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Smart grid monitoring with service differentiation via EPON and wireless sensor network convergence
Author's Accepted Manuscript
Smart grid monitoring with service differentiation via epon and wireless sensor network convergence Nima Zaker, Burak Kantarci, Melike ErolKantarci, Hussein T. Mouftah
www.elsevier.com/locate/osn
PII: DOI: Reference:
S1573-4277(14)00011-3 http://dx.doi.org/10.1016/j.osn.2014.01.010 OSN281
To appear in:
Optical Switching and Networking
Cite this article as: Nima Zaker, Burak Kantarci, Melike Erol-Kantarci, Hussein T. Mouftah, Smart grid monitoring with service differentiation via epon and wireless sensor network convergence, Optical Switching and Networking, http://dx. doi.org/10.1016/j.osn.2014.01.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Smart Grid Monitoring with Service Differentiation via EPON and Wireless Sensor Network Convergence
Nima Zaker, Burak Kantarci, Melike Erol-Kantarci and Hussein T. Mouftah School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Avenue, Ottawa, ON, Canada Tel: +1 (613) 562-5800 ext. 2151; Fax: +1 (613) 562-5664 Email: [email protected], [email protected], [email protected], [email protected]
Abstract Passive Optical Networks (PON) are recognized as a fundamental component of high-speed network access, and emerge as a solid networking solution for the smart grid. Smart grid aims to incorporate two-way communications between the customers and the utility, for the purposes of advanced monitoring and intelligent control of demand and supply of electricity. At the user-end, Wireless Sensor Networks (WSN) are ideal tools for energy consumption monitoring at residential premises, as well as, event and ambient monitoring at substations, power lines and vaults. In this paper, we target a seamless integration of WSN technology with PONs to provide Quality of Service (QoS) to both the data collected by billions of sensors and to the Fiber-To-The-Home/Building/Curb (FTTX) users in the smart grid environment. We present the design of a Fiber-Wireless Sensor Network (Fi-WSN) gateway where data prioritization is fundamental since ambient data collected from a home will have lower priority than an alarm generated at a smart grid asset. We show that the proposed gateway design is able to offer low delay for high priority packets while maintaining the delay of FTTX traffic and the reliability of the WSN at desired levels under various loads, concentrations of service levels and aggressive/non-aggressive bursting mechanisms. Key words: Ethernet passive optical networks, fiber-wireless networks, gateway design, quality of service, smart grid, wireless sensor networks.
Preprint submitted to Elsevier
27 January 2014
1
Introduction
Passive Optical Networks (PONs) have recently matured as a high-speed broadband access technology. As a particular case of PON supported networking, integrated fiber-wireless (FiWi) broadband access networks combine the speed and the reliability of optical networks with the flexibility and the wide coverage of wireless technologies [1,2]. PON supported networking plays a key role in smart grid communications as the next generation power grid calls for a robust communication infrastructure to support intense communications among the utilities, market players, suppliers and the consumers. In addition to a robust communication backbone, at the user end, reliable monitoring tools are required to enhance the operation of the power grid. FiWi and in particular, Fiber-Wireless Sensor Networks (Fi-WSNs) offer a unique solution for monitoring the smart grid [3,4]. In the Fi-WSN architecture, the data collected by the WSN from the smart grid environment is transmitted to the operators through a broadband access network, e.g. Ethernet Passive Optical Network (EPON). In the EPON architecture, each ONU is connected to the Optical Line Terminal (OLT) via a distribution fiber. ONUs can be also connected to a WiMAX base station through an optical fiber ring [5] in order to increase coverage. At the user end, a wireless device can be receiving service from the PON supported network or it can be a WSN that is monitoring a smart grid asset or a residential premise. For instance, WSNs can be deployed in substations that house transformers which step down the voltage for distributing the electricity through local feeders [6,7], or they can be deployed at overhead power lines to measure sagging, or at transmission/distribution towers to monitor vegetation around 2
the lines [8], or they can be utilized in renewable energy generation cites [9]. They can be further deployed at homes to monitor energy use [10]. Sensors collect ambient measurements such as temperature, humidity, etc., in addition they can monitor transformer overloading, line sagging, etc. where in general, the latter is more critical in terms of the operation of the power grid [11,12]. A typical scenario for this kind of alarm data would be a sensor reading at a substation indicating transformer overloading which may be further related with simultaneous electric vehicle charging from different locations of the same distribution system. To this end, integrated Fi-WSN network provides an ideal solution for Home Area Network (HAN) broadband access as well as WSNbased monitoring in smart grid assets. However, there are certain challenges that need to be addressed before Fi-WSN deployment is widely utilized in the smart grid. One of the fundamental challenges is to provide Quality of Service (QoS) for the heterogenous network architecture of the Fi-WSN and the traffic loads involved in smart grid monitoring tasks.
In smart grid monitoring, delivery of critical data in a timely manner is a key operational requirement. Lost data may result in incorrect control actions and risk the stability of the power grid, and in certain situations, delayed data may impact the operation of the grid worse than lost data. The operational delay requirements can be as tight as 500ms for certain smart grid applications. For this reason, differentiation of the collected WSN data is essential which means non-urgent data may be delayed while urgent alarm data is pushed faster towards the power grid operators.
In this paper, we present a QoS-aware gateway design for the Fi-WSN architecture employed in the smart grid environment. Our design aims to achieve data prioritization, maintain QoS for FTTX users and deliver WSN data in 3
a reliable manner 1 . The Fi-WSN gateway employs a burst assembly mechanism to differentiate between urgent and non-urgent packets. We evaluate the performance of our gateway design under various traffic types, aggressive and non-aggressive bursting mechanisms and under varying concentrations of traffic from different service classes. We show that the proposed gateway design attains low delay for high priority packets while maintaining the QoS of FTTX traffic and the reliability of the WSN. The rest of the paper is structured as follows. In Section II, we briefly review the related work on PON supported networking and smart grid monitoring. In Section III, we introduce the system model, and in Section IV we describe the gateway design in detail. Section V describes the simulation settings and the numerical results. Finally, Section VI concludes the paper and gives future directions.
2
Related Work
In this section, we first provide an overview of PON supported networking research and then we give a brief survey on recent advances in WSN-based smart grid monitoring studies.
2.1
PON supported networking
PON technology have recently emerged as a key component of broadband access [14]. Among various competing PON solutions, EPON and GPON solutions are widely preferred based on the existing telecommunication infras1
A simplified version of this work has been published in [13]
4
tructure, as well as the requirements. In this paper, we have selected EPON as the optical back-end technology as it is based on a globally accepted standard, i.e., Ethernet. EPONs offer high speed access with a simple design with seemless integration capability of IP-based networks [15,16]. In EPON, data is encapsulated in Ethernet frames and transmitted in PONs. For a survey on PON technologies, interested readers are referred to [14] and [15–17] for EPON technology. A more recent technology, FiWi have succeeded to consolidate the high-speed and low-latency advantages of EPONs with the flexibility and coverage of wireless networks.
FiWi networks can have two possible implementations, i.e. Radio over Fiber (RoF) and Radio and Fiber (R&F). In [18], the authors have investigated the RoF protocol where an optical carrier is modulated in the central office (CO) by radio frequency (RF) signals that are propagated in analog fiber link to remote antenna units (RAUs), then they are received through air by clients. Integration of WiMAX to the optical network has been considered in [19,20]. On the other hand, in [21], the authors have considered an R&F protocol which applies a different MAC protocol via a protocol translation interface that takes place at the optical-wireless borderline. As reported in [22], the advantage of the integration of EPON with WiMAX technology under R&F protocol is a good capacity match at the integrated ONU-Base Station (ONU-BS) gateway among other EPON-WiMAX integration benefits. Furthermore, integration of long term evolution (LTE) technology with Long-Reach PON (LR-PON) has been studied in [23]. QoS and energy-efficiency of FiWi networks have been explored in [24]. A recent study further investigates the capacity and delay bounds of FiWi networks [2]. The interested readers are referred to [25] for a pioneering survey of the FiWi networks and [26] for the challenges and 5
the advantages of FiWi networks. In this paper, we are focusing on an R&F implementation that consists of EPON technology at the optical end and a WSN at the wireless end. We propose a gateway design that incorporates a bursting mechanism to enhance QoS of the overall integrated network.
