A proposed network management protocol for Cognitive Radio Sensor ...

1st IEEE International Symposium on Telecommunication Technologies

A Proposed Network Management Protocol for Cognitive Radio Sensor Networks Suleiman Zubair*1, Norsheila Fisal2, Mohammed. B Abazeed3

Beijing University of Post and Telecommunication China [email protected]

UTM MIMOS CoE Universiti Teknologi Malaysia Johor, Malaysia [email protected]*1 , [email protected], [email protected]

an idea which is not cost effective for Sensor Networks. Maintaining one transceiver while still having access to more resources calls for unique Medium Access Control (MAC) solutions. Cognitive Radio/Multi-Channel MAC for Ad-Hoc networks is a well-researched area in the research community [6, 7, 8, 9, 10]. As clearly categorized in [6, 7, 8, 9, 10] these schemes can either be distributed [11, 12, 13], central [14], based on multiple radios [15, 16] or based on the channel access method CSMA/CA [11, 13] TDMA [12, 19] or a Hybrid of both CSMA/TDMA [17, 18]. However, geographical forwarding schemes generally support and are more robust with distributed implementations because of its simplicity and the prior advantage that the nodes are location aware either based on GPS or by making use of simple location awareness algorithms [20]. Further, we restrict ourselves to single transceiver nodes so as to minimize cost and reduce energy usage of operating multiple radios. Usually, most multiple radio schemes have one radio fixed on a control channel while the other switches between channels. Such schemes still have to contend with the energy of switching the data radio between channels added to the overhead of operating multiple radios. Hence we argue for the need of single radio nodes and make use of simplicity of the implemented scheme to counter the overhead of multiple channel operation. Still for simplicity, the chosen Medium Access Control scheme is CSMA/CA since the increased resource can be carefully designed to further mitigate the normal issues of packet loss due to the inherent collision prone nature of CSMA. Based on the aforementioned, we propose Light Distributed Geographical (LDG) a distributed channel selection algorithm for geographical forwarding in Multimedia Cognitive Radio Sensor Networks to simplify channel selection overhead for the dynamic spectral nature of CR environment. We leverage the strength of this algorithm on the dense deployment nature of Sensor Networks and utilize the dynamic spectral environment to our advantage. Nodes in LDG are assumed to have location awareness and are able to sense the spectrum to detect the spectral opportunities. Based on the dense nature of deployed nodes across the network, the nodes form virtual clusters based on similarities of spectral opportunities from area to area across the network with each cluster selecting a dynamic Control Channel whose stability is based on perceived interference. LDG is built upon our weight

Abstract— Running a Network Management Protocol is imperative to ensure network connectivity and stability especially in highly dynamic environment of future Cognitive Radio Sensor Networks. Such protocols have to be characterized by their light overhead in terms of energy, communication and implementation. A solution in this respect is hereby proposed to enable a node in a multichannel environment to quickly establish a control channel with neighboring nodes. The channel selection scheme leverages on the strength of both Dedicated Control Channel and Hopping schemes by implementing a simple weighting scheme and maximizing the use of idle listening periods. By identifying local minima nodes, it also has the potentiality of reducing route failure by 70% when utilized as a routing support. Keywords- Ad-Hoc Networks, Cognitive Radio Sensor Network, Control Channel, Network Management Protocol.

I.

INTRODUCTION

The main drives for interest in Cognitive Radio technology of late are the White Space phenomena and the congestion Issue of Industrial, Scientific and Medical (ISM) unlicensed bands. Added to this, the envisaged ubiquity of ad hoc sensor networks in next generation characterized with high content demand (multimedia) makes novel innovation in this area urgent. However, one of the main challenges cognitive radio introduces is the deafness problem whereby the best forwarding nodes at a particular point of need might have its radio tuned to another channel thereby making network connectivity vulnerable to random distortion. As a result, coordination of CR nodes to achieve full and stable network connectivity becomes the life line of any Cognitive Radio Network. Addressing this challenge in the realm of CRSN makes the issue more challenging because of the duty cycle of deployed nodes in the CRSN network and other resource constrain. Geographical forwarding schemes are generally noted for their simplicity and light weight which has made them the choice in credible protocols like [1, 2, 3, 4] however, when multimedia traffic is taken into consideration, to maintain/improve the effectiveness, throughput, delay and reduce access collision calls for an increase in resource. To the best of our knowledge, [5] alone has utilized multiple channels in geographical forwarding to reduce interference in literature. In this scheme, the nodes are assumed to have two transceivers *Corresponding Author

978-1-4673-4786-0/12/$31.00 ©2012 IEEE

Bala A. Salihu4

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based channel learning algorithm and the Network Area Discovery (NAD) algorithm.

II.

