Cognitive Radio Ad Hoc Networks for Smart GridCommunications: A Disaster Management

ApproachHossein Khayami†, Mohsen Ghassemi†, Kamyar Ardekani†, Behrouz Maham† and Walid Saad‡

†School of ECE, College of Engineering, University of Tehran, Iran‡ECE DEpartment, University of Miami, USA

Email: h.khayami, moh.ghasemi, kamyar.ardekany, bmaham@ut.ac.ir, walid@miami.edu

Abstract—The explosive growth in users’ demand in both areasof wireless communications and power generation has led todesign of new key technologies that will be dominant in thenear future; cognitive radio networks in communications andsmart grid in power field. This paper proposes a novel scenarioto marry these technologies together by using a cognitive radioad hoc network (CRAHN) as the foundation of smart gridcommunications. It discusses formation and throughput of sucha citywide network, the information transferred by the network,and how this structure can be relied upon in disasters, comparedto the state-of-the-art.

Index Terms—disaster management, cognitive radio, ad hocnetworks, smart grid communications, femtocell.

I. INTRODUCTION

Smart grid (SG) is envisioned to bring about fundamentalchanges in electrical power distribution and generation net-works in order to obviate shortages and limits of conventionalgrids [1], [2]. Some of the most significant features which aremade possible by realization of smart grid are:• Distributed generation of electrical energy;• More dependability on renewable energy sources;• Enhancement of customer service quality;• Realization of new business models of demand response

energy market mechanisms.Due to its large-scale and distributed nature, two-way flow

of information is an inevitable component of smart grid in-frastructure. Naturally, the transfer of critical grid informationheavily relies on a capable communication infrastructure. Inturn, this has resulted in significant research in the field ofsmart grid communications [3]. In such works, the main goalis to find, analyze and evaluate various communication tech-nologies as potential solutions that can satisfy the requirementsof smart grid communication infrastructure. Generally, thereare two mainstream solutions, wired and wireless communi-cations. The implementation of each of these technologiesin different parts of the smart grid network has its ownadvantages and drawbacks. In general:

This work was supported by the project numbered 91000529 entitled”Design and Performance Analysis of Smart Wireless Networks for Machine-to-Machine Communications”, funded by Iran National Science Foundation(INSF), and by the U.S. National Science Foundation (NSF) under GrantCNS-1253731.

Wired structures:• Provide exclusive mediums for separate links, resulting

in higher bandwidth and throughput.• Are more dependable on infrastructure and prone to

physical damages.Wireless structures:• Share the same medium with other wireless transmitters,

leading to limited bandwidth and throughput.• Are less dependable on infrastructure and more resistant

to external damages. [4].One of the conditions in which wireless solutions outdo theirwired alternatives, is during disasters; where using wirelesstechnologies would make it easier to keep the communicationinfrastructure of the grid in service.

Nonetheless, a fundamental limitation for using wirelesscommunication based on conventional technologies and regu-lations is the lack of sufficiently available frequency spectrumbandwidth.

The scarcity of the wireless frequency is currently a promi-nent issue in the wireless communications industry. A largeportion of wireless frequency spectrum is already licensedin many countries, leaving only a small share for newlyemerging applications. However, studies such as [5] indicatethat in average only a small percentage of the spectrum isutilized at a specific time in a specific location; indicatingthat the current wireless spectrum shortage is not inherentand is due to conventional wireless communications regulationschemes. To address this problem, the concept of cognitiveradio has been recently proposed as a means for allowingan efficient co-existence between unlicensed, secondary users(SUs) and licensed, primary users (PUs) [6]. In the cognitiveradio paradigm, the secondary users may use the licensedbands as long as primary users do not use them. As soon as aprimary user begins transmitting in their band, the secondaryusers must vacate these frequencies and find other unusedfrequency bands. Using various functions, such as dynamicspectrum sensing and access, cognitive radio enables radios tomonitor their surrounding radio environment so as to enablean opportunistic usage of spectrum by allowing the SUs tointelligently decide on which frequency bands (or spectrum

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holes) to use and at which time [7]. Ultimately, cognitive radiohas shown to yield a more efficient utilization of the wirelessspectrum, subsequently providing higher wireless capacities.

In addition, the information network of the grid may beused to transfer data that is helpful for disaster managementauthorities. Especially, information at early hours after occur-rence of a disaster is extremely valuable [8].They may use thisinformation to locate damages and plan for rescuing possiblevictims respectively.

