Secure Encryption and Compression in Wireless Body Sensor Networks

2019 ◽  
Vol 16 (8) ◽  
pp. 3608-3611 ◽  
Author(s):  
A. Sivsangari ◽  
R. Subhashini ◽  
S. Vigneshwari ◽  
B. Bharathi
Keyword(s):  
Energy Consumption ◽  
Data Transmission ◽  
Medical Diagnosis ◽  
Medical Information ◽  
Sensor Nodes ◽  
Body Sensor Networks ◽  
Memory Space ◽  
Proposed Model ◽  
Energy Constrained ◽  
Wireless Body Sensor Networks

The ECG information are essential in medical diagnosis and treatment. If any loss or alteration of medical information during their data transmission, it will affect the patient. The ECG information is very long and needs more memory space for storing the information. However, the sensor nodes are energy constrained and have less memory space. The energy consumption and security are the two important requirements in WBAN. In order to minimize the energy consumption, the proposed model exploits the compression. The compression can reduce the amount of data transmission.

scholarly journals Energy-efficient hierarchical routing in wireless sensor networks based on Fog Computing

2020 ◽  
Author(s):  
Ademola Abidoye ◽  
Boniface Kabaso
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Data Transmission ◽  
Energy Efficient ◽  
Fog Computing ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Hierarchical Routing

Abstract Wireless sensor networks (WSNs) have been recognized as one of the most essential technologies of the 21st century. The applications of WSNs are rapidly increasing in almost every sector because they can be deployed in areas where cable and power supply are difficult to use. In the literature, different methods have been proposed to minimize energy consumption of sensor nodes so as to prolong WSNs utilization. In this article, we propose an efficient routing protocol for data transmission in WSNs; it is called Energy-Efficient Hierarchical routing protocol for wireless sensor networks based on Fog Computing (EEHFC). Fog computing is integrated into the proposed scheme due to its capability to optimize the limited power source of WSNs and its ability to scale up to the requirements of the Internet of Things applications. In addition, we propose an improved ant colony optimization (ACO) algorithm that can be used to construct optimal path for efficient data transmission for sensor nodes. The performance of the proposed scheme is evaluated in comparison with P-SEP, EDCF, and RABACO schemes. The results of the simulations show that the proposed approach can minimize sensor nodes’ energy consumption, data packet losses and extends the network lifetime

Wireless Body Sensor Networks for Patient Health Monitoring

2020 ◽  
pp. 132-154
Author(s):  
Bhanu Chander
Keyword(s):  
Energy Savings ◽  
Health Management ◽  
Wireless Body Area Network ◽  
Research Field ◽  
Sensor Nodes ◽  
Medical Examiner ◽  
Body Sensor Networks ◽  
Area Network ◽  
Patient Health ◽  
Wireless Body Sensor Networks

Remote medical health management is the most attractive research field in the domain of WSN. Wireless body area network (WBAN) produces constant, unbroken observation of the patient. Basically, WBAN acts as the appliance of internet of things (IoT) which offers an opportunity to a medical examiner to supervise chronic disease. Dissimilar protocols, guidelines, policies have been developed and developing in the last decade. In WBAN, minute power sensor nodes deployed toward capturing unusual essential signs of patients at home, hospitals in support of analysis purpose and furthermore advise suitable procedures. The main goal of this chapter is to introduce a complete and advanced understanding of WBANs, energy savings methods, human activity monitoring procedures, challenges and research issues, applications, and a comprehensive literature survey.

scholarly journals An efficient hybrid model for secure transmission of data by using efficient data collection and dissemination (EDCD) algorithm based WSN

2020 ◽  
Vol 20 (1) ◽  
pp. 545
Author(s):  
Mustafa Mahmood Akawee ◽  
Mohanad Ali Meteab Al-Obaidi ◽  
Haider Mohammed Turki Al-Hilfi ◽  
Sabbar Insaif Jassim ◽  
Tole Sutikno
Keyword(s):  
Energy Consumption ◽  
Data Collection ◽  
Hybrid Model ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Security Issue ◽  
Proposed Model ◽  
Efficient Data ◽  
Secure Transmission ◽  
Efficient Data Collection

<span>Wireless Sensor Network (WSN) is one of the most important elements of the Internet of Things paradigm. Energy consumption is a vital issue in IoT and WSN.  Security primitives in the IoT are energy consuming. Addressed the security issue for transmitted data by IoT sensor node add another challenge in term of energy consumption. finding the satisfactory solutions that reduce power consumption at the same time as making sure the required security services is not always an easy undertaking. Therefore, in this article, we proposed an efficient hybrid model for secure transmission of data from sensor nodes to receivers in WSN applications.  The proposed model includes two algorithms Rivest–Shamir–Adleman (RSA) and efficient data collection and dissemination (EDCD). The key idea behind the proposed model is to prevent to secure sensed data if no significant change between the current data and the last transmitted data by the apply EDCD1 algorithm, which that will help in saving the sensor node energy. The reason for that the size of cipher data is so large compared to the sensed data, which that will increase the energy consumption.  The outcome results shown that the proposed model has a high performance compared to RSA in term of energy consumption.</span>

scholarly journals SPINE-BASED APPLICATION DEVELOPMENT ON HETEROGENEOUS WIRELESS BODY SENSOR NETWORKS

2010 ◽  
pp. 80-89
Author(s):  
Giancarlo Fortino ◽  
Stefano Galzarano ◽  
Roberta Giannantonio ◽  
Raffaele Gravina ◽  
Antonio Guerrieri
Keyword(s):  
Sensor Networks ◽  
Recognition System ◽  
Sensor Nodes ◽  
Physical Contact ◽  
Wireless Sensor ◽  
Body Sensor Networks ◽  
Application Development ◽  
Wide Range ◽  
Ubiquitous And Pervasive Computing ◽  
Wireless Body Sensor Networks