NETWORK MODEL

Each node in the network has a single transceiver with an Omni directional antenna and is location aware by implementing localization algorithm [20] or by the use of GPS. The network runs on a Distributed Node Duty Cycle (DC) operation defined by DC SDUDPHWHUį7KXVJLYHQDVOHHSIUDPH of Ts DQRGHLVDFWLYHIRUį× Ts sec and is asleep for (1 - į × Ts sec. the sleep states are not synchronized. We also consider that our network is composed of sensor nodes that are distributed according to a 2-D PoLVVRQGLVWULEXWLRQZLWKGHQVLW\ȡ For simplicity we assume a specific frequency band is chosen as the operation area for the sensors all channels having same bandwidth, channel ranking based on perceived interference (i.e. link stability), energy detection is used and the sensing results are assumed to be perfect. We assume an Ad Hoc network deployed without a specific topology that has sources of interference spread across the network area. The nodes are either assumed to be stationary or in limited motion typical to sensor networks. The presence of interference at a particular instance actually dynamically defines the network architecture by the formation of virtual clusters based on the channel list of each node after the sensing operation is implemented. The LGD algorithm dynamically creates the virtual clusters without the need of a cluster head at the expense of limited overhead incurred by implementing the Network Area Discovery (NAD) algorithm as described in section III. Based on the outcome of the implemented algorithms, the virtual clusters are interconnected.

Figure 1. A typical network scenario of nodes distributed into virtual clusters by various sources of interference

The protocol ensures network wide spectral connectivity by frequency hopping of the cluster intersecting nodes and detects local minima nodes a factor that is responsible for route failure. Since the virtual sectors are dynamic based on spectral changes, the intersecting nodes tend to also change accordingly thereby taking care of possible network holes due to intersecting node energy depletion. LDG does not claim to be a full MAC solution however it solves a vital issue of CR geographical forwarding schemes especially for resource constrained networks. The novelty of LDG lies in the simple channel selection scheme based on learning and the NAD algorithm that quickly ensures rendezvous for control messaging. Analytical simulation of the protocol reveals its light nature as compared to utilized methodology of the ERCC protocol and it’s like. The implemented Reverse Backoff and Representative Drop (RBRD) Scheme further reduces the energy due to collision and retransmission, shows a higher efficiency for control channel recovery across the cluster and also has the capability of extending the control channel coverage based on the type of interference perceived in the network area. The main contributions of this work are as follows; x Based on the little knowledge of the authors, LDG is the first approach that leverages Multichannel MAC on location awareness to further simplify geographical forwarding schemes. x Introduces a novel algorithm for dynamic virtual clustering in CRSN.

III.

THE PROPOSED PROTOCOL LDG

This section explains all the operational components of LDG, the Channel Learning Algorithm, Virtual Cluster Formation, the Network Area Discovery and the reverse backoff scheme. A. Channel Learning Algorithm (CLA) Using learning algorithms can greatly enhance the performance of a channel selection scheme because it makes nodes intelligently select channels that have high record performance. Most learning algorithms like Game Theory, Genetic Algorithms, and their likes can be computationally demanding on the energy constraint nodes especially when the overhead of channel switching is also taken into consideration. Thus light but efficient schemes are the best choice for sensor nodes. Our channel learning algorithm is quite similar to [25] however a fundamental difference in that the algorithm in [25] does take care of the problems of load distribution and channel interference which could occur in only one area of the network without affecting other network areas. Thus our channel solution takes care of this scenario by implementing a simple channel hopping that guarantees a quick rendezvous with the next common control selected by the neighboring virtual cluster.

The remaining of the work are arranged thus; section II presents the network model, section III describes all the operational parts of LDG, section IV makes a cross layer analysis of LDG. Section V presents related works, Section VI discusses the result of our analysis and VII concludes the article.

As shown in the algorithm Fig. 2, it is assumed that N number of Channels (C) are available to the network, thus, CN refers to Channel N. At setup, a node scans the entire available channels and determines the channels which are free of

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channel, in other words, it reduces the multiplicity of different control channels across the network and by implication help conserve node energy incurred by frequent switching between control channels. So once a nodes awakes based on its duty cycle (depending on the node network status) if its sensing result differs with its previous values such that the previous Control Channel is being occupied, it performs Network Area Discovery (NAD) and updates the channel usage information accordingly otherwise, it listens to its previous Control Channel for updates, updates its channel performance information list before it goes back to sleep if no other activity is needed.

interference, this channel list are arranged in channel ascending order and are all assigned a base Interference Weight IWbN . This weight is dynamically increased or decreased by the factor q based on its observed performance. With successful communication, this weight is gradually increased by 2 until it reaches the maximum reward IWh. Also, when interference is suddenly noticed it is reduced by 3 until it reaches a drop limit IWdN where it is automatically dropped from the pool. Arrange channels in pool in ascending order, Initialize weights for all channels IWbN and C N U b N Step 1. Track Channel Interference Performance IWb r q Step 2. Track Channel Usage Performance C NU b r p Step 3. Calculate TCW N Step 4. Receive any Performance Update from neighbors. Step 5. Rearrange Channels based on weight Repeat Steps.





