scholarly journals Routing Path Estimation Based on RWS Method for Competent Energy Dissipation Employing X-Layer Network

2019 ◽  
Vol 8 (2) ◽  
pp. 6296-6303
Keyword(s):  
Energy Dissipation ◽  
Error Probability ◽  
Scheduling Algorithm ◽  
Mobile Node ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Idle State ◽  
Proposed Model ◽  
Time Scheduling ◽  
Output Efficiency

In this paper, the implementation of Mobile Error Probability algorithm has been implemented to improve the output efficiency of the sensor nodes in the wireless network environment. According the WSNs is concerned, it’s very important to focus on residual energy of each node. The Mobile Error Probability algorithm support to this very strongly, it ultimately calculates the residual energy corresponding to the consumed energy by the node at each level of the beaconing. This is only applicable for the mobile node depending upon the distance between the nodes. If the distance exceeds the set limit, then only the node sends the beaconing signal else it will be in idle state. Besides, the above concept, the method of RWS is also been implemented to estimate the routing path which can be used for the transmission in minimum stipulated of time. Scheduling algorithm has been used for proper cycling of the node in various modes such as active, idle, sleep and dead conditions. All the above algorithms are been implemented in EQSR, ED as well as proposed model using NS-II simulation through results are also been examined.

Wireless Photoelectric Sensor Network Scheduling Algorithm Based on Divisible Load

2018 ◽  
Vol 13 (10) ◽  
pp. 1499-1504 ◽  
Author(s):  
Jiaqi Wu ◽  
Huahu Xu
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Scheduling Algorithm ◽  
Residual Energy ◽  
Initial Energy ◽  
Sensor Nodes ◽  
Load Scheduling ◽  
Divisible Load ◽  
Photoelectric Sensor ◽  
Divisible Load Scheduling

To discuss the divisible load scheduling in wireless photoelectric sensor networks, a load scheduling algorithm called EDDLT based on residual energy landscape is proposed. In the algorithm, the constant of the time needed for the induction and reporting unit data is adjusted to the variable parameters based on the residual energy. In addition, the ratio of the initial energy to the residual energy is used to carry out the effective load scheduling. By using this algorithm, when the load is allocated to each sensor node, the remaining energy of nodes is considered, and a lighter load is allocated to sensor nodes with less residual energy. EDDLT, compared to the standard divisible load scheduling method SDLT, the number of execution rounds is greatly increased when the first sensor node is dead. The experimental results showed that EDDLT had a certain effect on prolonging the lifetime of wireless photoelectric sensor networks. To sum up, the scheduling algorithm has good performance in exploring divisible load.

scholarly journals Reinforcement Learning based Node Sleep or Wake-up Time Scheduling Algorithm for Wireless Sensor Network

2020 ◽  
Vol 5 (4) ◽  
pp. 707-731
Author(s):  
Parag Verma ◽  
Ankur Dumka ◽  
Dhawal Vyas ◽  
Anuj Bhardwaj
Keyword(s):  
Reinforcement Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Scheduling Algorithm ◽  
Data Packet ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Time Scheduling ◽  
Time Band

A wireless sensor network is a collection of small sensor nodes that have limited energy and are usually not rechargeable. Because of this, the lifetime of wireless sensor networks has always been a challenging area. One of the basic problems of the network has been the ability of the nodes to effectively schedule the sleep and wake-up time to overcome this problem. The motivation behind node sleep or wake-up time scheduling is to take care of nodes in sleep mode for as long as possible (without losing data packet transfer efficiency) and thus extend their useful life. This research going to propose scheduling of nodes sleeps and wake-up time through reinforcement learning. This research is not based on the nodes' duty cycle strategy (which creates a compromise between data packet delivery and nodes energy saving delay) like other existing researches. It is based on the research of reinforcement learning which gives independence to each node to choose its own activity from the transmission of packets, tuning or sleep node in each time band which works in a decentralized way. The simulation results show the qualified performance of the proposed algorithm under different conditions.

