Honours Project Report. Smart Wireless Mesh Network

Transcription

1 Honours Project Report Smart Wireless Mesh Network Routing using I-BATMAN Richard Maliwatu Supervised by: Dr. Antoine Bagula Category Min Max Chosen 1 Requirement Analysis and Design Theoretical Analysis Experiment Design and Execution System Development and implementation Results, Findings and Conclusion Aim Formulation and Background Work Quality of Report Writing and Presentation Adherence to Project Proposal and Quality of Deliverables Overall general project evaluation Total Marks Department of Computer Science University of Cape Town 2012

2 Abstract The traditional wireless mesh networks are made up of nodes consisting of a single fixed communication channel for routing traffic. These nodes operate in the heavily congested ISM band and are affected by interference from other devices operating in the same frequency band. Despite the dynamicity of the environment, the traditional wireless mesh nodes are unable to vary their transmission and reception parameters such as transmit-power, carrier-frequency, and modulation strategy. On the other hand, the new paradigm of wireless mesh networks consists of smart nodes that are capable of sensing their spectral environment. The sensed information can be shared with other nodes and harnessed to optimize for some overall goal such as, achieving a cognitive routing model, network capacity or minimizing interference with other signals. In this report we investigate ways of quantifying the amount of interference being experienced by a particular node. We do this by considering the wireless network interface card properties which vary with changes in the wireless environment. For simplicity, we focus on locally measurable quantities using commodity wireless network interface cards. This includes, the RSSI, noise and channel busy time for a given interval. We observe from the experiment results that RSSI stays almost constant despite a significant increase in the amount of radio frequency interference. We also calculate the channel busy fraction from the channel busy time for a given time interval. We observe a sharp rise in the channel busy fraction as we increase the activity of the interferer. Using the hello packets of the existing protocol called Better Approach to Mobile Ad hoc Networking (BATMAN), information gathered from the nodes environment is piggybacked. We refer to the resulting protocol as Interference-aware BATMAN (I-BATMAN). Each node keeps a log of the received interference information about all its neighbours. When forwarding packets, the node uses the extra information about its neighbours to penalise links that are experiencing high levels of interference. We compare the performance of BATMAN and I-BATMAN: I- BATMAN outperforms BATMAN by a margin in terms of reduced packet delay but at the expense of increased packet loss.

3 Table of Contents 1 Introduction Background Routing in wireless mesh networks Cognitive nodes Cognitive routing Frequency management Interference in networks Interference models Signal strength values Measuring interference A general model of wireless interference COIM MIC iaware Discussion of the reviewed literature Adopted interference model Conclusion on the background chapter Design Introduction Design constraints Software and hardware needed for implementation Mesh network Mesh nodes Multi-radio configuration Interference estimation and metric design.. 18 i

4 3.5 Experiment design Exposing nodes to controlled interference Interference aware routing Interference mitigation Design summary Implementation Introduction Mesh node hardware specification Mode of operation Mesh node software specification Bridging the gap between kernel space driver and the user-space program Interference measurement Link quality indicators available from user-space SINR Channel busy fraction Accessing the hardware registers Using virtual interfaces Factoring interference into the BATMAN routing metric Channel selection Interference mitigation/adaptation Discussion on the implementation Performance testing Introduction Preliminary experiments Experiment one: determine the base values Experiment two: determine the best indicators for measuring interference Experiment three: determine indicator sensitivity to non interference Interference measurement results Discussion on the interference measurement.. 40 ii

5 5.5 Test-bed description and protocol testing Conclusion Future work Physical/MAC layer optimisation Classifying the nature or source of interference Determining the level of interference that amounts to poor communication Channel hopping References iii

6 List of Figures Figure 3.1: wireless links. 20 Figure 4.1: Alix 2d2 board Figure 4.2: CM9-GP mini PCI wireless interface card 25 Figure 4.3: Alix board casing Figure 4.4: Interference ranges are longer than transmission ranges Figure 4.5: Network graph Figure 5.1: Sender, receiver and interferer experiment setup 37 Figure 5.2 Test-bed topology iv

7 List of Tables Table 5.1: Wireless statistics of node B when the interferer is turned off. 37 Table 5.2: Wireless statistics of node B while the interferer is turned on Table 5.3: Wireless statistics of node B with microwave oven turned on Table 5.4: Comparison of packet delay, packet loss and route flapping. 42 v