Wireless sensor networks (WSNs) are a novel technology enabling new classes of applications and systems for ubiquitous and pervasive computing. In particular, WSNs for the human body, also known as Wireless Body Sensor Networks (WBSNs), will enable not only continuous, multi-purpose monitoring of people but also will support social interaction among people coming into physical contact. In these contexts, applications demand a wide range of functionalities, in terms of sensor types, processing performance, communication capabilities. Moreover the development of such applications has to deal with the issue of handling heterogeneous WBSNs since different kinds of sensor node architectures could be necessary to fulfill all the application requirements. This paper proposes an approach based on the SPINE frameworks (SPINE1.x and SPINE2) for the programming of signal processing applications on heterogeneous wireless sensor platforms. In particular, two integrable approaches based on the proposed frameworks are described that allow the development of applications for WBSNs constituted by heterogeneous sensor nodes. The approaches are exemplified through a human activity recognition system based on a WBSN composed of two types of sensor nodes, heterogeneous with respect to base software and hardware.

A QoS aware Framework to support Minimum Energy Data Aggregation and Routing in Wireless Sensor Networks

2009 ◽  
Vol 1 (4) ◽  
pp. 91-106 ◽  
Author(s):  
Neeraj Kumar ◽  
R.B. Patel
Keyword(s):  
Energy Consumption ◽  
Data Aggregation ◽  
Control Mechanism ◽  
Minimum Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Combine Data ◽  
Energy Data ◽  
Energy Constrained

In a wireless sensor network (WSN), the sensor nodes obtain data and communicate its data to a centralized node called base station (BS) using intermediate gateway nodes (GN). Because sensors are battery powered, they are highly energy constrained. Data aggregation can be used to combine data of several sensors into a single message, thus reducing sensor communication costs and energy consumption. In this article, the authors propose a QoS aware framework to support minimum energy data aggregation and routing in WSNs. To minimize the energy consumption, a new metric is defined for the evaluation of the path constructed from source to destination. The proposed QoS framework supports the dual goal of load balancing and serving as an admission control mechanism for incoming traffic at a particular sensor node. The results show that the proposed framework supports data aggregation with less energy consumption than earlier strategies.

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

Electronics ◽  
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.

scholarly journals Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 256 ◽  
Author(s):  
Haotian Chang ◽  
Jing Feng ◽  
Chaofan Duan
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Mobility Model ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Data Forwarding ◽  
Energy Constrained

Data forwarding for underwater wireless sensor networks has drawn large attention in the past decade. Due to the harsh underwater environments for communication, a major challenge of Underwater Wireless Sensor Networks (UWSNs) is the timeliness. Furthermore, underwater sensor nodes are energy constrained, so network lifetime is another obstruction. Additionally, the passive mobility of underwater sensors causes dynamical topology change of underwater networks. It is significant to consider the timeliness and energy consumption of data forwarding in UWSNs, along with the passive mobility of sensor nodes. In this paper, we first formulate the problem of data forwarding, by jointly considering timeliness and energy consumption under a passive mobility model for underwater wireless sensor networks. We then propose a reinforcement learning-based method for the problem. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate the validity of the proposed method. Our method outperforms the benchmark protocols in both timeliness and energy efficiency. More specifically, our method gains 83.35% more value of information and saves up to 75.21% energy compared with a classic lifetime-extended routing protocol (QELAR).

scholarly journals Effective Stochastic Modeling of Energy-Constrained Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Ali Shareef ◽  
Yifeng Zhu
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Petri Nets ◽  
Petri Net ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Optimal Energy ◽  
Maximum Utility ◽  
Energy Constrained

Energy consumption of energy-constrained nodes in wireless sensor networks (WSNs) is a fatal weakness of these networks. Since these nodes usually operate on batteries, the maximum utility of the network is dependent upon the optimal energy usage of these nodes. However, new emerging optimal energy consumption algorithms, protocols, and system designs require an evaluation platform. This necessitates modeling techniques that can quickly and accurately evaluate their behavior and identify strengths and weakness. We propose Petri nets as this ideal platform. We demonstrate Petri net models of wireless sensor nodes that incorporate the complex interactions between the processing and communication components of an WSN. These models include the use of both an open and closed workload generators. Experimental results and analysis show that the use of Petri nets is more accurate than the use of Markov models and programmed simulations. Furthermore, Petri net models are extremely easier to construct and test than either. This paper demonstrates that Petri net models provide an effective platform for studying emerging energy-saving strategies in WSNs.

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