Performance comparison of Routing Protocols over

Smart Utility Networks: A Simulation Study

Gopalakrishnan Iyer, Prathima Agrawal

Department of Electrical and Computer Engineering

Auburn University, Auburn, AL - 36849

gbi0001@tigermail.auburn.edu, pagrawal@eng.auburn.edu

Ruben Salazar Cardozo

Landis+Gyr, North America

30000, Mill Creek Avenue, Alpharetta, GA - 30022

ruben.salazar@landisgyr.com

Abstract— With the rapid increase in deployment of smart

meters across North America, utility resources such as water, gas

and electricity consumption data is being collected at a much

higher granular level. The huge amount of data is being used to

make demand-response applications smarter. A large part of this

deployment of smart utility networks and Advanced Metering

Infrastructure (AMI) is being done using the wireless mesh

architecture due to the low deployment costs offered by this

method. Although wireless environment parameters such as

fading and path loss differ widely from home, outdoor to

industrial, in-building scenarios, efficient protocols at the

Medium Access Control (MAC) and Physical (PHY) layers and

lower layer agnostic data routing protocols can be employed to

overcome these challenges. For such routing protocols to be

applicable to smart utility networks, reliability and scalability

are vital metrics which will determine the performance in such

networks. In this paper, we analyze reliability and scalability of

three such routing protocols, viz. RPL (IPv6 Routing Protocol for

Low power and Lossy Networks), LOAD (6LoWPAN Ad Hoc

On-Demand Distance Vector Routing) and a proprietary flavor

of Geographical Routing.

Keywords— Smart utility networks; Smart grid; wireless mesh

ad-hoc routing protocols; RPL; LOAD; Geographical routing

I. INTRODUCTION

Smart meters are devices which measure utility resource consumption, usually over a period of time or in units specified by the utility provider. These devices are comparable with sensors which are deployed in large scale to measure data that is important in analyzing a particular metric. The value in large scale collection of sensor data is greatly impacted by the collection method [1]. Advanced Metering Infrastructure (AMI) comprising of smart meters, collectors, transmission and distribution devices and automated aggregation systems are now being widely deployed as a part of the Smart Grid initiative [2]. A recent report states that over 36 million smart meter devices have been deployed as of 2012 in the United States alone. This is a 33% increase over 2011 deployments and is projected to be at 65 million by 2015 [3]. In a majority of such deployments, devices in the AMI have wireless interfaces for communication to collector devices. In addition, smart utility networks are characterized by harsh wireless environment conditions in which they are deployed. Wireless fading and path loss have spatial and temporal components which require complex models to study performance in such environments. The enormous scale of the network also makes scalable solutions an important part of achieving efficiency in

such networks. A typical smart meter network can range from a few thousand metering nodes reporting to a single collector to millions of metering end points reporting to the head-end systems. These networks can be deployed using a plethora of evolved communication technologies that are available today. Physical layer technologies such as wireless cellular, power-line communications, fiber-optical networks and wireless mesh networks are under consideration for deploying AMI.

The most popular technology yet has been low power wireless systems used in the wireless mesh architecture for deploying these networks. The wireless mesh architecture provides several advantages over the others including flexibility, minimal infrastructure, extensive network coverage and low deployment cost [1]. In addition, the usage of license free spectrum for deploying wireless mesh networks is highly lucrative in terms of deployment costs. The goal of deploying smart utility networks is for the utility to monitor resource consumption at a highly granular level and detect and correct imbalances in the service proactively. In addition, utility pricing information can be sent to these connected devices to allow consumers to modify their consumption pattern in synchronization with peak and off-peak demand based pricing. Google’s now retired PowerMeter initiative was one such device which provided the consumer with timely and decision making information about temporal fluctuations in consumption and time-of-day pricing [4]. In addition to demand response applications, utilities deploy such networks for continuous monitoring of the health of transmission and distribution lines using distribution automation devices such as Phasor Measurement Units (PMUs) to measure and analyze high-resolution data which is further processed by Supervisory Control and Data Acquisition (SCADA) systems to make critical decisions on re-routing resources to avoid cascading failures [5]. It is hence evident that an efficiently deployed smart utility network or AMI is vital to several key applications at the utility hierarchy. An efficient data routing protocol, which is agnostic to lower layer technologies is imperative to the efficiency and reliability of the smart utility network in general. In addition, inter-operability requirement is inherent to all devices in the AMI and must be the same with a routing protocol used in such a deployment. Three such IPv6 based routing protocols developed by the IETF (Internet Engineering Task Force) community viz. RPL [6] [7] and LOAD [8][9] have been studied herein. Additionally, a proprietary flavor of Geographical routing protocol developed by Landis+Gyr has been studied for comparison [10].