2.2
WSN-based Smart Grid Monitoring
WSNs have long been considered for monitoring critical infrastructures [27,28]. They have been recently studied for possible deployments in smart grid environments as well [29–32]. In [29], the authors discuss the fundamental challenges and opportunities of WSNs in the smart grid. In [30], the use of wireless multimedia sensors in the smart grid has been investigated. In [31], the authors have considered using WSNs at residential premises for the purpose of monitoring electricity use and appliance scheduling. Furthermore, in [32], the authors have proposed delay-aware medium access for WSNs that are deployed in critical smart grid assets such as substations. Previous studies demonstrate that WSNs will have wide use in the smart grid. In our work, we consider a heterogenous network architecture of EPON and WSNs for smart grid monitoring where QoS provisioning is addressed in a different way than the studies on WSNs. Interconnection of billions of sensors with a backbone network have been very recently considered. Since data from local WSNs will need to be processed by the smart grid operators, a robust communication architecture is required for data delivery from the field to the operators [5]. In [4], the authors have explored the viability of smart grid communications over next generation PONs. In [33], the authors have considered bandwidth scheduling in TDM-PON with 6
network coding under a smart grid scenario. The performance of electric vehicle and smart grid integration over a FiWi network have been further explored in [34]. In addition, security of a FiWi network under Denial of Service (DOS) attacks in a smart grid environment has been explored in [35]. Fi-WSN architecture has been initially proposed in [5] for smart grid deployment. The authors have discussed the challenges and possible use cases of Fi-WSN technology in the smart grid. Our paper addresses the service differentiation challenge in a smart grid environment using the Fi-WSN architecture, and proposes a gateway design that provides QoS for WSN data and FTTX traffic.
3
System Model and Requirements
Fi-WSN transmits the smart grid data to the central office (CO), through a hybrid radio and fiber access network. Front-end of the hybrid access network consists of a WSN which monitors the smart grid while the back-end adopts an FTTX technology which can be either EPON or GPON. In this study, EPON is deployed in the back-end of the R&F access network. Fig. 1 illustrates the infrastructure of the system under study where ONUs are connected to the splitter via distribution fibers that are coupled on the feeder fiber connected to the OLT at the CO. In the corresponding architecture, an ONU can be serving either to an FTTX subscriber or a group of FiWi users. It is worthwhile to note that a FiWi user can be either a wireless access node or a smart grid asset monitored by the WSN. The role of WSNs in smart grid monitoring is reliability assurance and ensuring efficient transmission, distribution and utilization of the electricity at the customer premises. In Table 1, we provide 7
FTTX Traffic
100 Mb/s ONU1
Central Office
WSN/FTTX Traffic Turbine 100 Mb/s ONU-WSN Gateway
Smart-Home
PHEV
Site link
WSN/FTTX
100 Mb/s
Substation
WSN/FTTX
Wind/Solar Farm
OLT
1:N Coupler
ONU-WSN Gateway
Feeder Fiber
100 Mb/s
. . . ONU-WSN . Gateway . . . .
100 Mb/s
100 Mb/s
WSN/FTTX Traffic ONU-WSN Gateway
Smart-Home
FTTX Traffic ONU 16 FTTX Traffic
Fig. 1. Fi-WSN architecture for the smart grid. Table 1 Smart grid application requirements.
Application
Latency
Reliability
Transformer monitoring
500-2000ms
99.0-99.99
Voltage and current monitoring
2000-5000ms
99.0-99.99
Automated feeder switching
300-2000ms
99.0-99.99
Demand response
2000ms
99.0-99.99
Direct Load Control
2000ms
99.0-99.99
PEV charging
2000ms-5 mins
99.0-99.99
the smart grid operational requirements for several applications based on a report of North American utility operators [36]. In the smart grid, latency requirements vary from several hundreds of miliseconds to several minutes. The Fi-WSN gateway requirements are as follows. 8
Message prioritization: The major challenge in the design of a Fi-WSN is introduced by the heterogeneity of the front-end network since the WSN serves the residential premises and wind turbines, substations which frequently report ambient data. Meanwhile, an unexpected phenomenon triggers alarm data to be transmitted requiring prioritized access of the corresponding WSN packets at the Fi-WSN gateway. Coordinated uplink transmission: A single feeder fiber bandwidth is shared among multiple users transmitting wired and wireless data to the CO. Therefore, it is crucial to ensure that the FTTX traffic is not disrupted by the WSN traffic. Any disruption in the FTTX traffic may lead to QoS degradation. On the other hand, the WSN traffic is expected to be delivered to the CO reliably with short delay and small delay variation. Reliable message delivery: As mentioned above, besides transmission of ambient data, unexpected phenomena in smart grid monitoring triggers alarm data. These are called urgent data, and they require to be delivered to the CO with low delay and low drop probability which may occur due to buffer overflow at the ONU.
4
Fi-WSN Gateway Design
We have designed a Fi-WSN gateway for the architecture in Fig. 1 by considering the requirements mentioned previously. WSN messages arrive at the sink node with their priority values. A high priority packet carries either alarm data from a critical asset of the smart grid such as a substation or an unexpected high load at the customer premises which may be due to electric 9
Sensor node WSN sink
High priority packet Low priority packet
Base Station
Fi-WSN Gateway CLASSIFIER
Priority Queueing
Burst Assembly Burst Aggregator
…...
To ONU Buffer
Fig. 2. A simplified view of the Fi-WSN Gateway.
vehicle charging. Fi-WSN gateway architecture is presented in a modular way in Fig. 2. The packets arriving at the Base Station are classified based on their priority levels, and enqueued into the corresponding priority queues at the FiWSN gateway. The Fi-WSN gateway is responsible for the coordination of dequeuing of the WSN packets and aggregating them to be transferred to the corresponding ONU buffer. The burst assembly mechanism forms the bursts, encapsulates them in the Class of Service (CoS) queues which finally undergo burst aggregation prior to the ONU buffer. 10
As seen in the figure, packets arriving to the gateway through the Base Station first undergo a classification phase where they are inserted into the priority queues. Then, the burst assembly mechanism forms the bursts that are sent to the ONU. Fig. 3 presents the flowchart of the burst assembly process in the Fi-WSN gateway. The notation used throughout the paper is defined in Table 2. Table 2 The notation used in the formulation Notation Explanation td :
Departure time of a low-priority packet at its source node.
td :
Departure time of a high-priority packet at its source node.
ST h:
Size threshold of the low-priority queue.
MP:
Monitoring period for the corresponding asset.
B:
Low priority queue occupancy.
B:
High priority queue occupancy.
ρi :
Priority level of packet-i.
νi (t):
Validity of packet-i at time-t.
Sb (t):
Burst size at time-t.
Sp :
Packet size.
As seen in the flowchart, Fi-WSN gateway continuously receives arriving packets through the WSN sink. If the incoming packet carries a high priority (urgent) flag, the gateway aims at forwarding it to the back-end ONU immediately; hence it checks the high priority buffer occupancy. Having high priority buffer occupied by at least one packet may occur due to either of the following conditions: i ) High priority buffer is already being dequeued; ii ) Low priority burst aggregation is in progress. In either case, incoming packet has to be stored in the high priority queue, and it will only be dequeued immediately, if high priority dequeuing is in progress. Otherwise, the packet will be dequeued at the end of the low-priority burst aggregation period. Since low-priority 11
START
t Å Current time
Wait for next arriving WSN packet NO New packet arrival? YES NO
YES ρi = 1
Insert packet to low priority queue BlÅBl+Sp
Insert packet to high priority queue BħÅBħ+Sp
Bl ≥ STh
NO
YES YES
Send packet to ONU buffer
NO Bħ=1 AND Bl
Packet stays in low priority buffer
CounterÅ0
Counter ≥ STh NO YES YES
NO νi(t)= TRUE
Bl Å Bl - STh.Sp Counter Å Counter+1
Get next packet in buffer iÅi+1
Fig. 3. Burst aggregation workflow.
packets do not carry delay-sensitive data, in order not to disrupt the ongoing FTTX traffic, low-priority packets are buffered until the corresponding queue length exceeds ST h. Burst assembly is a continuous process, and at any time t, the burst aggregator determines to either form a new burst or wait for new packets to arrive. This process is formulated by Eq. 1. The flowchart also presents, validity of the low-priority packets are checked prior to assembling them into the burst. Due to carrying ambient data, low-priority packets are buffered for a longer 12
time compared to the buffering time of high priority packets. This policy may introduce the risk of transmitting outdated data to the CO. An outdated message has to be dropped immediately from the burst assembly buffer and burst aggregation process proceeds with the next packet in the buffer. Since high priority packets report unexpected phenomena or urgent messages, they are always considered to be valid until they are received by the OLT at the CO. Thus, a high priority packet might be carrying an outdated message due to experiencing long routing delay within the WSN, however problem of validation of the corresponding message is handled by an upper layer protocol. Equation 2 formulates the validity function of the burst assembly process. A low priority packet is marked as invalid if the time elapsed since the generation of the corresponding packet is greater than a pre-determined monitoring period, M P . Otherwise the corresponding low-priority packet is marked as valid whereas a high priority packet is always marked as valid as mentioned above.