Gradually, based on the performance of the channels with regards to PRR as observed during routing and frequency of interference (i.e. channel stability) as observed from channel sensing, the best decisions are made. Thus just like any learning algorithm, it gradually converges its choices to the best values within a short time. B. Virtual Cluster Formation. The concept of high spectral correlation based on distance and the exploitation of frequency correlation is not a new issue in cognitive radio networks. Based on [21, 23, 24], the correlation of frequency band opportunities within an area is high with this correlation decreasing with distance. Also the concept of sensor spatial correlation has also been leveraged upon in sensor networks [22] to address Medium Access due to the dense nature of deployment. To the best of the knowledge of the authors, this work is the first to the attempt to Leverage the high spatial spectral correlation for virtual clustering in sensor nodes. Based on our proposed scheme, a node by virtue of its position can either be in a Homogeneous spatial spectral Area (HMA) or a Heterogeneous spatial spectral Area (HTA). A homogenous spatial spectral area is an area of space in which the result of sensing brings about highly similar available frequencies for transmission. Two HMAs are either different due to the distance between them and because of the existence of sources of interference that exist in one of them. Areas that exist between two or more intersecting HMA areas are referred to as Heterogeneous spatial spectral Area (HTA). Thus the sensing outcomes of node that reside or move into this area will be a mix of the intersecting clusters as illustrated in Fig. 1. A full flow chart of the implementation of NAD is as shown in figure 4. A neighborhood broadcaster counter kept by every node is used to monitor the cluster density which is referred to as the cluster CC confidence level. When this confidence level is below a minimum NAD is ignited in other channels.

Figure 2. The Channel Learning Algorithm

In order to ensure network load is distributed over all channels without unnecessarily overburdening one channel which could cause undesirable network congestion, we introduce another metric called the Channel Usage metric C N U b which is also dynamically increased or decreased by a value p based on the usage report gotten from the network. At setup, this value is C N U b and with every assignment for data transfer, this value is decreased gradually by 1. Thus the channels are graded based on two metrics, and. The final score a channel gets after the two metrics IWbN and C N U b are added together (1) determines its position in the channel list which is arranged in descending order. And when two channels have same score, they will be arranged in ascending channel number. TCW

N

IW

N b



r q  C N U b r p

(1)

Each node keeps a fixed number of channels in their pools. This number is dynamically replenished based on the interference metrics IWb r q . Since the algorithm is Network Layer centric, we maintain a large pool of channels to increase the probability of channel correlation among nodes across the network unlike [25] which dynamically expands and contracts the channel pool. Thus when the value of IWb r q reaches IWdN it is replaced by a new channel that has not been in the pool earlier. Two hop neighbors should be able to reassign channels, thus a two hop flag is initiated with every channel update, for every hop, this bit is decreased and when it gets to two hops from its origin, it is declared to be invalid for that region, this enhances channel reuse over the network.

C. Network Area Discovery Virtual cluster formation is fully decentralized. As shown in Fig. 3, the formation of the Virtual clusters is initiated by the broadcast of the NAD packet through the control channel during network initialization or by a new node just joining the network for the first time. All nodes in a cluster are associated via the cluster’s control channel which is assigned in a distributed manner by implementing the channel learning algorithm. After the distributed implementing of the channel learning algorithm the channel rank of each node in a specific area is expected to be highly correlated based on the Spatial

Channel usage update is done via the Route Request Reply (RREP) packet overhead by nodes and nodes that were asleep at that moment will update it when they wake up over the CC. Thus the advantage of our algorithm is that the most stable and best rated channel is established for control messaging, it increases the possibility of having network range control

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ranking immediately is switched to complete communication. Fig. 4 shows the importance of the local minima detection component in terms of route failure. The route failure which is defined as the ratio of the unsuccessful routes between each node in the network and the number of all possible routes. Although the route failure increases for both scenarios with the duty cycle, it is distinct that when local minima nodes are considered from inception, 70% of such failures can be averted.

spectral correlation as discussed in [21, 23, 24]. The channel having the highest ranking is automatically chosen as the default control channel. The channel ranking varies based on channel usage and interference as discussed in A above. However, the basic rule implemented by all nodes is that, the highest ranking channel at any point in time is assumed taken as the control channel. Next the NAD packet is broadcasted to all one hop neighbors for neighborhood area discovery and Control Channel Confirmation. The essence of broadcasting this packet to its one hop neighbor is not simply to keep a neighborhood table as commonly known in the Hello packet broadcast, however, this is to enable a particular node know whether it resides in a Homogeneous spatial spectral Area (HMA) or a Heterogeneous spatial spectral Area (HTA). So the NAD payload carries the location information and its binary compressed channel ranking table. The neighbors reply with both their location and channel information. The source uses the location information to determine its network area while the Channel ranking information is used to have an idea of the updated channel ranking of the cluster. If the outcome does not account for all surrounding areas (i.e. the four cardinal sectors), then it serially hops along its channel list sending a broadcast to search for the one hop neighbors in the area(s) unaccounted for. If it gets any reply it registers the channel as the control channel of the neighboring cluster(s) and saves their channel rank separately.

