A Novel Theoretical Probabilistic Model for Opportunistic Routing with Applications in Energy Consumption for WSNs

This paper proposes a new theoretical stochastic model based on an abstraction of the opportunistic model for opportunistic networks. The model is capable of systematically computing the network parameters, such as the number of possible routes, the probability of successful transmission, the expected number of broadcast transmissions, and the expected number of receptions. The usual theoretical stochastic model explored in the methodologies available in the literature is based on Markov chains, and the main novelty of this paper is the employment of a percolation stochastic model, whose main benefit is to obtain the network parameters directly. Additionally, the proposed approach is capable to deal with values of probability specified by bounded intervals or by a density function. The model is validated via Monte Carlo simulations, and a computational toolbox (R-packet) is provided to make the reproduction of the results presented in the paper easier. The technique is illustrated through a numerical example where the proposed model is applied to compute the energy consumption when transmitting a packet via an opportunistic network.

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Impact of Node Density on the QoS Parameters of Routing Protocols in Opportunistic Networks for Smart Spaces

Mobile Information Systems ◽

10.1155/2020/8868842 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Puneet Garg ◽

Ashutosh Dixit ◽

Preeti Sethi ◽

Plácido Rogerio Pinheiro

Keyword(s):

Routing Protocol ◽

Routing Protocols ◽

Smart Cities ◽

Opportunistic Networks ◽

Opportunistic Routing ◽

Maximum Output ◽

Opportunistic Network ◽

Smart Spaces ◽

Node Density ◽

Wide Range

The need and importance of Smart Spaces have been potentially realized by the researchers due to its applicability in the current lifestyle. Opportunistic network, a successor of mobile ad hoc networks and a budding technology of network, is a best-suited technology for implementing Smart Spaces due to its wide range of applications in real-life scenarios ranging from building smart cities to interplanetary communication. There are numerous routing protocols which are available in opportunistic network, each having their pros and cons; however, no research till the time of listing has been done which can quantitatively demonstrate the maximum performance of these protocols and standardize the comparison of opportunistic routing protocols which has been a major cause of ambiguous performance evaluation studies. The work here presents a categorical view of the opportunistic routing protocol family and thereby compares and contrasts the various simulators suited for their simulation. Thereafter, the most popular protocols (selecting at least one protocol from each category) are compared based on node density on as many as 8 standard performance metrics using ONE simulator to observe their scalability, realism, and comparability. The work concludes by presenting the merits and demerits of each of the protocols discussed as well as specifying the best routing protocol among all the available protocols for Smart Spaces with maximum output. It is believed that the results achieved by the implemented methodology will help future researchers to choose appropriate routing protocol to delve into their research under different scenarios.

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Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs

Electronics ◽

10.3390/electronics9010098 ◽

2020 ◽

Vol 9 (1) ◽

pp. 98

Author(s):

Rajkumar Singh Rathore ◽

Suman Sangwan ◽

Kabita Adhikari ◽

Rupak Kharel

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Energy Harvesting ◽

Duty Cycle ◽

Opportunistic Routing ◽

Sensor Nodes ◽

Wireless Sensor ◽

Echo State Network ◽

Proposed Model

Minimizing energy consumption is one of the major challenges in wireless sensor networks (WSNs) due to the limited size of batteries and the resource constrained tiny sensor nodes. Energy harvesting in wireless sensor networks (EH-WSNs) is one of the promising solutions to minimize the energy consumption in wireless sensor networks for prolonging the overall network lifetime. However, static energy harvesting in individual sensor nodes is normally limited and unbalanced among the network nodes. In this context, this paper proposes a modified echo state network (MESN) based dynamic duty cycle with optimal opportunistic routing (OOR) for EH-WSNs. The proposed model is used to act as a predictor for finding the expected energy consumption of the next slot in dynamic duty cycle. The model has adapted a whale optimization algorithm (WOA) for optimally selecting the weights of the neurons in the reservoir layer of the echo state network towards minimizing energy consumption at each node as well as at the network level. The adapted WOA enabled energy harvesting model provides stable output from the MESN relying on optimal weight selection in the reservoir layer. The dynamic duty cycle is updated based on energy consumption and optimal threshold energy for transmission and reception at bit level. The proposed OOR scheme uses multiple energy centric parameters for selecting the relay set oriented forwarding paths for each neighbor nodes. The performance analysis of the proposed model in realistic environments attests the benefits in terms of energy centric metrics such as energy consumption, network lifetime, delay, packet delivery ratio and throughput as compared to the state-of-the-art-techniques.

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A history-based energy-efficient routing protocol for opportunistic networks

10.32920/ryerson.14657526 ◽

2021 ◽

Author(s):

Khuram Khalid

Keyword(s):

Energy Consumption ◽

Routing Protocol ◽

Energy Efficient ◽

Opportunistic Networks ◽

Superior Performance ◽

Opportunistic Network ◽

Energy Aware ◽

Energy Efficient Routing ◽

One Simulator ◽

Overhead Ratio

In this thesis, a history-based energy-efficient routing protocol (called AEHBPR) for opportunistic networks (OppNets) is proposed, which saves the energy consumption by avoiding unnecessary packets transmission in the network and by clearing the buffer of nodes carrying the copies of the already delivered packets. The proposed AEHBPR protocol is evaluated using the Opportunistic NEtwork (ONE) simulator with both synthetic and real mobility traces, showing a superior performance compared to the History-Based Prediction for Routing (HBPR) protocol and AEProphet, in terms of average remaining energy, number of dead nodes, number of delivered messages, and overhead ratio, where AEProphet is the ProPHet routing protocol for OppNets on which the same energy-aware mechanism has been implemented.