⎧
⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ST h − · Sp i 1 − νi (t) ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨
Sb (t) = ⎪
B · Sp
⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩
B ≥
⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ST h ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬
B ≥ 1 ⎪ ⎪ ⎪ else
0
⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭
(1)
⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨
t − td ≤ M P
⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ∧ ρi = LOW ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬
νi (t) = ⎪ 0 t − t > M P ∧ ρ = LOW ⎪ i d ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩1
ρi = HIGH
13
⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭
(2)
Heterogeneity of the front-end network is not only denoted by the diversity amongst the monitored assets but also by the protocol suites used by the WSNs. Selection of the protocol suite (e.g., Zigbee, WiFi, etc) varies according to the application. For instance, for power-critical applications, Zigbee can be chosen due to its low-power consumption whereas WiFi is more viable to implement applications that require high bit-rates. The design presented above can provide a generic gateway implementation for heterogeneous FiWSN networks.
5
Numerical results
We evaluate the performance of our proposed Fi-WSN gateway under various scenarios by adapting the WSN simulation into the EPON simulator in [37]. In the simulations, EPON without WSN integration serves as the benchmark as we aim at investigating the impact of Fi-WSN framework on the service quality received by FTTX subscribers. Furthermore, we also compare the performance of the service quality introduced to the low priority and high priority messages which are differentiated at the Fi-WSN gateway. Table 3 presents the simulation settings. An EPON with a split ratio of 1:16 forms the backend of the Fi-WSN where each ONU is associated with a WSN. Each WSN terrain is assumed to span a region of 50m × 50m in which 50 sensor nodes are randomly deployed. Since sensor nodes are generally supposed to report ambient measurement data, each sensor node generates Constant Bit Rate (CBR) traffic, and we test our proposal under various bitrates. In fact, generation of high priority packets are event-driven. An event leading to an alarm message is expected to occur less frequently compared to the generation of ambient 14
measurement data. Therefore a packet which is generated by a WSN node is marked as a high priority message with a small probability, and the proportion between high priority and low priority packets (H : L) varies based on the scenario. FTTX users generate self-similar traffic with H=0.8 and varying load level between 0.05 and 0.9 Erlang. ST h values for burst assembly are determined empirically. In the WSNs, Geographic Routing (GR) has been selected as the routing scheme however, our proposed gateway architecture is transparent to the routing scheme in the WSN. 10% of an ONU’s buffer capacity is reserved for WSN traffic however, any unused space in the buffer can be utilized by the FTTX packets. In the simulations, average end-to-end (E2E) delay, maximum delay, packet delay variation and packet loss probability are the key performance metrics to evaluate the Fi-WSN gateway design. It is worthwhile to note that maximum delay denotes the highest delay value that is experienced by a packet of a given type throughout the simulation. For an FTTX packet, E2E delay is the sum of queuing delay at the ONU U OLT OLT (dON queue ), and the polling and granting delays (dpoll , dgrant ) introduced by the
OLT, respectively whereas the E2E delay experienced by a WSN packet is the SN sum of the routing delay in the WSN (dW route ), buffering delay at the sink node ON U (dsink queue ), queuing delay at the ONU (dqueue ), and the polling and granting deOLT lays (dOLT poll , dgrant ) introduced by the OLT as formulated in Eq. 3. In the plots,
each point represents the average of ten runs with 95% confidence intervals.
diE2E =
⎧ ⎪ ⎪ ⎪ SN sink ON U OLT OLT ⎪ ⎨ dW route + dqueue + dqueue + dpoll + dgrant W SN ⎪ ⎪ ⎪ ⎪ ⎩
U dON queue
+
dOLT poll
+
dOLT grant
⎫ ⎪ ⎪ ⎪ ⎪ ⎬
⎪ ⎪ ⎪ ⎭ FTTX ⎪ (3)
15
(a)
(b)
(c) Fig. 4. Average E2E delay vs. Target FTTX load; (a) Inter-arrival time in the WSN = 1s, (b) Inter-arrival time in the WSN = 0.5s, (c)E2E delay of smart grid messages under various packet inter-arrival times
16
Table 3 Simulation settings
WSN Terrain Size
50m×50m
Number of sensors
50 (single sink)
Sensor buffer capacity
20 packets
WSN traffic
CBR, 1/λ ∈ {500, 750, 1000} ms
WSN packet size
128B
H:L
1:9, 3:7
ST h
560, 640, 720 packets
WSN routing scheme
Geographic Routing
Rd , Ru
100Mbps, 1Gbps
ONU-OLT distance
10km ∼20 km
FTTX user traffic
Self-similar H=0.8
DBA in the back-end
IPACT Limited-service
Simulation duration
106 FTTX packets
ONU buffer capacity
1 MB
Number of ONUs/WSNs
16
ONU buffer capacity reserved for WSN traffic
0.1 MB
Max. transmission unit in the back-end
15500B
5.1
Scenario-1- H:L 1:9
In the first set of simulation results, we assume that only 10% of the WSN packets carry high priority messages. In Figs. 4.a-c, we illustrate the E2E delay per packet under varying FTTX load levels and inter-arrival times in the WSN. In order to ensure that the proposed Fi-WSN gateway design ensures reliable message delivery and coordinated uplink scheduling, we compare its performance evaluation to that of the conventional FTTX without WSN integration. 17
Fig. 4.a illustrates the results taken under 1s packet inter-arrival time in the WSN, and shows that integration of WSN does not increase the E2E delay of the FTTX packets. Moreover, due to the prioritization mechanism in the Fi-WSN gateway, high priority packets can be delivered with significantly less delay when compared to the low-priority packets.
In Fig. 4.b, inter-arrival time of the WSN packets is reduced to 0.5s, and more frequent arrival of WSN messages lead E2E delay per high priority packets to approach to that of the FTTX packets. Since high priority packets do not undergo a buffer threshold-based burst assembly, more frequent WSN messages introduce lower delay for the high priority packets. Fig. 4.c compares the performance of Fi-WSN design approach under various WSN packet interarrival times with respect to target load level at the ONUs. Two conclusions can be made based on the figure: 1) Burst assembly process at the Fi-WSN gateway leads high priority packets to experience less delay in comparison to the low priority packets, 2) Higher WSN packet inter-arrival time leads to a significant increase in both low and high priority packet delays.
Figs 5.a-b illustrate maximum delay per packet under 0.5s WSN packet interarrival time and two different ST h values (i.e., 640 and 720 packets, respectively) at the gateway. In Fig. 5.a, it is clearly seen that from moderate load levels towards heavy load levels, high priority packets have the same behavior with FTTX packets in terms of maximum delay whereas Fig. 5.b shows that increasing the ST h to 720 packets for the bursting mechanism increases the maximum delay experienced by the high priority packets. Indeed, this is an expected phenomenon as greater ST h values lead to larger bursts of lowpriority packets, which in turn will introduce longer buffering times for the high priority packets both at the sink and at the ONU. Therefore, in the Fi18
(a)
(b) Fig. 5. Maximum Delay vs. Target Load
Fig. 6. Packet Loss Ratio vs. Target Load
WSN gateway design, smaller ST h values should be preferred for the sake of better service quality for high priority WSN packets. 19
Besides, E2E delay and maximum delay, packet loss probability is another performance metric to evaluate the reliable message delivery to the CO through the Fi-WSN gateway. Fig. 6 illustrates the impact of the Fi-WSN gateway design on the packet loss probability due to buffer overflow at the ONU. We have evaluated the packet loss probability of our proposed Fi-WSN gateway design approach under various ST h values and WSN packet inter-arrival time of 0.5s. As seen in the figure, the proposed Fi-WSN gateway design scheme does not introduce higher packet loss probability to the optical back-end in the presence of the WSN traffic. Furthermore, ST h does not have a significant impact on the packet loss probability since FTTX and WSN packets share the same buffer, and WSN traffic intensity is significantly lower than that of the FTTX traffic beyond the ONU target load of 0.4 Erlang.
