scholarly journals Network Lifetime Analysis in IOT Environment in Healthcare Sectors Using Deep Learning Routing Approach

2021 ◽  
Author(s):  
Janaki K
Keyword(s):  
Deep Learning ◽  
Network Lifetime ◽  
Data Transmission ◽  
Trade Off ◽  
Data Routing ◽  
Viable Solution ◽  
Lifetime Analysis ◽  
Acquisition Speed ◽  
The Individual ◽  
The Internet Of Things

The Internet of Things (IoT) provides an improved flexibility in data collection, network deployment and data transmission to the sink nodes. However, depending on the application, the IoT network tends to consume lot of power from the individual devices. Various conventional solutions are provided to reduce the consumption of energy but most methods focus on increasing the data acquisition speed, data transmission and routing capabilities. However, these methods tend to fall under the trade-off between these three factors. Hence, in order to maintain the trade-off between these constraints, a viable solution is developed in this paper. A deep learning-based routing is built considering the faster acquisition of data, faster data transmission and routing path estimation with increasing path estimation. The paper models a Deep belief Network (DBN) to route the data considering all these constraints. The experimental validation is conducted to check the network lifetime, energy consumption of IoT nodes. The results show that the DBN offers greater source of flexibility with increased data routing capabilities than other methods.

scholarly journals Game theoretical approach for utility-based distributed balanced data routing in wireless sensor networks

2021 ◽  
Author(s):  
Afshin Behzadan
Keyword(s):  
Wireless Sensor Networks ◽  
Network Lifetime ◽  
Theoretical Approach ◽  
Data Transmission ◽  
Transmission Rate ◽  
Energy Levels ◽  
Wireless Sensor ◽  
Data Routing ◽  
Game Theoretical Approach ◽  
Information Energy

In this thesis, two distributed algorithms for the construction of load balanced routing trees in wireless sensor networks are proposed. In such networks load balanced data routing and aggregation can considerably decrease uneven energy consumption among sensor nodes and prolong network lifetime. The proposed algorithms achieve load balancing by adjusting the number of children among parents as much as possible. The solution is based on game theoretical approach, where child adjustment is considered as a game between parents and child nodes, in which parents arc cooperative and children are selfish players. The gained utility by each node is determined through utility functions defined per role. Utility functions determine the behavior of nodes in each role. At the game termination, each individual node gains the maximum benefit based on its utility function, and the network reaches the global goal of forming the balanced tree. The proposed methods are called Utility Driven Balanced Communication (UDBC) algorithm which is designed for homogenous environment, where all nodes are assumed to produce equal amount of information, and Heterogenous Balanced Data Routing (HBDR) algorithm which is proposed for heterogenous environment, where different applications use different aggregation functions, and nodes can be vary in terms of the amount of produced information, energy levels, data transmission rate and available of the amount of produced information, energy levels, data transmission rate and available bandwidth for transmission. The advantage of this work over similar work in the literature is the construction of more balanced trees which results in prolonging network lifetime, with the capability of adaption according to specific application needs for sensitivity to delay and reliability of data delivery.

scholarly journals Game theoretical approach for utility-based distributed balanced data routing in wireless sensor networks

2021 ◽  
Author(s):  
Afshin Behzadan
Keyword(s):  
Wireless Sensor Networks ◽  
Network Lifetime ◽  
Theoretical Approach ◽  
Data Transmission ◽  
Transmission Rate ◽  
Energy Levels ◽  
Wireless Sensor ◽  
Data Routing ◽  
Game Theoretical Approach ◽  
Information Energy