8 Chapter 1 Introduction Wireless Mesh Networks (WMN) are an emerging significant new technology envisaged to extend Internet access and other networking services in personal, local, campus, and metropolitan areas. The WMN popularity is due in part to their low upfront costs, rapid deployability, and suitability for hard-to-wire buildings and terrain. The key separating feature of mesh networks is the multi-hop relaying of packets across wireless links for communication between the participating nodes. The type of mesh network architecture being considered here is often referred to as infrastructure mesh [1]. Infrastructure mesh network architecture is the type of mesh network in which the end-user devices such as Personal Digital Assistants (PDAs) and laptops do not participate in the packet relay. Instead, mesh routers form a mesh of self-configuring, selfhealing links among themselves. Wireless mesh networks suffer numerous challenges such as traffic congestion due to limited bandwidth and interference due to the shared nature of the wireless medium and from other devices operating in the same frequency band. Currently, much effort is expended on the IEEE medium access control (MAC) layer to fully exploit novel physical (PHY) layer techniques. Nevertheless, in multi-hop scenarios where the quality of the links is constantly changing, performance depends on the routing protocol to properly choose optimal routes [2]. The aim of this project is to design and implement a wireless mesh routing protocol which modifies the routing decisions of a node based on the analysis of its spectrum environment. The project builds upon the state of the art wireless models which have been enabled by the advances in wireless technology. Our project has a specific focus on the implementation of cognitive routing in the emerging multi-radio multi-channel mesh networks of the future. The main tasks involved in this project are: i. Designing of cognitive nodes that learn about the interference in their environment. Quantifying this interference using appropriate models and designing a routing metric based on this. 1

9 ii. Designing and implementing a cognitive routing protocol in which the spectrum information gathered from the nodes environment is piggybacked on the existing protocol called BATMAN (Better Approach to Mobile Ad Hoc Networking). We refer to the resulting protocol as Interference-aware BATMAN (I-BATMAN). To clarify for the purposes of this report, please note that a channel is an assigned frequency range which a radio may use for data transmission; a collection of channels is termed a frequency spectrum (or when the context is clear, simply spectrum). Channel capacity is the upper bound on reliable throughput available to a node transmitting over a channel. When we use the term spectrum environment we mean the busyness (great or small) of the channel the node is transmitting over, and of other channels. This project attempts to address routing in the next generation wireless mesh networks. Our approach consists of designing and implementing smart nodes capable of sensing their spectrum environment upon which routing decisions would be made with the objective of achieving a cognitive routing protocol. The information about the spectrum environment is piggybacked over existing routing protocols such as BATMAN. In other words the routing metric assimilates this information and uses it to determine the properties of routes from which new routing decisions can be made thereby optimizing the overall routing process. The focus of this report is to investigate the following: i. Ways of quantifying the amount of interference being experienced by a particular node. ii. Efficient ways of sharing the information about interference among the nodes so as to influence the routing process. iii. Whether factoring interference in the routing metric enhances the routing process. The goal of any routing protocol is to determine the best path. Cognition at the PHY/MAC layer can be used to determine the path with the least amount of interference - in other words, a better path. However, we need to either come up with a scheme of sharing this information among the nodes without increasing overhead traffic, or, in the spirit of the BATMAN protocol, restrict this information to only the node itself to assist in its routing decisions. Furthermore, there is need to 2

10 balance the tasks of sensing and routing to avoid incurring excessive processing costs. That is to say, improving the quality of routing through sensing should not overly affect other aspects of networking such that there might be worse performance. 3

11 Chapter 2 Background 2.1 Routing in Wireless Mesh Networks Wireless Mesh Networks (WMN) are envisaged to extend Internet access and other networking services in personal, local, campus, and metropolitan areas. The key separating feature of mesh networks is the multi-hop relaying of packets across wireless links for communication between the participating nodes. However such networks suffer numerous challenges such as traffic congestion due to limited bandwidth and interference from other ISM band users [3], [4]. Many approaches such as directional and smart antennas, multiple input multiple output (MIMO) systems, and multi-radio/multi-channel systems have in recent years been proposed to increase capacity and flexibility of wireless systems [1], [5]. Smart wireless mesh network builds upon advanced wireless models enabled by the recent advances in the wireless technology and protocols to deliver improved quality of service. Smart wireless mesh networks consist of nodes that use cognition to gather information about their spectral environment and use this information to optimize some overall goal. Possible goals include achieving an optimal network capacity, minimizing interference with other signals, or providing robust security or jamming protection [6]. The aim of this project is to design mesh nodes that are capable of measuring the amount of interference in their environment so that this information can be used for routing purposes. The need to factor interference in the routing metric comes from the fact that an increase in interference reduces throughput as demonstrated in [7]. There is no standard way of measuring interference in available literature. Instead, most approaches characterise interference using its manifestations such as latency, jitter and packet loss, etc. However, building a routing metric around such approaches require probes or extra traffic which results in increased overhead traffic. The majority of existing routing metrics are highly sensitive to traffic load and detect congestion through the packet loss probability. However, packet loss does not always provide accurate 4