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In this paper, section II and III detail validation of simulation models and a brief overview of the routing protocols used for comparing performance. Section IV describes the simulation model and wireless environment used herein and section V presents the results obtained from the simulation experiment. Section VI discusses the implications of the results in terms of reliability and scalability of the analyzed routing protocols and concludes.

II. VALIDATION OF MAC AND PHY

Although the routing protocols have been designed to be

lower layer agnostic, in order to compare the performance of

multiple routing protocols, the underlying MAC and PHY

layers, the wireless channel conditions and wireless

environment have to be fixed according to the network

topology in use and the actual wireless environment in which

smart utility networks are deployed. In this analysis we have

chosen the IEEE 802.15.4 MAC/PHY standard as it is one of

the radios currently deployed at a large scale. Also, the future

standard 802.15.4g for Neighborhood Area Networks under

development is in close relationship with the chosen lower

layers. This standard has been adopted by the Zigbee Alliance

for AMI deployments [11]. Validation of the lower layer

model for analysis of the routing protocols is done herein [12]

to ensure that the used model is in close accordance with field

data from such networks. Packet success probability values

were computed for real network comprising of smart meters

and network devices. The packet success probability

distribution in the network was obtained as shown in Fig. 1.

These values represent the real world wireless environment

conditions and the channel characteristics are reflected in the

packet success probabilities at the link layer.

Modeling the wireless channel conditions and the wireless

environment in close correlation with field data was realized

by conducting a simulation experiment to determine the

packet success probability yielded by the simulation model’s

link layer, physical layer and wireless channel modules. This

experiment successfully validated the simulation model for

IEEE 802.15.4 MAC/PHY to be used as the lower layer

modules for comparing routing protocol performance.

Modules for RPL, LOAD and Geographical routing were

developed to be used as routing layers over the validated

MAC/PHY layers.

Fig. 1: Packet Success Rate field values from a real network

III. ROUTING PROTOCOL COMPARISON

The use of IPv6 for newly developed routing protocols have been an obvious choice by the IETF community due to the large scale deployments of devices for which these protocols were intended. IPv4 address space is in rapid exhaustion. The ARIN address allocation is set to exhaust in early 2014 [13]. The move to IPv6 for the Internet of Things (IoT) and new large scale networks like smart utility networks is inevitable. RPL, LOAD and a new version of LOAD called LOADng are three such IPv6 based routing protocols under development. RPL has advanced from the draft stage to [RFC 6550] recently. Although geographical routing has not been under a standards initiative, it has seen wide deployment in existing smart metering networks due to its ease of deployment and relatively low complexity. A brief overview of each of these routing protocols will establish a general idea of the mechanisms on the control plane and the data plane employed in order to route data packets. In all of the above routing protocols, traffic can be of the following types:

• P2P : Point to Point (Direct communication from one node to another)

• MP2P: Multi-Point to Point (Two or more nodes communicating with the collector or root node)

• P2MP: Point to Multi-Point (Root node or collector communicating with two or more nodes in the network)

Among the traffic types, MP2P is of the utmost importance as it can be imagined as several or all the smart meters reporting data to the collector node. P2P case is used when distribution automation devices communicate with each other without involving the root node. It is also the traffic type in most Home Automation Networks (HANs). P2MP is a rarely prevalent traffic type. This type of traffic is generated when the root or the collector sends out a firmware update or a multi-cast message to the nodes as an example.

A. An overview of RPL

RPL is based on construction of a DODAG (Destination

Oriented Directed Acyclic Graph) for the network which has

the collector or data aggregating device as the root of the

directed acyclic graph. It is a distance vector routing protocol

for Low Power and Lossy Networks (LLNs). The route is

constructed with a goal of optimizing an objective-function.