In this thesis, two distributed algorithms for the construction of load balanced routing trees in wireless sensor networks are proposed. In such networks load balanced data routing and aggregation can considerably decrease uneven energy consumption among sensor nodes and prolong network lifetime. The proposed algorithms achieve load balancing by adjusting the number of children among parents as much as possible. The solution is based on game theoretical approach, where child adjustment is considered as a game between parents and child nodes, in which parents arc cooperative and children are selfish players. The gained utility by each node is determined through utility functions defined per role. Utility functions determine the behavior of nodes in each role. At the game termination, each individual node gains the maximum benefit based on its utility function, and the network reaches the global goal of forming the balanced tree. The proposed methods are called Utility Driven Balanced Communication (UDBC) algorithm which is designed for homogenous environment, where all nodes are assumed to produce equal amount of information, and Heterogenous Balanced Data Routing (HBDR) algorithm which is proposed for heterogenous environment, where different applications use different aggregation functions, and nodes can be vary in terms of the amount of produced information, energy levels, data transmission rate and available of the amount of produced information, energy levels, data transmission rate and available bandwidth for transmission. The advantage of this work over similar work in the literature is the construction of more balanced trees which results in prolonging network lifetime, with the capability of adaption according to specific application needs for sensitivity to delay and reliability of data delivery.

scholarly journals An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
C. Jothikumar ◽  
Kadiyala Ramana ◽  
V. Deeban Chakravarthy ◽  
Saurabh Singh ◽  
In-Ho Ra
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Network Lifetime ◽  
Data Transmission ◽  
Base Station ◽  
Wireless Sensor ◽  
The Internet ◽  
The Internet Of Things

The Internet of Things grew rapidly, and many services, applications, sensor-embedded electronic devices, and related protocols were created and are still being developed. The Internet of Things (IoT) allows physically existing things to see, hear, think, and perform a significant task by allowing them to interact with one another and exchange valuable knowledge when making decisions and caring out their vital tasks. The fifth-generation (5G) communications require that the Internet of Things (IoT) is aided greatly by wireless sensor networks, which serve as a permanent layer for it. A wireless sensor network comprises a collection of sensor nodes to monitor and transmit data to the destination known as the sink. The sink (or base station) is the endpoint of data transmission in every round. The major concerns of IoT-based WSNs are improving the network lifetime and energy efficiency. In the proposed system, Optimal Cluster-Based Routing (Optimal-CBR), the energy efficiency, and network lifetime are improved using a hierarchical routing approach for applications on the IoT in the 5G environment and beyond. The Optimal-CBR protocol uses the k-means algorithm for clustering the nodes and the multihop approach for chain routing. The clustering phase is invoked until two-thirds of the nodes are dead and then the chaining phase is invoked for the rest of the data transmission. The nodes are clustered using the basic k-means algorithm during the cluster phase and the highest energy of the node nearest to the centroid is selected as the cluster head (CH). The CH collects the packets from its members and forwards them to the base station (BS). During the chaining phase, since two-thirds of the nodes are dead and the residual energy is insufficient for clustering, the remaining nodes perform multihop routing to create chaining until the data are transmitted to the BS. This enriches the energy efficiency and the network lifespan, as found in both the theoretical and simulation analyses.

Welfare tradeoff psychology is present in children and adults

2018 ◽  
Author(s):  
Rhea M Howard ◽  
Annie C. Spokes ◽  
Samuel A Mehr ◽  
Max Krasnow
Keyword(s):  
Social Networks ◽  
Social Context ◽  
Opportunity Cost ◽  
Trade Off ◽  
Contextual Features ◽  
Limited Experience ◽  
The Individual

Making decisions in a social context often requires weighing one's own wants against the needs and preferences of others. Adults are adept at incorporating multiple contextual features when deciding how to trade off their welfare against another. For example, they are more willing to forgo a resource to benefit friends over strangers (a feature of the individual) or when the opportunity cost of giving up the resource is low (a feature of the situation). When does this capacity emerge in development? In Experiment 1 (N = 208), we assessed the decisions of 4- to 10-year-old children in a picture-based resource tradeoff task to test two questions: (1) When making repeated decisions to either benefit themselves or benefit another person, are children’s choices internally consistent with a particular valuation of that individual? (2) Do children value friends more highly than strangers and enemies? We find that children demonstrate consistent person-specific welfare valuations and value friends more highly than strangers and enemies. In Experiment 2 (N = 200), we tested adults using the same pictorial method. The pattern of results successfully replicated, but adults’ decisions were more consistent than children’s and they expressed more extreme valuations: relative to the children, they valued friends more and valued enemies less. We conclude that despite children’s limited experience allocating resources and navigating complex social networks, they behave like adults in that they reference a stable person-specific valuation when deciding whether to benefit themselves or another and that this rule is modulated by the child’s relationship with the target.