The main contribution of this paper is to study the type ofinformation that can be helpful to speed up rescuing procedureduring and after a disaster and to propose a communicationnetwork which can reliably transfer this information. Further-more, in normal situations when there are no disasters, theproposed infrastructure can serve as a solution for smart gridcommunications.

Generally, during a disaster, the communication infrastruc-ture may get damaged and cannot be adequately relied on.To address this problem, we propose a novel architecturebased on a cognitive radio ad hoc network (CRAHN). Byefficiently exploiting cognitive abilities, the proposed networkis capable of using higher capacities when more frequencyspectrum is available, and is less prone to fluctuations infrequency availabilities and adverse SNR conditions. Thewireless and ad hoc nature of this proposed network makes itless dependable on centralized communication infrastructure,therefore less vulnerable to local damages. Even if some partsof the network go out of service, the whole network wouldremain operational.

Our initial analysis shows that, within a large-scale, city-wide deployment, the proposed architecture may suffer fromincreased delay and/or reduced throughput. To overcomethis problem, we propose the use of base stations that cancollect information from their associated cluster. Based oninformation-theoretic analyses of large CRAHNs, known asscaling laws, we will show the effect of adding base stationsto network parameters.

The rest of this paper is organized as follows: Section IIdiscusses the main issues pertaining to the management ofdisaster conditions, with a focus on communication resourcesdefects. Section III describes the system model of the proposedCRAHN. It also briefly discusses femtocells as a potentialapplication which can be deployed based on this structure.Finally, conclusions are drawn in Section IV.

II. DISASTER MANAGEMENT

In the early hours after the occurrence of a large-scaledisaster, such as earthquake, flood, and hurricane, three majorsteps have to be taken simultaneously to alleviate the disastereffects [9], [10].• Regular information collection;• Decision making regarding effective allocation of rescue

resources;• Providing high priority victims with rescue resources.To manage a disaster situation, the responsible authorities

are required to make quick and adequate decisions. Often,

such a decision is made based on the information gatheredat disaster management bases or control centers. Part of thisinformation is extracted from direct observations of people orauthorized forces, and the other part can be extracted from theSCADA system of the smart grid or other sensory informationcollected at each node, i.e., each building.

Despite the vital importance of early information collectionin disaster situations, it typically takes a long time for the firstdirect observations to be reported. Hence, automatic collectionand transmission of sensory data can be extremely beneficialto disaster management and rescue resource allocation.

A. Communication Requirements

In order to remain operational in a catastrophic condition,the network which is used in emergency and disaster requiresa number of specific functionalities and characteristics. Suchas:

• Resistance to local damage: the communication networkshould not allow a local damage to spread to othersections which can eventually be detrimental to the wholesystem. Moreover, if some of the nodes lose their con-nectivity, other nodes must remain connected to the des-ignated destination, i.e., one of the disaster managementbases so as to transfer their collected information [9].

• Independence from users: it should automatically sendemergency information and must not rely on the usersince the user may not be in a normal situation.

• Low power consumption: the nodes must not consumehigh power and have to be able to operate withoutexternal power sources until the power grid is broughtback in service or the situation back to normal operatingconditions (i.e. fully controlled by first responders or res-cue forces [11]. This implies that long-range transmissionis undesirable, especially in higher frequencies. In turn,this motivates use of frequency channels such as the VHFand UHF frequency bands that are typically used for TVtransmissions.

• Resistance to spectrum defects:the network must work inan unoccupied band and not be influenced by interfer-ence of other available wireless links, encouraging useof flexible radios to find a band with desired wirelesscharacteristics.

Furthermore, in addition to being operational under disastercircumstances, the network should satisfy the minimum delayand throughput requirements. These requirements are deter-mined by the amount and importance of information neededfor rescue operations or other decision making plans and canbe adaptable with respect to network conditions. In emergencysituations in which the available frequency bands are scarce,the information must be filtered and only necessary data shouldbe transmitted, as discussed next.

In Section III, we provide a network model suitable forsatisfaction of these characteristics.

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TABLE IDATA GATHERED IN NORMAL SITUATION

Smart Meters Sensors Phasor Measurement UnitUtility meters with two-way Monitoring the mechanical and electrical Phasor measurement in different locations

communication components of the grid to manage the power quality

TABLE IIDATA GATHERED DURING DISASTERS

Smart Meters Physical Damage Detectors Human Status DetectorsConsumption pattern monitoring Accelerometers CO2, voice, motion detectors

and fault detection (shake and motion detector application)

B. Smart grid and rescue data

In each node of the wireless network, data is collected fromthe smart meters or other sensors. This data is subsequentlysent to the decision making centers at the disaster managementbases. Table I and II show general categories of the datagathered in buildings and some of the related sensors.