5.2
Scenario-2- H:L 3:7
Second set of simulations adopt the settings in the previous subsection with a slight difference. Here we set the H:L proportion at 3:7. Thus, at a WSN node, 30% of all messages are of high priority whereas 70% of the messages report ambient measurements. This situation can occur when an unexpected phenomenon in the smart grid triggers other phenomena such as cascaded failures due to malfunctioning of protection switches. Figs. 7.a-c illustrate E2E delay under 1s, 0.75s and 0.5s WSN packet interarrival times, respectively when STh is set at 640 packets under varying ONU load levels. The following two conclusions can be made from the figures: i. Having more high priority packets which are sent immediately without undergoing burst assembly do not affect E2E delay of FTTX packets. Indeed, 20
(a)
(b)
(c) Fig. 7. Average E2E delay vs. Target FTTX load when H:L = 3:7; (a) Inter-arrival time in the WSN = 1s, (b) Inter-arrival time in the WSN = 0.75s, (c)Inter-arrival time in the WSN = 0.5s
21
this is an expected phenomenon, since high priority packets are not buffered at the sink, they are not transferred to the ONU buffer as bursts unlike the low-priority packets where we already know that burst assembly mechanism does not increase the average E2E delay of the FTTX packets. ii. High priority packets experience significantly higher E2E delay when compared to the FTTX packets. Furthermore, when the ratio of the high priority packets is higher, those packets experience significantly higher E2E delay when compared to the E2E delay of the high priority packets under Scenario-1. This is due to the fact that more high priority packets arrive at the high priority buffer of the Fi-WSN gateway, and high priority packets are not assembled into bursts, they are sent out one-by-one; hence E2E delay of high priority packets increases due to buffering delay at the sink. E2E delays of high priority messages under the two scenarios coincide when WSN packet inter-arrival time is 1s, i.e., packets arrive at the sink less frequently.
Figs. 8.a-c illustrate maximum packet delay when ST h is set at 640 packet under WSN inter-arrival time of 1s, 0.75s and 0.5s, respectively. The results demonstrate the same behavior with those illustrating E2E delays in Fig. 7. Similarly, having more high priority packets which are sent immediately without undergoing burst assembly does not affect maximum delay of FTTX packets. Besides, high priority packets experience significantly higher maximum packet delay when compared to the FTTX packets. Furthermore, when the ratio of the high priority packets is higher, those packets experience significantly higher maximum delay when compared to the maximum packet delay of the high priority packets under Scenario-1. The reasons of these two phenomena are related with the bursting behavior that is explained for Fig. 7.
As more high priority messages arrive at the Fi-WSN gateway in Scenario-2, 22
(a)
(b)
(c) Fig. 8. Maximum E2E delay vs. Target FTTX load when H:L = 3:7; (a) Inter-arrival time in the WSN = 1s, (b) Inter-arrival time in the WSN = 0.75s, (c)Inter-arrival time in the WSN = 0.5s
23
Fig. 9. The impact of monitoring period on the performance at a moderate FTTX load (0.6. Erlang)
the length of monitoring period may affect the delay of WSN packets carrying ambient measurement data. Furthermore, packet delay variation of the FTTX packets needs to be investigated. Hence, in Fig. 9 and Fig. 10, we present the impact of monitoring period on the delay performance and the impact of Fi-WSN integration on the packet delay variation of the FTTX packets, respectively. As seen in Fig. 9, when the sensors report less frequently (i.e., 1000ms of arrival rate at each node), the monitoring period does not have a significant impact on the delay performance as the wireless front-end of the Fi-WSN network is lightly loaded and utilization of the ONU buffer does not introduce additional delay nor does dropping low-priority packets reduce the average delay that any WSN packet is exposed to until it reaches at the OLT. On the other hand, when sensors report ambient smart grid data more frequently (i.e., arrival rate of 500ms), end-to-end delay of a WSN packet increases when the monitoring period is set beyond 3ms. The reason of this behavior can be explained as follows. When the WSN packets arrive more frequently as the MP increases, more WSN packets are marked as valid leading to increased buffer utilization 24
Fig. 10. The impact of Fi-WSN integration on packet delay variation of the FTTX packets
at the sink, as well as at the ONU. This leads to increased packet delay for the ambient smart grid measurement data, i.e, low-priority packets. Indeed, beyond 3ms, the increase in end-to-end packet delay is bounded above by the maximum packet delay value in Fig. 8.c, which is at the order of 4.2s.
Besides, in order to study the impact of Fi-WSN integration on the packet delay variation (PDV) of FTTX packets, we have compared the PDV of FTTX packets without WSN integration under different ONU loads. As seen in the Fig. 10, PDV of the FTTX packets are not impacted by the integration of WSN packets. This is due to the different traffic intensities in the wireless and optical back-end of the Fi-WSN network. Furthermore, as mentioned earlier, WSN packets utilize a small portion of an ONU buffer which does not lead to a significant variation in the dynamic bandwidth allocation process run at the OLT. 25
(a)
(b) Fig. 11. Average E2E delay vs. Target FTTX load under larger high priority bursts; (a) Inter-arrival time in the WSN = 1s, (b)Inter-arrival time in the WSN = 0.5s
5.3
Scenario-3- Larger High Priority Bursts
Next set of simulation results are taken by enforcing burst assembly mechanism for high packets at the Fi-WSN gateway. We set ST h to 70 packets for high priority messages and 640 for low priority packets. Furthermore, we also simulate a benchmark case where there is no differentiation between the packets at the Fi-WSN gateway. Figs. 11.a-b illustrate E2E delay when ST h for low priority messages is 640 packets under WSN packet inter-arrival time of 0.5s and 1s, respectively. As 26
Fig. 12. Maximum E2E delay vs. Target FTTX load under larger high priority bursts and inter-arrival time in the WSN = 0.5s
seen in the figures, introducing burst assembly procedure to high priority packets increases the E2E delay of these packets as high priority packets are forced to wait in the high priority queue at the Fi-WSN gateway. Besides, when the WSN packet inter-arrival time is increased to 1s, E2E delay of high priority packets significantly increases, and it exceeds even E2E delay of WSN packets when there is no differentiation between WSN messages. This is due to the fact that high priority packets are buffered for longer duration as WSN messages arrive less frequently. As we have seen that implementing burst assembly mechanism for the high priority packets at the Fi-WSN gateway can introduce limited benefit in terms of delay only under frequent arrival of WSN packets. We test the proposed Fi-WSN gateway design scheme with larger high priority bursts by setting 1/λ at 500ms. As expected, maximum delay behavior of the Fi-WSN gateway implementation coincides with its E2E delay behavior in Fig. 11. Finally, we test packet loss probability under this last scenario where high prioriority packets are assembled to bursts at the Fi-WSN gateway. As packet loss probability is a function of the traffic intensity and high priority bursts 27
Fig. 13. Packet Loss Ratio vs. Target Load under larger high priority bursts
do not increase the WSN traffic intensity at the sink, packet loss probability at the optical back-end is not impacted.
6
Conclusion
Smart grid calls for advanced monitoring solutions as well as an effective communication infrastructure to support the flow of high volume of data collected from smart grid assets and customer premises lying in a large geographic region. PON supported networking and in particular, Fiber-Wireless Sensor Network (Fi-WSN) architecture consolidates the high-speed, low latency advantage of EPONs with the advanced monitoring capability, flexibility, lowcost, and wide-coverage properties WSNs. With the Fi-WSN architecture it becomes feasible to deliver data from billions of sensors to smart grid operators in a fast and reliable manner. In this paper, we address the service quality provided to the WSN traffic and the FTTX traffic in Fi-WSN deployment in the smart grid. Considering smart grid application requirements, ambient data that represent non-critical sensor 28
readings are given low priority while alarm data that is raised from events that may risk the stability of the power grid are given high priority. The proposed QoS-aware Fi-WSN gateway design differentiates the delivery of non-urgent data and urgent data through service-level oriented bursting mechanisms. Our design delivers high priority smart grid data within specified delay bounds with high reliability while maintaining the desired QoS levels for FTTX users. Our results have shown that more frequent report messages by the WSNs enhance the service quality of urgent messages. On the other hand, transmitting the low priority messages in small bursts to the ONUs improves the service quality of the urgent messages. We have also evaluated the impact of burst formation for urgent and non-urgent data as well as the impact of various concentrations of high priority packets. We have shown that the smart grid messages carried via WSN do not introduce any performance degradation to the optical back-end in terms of packet loss probability under all of the tested scenarios. As a future work, we are planning to extend our work by improving the WSN performance for critical smart grid applications. We are also planning to further our investigation on the optimal selection of thresholds in the proposed gateway design.