12 information and can result in low-throughput routing decisions, either by selecting congested paths or by filtering-out non-congested paths [8]. The routing mechanism requires an accurate estimate of channel congestion level associated with a route in order to perform as predicted. We need to estimate channel congestion and have this information shared among the nodes so that packets are routed over least congested links. In addition, this must be done without generating extra traffic or incurring excess processor overhead to achieve an overall improvement in performance. 2.2 Cognitive nodes Cognitive node is a term being used to refer to a node with cognitive radio capabilities. Cognitive radios are fully programmable wireless devices that can sense their environment and dynamically adapt their transmission waveform, channel access method, spectrum use, and networking protocols as needed for good network and application performance [9]. The majority of research in cognitive radio technology has been motivated by the need to establish and maintain reliable communication while utilizing the radio spectrum in an efficient manner as well as the need to minimize human interaction in the deployment and maintenance of the wireless infrastructure. From the definition, it is evident that cognitive radio technology in general encompasses a wide range of degrees of cognition. Suffice to say, cognitive radio may be categorized according to the set of parameters or the parts of the spectrum considered in determining the transmission and reception changes. The author of [10] summarizes the tasks of a cognitive radio as follows: i. Radio-scene analysis, which encompasses the following: estimation of interference temperature of the radio environment; detection of spectrum holes. ii. Channel identification, which encompasses the following: estimation of channel-state information (CSI); prediction of channel capacity for use by the transmitter. iii. Transmit-power control and dynamic spectrum management. There are many practical applications of cognitive radio such as dynamic channel allocation to alleviate spectrum scarcity. However, this paper is primarily concerned with the use of cognitive 5

13 nodes in a wireless mesh network in order to achieve a cognitive routing model. This could be achieved by piggybacking the radio-scene analysis information such as interference temperature over the routing protocol control messages to influence the routing strategy. 2.3 Cognitive routing In recent years different metrics and protocols have been proposed by researchers to improve wireless mesh routing. The authors of [2] propose a taxonomy for WMN routing protocols with four classes: ad hoc-based; controlled-flooding; traffic-aware; and opportunistic, where classes are differentiated based on procedures employed in route discovery and maintenance. In a continued quest for improved routing in wireless mesh networks, the effect of introducing cognitive mechanisms in the routing module of a wireless network have been investigated. The results show that multi-user interference awareness as provided by the cognitive functions may improve network performance [11]. More recently, several routing protocols that make use of the nodes cognitive capabilities have been proposed to improve wireless mesh routing. These include Multi-hop Single-transceiver CRN Routing Protocol (MSCRP) in which nodes initiate route discovery procedure by broadcasting a Route Request (RREQ), which piggybacks the available channel information [12]; Spectrum Aware Mesh Routing (SAMER) protocol which exploits the available spectrum opportunistically and attempts to balance between long-term route stability and short-term opportunistic performance [13]; Opportunistic Service Differentiation Routing Protocol (OSDRP) for the dynamic cognitive radio networks [14]. Unlike all the other routing protocols considered so far, OSDRP is a cross-layer routing protocol which addresses a situation where the average available duration of the communication channel might be much shorter than the communication time. The other challenge facing wireless mesh networks is traffic congestion due to limited available bandwidth for supporting the large number of nodes in close proximity. This problem can be alleviated by the cognitive radio paradigm that aims at devising spectrum sensing and management techniques, thereby allowing radios to intelligently locate and use frequencies other than those in the 2.4GHz ISM band. An analytical model is proposed in [3] that allows mesh routers to estimate the power in a given channel and location due to neighbouring wireless LAN 6

14 traffic, thus creating a virtual map in space and frequency domains. However, [15] explains that power estimation alone often generates false alarms triggered by unintended signals because the detector cannot differentiate signal types and so, the use of sophisticated techniques in order to capture the temporal and spatial variations in the radio environment is suggested. One alternative digital signal processing technique might be the cyclostationary feature detection described in [16] which has the ability to differentiate modulated signals, interference and noise in low signal to noise ratios. Furthermore, for multi-hop wireless mesh network, where cost of the radios and battery consumption are not limiting factors, [17] proposes the use of coordinated multiple wireless network cards tuned to non-overlapping frequency channels. This optimizes channel capacity by allowing nodes to receive and transmit simultaneously. However, according to [18], when the node has multiple radios, the shortest path algorithm does not perform very well. 2.4 Frequency management Current wireless networks are characterized by a static spectrum allocation policy, where governmental agencies within a given geographical jurisdiction assign wireless spectrum to license holders on a long-term basis. However, a large portion of the assigned spectrum is used sporadically, leading to underutilization of a significant amount of spectrum [19]. Furthermore, technical changes such as the switchover to digital television leaves some frequencies unused [20]. The White Space Coalition, IEEE Working Group and other proposals have advocated the use of white spaces availed by the departure of analogue TV to provide wireless broadband Internet access [21]. Some of the early ideas proposed included hard-coding wireless devices with a database of all licensed bands such as TV stations. However this is not a viable long term solution because of possible alterations to licensed spectrum allocations after the devices have been manufactured. In addition, such an approach could affect other open frequency technologies in unpredictable ways. Therefore, Dynamic Spectrum Access (DSA) techniques have recently been proposed to alleviate these spectrum utilization inefficiencies [22]. 7