At the disaster management bases, given the gathered data,the responsible entities must determine whether each homeis abandoned or not and if a certain house is still occupied,estimate the number of people inside so as to assess thepopulation distribution before and after the disaster. This canbe, for example, done by analyzing the power consumptionpattern of each household in the last hours just before thedisaster. This process is called decision making.

Moreover, decision making is about identifying occurrenceof a fault and instantly informing disaster management centersso that they can take immediate actions. Such a promptemergency communication is critical because statistics haveshown that most deaths happen in the first hours after a disaster[12].

1) Local vs. Centralized: Data processing can be doneeither locally at the level of each node before being transmittedto a disaster management center, or in a centralized fashion ata central monitoring center. There are benefits and drawbacksfor each method. In centralized data collection, raw data mustbe sent to a monitoring center, such as the disaster manage-ment bases, on a regular basis and even more importantly,immediately after occurrence of an anomaly which can be anindication of a disaster. Naturally, transmitting such a largevolume of data can increase the overall power consumption ofthe transmission system and it also requires a high throughputwhich cannot be guaranteed during a disaster since manynodes may start transmitting data simultaneously. Nonetheless,a central collection of data, generally, allows decision makingbodies to make a more precise decision based on completeglobal data extracted from all nodes.

Local data processing involves filtering data and makingbasic decisions at each node. This can greatly reduce theamount of data that must circulate in the network as wellas the power consumption of transmitters. However, it maylead to a decrease in the accuracy of decisions due to theavailability of only a limited set of data (i.e., possible loss orlack of information).

Ideally, developing a system that can adaptively balancethe tradeoff between these two scenarios is desirable. Inother words, depending on the wireless spectrum availability,disaster characteristics, and disaster management approaches,the authorities can specify the level of localization. At oneend of the spectrum, each node sends all its raw data receivedfrom its sensors, and at the other end, the nodes only send theresults of the local decision making to the monitoring centers.

2) Prioritization: Each node of the ad hoc network receivesdata from different nodes that should also be sent alongwith its own data. Since important data must suffer lessdelay compared to less critical information, we propose aprioritization method in which each packet of data is assigneda priority index in the original node. This index is generatedusing a function:

Pi = f(Di, ni, Hi, Si) (1)

where Pi ∈ [0,1] is the priority index of node i, Di isa measure of damage caused to the building i which iscalculated based on the sensory data, ni represents the numberof residents inside the building at the time of disaster, Hi isthe degree to which the building is exposed to hazards suchas nearby gas stations, and Si designates how strategic thesite is. For instance, a hospital or a bank is of high strategicimportance.

When congestion occurs at a node, it rejects forwarding datapackets from each of its incoming ports with a probabilityproportional to the following:

Ppacketdropi = 1− Pi (2)

In this way, on the average, more bandwidth is providedfor nodes with higher priority and they suffer less delay. Atthe same time, other nodes are allowed to transmit with moredelay and less average throughput.

III. PROPOSED NETWORK MODEL

A. System Model

As a solution to the communication requirements discussedin Section II, we propose a cognitive radio ad hoc network(CRAHN) as the communication infrastructure. The proposedarchitecture is ad hoc in the sense that no particular infrastruc-ture is required to set up the network. Nodes connect to each

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Fig. 1. Proposed CRAHN Topology

other without prior configuration and deliver their informationcooperatively using a multi-hop structure. As a result, localizeddamage due to a disaster would not lead to malfunction of thewhole system. In case some of the nodes get damaged, thesystem can still connect functional nodes and pass informationto the destination, namely, the nearest disaster managementbase. Figure 1 illustrates a view of this network, prior to anydamage.

The system utilizes cognitive radio technology so that withineach region, the nodes can opportunistically transmit in thefrequency bands in which they detect no presence of primaryusers. As soon as a primary user is detected, the nodes areable to change their operating frequency band and use otherunused bands as secondary users via well-known dynamicaccess schemes (see [13]).

In order to cooperatively pass information and reliablydetect available frequency bands in the network, the nodes aregrouped in clusters. Each cluster is composed of nodes thathave similar decision about which frequency bands are avail-able and which are not (such clustering is popular in cognitiveradio networks, see [14]). The nodes in each cluster carry outdistributed sensing and share their sensing information so as todecrease the probability of false detection. Between differentclusters gateway nodes are deployed. These nodes serve asrouting bridges between two clusters and must be able tocommunicate in a frequency band that is not occupied byprimary users in either of the clusters. Figure 2 shows hownodes are laid out in clusters and their gateways.