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34
Smart grid monitoring with service differentiation via epon and wireless sensor network convergence Nima Zaker, Burak Kantarci, Melike ErolKantarci, Hussein T. Mouftah
www.elsevier.com/locate/osn
PII: DOI: Reference:
S1573-4277(14)00011-3 http://dx.doi.org/10.1016/j.osn.2014.01.010 OSN281
To appear in:
Optical Switching and Networking
Cite this article as: Nima Zaker, Burak Kantarci, Melike Erol-Kantarci, Hussein T. Mouftah, Smart grid monitoring with service differentiation via epon and wireless sensor network convergence, Optical Switching and Networking, http://dx. doi.org/10.1016/j.osn.2014.01.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Smart Grid Monitoring with Service Differentiation via EPON and Wireless Sensor Network Convergence
Nima Zaker, Burak Kantarci, Melike Erol-Kantarci and Hussein T. Mouftah School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Avenue, Ottawa, ON, Canada Tel: +1 (613) 562-5800 ext. 2151; Fax: +1 (613) 562-5664 Email: [email protected], [email protected], [email protected], [email protected]
Abstract Passive Optical Networks (PON) are recognized as a fundamental component of high-speed network access, and emerge as a solid networking solution for the smart grid. Smart grid aims to incorporate two-way communications between the customers and the utility, for the purposes of advanced monitoring and intelligent control of demand and supply of electricity. At the user-end, Wireless Sensor Networks (WSN) are ideal tools for energy consumption monitoring at residential premises, as well as, event and ambient monitoring at substations, power lines and vaults. In this paper, we target a seamless integration of WSN technology with PONs to provide Quality of Service (QoS) to both the data collected by billions of sensors and to the Fiber-To-The-Home/Building/Curb (FTTX) users in the smart grid environment. We present the design of a Fiber-Wireless Sensor Network (Fi-WSN) gateway where data prioritization is fundamental since ambient data collected from a home will have lower priority than an alarm generated at a smart grid asset. We show that the proposed gateway design is able to offer low delay for high priority packets while maintaining the delay of FTTX traffic and the reliability of the WSN at desired levels under various loads, concentrations of service levels and aggressive/non-aggressive bursting mechanisms. Key words: Ethernet passive optical networks, fiber-wireless networks, gateway design, quality of service, smart grid, wireless sensor networks.
Preprint submitted to Elsevier
27 January 2014
1
Introduction
Passive Optical Networks (PONs) have recently matured as a high-speed broadband access technology. As a particular case of PON supported networking, integrated fiber-wireless (FiWi) broadband access networks combine the speed and the reliability of optical networks with the flexibility and the wide coverage of wireless technologies [1,2]. PON supported networking plays a key role in smart grid communications as the next generation power grid calls for a robust communication infrastructure to support intense communications among the utilities, market players, suppliers and the consumers. In addition to a robust communication backbone, at the user end, reliable monitoring tools are required to enhance the operation of the power grid. FiWi and in particular, Fiber-Wireless Sensor Networks (Fi-WSNs) offer a unique solution for monitoring the smart grid [3,4]. In the Fi-WSN architecture, the data collected by the WSN from the smart grid environment is transmitted to the operators through a broadband access network, e.g. Ethernet Passive Optical Network (EPON). In the EPON architecture, each ONU is connected to the Optical Line Terminal (OLT) via a distribution fiber. ONUs can be also connected to a WiMAX base station through an optical fiber ring [5] in order to increase coverage. At the user end, a wireless device can be receiving service from the PON supported network or it can be a WSN that is monitoring a smart grid asset or a residential premise. For instance, WSNs can be deployed in substations that house transformers which step down the voltage for distributing the electricity through local feeders [6,7], or they can be deployed at overhead power lines to measure sagging, or at transmission/distribution towers to monitor vegetation around 2
the lines [8], or they can be utilized in renewable energy generation cites [9]. They can be further deployed at homes to monitor energy use [10]. Sensors collect ambient measurements such as temperature, humidity, etc., in addition they can monitor transformer overloading, line sagging, etc. where in general, the latter is more critical in terms of the operation of the power grid [11,12]. A typical scenario for this kind of alarm data would be a sensor reading at a substation indicating transformer overloading which may be further related with simultaneous electric vehicle charging from different locations of the same distribution system. To this end, integrated Fi-WSN network provides an ideal solution for Home Area Network (HAN) broadband access as well as WSNbased monitoring in smart grid assets. However, there are certain challenges that need to be addressed before Fi-WSN deployment is widely utilized in the smart grid. One of the fundamental challenges is to provide Quality of Service (QoS) for the heterogenous network architecture of the Fi-WSN and the traffic loads involved in smart grid monitoring tasks.
In smart grid monitoring, delivery of critical data in a timely manner is a key operational requirement. Lost data may result in incorrect control actions and risk the stability of the power grid, and in certain situations, delayed data may impact the operation of the grid worse than lost data. The operational delay requirements can be as tight as 500ms for certain smart grid applications. For this reason, differentiation of the collected WSN data is essential which means non-urgent data may be delayed while urgent alarm data is pushed faster towards the power grid operators.
In this paper, we present a QoS-aware gateway design for the Fi-WSN architecture employed in the smart grid environment. Our design aims to achieve data prioritization, maintain QoS for FTTX users and deliver WSN data in 3
a reliable manner 1 . The Fi-WSN gateway employs a burst assembly mechanism to differentiate between urgent and non-urgent packets. We evaluate the performance of our gateway design under various traffic types, aggressive and non-aggressive bursting mechanisms and under varying concentrations of traffic from different service classes. We show that the proposed gateway design attains low delay for high priority packets while maintaining the QoS of FTTX traffic and the reliability of the WSN. The rest of the paper is structured as follows. In Section II, we briefly review the related work on PON supported networking and smart grid monitoring. In Section III, we introduce the system model, and in Section IV we describe the gateway design in detail. Section V describes the simulation settings and the numerical results. Finally, Section VI concludes the paper and gives future directions.
2
Related Work
In this section, we first provide an overview of PON supported networking research and then we give a brief survey on recent advances in WSN-based smart grid monitoring studies.
2.1
PON supported networking
PON technology have recently emerged as a key component of broadband access [14]. Among various competing PON solutions, EPON and GPON solutions are widely preferred based on the existing telecommunication infras1
A simplified version of this work has been published in [13]
4
tructure, as well as the requirements. In this paper, we have selected EPON as the optical back-end technology as it is based on a globally accepted standard, i.e., Ethernet. EPONs offer high speed access with a simple design with seemless integration capability of IP-based networks [15,16]. In EPON, data is encapsulated in Ethernet frames and transmitted in PONs. For a survey on PON technologies, interested readers are referred to [14] and [15–17] for EPON technology. A more recent technology, FiWi have succeeded to consolidate the high-speed and low-latency advantages of EPONs with the flexibility and coverage of wireless networks.
FiWi networks can have two possible implementations, i.e. Radio over Fiber (RoF) and Radio and Fiber (R&F). In [18], the authors have investigated the RoF protocol where an optical carrier is modulated in the central office (CO) by radio frequency (RF) signals that are propagated in analog fiber link to remote antenna units (RAUs), then they are received through air by clients. Integration of WiMAX to the optical network has been considered in [19,20]. On the other hand, in [21], the authors have considered an R&F protocol which applies a different MAC protocol via a protocol translation interface that takes place at the optical-wireless borderline. As reported in [22], the advantage of the integration of EPON with WiMAX technology under R&F protocol is a good capacity match at the integrated ONU-Base Station (ONU-BS) gateway among other EPON-WiMAX integration benefits. Furthermore, integration of long term evolution (LTE) technology with Long-Reach PON (LR-PON) has been studied in [23]. QoS and energy-efficiency of FiWi networks have been explored in [24]. A recent study further investigates the capacity and delay bounds of FiWi networks [2]. The interested readers are referred to [25] for a pioneering survey of the FiWi networks and [26] for the challenges and 5
the advantages of FiWi networks. In this paper, we are focusing on an R&F implementation that consists of EPON technology at the optical end and a WSN at the wireless end. We propose a gateway design that incorporates a bursting mechanism to enhance QoS of the overall integrated network.
