigeration options a consistent methodology was developed. The refrigeration technologies are assessed regarding their efficiency, CO2-emissions and primary energy consumption. For CCHP systems an exergetic allocation method was implemented. Two scenarios with A) a compression chiller and B) an absorption chiller coupled to a natural gas CHP system were calculated exemplarily, showing a greater overall efficiency for the CCHP system, although the individual COP of the chiller is considerably lower.

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