15 Dynamic spectrum access can be enabled by cognitive radio technology which allows the unlicensed user to utilize the licensed channel in an opportunistic manner. In order to meet the diverse quality of service (QoS) requirements of various applications and handle the dynamicity of available spectrum, it is required that each user in the cognitive radio network meet the following conditions: Determine which portions of the spectrum are available Select the best available channel Coordinate access to this channel with other users Vacate the channel when a licensed user is detected. An attempt to meet the above conditions gives rise to four spectrum management challenges which must be addressed in order to achieve dynamic spectrum access which are Spectrum sensing, Spectrum decision, Spectrum sharing and Spectrum mobility [23]. A taxonomy has been provided in [24] where dynamic access is categorized broadly as dynamic exclusive model, open sharing model or hierarchical access model. Dynamic spectrum access is relatively a new phenomenon with much of the research still underway. The primary purpose of this paper is to explore the wide range of cognitive radio capabilities that can be harnessed to improve the routing process in wireless mesh networks. 2.5 Interference in networks There is a large number of devices out there but, since the devices follow the same protocol, they tend to work cooperatively. However, the many other devices operating in the unlicensed band affect the devices. These non types of interference typically do not work cooperatively with devices, and can cause significant loss of throughput. In addition, they can cause secondary effects such as rate back-off, in which retransmissions caused by interference trick the devices into thinking that they should use lower data rates than appropriate. The interference associated with Wireless links operating in the unlicensed spectrum may be categorised broadly as follows: 8

16 Uncontrolled interference which results from non-cooperating entities external to the network that use the same frequency band but do not participate in the MAC protocol used by network nodes. Controlled interference which results from the broadcast nature of wireless links where a transmission in one link in the network interferes with the transmission in neighbouring links depending on factors such as network topology, traffic in neighbouring nodes, etc. Further distinction can be made between intra-path interference and inter-path interference. Intra-path interference refers to interference resulting from transmissions on different links in a path whereas Inter-path interference refers to interference resulting from transmissions on links in separate paths [25]. An ideal measure of interference is one that considers all possible sources and causes of interference. 2.6 Interference measurement models There are two main interference models available in literature: the protocol model; and the physical model. The physical model, also known as the SINR (signal-to-interference-and-noise-ratio) model, is based on practical transceiver designs of communication systems that treat interference as noise. Under the physical model, a transmission is successful if and only if SINR at the intended receiver exceeds a threshold so that the transmitted signal can be decoded with an acceptable bit error probability [26]. On the other hand, under the protocol model, a transmission is considered successful when a node falls inside the transmission range of its intended transmitter and falls outside the interference range of unintended transmitters. The protocol model has been widely used in literature due to its simplicity and ability to mimic CSMA/CA network behaviour. However, there are arguments concerning the accuracy of the protocol model as well as its flexibility [26]. The physical model has the advantage of being less restrictive in that it does not employ the concept of transmission range or interference range and 9

17 is not restricted to any medium access mechanism. The physical model only depends on the signal strength values. 2.7 Signal strength values The IEEE standard defines a mechanism by which Radio Frequency energy is to be measured by the circuitry on a wireless network interface card. This numeric value is an integer with an allowable range of 0 to 255 called the Receive Signal Strength Indicator (RSSI). In reality, vendors do not measure 256 different signal levels. Instead, each vendor s network interface card has a specific maximum RSSI value ( RSSI_Max ). For example, Cisco chooses to measure 101 separate values for RF energy, and their RSSI_Max is 100. Symbol uses an RSSI_Max value of 31. The Atheros chipset uses an RSSI_Max value of 60. Therefore, it can be seen that the RF energy level reported by a particular vendor s NIC will range between 0 and RSSI_Max [27]. In MadWifi, which is a universal driver for Atheros-based wireless cards, the reported RSSI is the difference between the signal level and the noise level for each packet. Therefore, the reported RSSI is equivalent to the Signal-to-Noise Ratio and as such the terms can be used interchangeably. However, this does not hold for other drivers. This goes without saying that the actual signal strength values obtained at a particular node depends on the type of wireless NIC and the driver in use. 2.8 Measuring interference A general model of wireless interference The general model of wireless interference proposed in [28] takes radio dependent inputs namely, CCA (clear channel assessment) threshold, radio sensitivity, thermal noise, SINR threshold and measured inputs such as RSSI to calculate the throughput and packet loss rate between two nodes. The model focuses on one-hop traffic demands, which means that traffic is sent over one hop and not routed any further. In addition, the model requires use of custom traffic. 10

