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Performance Modeling of Cognitive Wireless Sensor Networks Applied to Environmental Protection Elyes Bdira and Mohamed IbnKahla Queen’s University, Kingston, ON Canada E-mail: [email protected] Abstract This paper presents a methodology, a theoretical framework, and some novel ideas on performance modeling and evaluation of application-specific cognitive wireless sensor networks applied to environmental protection. Cross-layer optimizations integrating the use of adaptive sleep, adaptive modulation and energy-aware higher layer processing and protocols are assumed. Routing and application layer processing are assumed to be dependent on lower- layer protocols, requirements, and constraints. Applications relevant to this study are forest monitoring, where the probability of network failure is the main parameter to be minimized, and endangeredspecies monitoring, where the probability of node failure is reduced by increasing the expected node life. Results are shown comparing expected node life of a cross-layered design to that of a traditional adaptive modulation system.

1. Introduction Research on Wireless Sensor Networks (WSNs) has seen an exponential growth as a fertile ground for innovation in many interdisciplinary areas such as signal processing, networking, distributed processing and pervasive computing [5]. Driving applications are diverse and range from military applications, health, environment, agriculture, and construction to mention but a few. Furthermore, perspectives from which the problem is being addressed often diverged along ISO layers with cross-layered approaches being a relatively recent endeavor. Most performance literature however still singled out one or two layers independently of the others [1]. In many WSN applications however, network architectures and protocols, power-aware routing and MAC, and even some minute physical layer intricacies can be closely interdependent and thus have to be addressed in a unified cross-layer design [2, 3, and 7]. One most important aspect in this design is the issue of performance modeling and the metrics used in the evaluation of WSNs for a particular application. It has been observed that most of the

performance literature assumes a passive network with no cognitive features except adaptation that uses a localized feedback constrained to the same layer or to two layers at most. As an example, physical layer and MAC layer--oriented papers evaluated and optimized adaptive modulation schemes or MAC layer protocols [10 and 11] independently of routing issues in multihop networks. On the other hand, energy-aware routing studies rarely addressed application-layer constraints such as distributed vs. centralized processing of information, or whether information relayed is urgent vs. essential even though recent literature is wealthy with cross-layer routing protocols cognition [6, 7 and 8]. On the other hand the literature on intelligent networks and cognitive radio is converging to one paradigm which suggests that network nodes with nearby sensors and actuators should have the ability to make local intelligent decisions using a more global feedback that integrates information at all layers relevant to its task. The optimization of such a network to achieve some static objectives may be an impossible task and in many cases not applicable at all as a distributed intelligence in the network would lead to a cognitive network that self-configures itself and “learns its way” to the best achievable sub-optimal and predictable performance. A suboptimal approach is adopted for performance modeling and evaluation of such a network using cross-layer design and evaluation of performance, robustness and reliability of the network assuming transparent layering where information is shared constantly between physical, access, network, and application layers. This paper is divided into four sections. In Section 2 we propose a Cognitive Wireless Sensor Network (CWSN) design applied for environment and habitat monitoring. In Section 3, a theoretical framework for an intelligent WSN based on artificial intelligence is briefly presented and customized to this particular application. In Section 4, a methodology for optimization and performance evaluation using analytical as well as simulation methods is presented. Section 5 illustrates one aspect of the analysis with expected future analytical work..

978-1-4244-4148-8/09/$25.00 ©2009 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE "GLOBECOM" 2009 proceedings.

2. Cognitive environmental monitoring, the framework. We assume a network of wireless sensors and actuators helped by a grid of fixed agent nodes as well as mobile nodes whose sole function is to help the whole network achieve the objectives of a specific application (Figure 1). All nodes in this network could be connected to the internet or eventually to a human interface, however the network is assumed to be an application-specific entity that is optimized to achieve the above-mentioned goals and meet some specific requirements. The application that is considered here is environmental protection which may include continuous monitoring of some mobile or fixed targets, as well as the possibility of localized control of some preventive or corrective action. The targets could be very small (such as a small animal), or a big geographical area (a Forest, a volcano base, or a river). The target could be fixed (a highway or a forest), slowly moving (a turtle) or with possible high speeds (deer and the like). The network would be designed to adapt in one of these possible environments and applications.

