"Performance Analysis of In-Network Caching in Content-Centric Advanced Metering

(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 7, No. 11, 2016
108 | P a g e
www.ijacsa.thesai.org
Performance Analysis of In-Network Caching in
Content-Centric Advanced Metering Infrastructure
Nour El Houda Ben Youssef
SAGE-LATIS/ ENISO University of
Sousse
ENSI University of La Manouba
WEVIOO
Tunisia
Yosra Barouni
Sofiane Khalfallah
Jaleleddine Ben Hadj Slama
SAGE-LATIS/ENISO University of
Sousse Tunisia
Khaled Ben Driss
WEVIOO
Tunisia
Abstract—In-network caching is a key feature of content-
centric networking. It is however a relatively costly mechanism
with hardware requirements besides placement/replication
strategies elaboration. As content-centric networking is proposed
in the literature to manage smart grid (SG) communications, we
aim, in this research work, to investigate the cost effectiveness of
in-network caching in this context. We consider, in particular,
the Advanced Metering Infrastructure (AMI) service that comes
into prominence since its outputs are imperative inputs of most
smart grid applications. In this research work, AMI
communication topology and data traffic are characterized.
Corresponding simulation environment is then built. Thereafter,
various placement and replacement strategies are compared in a
simulation study to be further able to propose a suitable cache
placement and replacement combination for AMI in Smart Grid.
Keywords—caching; placement; replacement; content-centric
networking; Named Data Networking; Advanced Metering
Infrastructure; Smart Grid
I. INTRODUCTION
As a heterogeneous, distributed and large scale system, the
Smart Grid Communication System (SGCS) is the subject of
many research works trying to propose a well-tailored
communication layer that guarantees smart grid requirements.
Among many solutions, CCN is proposed to offer real time
data transmission with inherent security levels and
competitive latency. This research work belongs to a series of
articles aiming at investigating content-centric networking
adequacy for smart grids. In [Error! Reference source not
found.], CCN performances were compared to the Internet
Protocol stack while managing smart grid communications. A
deeper qualitative and quantitative analysis is pursued in
[Error! Reference source not found.] studying CCN support
for renewable energy resources integration into SGs. The
aforementioned works allowed us to take a position in favor of
CCN as an eligible communication solution for this system. In
this article, the focus is granted to caching as one of the most
important building blocks of CCN. In fact, besides host
decoupling and content-based routing, in-network caching is
considered as one of the most relevant CCN features. The
main goal is to study the cost effectiveness and the
performance of this mechanism in smart grids. Advanced
Metering Infrastructure (AMI), a service responsible for the
generation of a huge amount of data such as power
consumption and electrical parameters data, is particularly
considered. This smart grid application can particularly avail
of in-network caching since many data flows disseminate the
same content to many nodes like price signals or maintenance
commands. Previous work [Error! Reference source not
found.] proposes CCN for enabling AMI service but the focus
is mostly on proposing a naming scheme to elaborate CCN-
AMI. A relatively poor performance assessment is presented
as only bandwidth consumption metric is studied. To the best
of our knowledge, no other research work dealt with content-
centric in-network caching impact on AMI performance.
This article leads off by an overview of the advanced
metering infrastructure detailing this service and its major
stakeholders. Afterward, part II exposes related works by
exploring research works investigating CCN use in smart
grids. Then content-centric networking is presented with a
particular focus on its in-network caching feature in part III.
Next, simulation environment and scenarios description is
presented in part IV. The last part of this article exposes and
analyses the results obtained by the conducted simulations.
II. STATE OF THE ART: CCN IN SMART GRIDS
Content-centric networking (CCN) in smart grids is being
in the center of an active research effort where researchers and
industrials are collaborating to propose a content-centric
communication layer for smart grids. The first endeavor to
enable SGCS using CCN was in [Error! Reference source
not found.] where a content-centric overlay network is
deployed to exchange smart grid data traffic. It, however, used
geographic routing preventing it to be considered as a pure
content-centric solution. Then, the authors of [Error!