18 2.8.2 COIM Channel Occupancy Interference Model (COIM) used in [7] estimates interference based on the channel occupancy using the carrier sensing mechanism while possible interfering nodes are utilizing the shared carrier. Channel occupancy is a value describing what fraction of a fixed interval the medium was sensed busy at a particular node. This can be measured by directly accessing the hardware registers for carrier sensing of the wireless network interface card. The activity on the medium is monitored for a specified interval and the corresponding channel occupancy is calculated. However, getting the register values for the cycle time and the channel busy time is only supported by a few Atheros-based network adapters and might not be possible with other chipsets MIC The Metric of Interference and Channel Switching proposed in [29] captures the shared nature of the wireless medium and exploits the extra resources available from multi-radio/multi-channel nodes. MIC consists of two components namely IRU (Interference-aware Resource Usage) and CSC (Channel Switching Cost). IRU and CSC represent two characteristics of mesh networks. The IRU captures the effects of inter-flow interference and the differences in the transmission rates and loss ratios of wireless links, while CSC captures the impact of intra-flow interference. In using this metric for load balanced routing, interference is taken into account by configuring each node in the network to log the fraction of channel busy time at its location, which indicates channel utilization [30] iaware The interference aware routing metric proposed in [25] uses the physical interference model to capture the interference experienced by links in the network. Interference is estimated based on the signal strength, sending rate and the background noise. The proposed method considers both controlled and uncontrolled interference and measures the parameters of the model using online data traffic. 11

19 2.9 Discussion of the reviewed literature The existing WLAN measurements and information about them are inadequate to move ahead to the next generation of WLAN. IEEE k-2008 is a new standard that provides information on measurements such as channel load intended to improve the way traffic is distributed within a network [31]. In an k compliant network, if the AP with the strongest signal is loaded to capacity, wireless devices connect to one of the underutilized APs despite having a weaker signal. The overall throughput is greater because the network resources are used more efficiently. The general wireless interference model proposed in [28] provides good insight into the general use of the physical model. The model has the disadvantage of requiring custom traffic to implement and being focussed on one hop traffic demands. This makes it unsuitable for mesh networks characterised by multi-hop relaying of packets. Wireless network interface card manufacturers have recognized the need for the card to directly estimate the channel load. Atheros WLAN chipsets is one such example of a wireless NIC which provides direct access to channel load information. The information provided by the Atheros proprietary interface indicates the fraction of time in which the wireless channel is detected busy due to the node s own activity or the neighbours activity. Routing metrics that do not take this quantity into account can yield low throughput by routing over congested paths or by filteringout non-congested paths [8]. The busy time fraction is an additional factor essential to discover high throughput paths, especially under congested conditions. The channel occupancy interference model used in [7] uses fraction of busy time to estimate interference experienced by a node. The model is aimed at predicting interference which in its strict sense is ideal for network planning purposes rather than routing metric design. However, the work presented introduces an important concept: sanity check. The channel occupancy is measured when the network is completely silent i.e. no traffic being generated. This value is then used to determine the level below which, the channel occupancy would have to be considered as noise generated by the environment. 12

20 Other approaches have been used to incorporate interference in the routing metric such as MIC [29] by scaling the link cost with the number of interfering neighbours. But as was observed in the experiments [25], the degree of interference caused by each interfering node is not the same but instead depends on the amount of traffic generated by the interfering node. However the proposed metric has the advantage of capitalising on extra resources from multi-radio/multichannel where available Adopted Interference model Out of the literature reviewed, the most closely related work was found in [25]. The physical model has been used because it does not depend on the medium access mechanism but instead relying on the signal strength values easily obtainable using regular wireless cards. In addition, the physical model has the advantage of measuring the parameters using online data without requiring special traffic which would otherwise increase the overhead. In order to quantify the amount of interference being experienced by a particular node as a result of sharing the communication channel with cooperating and non-cooperating entities, the method used in [25] uses three key interference parameters namely: i. Signal strength ii. Background noise iii. Sending rate The signal strength can be obtained from the wireless card driver whereas the background noise has to be read from a particular card register. The hostap [25] driver also requires modification in order to report the sending rate Conclusion on background chapter Interference is cited throughout literature as one of the major causes of performance degradation in Wireless Mesh Networks. Interference estimation experienced by nodes has been used in solutions for channel assignment, routing and so forth. The performance of these solutions depends on the procedure and accuracy of the interference estimation. The majority of work done in investigating the impact of interference on performance have assumed the existence of 13