There have been several efforts to analyze the capacity ofsuch networks. Due to the flexible nature of node layoutsand primary spectrum usage, commonly, upper bounds andlower bounds of the capacity are of interest. Also, since innetworks of interest, the number of nodes is very large, usuallyasymptotic analysis of network capacity is provided (see [15],[16], [17]). In our proposed network, this assumption is valid,because the number of buildings, which of each correspondingto one node in the network, is large.

Fig. 2. Clusters and Gateways

As shown in the next subsection, the throughput of eachnode declines as the number of nodes increases. As a result,the whole system throughout within a large-scale deploymentsuch as in a big city cannot be a purely ad hoc network.Base stations should be deployed throughout the network.Consistently, they can be installed in disaster managementbases, which are supposed to be reliable and resistant todamages caused by large-scale disasters. These stations areconnected to each other and to the central management baseusing high-speed wired or a satellite backhaul. Figure 3 showshow such base stations can be laid out and connected to oneother.

B. Scaling Laws

Extensive research has been done to derive scaling laws ofvarious cognitive ad hoc networks based on different assump-tions and scenarios. Here, we bring some of the findings todemonstrate how per-node throughput asymptotically scaleswith number of nodes.

In [18], it is shown that under certain assumptions, whichare valid in our proposed network model, both secondary andprimary networks achieve the same throughput scaling as whenthe other network is absent. These assumptions include:• Secondary nodes are aware of the locations of primary

nodes, however, primary nodes need not be aware ofexistence of the secondary network;

• Secondary nodes are denser than primary nodes.It is shown in [16] that in a random general CRAHN:

λn ∈ [Ω(Cα√n lnn

)), O(Cζ

n1/λ))] (3)

where λ(n) is the per-node throughput for a network with nnodes, Cα and Cζ are respectively the minimum link capacityover all links and sum of capacity over all available bands inthe CRAHN, and γ ≥ 2 is the path loss index.

The expression in (3) gives an insight on how the increaseof the node numbers results in a decrease of the per-nodethroughput and encourages the idea of using disaster man-agement bases as base stations. Thus, by limiting the numberof nodes assigned to each station we are able to provide theminimum required throughput for each node.

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Fig. 3. CRAHN with Nodes Assigned to Various Base Stations

Furthermore, it is shown in [19] that with adoption of hier-archical cooperation methods between nodes of the network,i.e., use of clustering and MIMO techniques, achieving theoptimal capacity (that is O( Cζ

n1/λ)) is possible.

C. CRAHN Nodes as Femtocells

The concept of small-cell networks has been used forincreasing capacity of cellular networks. Femtocells or homebase stations are the most recent members of this family whichhave lately attracted a lot of [20]. Assuming the conventionalcellular networks are out of service as a result of the dis-aster, the frequency bands assigned to them are consideredwhite spaces. Thus, considering significant frequency reuseof femtocell networks (FNs), these bands can be used forcommunication between mobile users and home base-stations.In contrast with the conventional FNs which have wired back-haul, our proposed FN uses the introduced wireless CRAHNto transmit data between buildings. Switching to FN coveragecan be triggered immediately after identifying a disaster andterminated when the cellular network is restored to its normalcondition. The objective of the FN is merely to enable makingemergency voice calls or sending voice/text messages for helprequest to rescue centers and contacts among users or otherregular services are not allowed inasmuch as communicationnetworks are prone to congestion in critical situations.

IV. CONCLUSION

In this paper, we have discussed how the use of CRAHNsas one an infrastructure of smart grid communications cansimultaneously benefit dependable data transfer in smart gridand help disaster management teams to make timely decisionsbased on smart grid data and other sensory information fromthe building. Furthermore, we have analyzed some of therequirements which the network structure should satisfy duringa disaster. Based on these requirements, a CRAHN networkwith interoperability features was proposed and described.

Then, we have discussed how this network can also serve asa femtocell network to provide cellular communication serviceduring a disaster.

This paper provided the general view of the idea of usingCARHN as a communication infrastructure for both smart gridand disaster management. For future work, a precise, closed-form expression for the relation of throughput and delay ofthe network as a function of parameters such as the numberof base stations, the layout of gateways, and the probabilityof false empty band detection, can be analyzed.

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