Reference source not found.] studied Information-Centric
Networking (ICN) performance in Real Time State Estimation
(RTSE) of smart grids. This work has been expanded in
[Error! Reference source not found.] and an information-
centric platform baptized C-DAX is proposed to RTSE in
active power distribution grids. Other research works dealt
with different smart grid subsystem, we noticed particularly
the adoption of ICN in home area networks for residential
energy management [Error! Reference source not found.].
ICN is also investigated to enable smart city services [Error!
Reference source not found.]; in particular vehicle-to-grid
communications has been studied. The aforementionedThis research work is elaborated under the umbrella of the PASRI-
MOBIDOC (www.pasri.tn) project in Tunisia funded by the European Union
and WEVIOO (www.wevioo.com).

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research works analyzed ICN performance in selected smart
grid subsystems but, despite its importance, no particular
attention is paid to caching policies assessment. Due to its
deep impact on networking performances, caching
mechanisms requires thorough analysis. The present research
work aims at filling this gap by probing into in-network
caching impact on AMI communications.
III. ADVANCED METERING INFRASTRUCTURE
A. Overview
Advanced metering infrastructure (AMI) is a smart grid
potential application on the consumer side, responsible for
metering services between electricity utility companies and
their customers. It is an integrated system of smart meters,
communication channels and meter data management systems.
Many relevant smart grid subsystems like Advanced
Distribution Infrastructure (ADI), Advanced Transmission
Infrastructure (ATI) and Asset Management (AM) rely on the
advanced metering infrastructure in order to properly achieve
their functions. AMI has many benefits to both customers and
power providers. On the consumer side, it allows him to be
well informed regarding prices and services which provide
him with the ability of managing its consumption patterns and
costs. On the power utility side, AMI has an impact on two
major services: the billing and Distribution/Transmission
operations. Receiving time stamped power consumption
information helps establishing an efficient billing system. In
addition, customer information processing and mining may
enable the utility offerings improvements. Moreover, accurate
reporting of various electrical parameters is essential for the
smart grid main purpose which is a reliable power
transmission and distribution.
B. Actors, roles and interactions
Realizing the power grid modernization relies on several
cooperating subsystems. Deploying an advanced metering
infrastructure can be considered as one imperative step toward
the smart grid since its outputs are the feeds of advanced
distribution and transmission infrastructures [Error!
Reference source not found.]. Most relevant AMI functions
are the following:
 Power data consumption readings,
 Electrical parameters reporting (phase, voltage, power
factor),
 Time-based pricing and billing,
Fig. 1. Architecture of an AMI
Achieving the aforementioned functionalities involves four
main actors (see Fig.1):
 Smart meters: a device deployed at the customer
premises responsible for periodic power consumption
data readings. It also contributes at transmission and
distribution operations by reporting some electrical
parameters like phase, voltage, etc. Smart meters have
to meet few requirements; communication skill can be
considered as the most imperative one as it allows the
device to achieve its major role. Smart meters must
provide users by an ergonomic display feature. It also
should support remote control commands to be
executed by distribution management systems in order
to maintain a reliable power grid.
 Electrical properties sensors: Voltage sensors (VS)
and phase measurement units (PMU) are electrical
field devices responsible for reporting real time
electrical properties used to monitor the power
distribution system.
 Communication layer: Each smart meter deployed in
the smart grid has IN/OUT data traffic to receive or
send. It has, in fact, to periodically send data about
power consumption or electrical parameters status to
remote collecting and processing nodes. In addition, it
receives operational commands and price signals from
operation centers. A communication layer managing all
these data flows is then an important AMI subsystem.
The richness of communication technologies landscape
allows many design choices. Wireless and wired
technologies can be combined to build a well tailored
communication layer considering bandwidth and
latency requirements at each level of the grid.