21 interference measurements and as such do not dwell much on the methods needed in estimating interference. This may be satisfactory for network planning purposes. The literature provides us with several techniques that can be used to improve the performance of a wireless mesh network. Particularly, the use of routing metrics that take into account the status of the spectral environment have been shown to deliver better route discovery and increased throughput in wireless mesh networks. However, incorporating cognition in the design of routing algorithms and protocols raises several new challenges related to efficiencies in route discovery, route stability and exchange of control information. In addition, implementing a cognitive routing model requires the nodes to sense the environment with a high degree of accuracy in order to avoid performance degradation due to false detections. Thus high quality sensing devices are needed alongside efficient spectrum sensing algorithms. However, increasing cognition generally increases complexity and may increase the amount of overhead traffic required for cooperation with other nodes. Furthermore, sensing and packet transmission cannot be done simultaneously in a single-radio node. Cognitive nodes have to stop transmitting while sensing, which may decrease spectrum efficiency. Therefore, spectrum efficiency and sensing accuracy is an important balance to strike. Using multiple radios enables simultaneous sensing and packet transmission but, at the expense of a complex routing algorithm. 14

22 Chapter 3 Design 3.1 Introduction The aim of this project is to design and implement a wireless mesh routing protocol which takes interference into account in determining optimal paths between two nodes. The majority of wireless mesh routing protocols are adaptations from ad-hoc network protocols. These protocols are concerned with ensuring the existence of a link rather than determining the quality of the link. We are interested in finding out whether a routing protocol can consider sending packets over links experiencing the least amount of interference as an added way of ensuring better throughput and less delay. Just to clarify, this research is not aimed at determining the impact of interference in wireless networks. A lot of work has been done in that direction [32], and research shows that equipment is susceptible to interference patterns from difference sources. The experimental approach is necessary in meeting the project objectives. To that end, a test bed will be used to facilitate testing and evaluation of results. This chapter briefly discusses the design constraints influencing some of the design choices, the software and hardware required for implementation and also describes the nature of experiments that will be conducted and how these will be conducted. Interference mitigation techniques have also been described. 3.2 Design constraints Firstly, the interference sensing module will be integrated into the existing mesh routing protocol called Better Approach to Mobile Ad hoc Networking (BATMAN) [33]. BATMAN was originally written in C programming language and highly optimized for embedded devices. This means that the overall architecture has to follow the original design to minimise the amount of changes to effect the proposed enhancements. In addition, since the evaluation is going to be done on the resulting protocol as a whole, it is important that the interference estimation mechanism be implemented efficiently without incurring excess gain penalties. Secondly, we 15

23 must be able to implement it on commodity wireless network interface cards (NICs) without changing their MAC or PHY layer implementations. 3.3 Software and Hardware needed for implementation As mentioned in the preceding section, the implementation is going to be done using commodity wireless network cards plugged into a computer running Linux. We will use the supporting drivers to every extent possible. Programming changes to the drivers and additional module for added function is going to be done in C using the GNU Compiler Collection (GCC) which comes with Ubuntu Linux distribution. 3.4 Mesh network The wireless mesh network test bed will be setup in the honours lab of the Computer Science building. The Mesh test bed will consist of a 3 x 4 wireless node grid. A small number of nodes should suffice because the scope of work does not include testing for scalability. Due to the nature of the experiments, tests are going to be carried out during quiet times i.e. when there is the least amount of external spectral influence in the room Mesh nodes Except for the laptop which will be used to monitor the activities, each node consists of an Alix.2d2 board [34] with a 500MHz AMD Geode processor and 256MB DDR RAM. Each board is also equipped with a CM9-GP radio card for wireless connectivity. The CM9 radio is based on the Atheros AR5212 chipset and is compatible with the Linux wireless software and is presumably easy to setup. The CM9 wireless card supports a number of modes, allowing a wide variety of test scenarios and connectivity options. It supports the IEEE a/b/g, IEEE g Super Mode, and IEEE a Turbo Mode. It is also highly configurable with WPA and WEP security options, transmission power control, and dynamic frequency selection support. This card provides enough features for the current implementation as well as for future improvements, expansions, or tests. 16