locally on maintenance and prevention of environmental catastrophes. This is a flexible topology that allows two-way communication between any of the two entities shown (sensor motes, relay nodes, mobile and fixed agents, local processors and central processors). The communication between any of the sensor nodes and the processors is guided and optimized by the use of a global feedback function that feeds back all information necessary at all layers to adapt the protocols of each layer to a changing environment. Information can be shared across all layers in this feedback. Figure 2 shows a block diagram of the global feedback used in the proposed framework

Figure 2: A block diagram showing The multi-layered feedback in the proposed congnitive sensor network

Figure 1: An intelligent WSN with local and central processors and agents. Note that solid lines are permanent connections. All other nodes including sensor mote nodes are connected wirelessly in a mesh network

The common unifying theme is the setting of a big number of low-cost sensor motes that transmit information to localize the target and/or to send some critical information about it to a distributed and/or centralized intelligent function within the network which may act independently via some local actuators or may require human intervention. The objective of this network is to achieve or exceed some requirement in monitoring of the target and possibly decide and act

This is a new paradigm in which the information is visible across layers in real-time and would require transparency of data. For example, In the WSN the IP packet maybe encapsulated by local agents so that mixed information is visible locally at all WSN layers. Details on such protocols are out of the scope of this paper.

3. An Intelligent WSN Paradigm The general framework assumes an autonomous cognitive network that uses distributed mobile agents to perform tasks locally and/or a central monitoring and control entity [3]. The relevant application dictates some particular properties for the cognitive network listed in the next paragraph.


3.1 The concept of cognitive network of agents Cognition paradigms are quite elaborate in artificial intelligence and may not be fully applicable to network intelligence. We apply here a subset of the subsumption architecture model developed in [4] to an intelligent network of wireless sensors using relatively simple intelligent agents. The main aspects of the cognitive network are behavior-oriented architecture with agents that have a sensor-based robust behavior with slow rate of processing, distributed control, small, cheap, low power hardware and the assumption that there is uncertainty in sensing. 3.2 Three examples of network intelligence We list below three examples of network intelligence that uses global feedback across-layers. A) An Adaptive sleep MAC: The energy available to each node is fed back to determine a maximum average power. This in turn dictates a maximum duty cycle for the node and a constraint on the maximum sleep time. In conjunction, an Adaptive modulation Physical Layer uses feedback on channel quality. This feedback is used to determine minimum average power and maximum date rate achievable in each instance given the required Quality of Service (QoS). The objective is to minimize packet transmission time while minimizing average power consumption. B) An adaptive MAC Physical-Layer-aware routing. Information on neighbor node available energies is fed back and used to decide whether a single-link or redundant broadcast mode routing should be used. This may achieve diversity to avoid retransmissions but minimize power use of nodes and minimize node failure. C) Redundant routing mechanisms applicable to WSN with dying nodes. In a system where energy available to some nodes is reaching low levels, redundant routing must be used more carefully to avoid counter-productive and wasteful use of energy. A cross layer design links all three aspects. To the above aim, The following procedure is proposed: 1) Energy levels of neighboring nodes are communicated often to all nodes by local agents who have continuous supply of energy. 2) Source nodes transmit RTS (Request to Send) broadcast mode at low power to all neighbors. If CTS

(Clear to Send) is received from more than one neighbor node which all are vulnerable (with depleted energy), data is transmitted to all nodes. otherwise only a “good node” is asked to route data. if more than one vulnerable node are the only options and all nodes are within the same range then the node with the best Received Signal Strength Indicator (RSSI) is asked to route data at a low transmit power according to RSSI and available energies in neighboring nodes. If an ACK is not received, the next priority node routes it without waiting for further request and so on. The novelty in the above procedure is in optimizing the redundant routing to allow transmission success without causing vulnerable nodes to waste resources uselessly in the process. Work is in progress to compare performance of WSN’s with above intelligent characteristics using the following performance modeling methodology.

4. Performance Modeling Procedure In general, performance modeling in the context of an environmental monitoring WSN has an objective of minimizing node and network failure. The objective of the network being to continuously provide information from and possibly control the mobile nodes, all performance metrics have to eventually lead to this goal. For the purpose of conciseness, the proposed methodology is summarized briefly in a diagram shown in Figure 3. This block diagram shows how the metrics used for all layers are linked in one design and evaluation process.

Figure 3: A flow chart illustrating the model on cognitive network performance evaluation and design

The next section shows one instance where the last two layers are designed and evaluated interdependently.