 Data management system: It is an integrated system
for managing the huge amount of data received from
customers at the utility company premises. Data
collectors are first deployed in neighborhoods to
collect data from smart meters in Home Area networks
(HANs). It includes then meter data management
system (MDMS) that can be seen as a database

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responsible for gathering and storing metering data
from data collectors in neighborhood area networks
(NANs). This system benefits, nowadays, from big
data technologies and data centers expansion. The data
management system also includes a billing system and
a customer information system and has several
interfaces with distribution operators such as
distribution management system. Indeed this latter
needs power quality information sent by smart devices
deployed throughout the grid in order to monitor the
power distribution system and trigger operational
commands if needed.
To achieve advanced metering functions, the
aforementioned actors need to interact and exchange data. The
most relevant data flows are power consumption information
sent periodically by smart meters and collected in data
collectors deployed in neighborhoods. Power consumers
receive also real time power pricing allowing them to have an
active role regarding their consumption patterns. AMI data
traffic description provided in Table 1 was elaborated after
consulting various research articles surveying smart grid
services [Error! Reference source not found., Error!
Reference source not found.].
IV. CACHING IN CONTENT-CENTRIC NETWORKING
A. Content-centric networking in a nutshell
Till now, communication solutions proposed to manage
computer networks are mostly based on addressing hosts and
maintaining a bi-host connection used to exchange data.
However, new communication patterns are content
dissemination oriented which lead to the imperative need of a
recast of existing networking paradigms. This fact has
enriched the literature, since the early 2000s, by several
research works [Error! Reference source not found., Error!
Reference source not found., Error! Reference source not
found.] proposing the recast of the Internet and the adoption
of new communication tenets. Although content-centric
networking has first been elucidated in [Error! Reference
source not found.] to cater for the Internet recast, it has been
explored by many researchers to grant required networking
performances in diverse use cases such as social networking,
vehicular networking, smart cities, etc. This attraction is due
to many reasons. Building the whole communication process
around the content unlike traditional paradigms where hosts
are in the center of communications can be considered as the
major attractive treat of CCN. Indeed we pay more attention
on retrieving the desired content then knowing the host
providing it. To do so content needs to be identified instead of
addressing hosts this guaranteed by a naming strategy: the
second pillar of CCN. A content-based routing protocol is also
used to manage content travel from its producer to its
consumer. In-network caching, an important building block of
CCN, ardently seduces researchers as it promises enhanced
networking performances.
B. In-network caching
CCN is characterized by in-network caching of data that
improves network quality of services especially delivery
latency. Satisfying data requests is not obligatory through
locating the original data source but can also be done from
multiple data stores. We find, in the literature, many caching
policies that can be categorized in in-path caching or off-path
caching. In the first one, only data replicas found along the
path taken by the name resolution request are exploited. In
contrast, the second one allows exploiting caches outside this
path [Error! Reference source not found.]. We can also
differentiate caching policies based on router cooperation
while placing content cache. Cooperative or non-cooperative
strategies are mostly known [Error! Reference source not
found.].
In order to achieve in-network caching, placement and
replacement algorithms need to be deployed. First of all, one
needs to decide where to cache content; would it be in each
node or in some selected ones. This decision leads us to the
elaboration of a cache placement strategy like Leave Copy
Everywhere (LCE) or Leave Copy Down (LCD), etc [Error!
Reference source not found.]. It is also common to place
caches probabilistically or based on content proximity to its
consumer [Error! Reference source not found.]. A cache
replacement strategy is also required since we wouldn’t like to
exceed the cache size. Least Recently Used (LRU), Least
Frequently Used (LFU) and First In First Out (FIFO) are the
most usual replacement policies in the literature.
V. PERFORMANCE ANALYSIS OF CACHING FOR
ADVANCED METERING INFRASTRUCTURE
A. Simulation tools
Being in the heart of an intensive research effort, content-
centric networking is the subject of many research projects
which lead us to many implementations of this paradigm.