24 It is desirous to equip each node with an IEEE a/b/g card because, a bandwidth difference of 10MHz can be obtained when switching between IEEE b and IEEE a. Hence, by making some nodes use their IEEE a cards (20MHz width) while the others use their IEEE b cards (30MHz width), we will be able to experiment with different scenarios in which band switching/selections occurs across bands with different widths as mentioned in [35]. In addition, there is a distribution of Linux specifically meant for Alix boards Debian for Alix. Debian for Alix is the operating system of choice because of its light weight, easy of installation and wider online support. Furthermore, this distribution of Linux comes with the required wireless card drivers which support Atheros based wireless cards. This will lessen implementation complexities Multi-Radio configuration A selected pair of nodes will each be configured with two wireless network interface cards, one primary and another secondary card. The primary wireless network interface card is for the regular transmission and reception of packets. The secondary wireless network interface card allows us to overcome the limitations of current driver architectures which do not allow us to perform certain kind of monitoring for a given mode of operation. The secondary card will be tuned to the same channel as the primary wireless network interface card so that it is able to pickup all the packets in that channel. With this configuration, we are able to monitor the following parameters regardless of the mode of operation: Signal strength, transmission rate, MAC sequence number and length of all frames received Type of packet errors (e.g., CRC, PHY errors) Medium-busy periods as sensed by the wireless network interface card. This is the time during which there are active transmissions on the channel using any technology. MAC-busy time. This is the time during which transmissions are active. This is computed using packets decoded in the packet trace clients that can be heard [36]. This information is useful in understanding what is going on in the node s wireless environment 17

25 Using multiple wireless network interface cards will also help us explore channel hoping as a way of mitigating interference. The secondary card serves as a means of coordinating the nodes or maintaining connectivity on the previous channel while the primary card hops to a different channel and waits for the entire network to converge to a common channel Interference estimation and Metric design Radio frequency interference around the node can be estimated by monitoring wireless network interface card properties whose values change as the interference level varies. These values include the RSSI, noise, sending rate and channel busy time. The routing metric design was based on the analytical model that captures the MAC protocol operations to predict both throughput and delay of multi-hop flows [8]. The main idea is to use a locally measurable quantity namely channel busy fraction to capture both inter-flow and intra-flow interference. All existing routing metrics are highly sensitive to traffic load and are capable of detecting congestion using the packet loss probability. However, packet loss alone does not always provide accurate information and may lead to low-throughput routing decision, either by selecting congested paths or by filtering-out non-congested paths. Under congested conditions, the busy fraction is an additional factor useful in discovering high throughput paths [8]. 3.5 Experiment design In meeting the goal of measuring interference experienced by each node, in line with the overall objective of designing and implementing an interference aware mesh routing protocol, the following tasks will have to be performed. Firstly, we will have to perform a sanity check as described in [7]. In other words, we will have to take note of the values for properties such as channel occupancy and signal strength while our mesh test bed is completely silent i.e. with no traffic being generated. This can be achieved by performing the tests late in the night in a room where potential sources of interference are controlled. Once this floor has been established, any changes in the values will be attributed to some spectrum activity. Because of the presence of other wireless networks in the building, the bulk of experiments will have to be done during times when there is little external wireless traffic such as late in the night. 18

26 3.5.1 Exposing nodes to controlled interference After establishing the floor values, the next step is to monitor the accuracy of the interference estimation mechanism. This is going to be done by exposing a particular node to controlled interference. There are several ways to expose the effects of interference. However, we are interested in controlled-interference because of the need for reproducibility of experiments. It is a generally accepted scientific practice that experimental results should be reproducible by others. The following methods of generating controlled interference will be used: i. The interference can be introduced by an operation as simple as moving a hand around the antenna. The amount of fluids in muscle tissue of the hand is expected to affect the RSSI and other wireless characteristics. ii. Using the microwave oven (wide-band interference) in close proximity with the node iii. Bluetooth (narrow-band interference) iv. Internally configuring a node to receive excessive traffic or alternatively, using a program to continuously send modulated data. If the interference sensing mechanism performs as expected, all of the above actions aimed at artificially exposing a node to controlled interference are expected to vary the properties being used to quantify interference above the floor values such as SINR or the channel busy fraction explained in detail in chapter 4. In case the interferer cannot be placed close to the node that we need to interfere with, we may decide to use multiple interferers to increase the level of generated noise. This can be done by exploiting the additive interference property that states that the total interference is the sum (in db) of each individual interferer. The additive interference property applies when using multiple nodes as interferers. In other words if one of the interfering nodes rises its transmission power by 3 dbm, the overall noise floor is expected to increase approximately in the order of 3 db. However, it is hard to obtain an exact increase of interference, due to the unpredictability of radio propagation among the interferers. It may just be easier to control the additive interference at low transmission powers than use a single interferer a high transmission power. 19