Figure 4. Route Failure Rate with and without Local Minima Awareness [3]

D. Reverse Backoff and Representative Drop (RBRD) Scheme for LDG Protocol The network area for any system that operates NAD is partitioned into priority regions. The nodes in its one hop region are defined as nodes that fulfill the criteria of having received the NAD packet with the Signal to Noise Ratio (SNR) above a certain threshold limit of þ. It is expected that nodes that fulfill this criteria are distributed around two or three priority regions. The value of is carefully selected in order to limit network traffic as a result of implementing the NAD protocol to the minimum. Based on the concept presented in [3] we present a reverse modified version which we call, Reverse Backoff and Representative Drop scheme. Each priority region Ai is made to correspond to a backoff window size CWi thus, based on a neighbors location, it backs off for ¦i 1 CW j  cwi , where j 1

Figure 3. Flow chart of NAD Implementation

cwi is randomly selected such

that CWi  >0, CWmax @ where CWmax CWi  CWi 1 . Hence based on how close a neighbor is to the NAD requesting node, this backoff scheme prioritizes them accordingly in their priority area. When a node gets a NAD packet, it backs off for i 1 ¦ CW j  cwi as corresponds to its priority area. During the

So this node will have a hoping schedule based on the number of clusters it connects and also updates the channel ranking of each cluster on wake up. However, if the multicast message sent during hoping does not still brings about any result, then the node declares that area as local minima and will not engage in contesting for routing packets. There is the possibility that a node wakes up and the control channel was changed during its sleep period, the node solves this scenario by simply hopping along its previous channel rank while implementing NAD until it reconnects with its cluster. Our channel ranking method as described above makes the time node rendezvous with CC very short. Also if communication was suddenly interrupted by interference, the next channel (which by default is the backup channel) according to the

j 1

period a node is backing off, if it overhears a RNAD that fully represents its position to the NAD requesting node, it refrains from sending (i.e. what we represent as Representative Drop.) otherwise, it observes its backoff period and tries to access the channel after its backoff period expires. Our Representative Drop scheme further helps in reducing collision and channel contention thereby conserving energy.

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IV.

PRELIMINARY RESULTS

Fig. 6 shows the energy demand for both threshold frequencies of 20dB and 30dB to be the same while at 10dB, a major difference is recorded which indicates the optimal frequency to ensure more energy conservation be above 20dB. Fig. 7 shows the energy demand based on network node status. That for the heterogeneous node is more because of the extra switching it encores because of its network position. However this overhead is kept at minimum by the CLA which ensures that the most probable CC for the next cluster is not far below in the channel pool.

To investigate the efficiency of the protocol, we perform mathematical analysis using MATLAB on a network of 300 nodes randomly distributed in an area of 100 by 100 m2. LDG was implemented in the network and the expected overhead is analyzed with respect to duty cycle in varying one hope distance coverage and frequencies. Fig. 5 represents the energy demand of implementing LDG virtual cluster of varying sizes. The energy demand increases with the radius of the cluster as well as with the duty cycle. This is because the number of nodes that hears the broadcast increases with both the duty cycle and radius of the cluster.

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Figure 7. Average Energy Demand of LDG for Homogenous and Heterogenous Nodes

Figure 5. Average Energy Demand of LDG at various Cluster Radius.

This demand is somewhat similar when a low duty cycle is implemented. However, such low duty cycles (0.001-0.01) cannot guarantee a QoS based connectivity throughout the network. The demand experiences a sharp divergence around the 0.18 which indicates a probable operation value that can be implemented to ensure network connectivity. When set at lower SNR threshold values, we have more nodes that meet the criteria thus the energy expended waiting to send amounts for the increase with increased duty cycle.

V.

Recent works has shown the imperativeness of integrating Dynamic Spectrum Access component into the WSN based on this we have presented a light protocol based on a simple learning channel allocation algorithm that establishes a control channel (CC) for neighboring nodes and automatically recovers from when this CC channel experiences interference. We have been able to establish the best operating values in terms of duty cycle and SNR threshold frequencies. Our future work is to integrate LDG into a MAC protocol to investigate its performance with data transfer.

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LDG at SNR=30dB LDG at SNR=20dB LDG at SNR=10dB

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Figure 6. Average Energy Demand of LDG at various SNR thresholds

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