Among many, Named Data Networking (NDN) project
(https://named-data.net/) is taking the lead by producing a
large panel of deliverables such as core solution
implementation, periodic technical reports, testbeds and
simulation modules. In the present research work, we use
NDNSIM, the NDN simulation module under NS-3, to
simulate content- centric communications. Network simulator-
3 (NS-3): https://www.nsnam.org/) is an event-based
simulator widely used by the research community due to the
richness of its libraries. We first built a networking topology
according to figure 1 in order to reproduce AMI
communication environment. Since no consensus has been
made on the communication technologies to use while
building the smart grid communication system, we have
abstracted from the physical layer. In fact, the networking
landscape offers many alternatives to enable smart grid
communications; for instance home area networks (HANs)
can exploit narrowband power line communication as wired
technology or Bluetooth or Wi-Fi as wireless technology. The
common aspect at this level of the grid is a low data rate. As
for neighborhood area networks (NANs), cellular networks of
broadband power line communications can be adopted. Finally
the wide area network (WAN) can be achieved by optical fiber
technology or fourth and fifth cellular network generations
such as LTE [Error! Reference source not found.]. Since
assessing physical technologies performances in smart grids
requires a dedicated research work, we excluded this task from
our scope and therefore decided to use point to point links in

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NS-3 with well studied characteristics according to the requirements of each smart grid communication level.
TABLE I. AMI DATA FLOWS
B. Simulation topology
To conduct simulations, the AMI topology has been built
under NS-3 with the restriction of our scope to 5 Km radius
area. Based on a prior research work [Error! Reference
source not found.] describing a smart grid communication
infrastructure in Canada, it has been noticed that the
communication infrastructure generally follows the electrical
one which implies that the smallest branch of the adopted
topology contains 10 smart meters according the north
American norms [Error! Reference source not found.]. In
fact, within 5 km, 50 substations are deployed and 120
transformers are connected to each substation. Then, 10 homes
will be served by each transformer leading to 10 smart meters
wired to each one.
Particular nodes of the AMI application described in figure
1 have been added to our topology. Data collectors are
deployed at neighborhood area network (NAN) while meter
data management systems (MDMS) and distribution
management system (DMS) were added at wide area network
(WAN). To sum up the smallest branch of our topology counts
a total of 14 nodes: 10 smart meters, 1 transformer, 1
substation, 1 MDMS and 1 DMS.
Fig. 2. Downstream interest processing in NDN
C. Simulation scenarios
Ndnsim used to simulate content-centric networking
implements Named Data Networking [Error! Reference
source not found.] components under NS-3. It reproduces all
its important building blocks: NDN core protocol, pending
interest table (Pit), forwarding information base (Fib), Content
Store (Cs) and applications. The following scenario exposes
their interaction to manage communications. From a content-
centric point of view, exchanging content occurs in two steps:
a publisher producing content and a subscriber expressing
interest in receiving it. Each network node has three data
structures: a content store used for in-network caching, a
pending interest table used to store received interests if the
node can't satisfy them by a cache hit and a forwarding
information base that stores possible forwarding paths to
process incoming interests. When an interest is received, the
Content Store (CS) is firstly checked. If the desired content is
stored locally in the CS, then the node send it through the
interest incoming face otherwise the Pending Interest Table is
checked. If an entry with the same interest is found in the PIT,
the interest incoming face is concatenated to existing faces
and the interest itself is dropped. Interest existence in the PIT
means that the node already received an interest in retrieving
the same content and is waiting for a response. Once the
desired content is received, it will be send to each face in the
corresponding PIT entry. The final step is to check the
Forwarding Information Base (FIB); it is carried out only if
the interest does not exist in the PIT. Two scenarios are
possible. Whether we find a corresponding entry for the
interest which means that the node will forward it to the nodes
found in the FIB and waits for them to send back the desired
content. Or no entry is found in the FIB, this is the worst case
since it means that the node has no idea how to find the
corresponding content and will simply drop the interest (see
figure 2). The upstream data processing is illustrated by figure
3. A node receiving a data packet first checks its content store
to execute the caching policy. Of course whether to cache the
content or not depends on the adopted caching placement
strategy. For instance, if LCE (Leave Copy Everywhere)
strategy is chosen, every node receiving a data packet appends
it to its own CS. A replacement policy is also needed to define
the way a CS is refreshed when the threshold size is reached.
