Mobility and Energy Aware Routing Protocol for Healthcare IoT Application

 

S. Sankar¹, Dr. P. Srinivasan²*

¹Research Scholar, School of Computer Science and Engineering, VIT University, Vellore-632014, Tamilnadu, India

²Associate Professor, School of Information Technology and Engineering, VIT University, Vellore-632014, Tamilnadu, India

 

ABSTRACT:

Internet of Things (IoT) is one of the emerging technologies in the world and it changes the life style of the people. Nowadays, there is a drastic increase of sensor enabled physical devices connected in the internet and at the same time, quick responsiveness is very important for transferring the data from source to destination. Hence, routing plays a crucial role in IoT. In IoT routing protocol, the requirement is dynamically varying depends on the on the application. The mobility based IoT application like healthcare, smart school, animal tracking and etc. The term mobility denotes entire node can move dynamically from one place to another place within the coverage area of the network. In this paper, we introduce the Mobility and Energy aware routing protocol (ME-RPL), which consider the metrics  received signal strength (RSSI), expected transmission count (ETX), battery depletion index (BDI). The DODAG root node sends the DODAG Information Object (DIO) control messages to the entire participant node in LLN. If the participant node is within the coverage of existing DODAG, then it can choose the preferred parent based on the routing metric. The simulation has done using COOJA simulator in different IoT mobility scenario. The proposed work (ME-RPL) provides the better result in terms of packet delivery ratio and network lifetime, than other similar protocol RPL, mRPL, D-RPL.

 

 

 

INTRODUCTION:

The Internet of Things (IoT) has deliberated for enabling the physical devices to connect over  the  internet,  that's  able  to  exchange  the  data  between  the  physical  devices  in  heterogeneous  wireless  sensor network  environment [1, 2]. The IoT applications are smart home, smart city, ambient assisted living, smart agriculture and smart healthcare, etc. The healthcare is important IoT application because it improves the patient experience and saves the human life [3, 4].

 

 

 

Medical and healthcare is one of the popular applications in IoT [5, 6]. The IoT technology recently adopted in medical applications like chronic diseases, remote health monitoring, fitness programs, and elderly care.Nowadays, IoT involved in medical field and it changes the medical diagnostic devices into smart devices. Hence, the usages of healthcare IoT is increasing rapidly and decrease the health care expenses [5].

 

In IoT, highly resource constrained devices connected over the internet. So, a huge amount data losses are happening during the data transmission in the IoT network called “Low Power and Lossy Network (LLN)”. Thus, IETF is standardized the RPL routing protocol for LLN. RPL is a distance vector, source routing protocol and It is a proactive routing protocol. It follows the Destination oriented Directed Acyclic Graph (DODAG). In healthcare IoT, mobility is a challenging task, to take efficient route, to transfer the data [5]. In mobility environment, the participant node connects with DODAG based on the RSSI value [2].

 

Alok Kulkarni et’ al reviewed an application of IoT in healthcare sector, and how IoT achieved the healthcare requirement in affordable costs. This work explained the IoT functionalities, and implementation using wireless and sensing techniques for healthcare applications [6][7]. Hassan, Ali, et al presented a composite metric based energy efficient routing protocol for LLN, which considered the metrics ETX, RER and BDI [8]. M. Riazul Islam et’al surveyed on IoT based healthcare technologies, which provided the state-of the-art of IoT network, and platform architecture for IoT based healthcare solutions. Followed by, this work analyzed different IoT privacy features and security, including attack taxonomies and threat model from the healthcare perspective. Moreover, it proposed an intelligent collaborative security model to reduce the security risk [9].

 

Kevin C. Lee et’al reviewed on routing protocol for vehicle-to-vehicle communication, through direct or via multiple hops. LLN follows the tree based routing and many RPL elements are transferable from wireless to vehicular environments. It fine-tuned the RPL parameter and outperformed in the VANET environment than existing available RPL protocols [10]. El Korbi et’al investigated the mobility issues in LLN. RPL is a routing protocol and it supports low storage, power and processing devices in wireless sensor networks. It proposed new mechanism that reduced the traffic congestion and improved the mobility [11].

 

Jeong Gil Ko and Marcus Chang discussed the current generation low power wireless sensor network protocols and it supported the efficient data collection without consideration of mobility. This work proposed a mobility based data collection of LLN. It is used fuzzy estimator to analyze the link quality, which reduced the traffic congestion and reconfigured the node immediately [12]. Hossein Fotouhi et’al discussed about the algorithms network topology changes in the trickle timer and neighbor discovery algorithm. It introduced the proactive routing hand off mechanism as dubbed the smart hop within RPL, which solve the mobility issues [13-15].