scholarly journals RSSI-Based for Device-Free Localization Using Deep Learning Technique

Smart Cities ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 444-455
Author(s):  
Abdul Syafiq Abdull Sukor ◽  
Latifah Munirah Kamarudin ◽  
Ammar Zakaria ◽  
Norasmadi Abdul Rahim ◽  
Sukhairi Sudin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Statistical Features ◽  
Received Signal Strength Indicator ◽  
Privacy Concerns ◽  
Physical Discomfort ◽  
Learning Technique ◽  
Device Free ◽  
The Internet Of Things

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

Improving the Accuracy-Latency Trade-off of Edge-Cloud Computation Offloading for Deep Learning Services

2020 ◽  
Author(s):  
Xiaobo Zhao ◽  
Minoo Hosseinzadeh ◽  
Nathaniel Hudson ◽  
Hana Khamfroush ◽  
Daniel E. Lucani
Keyword(s):  
Deep Learning ◽  
Computation Offloading ◽  
Trade Off ◽  
Cloud Computation

scholarly journals Task allocation and site fidelity jointly influence foraging regulation in honeybee colonies

2017 ◽  
Vol 4 (8) ◽  
pp. 170344 ◽  
Author(s):  
Thiago Mosqueiro ◽  
Chelsea Cook ◽  
Ramon Huerta ◽  
Jürgen Gadau ◽  
Brian Smith ◽  
...  
Keyword(s):  
Site Fidelity ◽  
Collective Behaviour ◽  
Trade Off ◽  
Agent Based ◽  
Resource Collection ◽  
Group Members ◽  
Collective Foraging ◽  
The Individual ◽  
Colony Level ◽  
And Task

Variation in behaviour among group members often impacts collective outcomes. Individuals may vary both in the task that they perform and in the persistence with which they perform each task. Although both the distribution of individuals among tasks and differences among individuals in behavioural persistence can each impact collective behaviour, we do not know if and how they jointly affect collective outcomes. Here, we use a detailed computational model to examine the joint impact of colony-level distribution among tasks and behavioural persistence of individuals, specifically their fidelity to particular resource sites, on the collective trade-off between exploring for new resources and exploiting familiar ones. We developed an agent-based model of foraging honeybees, parametrized by data from five colonies, in which we simulated scouts, who search the environment for new resources, and individuals who are recruited by the scouts to the newly found resources, i.e. recruits. We varied the persistence of returning to a particular food source of both scouts and recruits and found that, for each value of persistence, there is a different optimal ratio of scouts to recruits that maximizes resource collection by the colony. Furthermore, changes to the persistence of scouts induced opposite effects from changes to the persistence of recruits on the collective foraging of the colony. The proportion of scouts that resulted in the most resources collected by the colony decreased as the persistence of recruits increased. However, this optimal proportion of scouts increased as the persistence of scouts increased. Thus, behavioural persistence and task participation can interact to impact a colony's collective behaviour in orthogonal directions. Our work provides new insights and generates new hypotheses into how variations in behaviour at both the individual and colony levels jointly impact the trade-off between exploring for new resources and exploiting familiar ones.

scholarly journals One for All: Neural Joint Modeling of Entities and Events

2019 ◽  
Vol 33 ◽  
pp. 6851-6858 ◽  
Author(s):  
Trung Minh Nguyen ◽  
Thien Huu Nguyen
Keyword(s):  
Deep Learning ◽  
Recent Work ◽  
State Of The Art ◽  
Contextual Information ◽  
Joint Modeling ◽  
Event Extraction ◽  
Event Trigger ◽  
The Individual ◽  
Novel Model ◽  
Argument Roles

The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.

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