2.2
WSN-based Smart Grid Monitoring
WSNs have long been considered for monitoring critical infrastructures [27,28]. They have been recently studied for possible deployments in smart grid environments as well [29–32]. In [29], the authors discuss the fundamental challenges and opportunities of WSNs in the smart grid. In [30], the use of wireless multimedia sensors in the smart grid has been investigated. In [31], the authors have considered using WSNs at residential premises for the purpose of monitoring electricity use and appliance scheduling. Furthermore, in [32], the authors have proposed delay-aware medium access for WSNs that are deployed in critical smart grid assets such as substations. Previous studies demonstrate that WSNs will have wide use in the smart grid. In our work, we consider a heterogenous network architecture of EPON and WSNs for smart grid monitoring where QoS provisioning is addressed in a different way than the studies on WSNs. Interconnection of billions of sensors with a backbone network have been very recently considered. Since data from local WSNs will need to be processed by the smart grid operators, a robust communication architecture is required for data delivery from the field to the operators [5]. In [4], the authors have explored the viability of smart grid communications over next generation PONs. In [33], the authors have considered bandwidth scheduling in TDM-PON with 6
network coding under a smart grid scenario. The performance of electric vehicle and smart grid integration over a FiWi network have been further explored in [34]. In addition, security of a FiWi network under Denial of Service (DOS) attacks in a smart grid environment has been explored in [35]. Fi-WSN architecture has been initially proposed in [5] for smart grid deployment. The authors have discussed the challenges and possible use cases of Fi-WSN technology in the smart grid. Our paper addresses the service differentiation challenge in a smart grid environment using the Fi-WSN architecture, and proposes a gateway design that provides QoS for WSN data and FTTX traffic.
3
System Model and Requirements
Fi-WSN transmits the smart grid data to the central office (CO), through a hybrid radio and fiber access network. Front-end of the hybrid access network consists of a WSN which monitors the smart grid while the back-end adopts an FTTX technology which can be either EPON or GPON. In this study, EPON is deployed in the back-end of the R&F access network. Fig. 1 illustrates the infrastructure of the system under study where ONUs are connected to the splitter via distribution fibers that are coupled on the feeder fiber connected to the OLT at the CO. In the corresponding architecture, an ONU can be serving either to an FTTX subscriber or a group of FiWi users. It is worthwhile to note that a FiWi user can be either a wireless access node or a smart grid asset monitored by the WSN. The role of WSNs in smart grid monitoring is reliability assurance and ensuring efficient transmission, distribution and utilization of the electricity at the customer premises. In Table 1, we provide 7
FTTX Traffic
100 Mb/s ONU1
Central Office
WSN/FTTX Traffic Turbine 100 Mb/s ONU-WSN Gateway
Smart-Home
PHEV
Site link
WSN/FTTX
100 Mb/s
Substation
WSN/FTTX
Wind/Solar Farm
OLT
1:N Coupler
ONU-WSN Gateway
Feeder Fiber
100 Mb/s
. . . ONU-WSN . Gateway . . . .
100 Mb/s
100 Mb/s
WSN/FTTX Traffic ONU-WSN Gateway
Smart-Home
FTTX Traffic ONU 16 FTTX Traffic
Fig. 1. Fi-WSN architecture for the smart grid. Table 1 Smart grid application requirements.
Application
Latency
Reliability
Transformer monitoring
500-2000ms
99.0-99.99
Voltage and current monitoring
2000-5000ms
99.0-99.99
Automated feeder switching
300-2000ms
99.0-99.99
Demand response
2000ms
99.0-99.99
Direct Load Control
2000ms
99.0-99.99
PEV charging
2000ms-5 mins
99.0-99.99
the smart grid operational requirements for several applications based on a report of North American utility operators [36]. In the smart grid, latency requirements vary from several hundreds of miliseconds to several minutes. The Fi-WSN gateway requirements are as follows. 8
Message prioritization: The major challenge in the design of a Fi-WSN is introduced by the heterogeneity of the front-end network since the WSN serves the residential premises and wind turbines, substations which frequently report ambient data. Meanwhile, an unexpected phenomenon triggers alarm data to be transmitted requiring prioritized access of the corresponding WSN packets at the Fi-WSN gateway. Coordinated uplink transmission: A single feeder fiber bandwidth is shared among multiple users transmitting wired and wireless data to the CO. Therefore, it is crucial to ensure that the FTTX traffic is not disrupted by the WSN traffic. Any disruption in the FTTX traffic may lead to QoS degradation. On the other hand, the WSN traffic is expected to be delivered to the CO reliably with short delay and small delay variation. Reliable message delivery: As mentioned above, besides transmission of ambient data, unexpected phenomena in smart grid monitoring triggers alarm data. These are called urgent data, and they require to be delivered to the CO with low delay and low drop probability which may occur due to buffer overflow at the ONU.
4
Fi-WSN Gateway Design
We have designed a Fi-WSN gateway for the architecture in Fig. 1 by considering the requirements mentioned previously. WSN messages arrive at the sink node with their priority values. A high priority packet carries either alarm data from a critical asset of the smart grid such as a substation or an unexpected high load at the customer premises which may be due to electric 9
Sensor node WSN sink
High priority packet Low priority packet
Base Station
Fi-WSN Gateway CLASSIFIER
Priority Queueing
Burst Assembly Burst Aggregator
…...
To ONU Buffer
Fig. 2. A simplified view of the Fi-WSN Gateway.
vehicle charging. Fi-WSN gateway architecture is presented in a modular way in Fig. 2. The packets arriving at the Base Station are classified based on their priority levels, and enqueued into the corresponding priority queues at the FiWSN gateway. The Fi-WSN gateway is responsible for the coordination of dequeuing of the WSN packets and aggregating them to be transferred to the corresponding ONU buffer. The burst assembly mechanism forms the bursts, encapsulates them in the Class of Service (CoS) queues which finally undergo burst aggregation prior to the ONU buffer. 10
As seen in the figure, packets arriving to the gateway through the Base Station first undergo a classification phase where they are inserted into the priority queues. Then, the burst assembly mechanism forms the bursts that are sent to the ONU. Fig. 3 presents the flowchart of the burst assembly process in the Fi-WSN gateway. The notation used throughout the paper is defined in Table 2. Table 2 The notation used in the formulation Notation Explanation td :
Departure time of a low-priority packet at its source node.
td :
Departure time of a high-priority packet at its source node.
ST h:
Size threshold of the low-priority queue.
MP:
Monitoring period for the corresponding asset.
B:
Low priority queue occupancy.
B:
High priority queue occupancy.
ρi :
Priority level of packet-i.
νi (t):
Validity of packet-i at time-t.
Sb (t):
Burst size at time-t.
Sp :
Packet size.
As seen in the flowchart, Fi-WSN gateway continuously receives arriving packets through the WSN sink. If the incoming packet carries a high priority (urgent) flag, the gateway aims at forwarding it to the back-end ONU immediately; hence it checks the high priority buffer occupancy. Having high priority buffer occupied by at least one packet may occur due to either of the following conditions: i ) High priority buffer is already being dequeued; ii ) Low priority burst aggregation is in progress. In either case, incoming packet has to be stored in the high priority queue, and it will only be dequeued immediately, if high priority dequeuing is in progress. Otherwise, the packet will be dequeued at the end of the low-priority burst aggregation period. Since low-priority 11
START
t Å Current time
Wait for next arriving WSN packet NO New packet arrival? YES NO
YES ρi = 1
Insert packet to low priority queue BlÅBl+Sp
Insert packet to high priority queue BħÅBħ+Sp
Bl ≥ STh
NO
YES YES
Send packet to ONU buffer
NO Bħ=1 AND Bl
Packet stays in low priority buffer
CounterÅ0
Counter ≥ STh NO YES YES
NO νi(t)= TRUE
Bl Å Bl - STh.Sp Counter Å Counter+1
Get next packet in buffer iÅi+1
Fig. 3. Burst aggregation workflow.
packets do not carry delay-sensitive data, in order not to disrupt the ongoing FTTX traffic, low-priority packets are buffered until the corresponding queue length exceeds ST h. Burst assembly is a continuous process, and at any time t, the burst aggregator determines to either form a new burst or wait for new packets to arrive. This process is formulated by Eq. 1. The flowchart also presents, validity of the low-priority packets are checked prior to assembling them into the burst. Due to carrying ambient data, low-priority packets are buffered for a longer 12
time compared to the buffering time of high priority packets. This policy may introduce the risk of transmitting outdated data to the CO. An outdated message has to be dropped immediately from the burst assembly buffer and burst aggregation process proceeds with the next packet in the buffer. Since high priority packets report unexpected phenomena or urgent messages, they are always considered to be valid until they are received by the OLT at the CO. Thus, a high priority packet might be carrying an outdated message due to experiencing long routing delay within the WSN, however problem of validation of the corresponding message is handled by an upper layer protocol. Equation 2 formulates the validity function of the burst assembly process. A low priority packet is marked as invalid if the time elapsed since the generation of the corresponding packet is greater than a pre-determined monitoring period, M P . Otherwise the corresponding low-priority packet is marked as valid whereas a high priority packet is always marked as valid as mentioned above.