 

P. Thubert et’al proposed IPv6 routing protocol for low power and lossy networks (RPL) and it is standardized by IETF. This RFC 6550 (Objective function-0) pointed out the highly resource constrained devices connected in LLN. It follows the metric as hop count and it addressed the problem of point-to-point and multipoint-to-point traffic [16]. O. Gnawali et’al proposed the Minimum Rank with Hysteresis Objective Function (MRHOF) and it is standardized by IETF. It is considered the routing metric as either link quality level (LQL) or hop-count (HC) or expected transmission count (ETX) [17].

 

MATERIAL AND METHODS:

The Internet of Things (IoT) supports wide range of applications using different standards and technologies. The IoT devices are mostly resource-constrained devices connected over the internet. In this paper, we propose mobility and Energy aware RPL (ME-RPL), which consider the composite  routing metrics received signal strength (RSSI), battery depletion index (BDI), expected transmission count (ETX). During the route selection, if the participant node is within the RSSI range of existing DODAG nodes, it can choose the parent from DODAG node. The preferred parent selection based on rank, which is calculated from link metric (ETX) and node metric (BDI). The proposed ME-RPL follows the additive property. It considers the minimal value of the route cost. CC2420 node is used in this proposed work. The RSSI value calculates from equation (1).

 

P = RSSI_VAL + RSSI_OFFSET [dBm]                     (1)

 

CC2420 node RSSI value has represented based on the distance of the node and RSSI value denoted as dBm. The RSSI value ranges are greater than -50 dBm indicates 100% quality signal and the value ranges are less than               -100dBm indicates 0% quality signal.  The RSSI values are mentioned in the table-1.

 

Table-1 CC2420 RSSI Value

Distance	RSSI Value (dBm)
2m	-52.47
4m	-53.35
6m	-58.15
9m	-63.17
12m	-63.7
15m	-70.27
20m	-76.34
25m	-82.89

 

A.   Expected Transmission Count:

ETX is a link metric and it predicts the link quality based on transmission and including retransmission. The ETX metric calculates from equation (2) and (3).

 

Link ETX:

It represents the forward and reverse data delivery of link. The link ETX calculation formula is given below.

                             (2)

 

Route ETX:

It calculates the link quality from root to leaf node of the path. The Route ETX calculation formula is given below.

                             (3)

B.   Battery Depletion Index

The battery depletion index is derived from residual energy. The residual energy calculates in each node of RER (Xi) i.e., based on initial energy and avail energy. The residual energy calculates from equation (4). 

                         (4)

The BDI calculates the node “X_i” as (1- Residual Energy). The BDI(X_i )calculates from equation (5)

 

                        (5)

 

The BDI of Path P_y calculates from Equation (5). It follows the productive rule.             

                           (6)

 

C.   Objective Function

The objective function (OF) defines how to choose RPL node and optimize the route in RPL instance. The proposed mobility based composite metric as objective function and it improves the mobility and energy utilization. This objective function (ME-RPL) specifically concentrates on mobility issue.

 

min OF-ME(ETX,BDI)= w1*ETX (Pi) + w2*BDI (Pi) (7)

 

The proposed objective function is represented in equation (7) and we have varied the weight values and evaluated the performance of the objective function. Finally, ME-RPL obtains the optimal route from the weight values of w1=0.5 and w2=0. 5.

 

Parent Selection Algorithm

 

Input: Node N, ParentNodeID, ParticipantNode_ParentID, BestParent_Rank=∞;

Output: Preferred_Parent (N)

for   Participant Node_ Neighbors  ∈  Parent _List  do

if ParticipantNode_Neighbhors(i)_RSSI >=Threshold then

Preferred_Parent← ParticipantNode_Neighbhors (i);

end

end

for Preferred_Parent ∈ Parent _List  do

Rank (N ) ←Rank(PN ) + Rank increase;

Rank increase ← Step + MinHopRankIncrease;

Step= w1*BDI (P_i) +w2* ETX (P_i);

If BestParent_Rank>=Prefered_ParentRank(P) then

BestParent_Rank←Prefered_ParentRank (P);

End if

end

while Prefered_ParentRank(P)= BestParent_Rank  do

SenderNode_ParentID←Preferred_ParentNodeID;

end

 

The parent selection algorithm and workflow is discussed in below.

 

 

 

 

 

 

 

 

 

 

 

Parent Selection Work Flow:

 

Fig 1: ME-RPL Design Work Flow

 

RESULT AND DISCUSSION:

The mobility and energy aware RPL (ME-RPL) is simulated and evaluated for healthcare application using COOJA wireless sensor network simulator [18-20]. We have used 25 nodes and 1 sink node in a 150m *150m simulation area. CC2420 nodes movement based on random waypoint mobility model at 0-2 m/s. The trickle time interval I_min and I _doubling are 8 and 6 respectively.  The performance of proposed ME-RPL provides the better result than RPL, mRPL and D-RPL.  The simulation parameters are represented in Table-2.

