Using a Genetic Algorithm to Optimize the Performance of ... - CiteSeerX

Using a Genetic Algorithm to Optimize the Performance of a Wireless Sensor Network Jin Fan, D.J.Parish High Speed Network Group of Loughborough University, Loughborough, LE11 3TU Email:{eljf2,D.J.Parish}@lboro.ac.uk

Abstract - The emerging field of wireless sensor networks (WSNs) combines sensing, computation, and wireless communications into a single tiny device. With the strict energy constraints and application-specific characteristics, WSNs distinguish themselves from other traditional networks. A huge number of new communication protocols considering the WSNs’ features are designed to meet the heterogeneous application requirements. Therefore, the decision to select the optimal set of protocols for a given task before a WSN’s practical deployment is an important issue. The purpose of this report is to present a proposed performance optimization system for WSNs with the given application and relative QoS requirements. The performance of a WSN can be modeled into a linear formula in terms of the multiple of assigned weight vectors and the metrics generated by simulations. This offline optimization procedure can be addressed by genetic algorithms. Index Terms – Wireless Sensor Network, Performance Optimization, Genetic Algorithm I. INTRODUCTION Wireless Sensor Networks (WSNs) are collections of autonomous devices which have to work flexibly with each other to achieve an assigned goal. Each sensor node is equipped with a certain type of sensor, such as seismic, temperature, humidity, acoustic and radar, low sampling rate magnetic, thermal[1] and so on. On the basis of such sensing ability, combined with wireless connection and computation technology, they are expected to be deployed in a broad range of applications due to the sensor nodes characteristics of lowcost, low-power and easily-manufactured. However, the limited computing resources, severe energy constraints of the sensor nodes, and the application-specific characteristics, present major challenges to meet such an expectation. One of the key challenges which needs to be addressed is the application-specific characteristic of wireless sensor networks. In other words, a WSN’s infrastructure directly depends on the different application scenarios. [1] For the best performance of a given task, the optimal WSN infrastructure should be selected out of the hundreds of possible network solutions before the practical deployment. At present, researchers have to face the problem that most of the performance evaluation of

ISBN: 1-9025-6016-7 © 2007 PGNet

wireless sensor networks can only be obtained from simulations or practical deployments. This is potentially an inefficient approach to the problem. A more efficient analysis of the problem space would be of considerable value before setting up a WSN. Therefore, our research motivation is to develop an offline optimization procedure (to search for the optimal set of protocols) to achieve the best performance under given application scenarios. This would be used before practical deployment. The proposed offline performance optimization system is designed to find the optimal set of the protocols out of hundreds of configuration possibilities in terms of the performance. The performance evaluation should consider all the performance aspects to assess a WSN comprehensively under heterogeneous network configurations. A performance function is derived in the work to model the performance of a WSN in terms of the energy consumption, end-to-end delay and loss metrics by multiplying different weight value for each metric. The weight vector should be identified by the given application. Intelligent optimization techniques are an efficient way of solving the searching part of our system. Genetic algorithms (GAs) have shown their capability of searching problems in many research areas including numerical optimization and combinatorial optimization problems. There are also several other optimization techniques such as simulated annealing, tabu search, etc. [2] other than GAs. However, the GAs’ ability for searching, fast convergence and fast evaluation distinguish themselves from other decision and optimization algorithms. The remainder of the paper is organized as follows: a short survey on protocols of WSNs is presented in section II. WSN performance modeling is introduced in section III. Section IV presents the background of Genetic Algorithms and the proposed offline performance optimization system. Section V lists the available simulation tools and target WSN application scenarios. Future work is discussed in section VI. II. COMMUNICATION PROTOCOLS OF WSNS As we mentioned before, communication between each node in a WSN (due to the genuine inherent characteristics)