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Energy Transmission and Equilibrium Scheme in Data Communication Opportunistic Networks

Applied System Innovation ◽

10.3390/asi3040054 ◽

2020 ◽

Vol 3 (4) ◽

pp. 54

Author(s):

Kun Wang ◽

Guoli Feng ◽

Lizhong Zhang ◽

Jia Wu

Keyword(s):

Energy Consumption ◽

Energy Supply ◽

Data Communication ◽

Opportunistic Networks ◽

Energy Utilization ◽

Opportunistic Network ◽

Good Communication ◽

Data Packets ◽

Wireless Communication Network ◽

Communication Scheme

In data communication, a good communication scheme can improve the transmission of data packets among nodes. The opportunistic network is a convenient wireless communication network and its model is easily applied in data communication. Energy consumption among nodes in the opportunistic network is an important parameter. The over-consumption of energy may cause the nodes to be dead, and then many useful data packets would be lost. Especially in data communication, this tendency is obvious. However, many researchers rarely consider energy consumption in the opportunistic network. This paper suggests a scheme in which data packets are transmitted among nodes. Energy supply and equilibrium is found in opportunistic networks. This scheme not only supplies energy to active nodes, but also considers inactive nodes to energy supply objects. Then, this scheme accomplishes data packets transmission and improves energy utilization in the opportunistic network. With the evidence of simulation and comparison of the epidemic algorithm, the direct delivery algorithm, and spray and wait algorithm in the opportunistic network, this scheme can be an equilibrium for energy consumption, for improving the delivering ratio, and the size of the cache time.

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A history-based energy-efficient routing protocol for opportunistic networks

10.32920/ryerson.14657526.v1 ◽

2021 ◽

Author(s):

Khuram Khalid

Keyword(s):

Energy Consumption ◽

Routing Protocol ◽

Energy Efficient ◽

Opportunistic Networks ◽

Superior Performance ◽

Opportunistic Network ◽

Energy Aware ◽

Energy Efficient Routing ◽

One Simulator ◽

Overhead Ratio

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Ferry Node Identification Model for the Security of Mobile Ad Hoc Network

Security and Communication Networks ◽

10.1155/2021/6682311 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Zhifei Wang ◽

Gang Xu ◽

Na Zhang ◽

Zhihan Qi ◽

Fengqi Wei ◽

...

Keyword(s):

Ad Hoc Network ◽

Network Performance ◽

Ad Hoc ◽

Mobile Ad Hoc Network ◽

Opportunistic Networks ◽

Opportunistic Network ◽

Evaluation Indicator ◽

Proposed Model ◽

Mobile Ad Hoc ◽

Identification Model

An opportunistic network is a special type of wireless mobile ad hoc network that does not require any infrastructure, does not have stable links between nodes, and relies on node encounters to complete data forwarding. The unbalanced energy consumption of ferry nodes in an opportunistic network leads to a sharp decline in network performance. Therefore, identifying the ferry node group plays an important role in improving the performance of the opportunistic network and extending its life. Existing research studies have been unable to accurately identify ferry node clusters in opportunistic networks. In order to solve this problem, the concepts of k-core and structural holes have been combined, and a new evaluation indicator, namely, ferry importance rank, has been proposed in this study for analyzing the dynamic importance of nodes in a network. Based on this, a ferry cluster identification model has been designed for accurately identifying the ferry node clusters. The results of the simulations conducted for verifying the performance of the proposed model show that the accuracy of the model to identify the ferry node clusters is 100%.

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

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666191026092311 ◽

2019 ◽

Vol 09 ◽

Cited By ~ 1

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.

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Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

Urban Rail Transit ◽

10.1007/s40864-021-00151-w ◽

2021 ◽

Author(s):

Ahmad Reza Jafarian-Moghaddam

Keyword(s):

Energy Consumption ◽

Travel Time ◽

Pollutant Emissions ◽

Scheduling Problems ◽

Train Scheduling ◽

Multi Objective ◽

Consumption Ratio ◽

Proposed Model ◽

Objective Model ◽

Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

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On modeling and dynamics of a multiple launch rocket system

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410020982243 ◽

2021 ◽

pp. 095441002098224

Author(s):

Bo Li ◽

Xiaoting Rui ◽

Guoping Wang ◽

Jianshu Zhang ◽

Qinbo Zhou

Keyword(s):

Monte Carlo ◽

Multibody Systems ◽

Euler Method ◽

Dynamics Analysis ◽

Analysis Problem ◽

Dynamic Design ◽

Proposed Model ◽

And Control ◽

Rocket System ◽

Complete Process

Dynamics analysis is currently a key technique to fully understand the dynamic characteristics of sophisticated mechanical systems because it is a prerequisite for dynamic design and control studies. In this study, a dynamics analysis problem for a multiple launch rocket system (MLRS) is developed. We particularly focus on the deductions of equations governing the motion of the MLRS without rockets by using a transfer matrix method for multibody systems and the motion of rockets via the Newton–Euler method. By combining the two equations, the differential equations of the MLRS are obtained. The complete process of the rockets’ ignition, movement in the barrels, airborne flight, and landing is numerically simulated via the Monte Carlo stochastic method. An experiment is implemented to validate the proposed model and the corresponding numerical results.

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Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Electronics ◽

10.3390/electronics10080920 ◽

2021 ◽

Vol 10 (8) ◽

pp. 920

Author(s):

Liesle Caballero ◽

Álvaro Perafan ◽

Martha Rinaldy ◽

Winston Percybrooks

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Mobile Robot ◽

Energy Budget ◽

Dynamic Models ◽

Pearson Correlation ◽

Experimental Conditions ◽

Grid Map ◽

Proposed Model ◽

Exploration Task

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

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