The objective function is constructed using a combination of

metrics and constraints to compute the best path for routing

data packets to the destination. Applications such as outage

reporting may require hard deadlines on delivery and can use

an objective function to minimize latency as an example. The

root node initiates formation of the graph using the DODAG

Information Object (DIO) control message. Consider node j

receiving a DIO from node i. Upon receiving the DIO

message the jth

node will compute its own rank Rj according to

eq. (1) and determine its position in the DODAG with respect

to other nodes. Parameter β defines the granularity of the

network topology. It is defined as minimum rank increase and

can be adjusted as required during deployment. Fig. 2

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illustrates an RPL instance after formation of the DODAG.

DIOs are used to form up-routes for MP2P traffic.

�� = ����� � �� (1)

RPL makes use of the trickle-timer [14] in order to control

flooding at the control plane. Flooding of control packets in

smart utility networks would be particularly costly where node

resources such as energy and computing power are scarce and

highly constrained. The adaptive trickle-timer is crucial to the

efficiency of RPL.

Fig. 2: Instance of RPL after DODAG formation

B. LOAD routing protocol

LOAD was originally designed to be a light-weight modification of the popular Ad-hoc On-demand Distance Vector routing protocol (AODV) for 6lowpan networks [15]. It has been simplified by not requiring the intermediate nodes to reply a RREQ (Route Request) control packet with the RREP (Route Reply). Instead, only the destination for which the RREQ was intended replies to the route-request. In addition, a table maintaining RREQ for each node has been eliminated. Also, LOAD does not use the precursor list of AODV in order to simplify the routing table structure. LOAD does not use the destination sequence number as gratuitous RREPs are no longer permitted. The accumulated route cost such as LQI and the number of hops from the source to the destination are the preferred routing metrics in LOAD. LOAD uses periodic HELLO messages, governed by a timer to maintain route state information. The process of LOAD control plane message exchange is illustrated in Fig. 3.

Fig. 3: Exchange of LOAD control messages

Source node S initiates a route request destined for D and is forwarded to D by intermediate nodes N1 and N2. Destination D replies to the RREQ by sending a route reply (RREP) via the reverse route in the RREQ message (i.e. via N2 and N1 to S).

After S receives the RREP, data exchange begins. Additionally, if the data packet fails at any node, a route error (RERR) message is issued to the preceding node. In the LOAD control plane, there is no separate mechanism in comparison to RPL for up and down routes. Each source sending RREQ to a particular destination receives a RREP with the reverse route to the destination node contained in the message. This reverse route is used to reach the destination. Thus, a special hierarchy cannot be established in LOAD wherein the root node, participating nodes and leaf nodes, all discover each other by the same mechanism and maintain routes using periodic HELLO messages.

LOADng is modification of LOAD to optimize it for low power and lossy networks. It aims to reduce the number of control packets exchanged to reduce the network overhead. It employs similar techniques as LOAD by eliminating gratuitous route reply (RREP). LOADng is also a reactive protocol, i.e. routes are discovered only when a data packet is available at the buffer of a router and when the router has no route for its destination. Optimized flooding is explicitly supported, reducing the overhead incurred by RREQ forwarding. It is layer-agnostic as it may be used at layer 3 as a "route over" routing protocol, or at layer 2 as a "mesh under" routing protocol. LOADng builds and maintains a per-destination route. High level differences between LOAD and LOADng is summarized in Table 1.

TABLE 1: MECHANISM DIFFERENCES BETWEEN LOAD AND LOADNG

C. Geographical Routing Protocol

The proprietary flavor of Geographical routing protocol

developed by Landis+Gyr is built around P2P traffic

exchanges. Nodes poll each other using two mechanisms

called TICKLE (similar to request) and ACK

(acknowledgement of the TICKLE). These control exchanges

contain geographical information in the form of co-ordinates

(latitude, longitude and altitude) of the nodes. In addition, they

signal if a node is ready to receive a data packet. In order to

make routing decisions, the nodes are aware of the

geographical location of the collector which is the upstream

destination. The collector is also aware of the geographical

location of target nodes for downstream communications. The

node initiating route discovery builds a scan table to select a

neighbor which is geographically proximate to the destination

node and computes its distance using eq. (2).