Priority-based Task Pre-processing in IoT Sensory Environments

2019 ◽  
Vol 13 ◽  
Author(s):  
U.N.V.P. Rajendranath ◽  
V. Berlin Hency
Keyword(s):  
Internet Of Things ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Resource Utilisation ◽  
The Internet ◽  
Network Overload ◽  
Task Sets ◽  
The Internet Of Things

Background: The motive of the internet of things (IoT) is to monitor and to control the devices that are connected to the internet. In IoT sensory environments, the application queries for the physical quantities in the spatiotemporal domain. The interaction between the sensors and the applications from the internet is the next big thing in the era of the internet of things. To minimize the resource utilisation, task scheduling mechanisms are implemented to the network. The survey on various patents of task scheduling is revised. Method: The PRITRAPS (Priority-based Task aware Pre-processing and Scheduling) is a mechanism that is employed in real time scenarios of industries. In which different applications units are accessing the gateway unit to measure and monitor the parameters of different service types. PRITRAPS employs priority among the tasks to reduce the network load. Results: The QoS parameters of the system are analysed and compared with the previous methodologies. The PRITRAPS mechanism consists of a task pre-processor unit, Scheduler and EMS module within the gateway unit. The scheduling algorithm employs in PRITRAPS is EDF (Earliest Deadline First) algorithm. The pre-processing task unit decreases the number of tasks by choosing the tasks having similar spatial and temporal requirements. The residual energy of the sensor nodes can help the scheduler for deciding the sensor nodes in respective of task requirements. The scheduler finds the best potential nodes and assigns them to the task for processing. Conclusion: To reduce the tasks arrivals at the wireless sensor unit, a priority based CCTs (Critical Covering Task sets) is proposed, and it effectively reduces the packet congestion and network overload. The results obtained are satisfactory and proven that PRITRAPS outperform TRAPS in energy consumption of a node by processing the tasks on the node. PRITRAPS require only 50% of the time that has been taken by TRAPS for serving the tasks. The PRITRAPS mechanism is implemented in NS3 simulator and tested for different task sets.

The Research of Data Gathering Protocol for Mobile Sensor Networks

2011 ◽  
Vol 216 ◽  
pp. 621-624
Author(s):  
Xin Lian Zhou ◽  
Jian Bo Xu
Keyword(s):  
Energy Efficient ◽  
Cluster Head ◽  
Mobile Sink ◽  
Mobile Node ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Node Mobility ◽  
Sink Node ◽  
Mobile Sensor ◽  
The Impact

This paper first proposed an energy-efficient distributed clustering technology for mobile sensor nodes and sink node mobility, select the higher residual energy and the nearest node from fixed nodes as cluster heads responsible for collecting sensed data, and all the fixed nodes form routing backbone to forward data, both can save energy and avoid cluster head away. Then, proposed a cross-layer scheduling mechanism to avoid the impact of mobile node and meet expectations cluster coverage. With energy-efficient clustering technology, efficient network topology control technology and mobile sink node, the data collection algorithm MSDBG, not only has considered mobility of nodes and energy saving, but also has achieved prolonging network lifetime.

scholarly journals Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 411
Author(s):  
Saba Awan ◽  
Nadeem Javaid ◽  
Sameeh Ullah ◽  
Asad Ullah Khan ◽  
Ali Mustafa Qamar ◽  
...  
Keyword(s):  
Trust Management ◽  
Evaluation Model ◽  
Residual Energy ◽  
Secure Routing ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Malicious Nodes ◽  
Public And Private ◽  
Proposed Model

In this paper, an encryption and trust evaluation model is proposed on the basis of a blockchain in which the identities of the Aggregator Nodes (ANs) and Sensor Nodes (SNs) are stored. The authentication of ANs and SNs is performed in public and private blockchains, respectively. However, inauthentic nodes utilize the network’s resources and perform malicious activities. Moreover, the SNs have limited energy, transmission range and computational capabilities, and are attacked by malicious nodes. Afterwards, the malicious nodes transmit wrong information of the route and increase the number of retransmissions due to which the SNs’ energy is rapidly consumed. The lifespan of the wireless sensor network is reduced due to the rapid energy dissipation of the SNs. Furthermore, the throughput increases and packet loss increase with the presence of malicious nodes in the network. The trust values of SNs are computed to eradicate the malicious nodes from the network. Secure routing in the network is performed considering residual energy and trust values of the SNs. Moreover, the Rivest–Shamir–Adleman (RSA), a cryptosystem that provides asymmetric keys, is used for securing data transmission. The simulation results show the effectiveness of the proposed model in terms of high packet delivery ratio.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN

2016 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Saadiah Yahya ◽  
Mohd Nasir Taib ◽  
Ahmad Ihsan Mohd Yassin ◽  
Razulaimi Razali
Keyword(s):  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Cluster Head Selection ◽  
Energy Usage ◽  
Network Cluster ◽  
Fuzzy Logic Approach ◽  
The Stability

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy. 

Sign in / Sign up

Export Citation Format

Share Document