distinguishes WSNs from other wireless networks. Hence, many new algorithms have been proposed for the communication problems in WSNs. These protocols have to be designed with the concern for the inherent features along with the application and architecture requirements. A medium access control (MAC) protocol takes a vital role in determining the efficiency of wireless channel bandwidth sharing and energy cost of communication in WSNs. Traditional MAC protocols focus on improving fairness, latency, bandwidth utilization and throughput (which are secondary considerations for WSNs). However, they lack energy conserving mechanisms. Studies reveal that energy wastage in existing MAC protocols occurs mainly from collision, overhearing, control packet overhead and idle listening [3]. MAC protocols for WSNs should try to avoid the above mentioned energy waste. While allocating shared wireless channels fairly among sensor nodes, MAC protocols should prevent nodes from transmitting at the same time. Wei Ye in references [4][5] introduced an energy efficient MAC protocol presented as sensor-MAC(S-MAC) for WSNs. Building on contention-based protocols like IEEE802.11MAC protocols, S-MAC tries to retain the flexibility of contentionbased protocols while improving energy efficiency in multihop networks. Besides energy efficiency, S-MAC has good scalability and collision avoidance capability by using a combined scheduling and contention scheme. In reference [15], the IEEE 802.15.4 MAC protocol is discussed to address the need for low rate, low power, low cost wireless networking, (i.e. Low Rate Wireless Personal Area Networks (LRWPAN)). To achieve low power consumption, any device in the network can only perform data transmission with slotted CSMA/CA (Carrier Sense, Multiple Access/Collision Avoidance) in the Contention Access Period (CAP) within the superframe period in a beacon-enabled mode. In the beaconless mode, the protocol is just the CSMA/CA protocol. I.F.Akyildiz in reference [1] also introduced some other MAC protocols for WSNs, such as hybrid TDMA/FDMA which is based on a hardware approach to minimize the energy consumption. Routing problems in WSNs are equally challenging. Firstly, it is not possible to put global addressing into the deployment of a large number of sensor nodes as the overhead of ID maintenance is high. Therefore, traditional IP-based protocols may not be applied to WSNs. Secondly, sensor nodes which are deployed in an ad-hoc manner need to be self-organized. Furthermore, the nodes are constrained in terms of energy, processing, and storage ability so that routing protocols in WSNs require careful resource management. Meanwhile, the high probability of data redundancy needs to be exploited by the routing protocols to improve the energy efficiency and bandwidth utilization. New algorithms (which have taken consideration of these genuine WSN features) have been proposed for the routing problems we have mentioned above. The routing techniques like data aggregation, in-network processing, clustering, different node role assignment and data centric methods are employed in different algorithms. In [7],

Al-Karaki classified those protocols into flat, hierarchical, or location-based catalogues according to the network structure. In flat networks, all involved nodes play the same role in the network. Sensor Protocols for Information via Negotiation (SPIN) and directed diffusion are the most significant protocols of this catalog. They were shown to save energy through data negotiation and elimination of redundant data. These two protocols motivated the design of many other protocols which follow a similar concept. LEACH (Low Energy Adaptive Clustering Hierarchy) protocol proposed by Heinzelman et. al. is a typical cluster-based or so-called hierarchical routing protocol. By the way of the creation of clusters and assigning special tasks to cluster heads, LEACH can greatly contribute to the system lifetime and energy efficiency. By contrast, location-based protocols utilize the position information to carry on the assigned task in respective regions rather than the whole network. III. WSN PERFORMANCE MODELING The main aim of our work is to find a selection of appropriate algorithms for WSNs for the optimal performance of the entire network, then configure them into each involved node before their deployment. This offline procedure is to be achieved by identifying the optimal performance of a WSN for certain tasks under different sets of protocol configurations. The work starts from the research question “how do we evaluate the WSNs performance under different protocols configurations through simulation?” We first need to identify several representative scenarios to cover most of the fundamental characteristics of WSNs. Secondly, we measure the performance under different protocols configured in each scenario. Next, we can use the unique GA approach to search the optimal selection of protocols for the scenario. And finally, we configure these protocols into practical sensor node deployments. The study will be achieved by: z Identify the current state of WSNs and investigate several significant protocols proposed recently. z Study the existing parameters in the WSNs to extract the performance measurements z Identify a set of scenarios to capture most of the fundamental characteristics of WSNs z Identify what kind of GA to be used, and choose the appropriate fitness function of the selected GA. z Investigate several simulation tools for WSN, and select one which can meet our requirements. z Set up a simulation testbed to incorporate the offline optimization before practical deployment. As discussed in section II, hundreds of communication techniques are available for people to choose from before the node practical configuration. How to identify the optimal set

of protocols to get the best performance is our main research question. We propose a GA as our optimization tool. A Genetic Algorithm (GA) is a stochastic search technique based on the mechanism of natural selection and recombination. It starts with an initial population of individuals, i.e. a set of randomly generated candidate solutions. The solutions are represented by chromosomes, which are collections of numbers or symbols that map onto parameters of the problem. Individuals are evolved from generation to generation, with selection, crossover (mating), and mutation operators. These operations provide an effective combination of exploration of the global search space and pressure to converge to the global minimum. The solution quality is measured by a fitness function. GAs have been already used in several optimization procedures in wireless sensor networks field. Qiu etc [8] proposed an approach that using distributed GA allocates different detection methods to different sensor nodes. As a result, the required detection probability can be achieved while the network lifetime is maximized. Also, in [9], Multiobjective genetic algorithm was used as an optimizer for monitoring a critical facility in a sensor network. When applied to our research work, two important issues need us to be addressed z z

How to represent each set of protocols as population in GA.

M

is

the

vector

performance i.e. M ( X ) = [ ES ( X ), Ds ( X ), Ls ( X )] .