This policy describes which content to replace by the new
incoming one; note that various replication and replacement
strategies have been developed above in part III.
Fig. 3. Upstream Data processing in NDN
Id Source Actor Exchanged Information Destination Actor Packet size (bytes) Frequency
1 Smart meter Power consumption data
Meter data mangement
system (MDMS) 200 Every hour
2 Billing system Real time prices Smart meter 210
Every 15
minutes
3
Distribution management
system (DMS)
Operationl commands
Smart meter 150
Triggered by
events
4
Voltage sensor (smart meter)
Voltage, power factor
system (DMS)
250 Every 5 minutes
5 Voltage sensor (transformer)
Voltage, power factor Distribution management
system (DMS)
250 Every 5 minutes
6
Phase measurment unit
(transformer)
Voltage Phase
system (DMS)
1536 Every 5 minutes

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To assess in-network caching performances for AMI
service, we deployed consumer and producer applications in
the aforementioned topology nodes in order to simulate AMI
data traffic described in Table 1. The first simulation scenario
aims at globally evaluating caching benefit for AMI. To do so,
metrics were compared after enabling and disabling the
caching functionality. Further simulation scenarios dig deeper
into caching replacement policy and cache replication strategy
evaluation. Simulations were run for 7200 seconds allowing to
all data traffic to occur and in-network caching algorithms
(placement and replacement) to take effect.
D. Simulation Results
1) In-network caching impact on global performances
The results of the first simulation show a reduced
communication delay at each node of the topology and a
reduced throughput (see fig 4). We remind that in this first
simulation, the same AMI data traffic has been generated and
two scenarios were compared; first with disabled in-network
caching and second after enabling this feature. The delay
represents the amount of time needed to satisfy an interest, it
includes the queuing delay and the propagation delay. The
reduction of communication delay is due to faster retrieval of
data packets since closer nodes are satisfying data requests
thanks to their content stores. The throughput is reduced
significantly in comparison to that of the case without in-
networking caching. Indeed less packets are travelling toward
and backward source nodes since many interests are being
served by content store hits.
2) Replacement strategy evaluation
After globally noticing in-network caching benefit while
managing advanced metering communications, our goal is
furthermore to investigate replacement strategy for AMI.
Metrics evaluated at this level are:
- Hop count: the number of hops a data packet needs to
travel in order to reach its requester.
- Cache hits: specifies the number of interests that were
satisfied from the content store (CS).
Simulations were run using the same AMI topology and
data traffic described in previous sections. Three replacement
strategies are proposed by NDNSIM to insert new content
packets once the content store size has been reached: FIFO
(First In First Out), LRU (Least Recently Used) and LFU
(Least Frequently Used). We vary cache size and notice that
cache hits is proportional to cache size. It is a rational result
since nodes with bigger cache size are able to store more data
packets allowing, then, to satisfy more interests from CS. This
fact is the reason behind the hop count decrease as the CS size
increases. Indeed, as more interests are served from caches,
shorter paths are taken by data packets to reach their
requesters. Regarding the eviction strategies comparison,
FIFO is having the lowest interest number satisfied from
content stores (see fig 5). As for LRU and LFU, they are
closely competing with a slight advantage for LFU strategy.