5. Performance evaluation of a WSN using cognitive cross-layer model In this part, An adaptive modulation, adaptive sleep combination is proposed where the transmission time, data rate, and available energy level in sensor mote or in the routing nodes are parameters that are combined to achieve minimal probability of sensor-node failure. This problem is applicable in networks where each sensor node is attached to a particular monitored target (a wild animal for example). In the following paragraphs, we apply part of the methodology presented in previous sections to show how a subset of the parameters is optimized in a cognitive selfconfiguring network. A cross-layer simulation is used to minimize the probability of node failure (for application 1) and network failure (for application 2), and is guided by some analytical results [10 and 11] on optimum scheduling and adaptive sleep combined with some analytical and simulation work on adaptive modulation [1, 12]. Some definitions follow. 5.1 Definitions: Here we define a few terms: 1) Critical sensor node: a node that communicates critical information about a target which can only be acquired by this node (example: a sensor node that is attached to an endangered animal and sends information about its location and its whereabouts) 2) Redundant sensor node: a node that transmits information about a target but its loss causes loss of information only if combined with other losses in the regions around it. 3) Node failures: a node failure occurs when communication with one critical-sensor mote is lost due to energy loss or malfunction. 4) Network Failure: This is a task-specific failure of the network. It is defined as the event where critical information about a target cannot be exchanged. This may be caused by the loss of a critical node making the network a disconnected graph for that particular task or by the loss of many redundant nodes. The probability of this event happening is difficult to compute analytically even in the most simple scenarios, however estimates can be computed using some reasonable assumptions on the relationship between network failure and node failures. 5) Adaptive duty cycle: In this adaptive sleep mechanism, a sensor node is put to sleep and is

assumed off (with very small power consumption in the order of a micro-amp) a variable percentage of the time (D1). It is on receive mode only for a fixed percentage of the time D2, and in full active mode a variable percentage of the time D3 such that D1+D2+D3=1 6) Adaptive modulation: In any hop between two nodes an adaptive variable rate modulation is assumed that depends on three factors: Channel constraints, allocated Time, and maximum power allowed. It also is controlled by feedback coming from all layers that may add more constraints. 5.2 A model for design of adaptive sleep and adaptive modulation A) Channel Parameters: Due to the fact that the WSN is spread through a variable terrain which would cause each link to have different propagation modes and fading environments, a single fading and propagation model may not be possible [9]. Here, we assume probabilistic Markov-chain transitions for each mobile mode as the transceiver reconfigure to adapt to fading environment transitions. The transitions could be from Rician to Rayleigh fading for example or from line-of sight to Log-Normal Shadowed. More generally, the channel could vary from any fading state to another. Since mobiles are assumed to be slowly moving, it is assumed that transceivers would reconfigure in time. B) Adaptive modulation parameters: discrete-rate adaptation with variable available power subject to constraints of adaptive sleep, and a random signal to noise ratio subject to channel parameters. MQAM modulation is assumed with up to M=64 and thresholds for rate switches are optimized as in [1] and [12]. C) Relationship between available power, available energy and active duty cycle: The following simplified formula is used: D 3P < x E , Where D3 is the active time, P is the available power as fed back by the adaptive sleep algorithm, E is the available energy at the transmitting node, and x is an adaptation fraction to be determined. D) Relationship between adaptive modulation and adaptive sleep: The relationship is guided by the need to maximize the life of each critical node and by the need to minimize network failures in all cases by minimizing energy use per node. We


assume a variable packet of information to be transmitted to a central processor every T seconds (as a maximum) with a Poisson arrival process. The variable sleep/variable modulation algorithm adjusts sleep times and modulation rates given channel constraints to achieve the lowest average energy use thus maximizing node life. E) Optimization model and procedure: The relationship between MAC and physical layer is used in the following procedure to maximize node life by minimizing average power consumption. Let γ be the instantaneous received SNR, and γth a threshold to be optimized. The simulation model is summarized in the following steps: a) Packets are generated randomly using a standard Poisson process model at a given packet rate (1 packet of 640 bytes and a variable rate using a Bandwidth of 100KHz). b) In each run a new channel state is generated and used to select a new modulation rate, M transmission power P, and transmit time as shown in Table 1. c) Different variable modulation schemes are compared for different channel quality parameters and variable power control errors. d) Log-Normal shadowing is considered with different runs generating different correlated fading states. e) The behavior of one link is observed over a period of one full day and probability of failure is assessed over a number of years.

