Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN

Abstract

The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT

“…Khan et al [ 18 ] proposed the Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, Signal-to-Noise Ratio (SNR), and available network bandwidth.…”

“…Hence, routing protocols play vital role in providing effective communication between the sensors, to prolong the overall lifetime of networks via minimizing energy consumption that required forwarding data from sensor nodes to a medical-related server efficiently [21]. e traditional routing protocol is not a suitable solution for this type of network due to resource limitations [22], where, in the last decade, many works have been proposed by different researchers who focused on developing adaptive and robust routing protocols [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. e various works use the congestion control techniques and maximizing battery efficiency to extend the network lifetime.…”

“…e proposal is based on a system, in which tiny sensors nodes are placed on the human body to sense important health-related parameters and forward them to two sink nodes. It is designed to stabilize the operation of WBANs by choosing the best (i) Emergency data will get higher priority and less delay over normal data (i) Packets drop ratio is high (ii) e node with greater fitness value will be selected as a next-hop node (ii) Network lifetime is less (iii) SNR parameter is used for better selection of path between sender and receiver (iii) End-to-End delay is high ELR-W [14] (i) A link efficiency-oriented network model is presented considering beaconing information and network initialization process (i) Network lifetime is less (ii) Path cost calculation model is derived focusing on energy aware link efficiency (ii) High End-to-End delay EH-RCB [15] (i) Clustering approach to enhance nodes connectivity with each other to balance out load on single sink node (i) Network lifetime is far short (ii) CF is calculated using node total energy, distance from other nodes, link SNR and required transmission power (ii) Packet delay is high EB-MADM [16] (i) Dynamic cluster head selection (i) Path loss is high (ii) An optimum node as cluster head which has higher residual energy level (ii) Network lifetime is less (iii) Selects a new cluster head for each transmission round (iv) Cooperative effort of cluster nodes PriNergy [17] (i) Selecting appropriate parent member node in the RPL protocol (i) Network lifetime is less (ii) Increasing network efficiency in terms of optimal speed of packet transmission in the IoT environment (ii) Packet drop is high EHCRP [18] (i) Link efficiency network model is presented which calculates the capability of the forwarder node in terms of its ability to send received/sensed data (i Journal of Healthcare Engineering forwarder node, which is based on optimal calculated Cost Function (CF).…”

Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks

“…CEPRAN [ 25 ] uses a cooperative approach for communication to enhance energy efficiency and reliable communication. EHCRP [ 26 ] consider several parameters for routing decisions to achieve the desired goal, i.e., energy efficiency. Similarly, the authors in [ 27 ] also aim to efficiently use the energy of the sensor nodes, while [ 28 ] aims to do the same but considers only critical data (CD).…”

“…for the CD and DCD packets and after receiving DP and NHNWLQ, it looks at NHNWLQ, and NNs with PDi,j, LC ≤ PDthre are listed into NHNPD (Next-Hop Neighbors with acceptable path delay) (lines[19][20][21][22]. DP is dropped if NHNPD is empty (lines 23-24) and if there is only one NN in NHNPD, then it is selected as SNH (lines[25][26]. DP is sent towards DCQ if it is DCD packets (lines 27-28); otherwise it is sent towards CDQ (lines…”