⎧
⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ST h − · Sp i 1 − νi (t) ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨
Sb (t) = ⎪
B · Sp
⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩
B ≥
⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ST h ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬
B ≥ 1 ⎪ ⎪ ⎪ else
0
⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭
(1)
⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨
t − td ≤ M P
⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ∧ ρi = LOW ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬
νi (t) = ⎪ 0 t − t > M P ∧ ρ = LOW ⎪ i d ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩1
ρi = HIGH
13
⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭
(2)
Heterogeneity of the front-end network is not only denoted by the diversity amongst the monitored assets but also by the protocol suites used by the WSNs. Selection of the protocol suite (e.g., Zigbee, WiFi, etc) varies according to the application. For instance, for power-critical applications, Zigbee can be chosen due to its low-power consumption whereas WiFi is more viable to implement applications that require high bit-rates. The design presented above can provide a generic gateway implementation for heterogeneous FiWSN networks.
5
Numerical results
We evaluate the performance of our proposed Fi-WSN gateway under various scenarios by adapting the WSN simulation into the EPON simulator in [37]. In the simulations, EPON without WSN integration serves as the benchmark as we aim at investigating the impact of Fi-WSN framework on the service quality received by FTTX subscribers. Furthermore, we also compare the performance of the service quality introduced to the low priority and high priority messages which are differentiated at the Fi-WSN gateway. Table 3 presents the simulation settings. An EPON with a split ratio of 1:16 forms the backend of the Fi-WSN where each ONU is associated with a WSN. Each WSN terrain is assumed to span a region of 50m × 50m in which 50 sensor nodes are randomly deployed. Since sensor nodes are generally supposed to report ambient measurement data, each sensor node generates Constant Bit Rate (CBR) traffic, and we test our proposal under various bitrates. In fact, generation of high priority packets are event-driven. An event leading to an alarm message is expected to occur less frequently compared to the generation of ambient 14
measurement data. Therefore a packet which is generated by a WSN node is marked as a high priority message with a small probability, and the proportion between high priority and low priority packets (H : L) varies based on the scenario. FTTX users generate self-similar traffic with H=0.8 and varying load level between 0.05 and 0.9 Erlang. ST h values for burst assembly are determined empirically. In the WSNs, Geographic Routing (GR) has been selected as the routing scheme however, our proposed gateway architecture is transparent to the routing scheme in the WSN. 10% of an ONU’s buffer capacity is reserved for WSN traffic however, any unused space in the buffer can be utilized by the FTTX packets. In the simulations, average end-to-end (E2E) delay, maximum delay, packet delay variation and packet loss probability are the key performance metrics to evaluate the Fi-WSN gateway design. It is worthwhile to note that maximum delay denotes the highest delay value that is experienced by a packet of a given type throughout the simulation. For an FTTX packet, E2E delay is the sum of queuing delay at the ONU U OLT OLT (dON queue ), and the polling and granting delays (dpoll , dgrant ) introduced by the
OLT, respectively whereas the E2E delay experienced by a WSN packet is the SN sum of the routing delay in the WSN (dW route ), buffering delay at the sink node ON U (dsink queue ), queuing delay at the ONU (dqueue ), and the polling and granting deOLT lays (dOLT poll , dgrant ) introduced by the OLT as formulated in Eq. 3. In the plots,
each point represents the average of ten runs with 95% confidence intervals.
diE2E =
⎧ ⎪ ⎪ ⎪ SN sink ON U OLT OLT ⎪ ⎨ dW route + dqueue + dqueue + dpoll + dgrant W SN ⎪ ⎪ ⎪ ⎪ ⎩
U dON queue
+
dOLT poll
+
dOLT grant
⎫ ⎪ ⎪ ⎪ ⎪ ⎬
⎪ ⎪ ⎪ ⎭ FTTX ⎪ (3)
15
(a)
(b)
(c) Fig. 4. Average E2E delay vs. Target FTTX load; (a) Inter-arrival time in the WSN = 1s, (b) Inter-arrival time in the WSN = 0.5s, (c)E2E delay of smart grid messages under various packet inter-arrival times
16
Table 3 Simulation settings
WSN Terrain Size
50m×50m
Number of sensors
50 (single sink)
Sensor buffer capacity
20 packets
WSN traffic
CBR, 1/λ ∈ {500, 750, 1000} ms
WSN packet size
128B
H:L
1:9, 3:7
ST h
560, 640, 720 packets
WSN routing scheme
Geographic Routing
Rd , Ru
100Mbps, 1Gbps
ONU-OLT distance
10km ∼20 km
FTTX user traffic
Self-similar H=0.8
DBA in the back-end
IPACT Limited-service
Simulation duration
106 FTTX packets
ONU buffer capacity
1 MB
Number of ONUs/WSNs
16
ONU buffer capacity reserved for WSN traffic
0.1 MB
Max. transmission unit in the back-end
15500B
5.1
Scenario-1- H:L 1:9
In the first set of simulation results, we assume that only 10% of the WSN packets carry high priority messages. In Figs. 4.a-c, we illustrate the E2E delay per packet under varying FTTX load levels and inter-arrival times in the WSN. In order to ensure that the proposed Fi-WSN gateway design ensures reliable message delivery and coordinated uplink scheduling, we compare its performance evaluation to that of the conventional FTTX without WSN integration. 17
Fig. 4.a illustrates the results taken under 1s packet inter-arrival time in the WSN, and shows that integration of WSN does not increase the E2E delay of the FTTX packets. Moreover, due to the prioritization mechanism in the Fi-WSN gateway, high priority packets can be delivered with significantly less delay when compared to the low-priority packets.
In Fig. 4.b, inter-arrival time of the WSN packets is reduced to 0.5s, and more frequent arrival of WSN messages lead E2E delay per high priority packets to approach to that of the FTTX packets. Since high priority packets do not undergo a buffer threshold-based burst assembly, more frequent WSN messages introduce lower delay for the high priority packets. Fig. 4.c compares the performance of Fi-WSN design approach under various WSN packet interarrival times with respect to target load level at the ONUs. Two conclusions can be made based on the figure: 1) Burst assembly process at the Fi-WSN gateway leads high priority packets to experience less delay in comparison to the low priority packets, 2) Higher WSN packet inter-arrival time leads to a significant increase in both low and high priority packet delays.
Figs 5.a-b illustrate maximum delay per packet under 0.5s WSN packet interarrival time and two different ST h values (i.e., 640 and 720 packets, respectively) at the gateway. In Fig. 5.a, it is clearly seen that from moderate load levels towards heavy load levels, high priority packets have the same behavior with FTTX packets in terms of maximum delay whereas Fig. 5.b shows that increasing the ST h to 720 packets for the bursting mechanism increases the maximum delay experienced by the high priority packets. Indeed, this is an expected phenomenon as greater ST h values lead to larger bursts of lowpriority packets, which in turn will introduce longer buffering times for the high priority packets both at the sink and at the ONU. Therefore, in the Fi18
(a)
(b) Fig. 5. Maximum Delay vs. Target Load
Fig. 6. Packet Loss Ratio vs. Target Load
WSN gateway design, smaller ST h values should be preferred for the sake of better service quality for high priority WSN packets. 19
Besides, E2E delay and maximum delay, packet loss probability is another performance metric to evaluate the reliable message delivery to the CO through the Fi-WSN gateway. Fig. 6 illustrates the impact of the Fi-WSN gateway design on the packet loss probability due to buffer overflow at the ONU. We have evaluated the packet loss probability of our proposed Fi-WSN gateway design approach under various ST h values and WSN packet inter-arrival time of 0.5s. As seen in the figure, the proposed Fi-WSN gateway design scheme does not introduce higher packet loss probability to the optical back-end in the presence of the WSN traffic. Furthermore, ST h does not have a significant impact on the packet loss probability since FTTX and WSN packets share the same buffer, and WSN traffic intensity is significantly lower than that of the FTTX traffic beyond the ONU target load of 0.4 Erlang.