�� = ���� − ��� + ��� − ��� + ��� − ��� (2)

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Node j receiving the geographical information from node i

computes distance dji and stores in a table. Sorted according to

shortest distance, each node is then polled to transmit data if

available at buffer. This selection method also considers the

dynamically changing link quality in a wireless environment

and combines geographical proximity with a weighted link

quality indicator metric in order to select the least cost

neighbor. Fig. 4 illustrates source S selecting next hop node A

to forward data to destination D by geographical proximity.

This implementation of geographical routing allows for a

minimal state routing approach and can adapt to the highly

temporal characteristics wireless medium. It has been

deployed in large scale networks while maintaining the same

level of complexity.

Fig. 4: Geographical routing mechanism

Large scale deployment of nodes with geographical

routing initially required feeding GPS co-ordinates into the

nodes manually. This method has now been deprecated and a

geographical co-ordinate system based on the node’s link

layer address has been developed for rapid deployment.

IV. SIMULATION MODEL AND WIRELESS ENVIRONMENT

The discrete event simulator OMNeT++ along with the wireless sensor network framework Castalia was used to develop the simulation model. The 802.15.4 MAC and PHY modules provided with the Castalia framework were used as lower layers under the routing modules of RPL, LOAD and Geographical routing. These modules were developed with the routing protocol drafts and related documentation as guide [16] [17]. The experiment was performed for varying network sizes starting from 25 nodes up to 7500 nodes. In each case there was only 1 collector node. The spatial distribution data of the network was obtained from a Landis+Gyr real deployed network. Fig. 5 shows the spatial distribution of one such network comprising of 7500 nodes and a single collector at the center of the grid. Although network size was varied, the density remained constant in order to obtain consistent results.

The parameters used in the physical layer module of Castalia were set to emulate a low power 2.4 GHz RF transceiver. The path loss exponent β

’ = 2.7 and standard

deviation σ = 4.0 of a Gaussian distribution were used to compute the path loss (PLd) suffered by each packet received from a node at distance d, using eq. (3). The wireless channel module parameters were set to the value of 60 dB for path loss

at distance d0 (PLd0) of 1m. A radio range of 460m was set by re-calculating d in eq. (3) using the values of path loss at distance of 1m as 60dB, receiver sensitivity of -110 dBm, maximum transmitted power of 30dBm and noise floor for a bandwidth of 5 MHz as -107 dBm.

Fig. 5: Spatial distribution of the 7500 node network

��� = ���� + 10��′�log#� � �0� + $��%&��0, (� (3)

The statistical values for total hop count, end-to-end delay and packet delivery ratio were collected from each simulation run. During the entire run of the simulation, packets generated by the control plane of each of the three routing protocols were used to calculate the control overhead. A summary of simulation parameters used is shown in Table 2.

TABLE 2: SIMULATION PARAMETERS USED

V. SIMULATION RESULTS

The traffic scenario simulated was MP2P, wherein all meters

send periodic data packets to the collector. This traffic

scenario is the most prevalent in smart utility networks. The

application packet rate was set at 1pakcet/second. For a total

simulation time of 100 seconds, the application layer

generated a total of 100 packets. Each node transmitted 100

packets at 1 packet/second towards the collector. The statistics

of hop count, delay and packet delivery ratio were gathered

for the entire network. At each node, the total number of

control packets corresponding to each routing protocol was

measured.

Fig. 6 shows the hop count histogram for a 500 node

intermediate network size. A maximum of 7 hops was

observed for each of the three routing protocols, RPL, LOAD

and Geographical routing. Among the three RPL places a

maximum number of nodes 1 hop away from the root which is

a good metric of its performance. Fig. 7 shows the delay data

histogram for the 500 node real network for RPL, LOAD and

Geographical routing protocols. Overall, the packets recorded

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Fig. 6: Hop-count histogram for 500 node network Fig. 7: Delay histogram for 500 node network Fig. 8: Packet delivery ratio comparison