 

Table 2. Simulation configuration for Experiments

Operating syste	Contiki 207
Node Type	CC2420
Number of Nodes	25 nodes + 1 sink node
Minimum DIO Interval	8
DIO interal doubling	6
Routing Protocol	RPL
MAC/Adaptation	ContikiMAC/6LowPAN
Layer	Unit Disk Graph Medium
Radio Environment	(UDGM)
Number of Nodes	25
Transmission Range	50m
Simultaion Duration	24 Hrs
Full Battery	3000 mj
Transmission Range	150*150m2
Data Packet Timer	60 sec
Node movement	Random way point, 0-2 m/s
RPL Parameter	MinHopRankIncrease = 256

HEALTHCARE APPLICATION:

The healthcare application most important application in IoT and it saves our human life. This simulation is taken into account 25 nodes, 1 sink node, and the node type as CC2420. The Healthcare IoT performances are discussed below.

 

i. Packet Delivery Ratio:

Packet Delivery ratio (PDR) represents numbers of packets are received successfully by receiver from the total number of packets sent by sender. The proposed ME-RPL provides the average PDR of 92%. It reduces the hand over time in various mobility situations. The packet delivery is represented in table 3.

 

Table-3: Routing Protocol vs Packet Delivery Ratio (RDR)

 

Figure.2 shows that graphical representation of packet delivery ratio. The packet delivery ratio values are 42, 88, 90 and 92% for the routing protocol RPL, M-RPL, D-RPL and ME-RPL.

 

Figure.3 Routing Protocol vs Packet Delivery Ratio (RDR)

 

ii. Energy Consumption:

The simulation has conducted for one hour. The average energy consumption per packet at each node is represented in table-3. The proposed ME-RPL takes less energy consumption than RPL, mRPL, D-RPL.

 

Table-4 Routing protocol vs. Energy consumption

 

The figure 4 shows that the energy consumption with respect to the various routing protocol.

 

Figure 4. Routing protocol vs. Energy consumption

 

ANIMAL TRACKING:

Animal Tracking is one of the in IoT applications, as it traces the animal and environment. This simulation is taken into account 25 nodes, 1 sink node, and the node type as CC2420. Animal Tracking performances are discussed in below.

 

i. Packet Delivery Ratio:

Packet delivery ratio (PDR) represents that numbers of packets received successfully by receiver and total number of packets sent by sender. The proposed ME-RPL provides the average PDR of 92%. It reduces the hand over time in various mobility situations. The packet delivery is represented in table 5.

 

Table-5: Routing Protocol vs. Packet Delivery Ratio (PDR)

 

Figure.5 shows that the graphical representation of packet delivery ratio. The packet delivery ratio values 35, 67, 78 and 80% for RPL, M-RPL, D-RPL and ME-RPL.

 

 

Figure 5. Routing Protocol vs Packet Delivery Ratio (RDR)

 

ii. Energy Consumption:

The simulation has conducted in one hour. The average energy consumption per packet at each node is represented in table-6. The proposed ME-RPL takes less energy consumption than RPL, mRPL, D-RPL. It considers the mobility based on RSSI value.

 

Table-6 Routing protocol vs Energy consumption

Routing Protocol	Energy(MJ)/ min
RPL	2.8
M-RPL	1.5
D-RPL	1.3
ME-RPL	1.1

 

The figure 6 shows that energy consumption with respect to the various routing protocol. The energy consumptions are 2.5, 1.5, 1.3 and 1.1 MJ/min for the routing protocol RPL, M-RPL, D-RPL and ME-RPL.

 

Figure 6. Routing protocol vs. Energy consumption

 

CONCLUSION:

In this paper, we proposed a mobile and energy aware routing protocol (ME-RPL), which considers the metric BDI and ETX, to select the best route, for transferring the data efficiently. The simulation is conducted for healthcare and animal tracking application, to improve the network lifetime. The proposed protocol ME-RPL is used the composite routing metric, for improving the packet delivery ratio and network lifetime. If the participant node is within the coverage of DODAG, It chooses the preferred parent from DODAG. The simulation result shows that the proposed ME-RPL has provided the packet delivery ratio 92% for healthcare application and 80% for animal tracking likewise energy consumption 1.2 MJ/min for healthcare application and 1.1MJ/min for animal tracking.

 

REFERENCES:

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Accepted on 14.04.2018        © RJPT All right reserved

Research J. Pharm. and Tech 2018; 11(7):3139-3144.