27 3.6 Interference aware routing Once we are satisfied with the interference estimation mechanism and have this information piggybacked onto the routing protocol, we are interested in observing the effect on the routing pattern. We are particularly interested is determine whether or not incorporating this information in the routing metric translates in improved overall network performance being ensuring that packets are routed over links with better throughput and least delay. Consider the wireless links in figure 3.1 below. Suppose node 1 has a packet to send to node 6. Node 1 knows about node 3, node 2 and node 4 as its neighbours. If node 1 further knows the estimated level of interference being experienced by each of its neighbours, we are interested in having node 1 weigh the links with this extra piece of information alongside Figure 3.1: wireless links Interference mitigation The unlicensed Industrial, Scientific and Medical (ISM) band does not require coordination among the deployed devices. The regulations by the Federal Communications Commission (FCC) and the International Telecommunication Union (ITU) such as limiting transmission power and requiring devices to spread their signals promotes co-existence among devices that use the ISM band. Furthermore, wireless technologies often employ MAC and PHY layer mechanisms in addition to the basic FCC or ITU regulations to enhance the co-existence. For instance, Bluetooth adaptively hops frequencies to lessen the impact of interference on networks. 20

28 The mechanism already used by to mitigate noise and interference include i.a MAC protocol that avoids collisions; ii.lower transmission rates that accommodate signal-to-interference-plus-noise (SINR) ratios; iii.signal spreading that tolerates narrow-band fading and interference; and iv.phy layer coding for error correction. However, despite these mechanisms put in place to promote co-existence, wireless networks by their nature still suffer the consequences of interference. As a matter of fact, generic mechanism aimed at accommodating other transmitters such as carrier sense also increases susceptibility to interference from other technologies [37]. Therefore, mitigation techniques are required to obliterate the effects of selfish or malicious interferers i.e. devices that use their own protocol for their own sole benefit or wireless jamming that takes up the communication channel without doing any useful work. According to the experiments carried out in [37], mitigation techniques employed in software such as changing packet sizes, rates, modulations, Clear Channel Assessment (CCA) thresholds and modes, adding Forward Error Correction (FEC) and measures expected to mitigate interference such as high sender transmit power, high receiver selectivity have been shown to be ineffective due to reception path limitations. The researchers suggest channel hopping i.e. switching to a pseudo-random channel rapidly and occupy it for a short period before switching again as the most effective way to gracefully tolerate interference without requiring major hardware changes. Separating the interferer and receiver by 5MHz or more has been shown to substantially mitigate the effects of interference. We are interested in exploring the performance gains provided by channel switching as a way of tolerating interference. 3.8 Design summary This chapter recaps the project objectives and points out some of the steps that will be taken to realise an interference aware wireless mesh routing protocol. Details of the hardware to be used and the series of preliminary tests that will be carried out and how these will be carried out have also been given. The chapter goes on to describe channel hoping as the ultimate avenue towards 21

29 interference mitigation by separating the center frequency of an channel between the node and the interferer. 22

30 Chapter 4 Implementation 4.1 Introduction This chapter provides details of how the interference sensing component of the wireless mesh routing protocol was implemented. The objective was to implement a module that gathered wireless activity statistics for a particular node. This information is then shared among the nodes participating in the mesh network so that routing decisions take this information into account as an added indicator of good quality links over which to forward packets. Radio frequency interference cannot be measured but can be estimated by monitoring its manifestations. There are four primary metrics for capturing the quality of a wireless link. These are RSSI (Received Signal Strength Indication), SINR (Signal-to-Interference-plus-Noise Ratio), PDR (Packet- Delivery Ratio), and BER (Bit-Error Rate) [38]. PDR provides a good characterisation of the link quality. However, this is highly dependent on the packet size as well as the transmission rate. BER provides a better characterisation of the link quality but, this requires repeated computation over time. Therefore we decided to focus on RSSI and SINR because PDR and BER would require extra probing traffic to compute. The aim was to obtain wireless network interface card statistics while the nodes are connected in ad-hoc mode. The statistics can then be used to estimate the amount of interference being experienced by the node. The estimated level of interference can then be integrated into the mesh routing protocol metric. We decided to extend the existing protocol BATMAN [33]. BATMAN routes packets across links over which it has received the highest number of hello packets called originator messages or OGMs. Once we have a measure of interference let us call it IR, the biggest challenge was to correlate IR with the OGM count. In contrast with other routing protocols in which the metric is an expression of throughput by a specified function, BATMAN characterizes the link by the number of OGMs received. The protocol tries to increase the probability of packet delivery by forwarding packets across the link over which the node has received many OGMs faster. 23