On the other hand, the highest hop count reduction is observed
with the LRU replacement strategy leaving FIFO and LFU
behind. We remind that, if required, LRU removes content on
recency of use basics; most recently used content is kept
longer in CS. As for LFU, popularity of retrieval is what
governs packet data eviction. We notice also, based on the
curve allure, that the effectiveness of replacement strategies is
more obvious when the content store (CS) size is beneath 50
packets. Thus, high content store size (beyond 50 packets)
dissolves the replacement strategy impact on cache hits and
hop count. Indeed, data packets won’t get evicted from the CS
unless storage threshold reached.
3) Placement strategy evaluation
The last stage of our simulation study aims at assessing
various placement strategies. Placement strategy refers to a set
of rules deciding where to cache content. In our context, four
placement policies have been considered:
Fig. 4. Caching impact on delay and throughput

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Fig. 5. Cache replacement policy evaluation
 LCE (Leave Copy Everywhere): this strategy allows
the insertion of content in every node along its path
toward its requester. Each content packet will be then
stored in the content store of all nodes crossed while
traveling back to the consumer.
 Probabilistic placement strategy: A normal
distribution decides whether a content is stored in the
content store or not.
 Probcache placement strategy [Error! Reference
source not found.]: Content is cached with a
probability depending on two factors: remaining
caching capacity on the downloading path and cache
weight depending on content closeness to its
requester. Probcache is considered as a cooperative
caching mechanism since caching decision depends
on factors provided by all routers along the path.
 Leave copy in none-constrained devices (LCNCD):
Since we are assessing caching for a particular smart
grid service, we take into consideration, in this
placement strategy, the fact that smart meters are
constrained devices. In this last placement strategy we
only deploy content stores in none-constrained
devices which exclude smart meters from in-network
caching.
Fig.6 illustrates the results obtained after running the
simulations described above with wider topology size. We
varied branches number of the tree topology shown in fig.1 to
reach a maximum of 5 data collectors and 50 smart meters.
We observe a trivial impact of the cache placement strategy on
the cache hits. The incidence on hop count is however more
significant, especially with an important disadvantage of the
probabilistic placement strategy that needs more hop number
to satisfy interests. The most substantial result at this level is
revealed by the superposition of LCE and LCNCD (Leave
Copy in None-Constrained Devices) curves which imply that
disabling the caching feature in the smart meters does not
degrade the system performances. This result is due to the tree
topology used in our simulations. Positioning smart meters at
the edge of the network alters caching usefulness at their level.
Probabilistic placement strategy is also less beneficial from
delay point of view. Fig.7 shows, in fact, high packet
transmission delays when normal probabilistic distribution
governs cache placement. It strengthens, also, the fact that
disabling cache in constrained field devices doesn’t affect
network performance as the transmission delay is not
influenced with LCNCD placement policy.
VI. CONCLUSION
Advanced metering infrastructure in smart grids is
responsible for sensing, measuring, collecting and sending
consumption data and electrical parameters. This key role
brings up AMI design weight on smart grid effectiveness. In
the meanwhile, content-centric networking with its ubiquitous
in-network caching feature is increasingly gaining attention as
the future networking trend. It’s premised that caching content
on the delivery path can improve data delivery. Our goal was
to investigate this assumption in handling AMI
communication requirements. A simulation-based analysis
reproducing AMI communication topology and data traffic
showed reduced data delivery delay once in-network caching
activated. It showed also inadequacy of FIFO replacement
strategy for this context and better performances for content-
popularity based strategies. Placement strategies analysis
allowed us to show that disabling caching in smart meters
does not affect the SGCS performance. It consolidates then
our position on the adequacy of CCN to smart grids. This
work concludes our series of articles investigating content-
centric networking for smart grid communications. Our
perspectives are the design of a content-centric smart grid
communication infrastructure providing SGCS requirements
such as latency, data transmission delay, quality of service and
interoperability.

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Fig. 6. Cache placement policy evaluation
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Fig. 7. Impact of placement strategy on transmission delay

"Performance Analysis of In-Network Caching in Content-Centric Advanced Metering

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