the least delay with RPL. The data shows that on an average,

packets suffer 160msecs, 173msecs and 339msecs of delay for

RPL, Geographical routing and LOAD respectively. Fig. 8

shows the packet delivery ratio (PDR) comparison of RPL,

Geographical routing and LOAD. RPL and Geographical

routing perform consistently between 95%-98% for all

network sizes. LOAD has the lowest PDR of 58% for the

largest network size. It performs reasonably well for smaller

networks in terms of reliability. Fig. 9 shows the control

packet comparison of the three routing protocols. RPL shows

a linear increase in the control packets with network size while

both LOAD and Geographical routing have an exponential

increase trend. LOAD generates significantly more number of

control packets throughout all network sizes in comparison to

RPL and Geographical routing.

Fig. 9: Control messages in RPL, LOAD and Geographical routing

VI. CONCLUSION AND FUTURE WORK

The data from the simulation results shows that although

LOAD has been designed to low power applications such as

deployment in smart utility networks, it does not perform well

in both route formation and end to end delay. In terms of

reliability, LOAD does not fare well with low packet delivery

ratios indicative of large number of dropped data packets. In

terms of scalability, LOAD control messages grow

exponentially with network sizes and can be considered not-

scalable upon initial analysis. The next generation LOAD i.e.

LOADng claims to mitigate these issues by design and an

implementation of LOADng will have to be studied for

performance to address these claims. In the near future, we

intend to develop an analytical model for scalability depending

on wireless environment conditions, independent of any

routing protocol. With further simulations the analytical model

can be validated to arrive at an experimental upper bound on

capacity of each collector node.

REFERENCES

[1] Ian. F. Akyildiz and Xudong Wang, “A Survey on Wireless Mesh Networks," IEEE Communications Magazine, vol. 43, Sept. 2005.

[2] “NIST Framework and Roadmap for Smart Grid Interoperability Standards,” US Dept. of Commerce, Release 1.0

[3] “Utility Scale Smart Meter Deployments, Plans and Proposals,” Institute for Electric Efficiency, May 2012.

[4] Google PowerMeter, http://www.google.com/powermeter/about/

[5] Carl H. Hauser; David E. Bakken; Anjan Bose, “A Failure to Communicate,” Power and Energy Magazine, IEEE, pp. 47-55, Mar-Apr, 2005.

[6] T.Winter; P. Thubert, “RPL: IPv6 Routing Protocol for Low Power and Lossy Networks,” draft-ietf-roll-rpl-19

[7] T. Watteyne; K.Pister; D. Barthel; M. Dohler; I. Auge-Blum, “Implementation of Gradient Routing in Wireless Sensor Networks,” IEEE GLOBECOM, Hawaii, December 2009.

[8] Kim K., Daniel S., et. al., “6lowpan Ad-hoc On-demand Distance Vector Routing Protocol (LOAD),” draft-daniel-6lowpan-load-adhoc-routing-03.

[9] Clausen T., et. al. “The Lightweight On-demand Ad hoc Distance-vector Routing Protocol – Next Generation (LOADng),” draft-clausen-lln-loadng-08.

[10] B. Lichtensteiger; B. Bjelajac; C. Müller and C. Wietfeld, “RF Mesh Systems for Smart Metering: System Architecture and Performance,” IEEE SmartGridComm, Maryland, October 2010.

[11] Zigbee Alliance, “Understanding Zigbee Gateway,”, Sep. 2010 http://www.zigbee.org/LearnMore/WhitePapers.aspx

[12] Iyer G.,Agrawal P., Monnerie E.,Cardozo R., “Performance Analysis of Wireless Mesh Routing Protocols for Smart Utility Networks,” IEEE SmartGridComm, Brussels, Oct 2011.

[13] A report on IPv4 address space, http://www.potaroo.net/tools/ipv4/index.html

[14] Levis P., et.al, “The Trickle Algorithm,” IETF RFC 6206.

[15] Perkins C., et.al, “Ad hoc On-Demand Distance Vector (AODV) Routing,” IETF RFC 3561

[16] Varga A., et.al., “An overview of the OMNeT++ simulation environment,” ICST, Brussels, Belgium, 2008.

[17] Castalia Framework for OMNEST simulator, http://castalia.npc.nicta.com.au/

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