5.3 Simulation results Figures 4 and 5 compare the expected energy consumption per year of a low-power wireless node using two adaptation methods: The results in Figure 4 assume a system that uses adaptive modulation in the physical layer (using 3 to 5 stages of MQAM adaptive modulation [1]) but with no feedback to the MAC layer affecting scheduling and adaptive sleep. The results in Figure 5 on the other hand are for a system that uses the cognition algorithm described above where sleep time is dependent on feedback about channel conditions and modulation rate. Our simulation assumes less–then-perfect power control with an average error of 2dB, Log-Normal distribution with a 8dB standard deviation, and assumes scheduling of unsent packet in the immediate next frame. Other parameters assumed are a total initial energy per node of 100mA, active current consumption of 100mA transmit, 20 mA receive, and 0.002 mA standby. These results are sub-optimal, while the results of Figure 4 assume optimal efficiency at the physical layer for a discrete-rate discrete-power adaptive QAM system with optimal power control. Figure 6 shows results of the simulation vs. data traffic and shows that node life is more affected by lower SNR at high traffic intensity. This illustrates that the cognitive procedure works best when traffic is bursty but has low average intensity. This assumption is well suited for environmental monitoring applications such as wild life monitoring.

Table 1: Adaptive modulation and sleep scheduling used for a transmission of packet of length L with overhead H. We assume random number N packets are to be sent in every period T and D is the time it takes to send one packet. P(i) are powers assigned as in shown in [1]. Fading range

SNR

0 ≤ γ / γ th < 2

Modulati on rate M(γ) 0

0

2 ≤ γ / γ th < 4

2

P(1)

4 ≤ γ / γ th < 8

4

8 ≤ γ / γ th < 64

64 ≤ γ / γ th < ∞

P(γ) / P ave

Trans. Time Needed 0

Sleep Time 1 1-ND/T

P(2)

ND= N * ( L+H)/R ND/2

16

P(3)

ND/4

64

P(4)

ND/6

1(ND/4T) 1(ND/6T)

1-ND/2T

Figure 4: Estimated Node Life vs. Average SNR in channel in an adaptive system with no cross-layer feedback (Theoretical Physical Layer Optimizations based on [1].


processing using intelligent agents capable of proposing and enforcing local processing of data that is not required in the central processor.

7. References [1] A. Goldsmith, Wireless Communications, Cambridge University Press, 2005. [2] C. Wijting, & R. Prasad, “A Generic Framework for Cross-Layer Optimisation in Wireless Personal Area Networks”, In Wireless Personal Communications Journal, Vol 29, 2004

Figure 5: Estimated Node Life vs. Average SNR in channel when using adaptive sleep with adaptive modulation.

[3] M. Sifalakis et al. "A Functional Composition Framework for Autonomic Network Architectures ". Proceedings of 2nd IEEE International Workshop on Autonomic Communications and Network Management, Salvador, Bahia, Brazil, April 7-11, 2008. [4] R.A. Brooks, "How to build complete creatures rather than isolated cognitive simulators," in K. VanLehn (ed.), Architectures for Intelligence, pp. 225-239, Lawrence Erlbaum Associates, Hillsdale, NJ, 1991 [5] F. Akyildiz, X. Wang, W. Wang, ‘Wireless Mesh Networks: a Survey,’ Computer Networks, Vol. 47 (2005). [6] A. Ghosha and S. K. Das “Coverage and Connectivity issues in Wireless Sensor Networks: A Survey.” Pervasive and Mobile Computing, pp. 303–334, Elsevier, 2008. [7] M. Sifalakis, M. Fry, D. Hutchison, “A Common Architecture for Cross Layer and Network Context Awareness”, in Self-Organizing Systems, Springer Berlin/Heidelberg 2007.

Figure 6: Node Life vs. Traffic Intensity (same parameters as Figure 5)

6. Conclusions and Future Work This paper presents a framework for performance modeling and design of cognitive wireless sensors networks, and some novel ideas for the design of application-specific Cognitive WSNs that are economic and reliable. The main driving idea is the sharing of information across layers in a generalized feedback that is available to all capable nodes that may use it to optimize the network availability using metrics that are customized to the application at hand. Results of a realistic simulation of a cross-layer design show that it is achieving better node-life for highly adaptive 5-stage MQAM with adaptive sleep. Future work will focus on more modeling and analysis of the general cognitive network framework presented including the use of cognitive routing and distributed

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