5.2
Scenario-2- H:L 3:7
Second set of simulations adopt the settings in the previous subsection with a slight difference. Here we set the H:L proportion at 3:7. Thus, at a WSN node, 30% of all messages are of high priority whereas 70% of the messages report ambient measurements. This situation can occur when an unexpected phenomenon in the smart grid triggers other phenomena such as cascaded failures due to malfunctioning of protection switches. Figs. 7.a-c illustrate E2E delay under 1s, 0.75s and 0.5s WSN packet interarrival times, respectively when STh is set at 640 packets under varying ONU load levels. The following two conclusions can be made from the figures: i. Having more high priority packets which are sent immediately without undergoing burst assembly do not affect E2E delay of FTTX packets. Indeed, 20
(a)
(b)
(c) Fig. 7. Average E2E delay vs. Target FTTX load when H:L = 3:7; (a) Inter-arrival time in the WSN = 1s, (b) Inter-arrival time in the WSN = 0.75s, (c)Inter-arrival time in the WSN = 0.5s
21
this is an expected phenomenon, since high priority packets are not buffered at the sink, they are not transferred to the ONU buffer as bursts unlike the low-priority packets where we already know that burst assembly mechanism does not increase the average E2E delay of the FTTX packets. ii. High priority packets experience significantly higher E2E delay when compared to the FTTX packets. Furthermore, when the ratio of the high priority packets is higher, those packets experience significantly higher E2E delay when compared to the E2E delay of the high priority packets under Scenario-1. This is due to the fact that more high priority packets arrive at the high priority buffer of the Fi-WSN gateway, and high priority packets are not assembled into bursts, they are sent out one-by-one; hence E2E delay of high priority packets increases due to buffering delay at the sink. E2E delays of high priority messages under the two scenarios coincide when WSN packet inter-arrival time is 1s, i.e., packets arrive at the sink less frequently.
Figs. 8.a-c illustrate maximum packet delay when ST h is set at 640 packet under WSN inter-arrival time of 1s, 0.75s and 0.5s, respectively. The results demonstrate the same behavior with those illustrating E2E delays in Fig. 7. Similarly, having more high priority packets which are sent immediately without undergoing burst assembly does not affect maximum delay of FTTX packets. Besides, high priority packets experience significantly higher maximum packet delay when compared to the FTTX packets. Furthermore, when the ratio of the high priority packets is higher, those packets experience significantly higher maximum delay when compared to the maximum packet delay of the high priority packets under Scenario-1. The reasons of these two phenomena are related with the bursting behavior that is explained for Fig. 7.
As more high priority messages arrive at the Fi-WSN gateway in Scenario-2, 22
(a)
(b)
(c) Fig. 8. Maximum E2E delay vs. Target FTTX load when H:L = 3:7; (a) Inter-arrival time in the WSN = 1s, (b) Inter-arrival time in the WSN = 0.75s, (c)Inter-arrival time in the WSN = 0.5s
23
Fig. 9. The impact of monitoring period on the performance at a moderate FTTX load (0.6. Erlang)
the length of monitoring period may affect the delay of WSN packets carrying ambient measurement data. Furthermore, packet delay variation of the FTTX packets needs to be investigated. Hence, in Fig. 9 and Fig. 10, we present the impact of monitoring period on the delay performance and the impact of Fi-WSN integration on the packet delay variation of the FTTX packets, respectively. As seen in Fig. 9, when the sensors report less frequently (i.e., 1000ms of arrival rate at each node), the monitoring period does not have a significant impact on the delay performance as the wireless front-end of the Fi-WSN network is lightly loaded and utilization of the ONU buffer does not introduce additional delay nor does dropping low-priority packets reduce the average delay that any WSN packet is exposed to until it reaches at the OLT. On the other hand, when sensors report ambient smart grid data more frequently (i.e., arrival rate of 500ms), end-to-end delay of a WSN packet increases when the monitoring period is set beyond 3ms. The reason of this behavior can be explained as follows. When the WSN packets arrive more frequently as the MP increases, more WSN packets are marked as valid leading to increased buffer utilization 24
Fig. 10. The impact of Fi-WSN integration on packet delay variation of the FTTX packets
at the sink, as well as at the ONU. This leads to increased packet delay for the ambient smart grid measurement data, i.e, low-priority packets. Indeed, beyond 3ms, the increase in end-to-end packet delay is bounded above by the maximum packet delay value in Fig. 8.c, which is at the order of 4.2s.
Besides, in order to study the impact of Fi-WSN integration on the packet delay variation (PDV) of FTTX packets, we have compared the PDV of FTTX packets without WSN integration under different ONU loads. As seen in the Fig. 10, PDV of the FTTX packets are not impacted by the integration of WSN packets. This is due to the different traffic intensities in the wireless and optical back-end of the Fi-WSN network. Furthermore, as mentioned earlier, WSN packets utilize a small portion of an ONU buffer which does not lead to a significant variation in the dynamic bandwidth allocation process run at the OLT. 25
(a)
(b) Fig. 11. Average E2E delay vs. Target FTTX load under larger high priority bursts; (a) Inter-arrival time in the WSN = 1s, (b)Inter-arrival time in the WSN = 0.5s
5.3
Scenario-3- Larger High Priority Bursts
Next set of simulation results are taken by enforcing burst assembly mechanism for high packets at the Fi-WSN gateway. We set ST h to 70 packets for high priority messages and 640 for low priority packets. Furthermore, we also simulate a benchmark case where there is no differentiation between the packets at the Fi-WSN gateway. Figs. 11.a-b illustrate E2E delay when ST h for low priority messages is 640 packets under WSN packet inter-arrival time of 0.5s and 1s, respectively. As 26
Fig. 12. Maximum E2E delay vs. Target FTTX load under larger high priority bursts and inter-arrival time in the WSN = 0.5s
seen in the figures, introducing burst assembly procedure to high priority packets increases the E2E delay of these packets as high priority packets are forced to wait in the high priority queue at the Fi-WSN gateway. Besides, when the WSN packet inter-arrival time is increased to 1s, E2E delay of high priority packets significantly increases, and it exceeds even E2E delay of WSN packets when there is no differentiation between WSN messages. This is due to the fact that high priority packets are buffered for longer duration as WSN messages arrive less frequently. As we have seen that implementing burst assembly mechanism for the high priority packets at the Fi-WSN gateway can introduce limited benefit in terms of delay only under frequent arrival of WSN packets. We test the proposed Fi-WSN gateway design scheme with larger high priority bursts by setting 1/λ at 500ms. As expected, maximum delay behavior of the Fi-WSN gateway implementation coincides with its E2E delay behavior in Fig. 11. Finally, we test packet loss probability under this last scenario where high prioriority packets are assembled to bursts at the Fi-WSN gateway. As packet loss probability is a function of the traffic intensity and high priority bursts 27
Fig. 13. Packet Loss Ratio vs. Target Load under larger high priority bursts
do not increase the WSN traffic intensity at the sink, packet loss probability at the optical back-end is not impacted.
6
Conclusion
Smart grid calls for advanced monitoring solutions as well as an effective communication infrastructure to support the flow of high volume of data collected from smart grid assets and customer premises lying in a large geographic region. PON supported networking and in particular, Fiber-Wireless Sensor Network (Fi-WSN) architecture consolidates the high-speed, low latency advantage of EPONs with the advanced monitoring capability, flexibility, lowcost, and wide-coverage properties WSNs. With the Fi-WSN architecture it becomes feasible to deliver data from billions of sensors to smart grid operators in a fast and reliable manner. In this paper, we address the service quality provided to the WSN traffic and the FTTX traffic in Fi-WSN deployment in the smart grid. Considering smart grid application requirements, ambient data that represent non-critical sensor 28
readings are given low priority while alarm data that is raised from events that may risk the stability of the power grid are given high priority. The proposed QoS-aware Fi-WSN gateway design differentiates the delivery of non-urgent data and urgent data through service-level oriented bursting mechanisms. Our design delivers high priority smart grid data within specified delay bounds with high reliability while maintaining the desired QoS levels for FTTX users. Our results have shown that more frequent report messages by the WSNs enhance the service quality of urgent messages. On the other hand, transmitting the low priority messages in small bursts to the ONUs improves the service quality of the urgent messages. We have also evaluated the impact of burst formation for urgent and non-urgent data as well as the impact of various concentrations of high priority packets. We have shown that the smart grid messages carried via WSN do not introduce any performance degradation to the optical back-end in terms of packet loss probability under all of the tested scenarios. As a future work, we are planning to extend our work by improving the WSN performance for critical smart grid applications. We are also planning to further our investigation on the optimal selection of thresholds in the proposed gateway design.
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