Ds ( X ) is the delay performance of the WSN with the X set Ls ( X )

of protocol configurations is the loss performance of the WSN with the X set of

protocol configurations Energy consumption Performance

f performance =W • M(X) = we • ES (X) + wd • Ds (X) + wl • Ls (X) where

f performance

(1)

is the performance function of each

architecture of the WSN, and vector X is the set of protocols we may use. W is the weight vector for different performance metricsˈi.e. W = [ w , w , w ] ˈ it is designed to emphasize the e

d

l

ES ( X ) depends on the

different network architecture and we designed it as a percentage. It can be defined as

ETOTAL − EUSED ( X ) ETOTAL

ES ( X ) =

(2)

ETOTAL is the total energy available for a sensor node, EUSED ( X ) can be calculated from the simulation result which

where

we will present in the simulation step. The delay performance Ds ( X ) and loss performance

Ls ( X )

also depend on the WSN architecture solution. Their definitions are formulated in the same way as ES ( X ) .

Ls ( X ) =

P − Ls ( X ) P

Ds ( X ) = In the modeling of our problem, heterogeneous network architecture solutions directly affect the network performance in terms of energy consumption, delay and loss. For the different applications, the evaluation standard may change. Hence, the performance function is derived for the optimization process which is implemented by genetic algorithms.

metrics

ES ( X ) represents for the energy consumption performance;

Identifying the appropriate fitness function to verify the optimal WSN performance

Performance function derivation

of

Daverage T

(3)

(4)

Where P is the total packets that passed through the network, T stands for the MAXIMUN DELAY for the network. IV. SYSTEM SOLUTION In the design of the proposed scheme, firstly, the end user will offer the information of the target application and the required performance to our optimization system. The information should include the network topology, application traffic and the importance of the metrics in the system for the offered application. According to this information, the system will come up with a relative Weight vector for different metrics for our performance function derived by the proposed scheme, optimize the overall performance function and determine which set of protocols that should be applied for the application using GAs. Finally, it will choose the right set of protocols and configure them into practical nodes. The system flow chart is shown in Figure 1.

importance of each metric for different applications. The system we propose still has some issues which need to be considered in the future. First, the value T in (4) i.e. the

maximum delay in the network is quite difficult to calculate. We make a rough assumption using the maximum delay of all the simulations, Secondly is how to make the values of ES ( X ) , Ds ( X ) and Ls ( X ) in the same scale so that they can play their parts in the performance function. These questions will be answered in the future.

GloMoSiM

SENSE

OpNet

motes Standard API used between the different simulation layers. The simulation is built on top of Parsec ISO/OSI C++ Offers different (componentbattery models, port model) simple network and application layers, and a IEEE 802.11 implementation. ISO/OSI -----TABLE 1 SIMULATION SOFTWARES OF WIRELESS SENSOR NETWORKS ISO/OSI

A. NS-2 overview The NS-2 simulation environment [12] offers great flexibility in investigating the characteristics of sensor networks because it already contains flexible models for energy constrained wireless ad-hoc networks. In the NS-2 environment, a sensor network can be built with many of the same set of protocols and characteristics as those available in the real world. The mobile networking environment in NS-2 includes support for each of the paradigms and protocols shown in Figure 2 below. The wireless model also includes support for node movements and energy constraints. At the beginning of the work, NS2 was used as the simulation tool for the optimization system proposed. Although NS2 is open-source and well developed in recent years, it still has some disadvantages when applied to a WSN. 1. no application layer simulation behavior (which can be solved by NRL’S extension to NS-2[10]) 2. limited sensor network protocols available FIGURE 1 PROPOSED SYSTEM FLOW CHART

V. SIMULATION TOOLS: There is no one-for-all simulator capable of precisely emulating WSNs (an application-specific network requesting on-demand design specification). Here we investigate several simulation tools available for WSNs in TABLE 1. Each of them has its own expertise. Simulator NS2[46]

Simulation model ISO/OSI

Languages

Description

OTCL, C++ (Objectoriented)

Includes huge number of protocols, traffic generators and tools to simulate TCP, routing, and multicast protocols over wired and wireless. modeling the presence of phenomena transmitted through a designated channel in NS2[10] Simulates TinyOS

NRL’s sensor extension to NS-2

ISO/OSI

OTCL, C++ (Objectoriented)

TOSSIM

At bit level

NesC[11]

leach, spin, flooding etc protocols have not yet been incorporated into USC’s ns-2 distributions [12], but they can be retrieved from their respective developers’ sites

FIGURE 2

PARADIGMS AND PROTOCOLS AVAILABLE IN NS2.28 FOR WIRELESS NETWORKING IN NS-2.

Parameters

Values

A shell script can be designed to establish an interface to carry out the optimization procedure. The aims of this interface should include: extracting the important information from the application, assigning a weight vector for each application, and calling the genetic algorithm to select the network solution for the network until we get the optimal set of protocols. Finally the interface will automatically configure these protocols into practical sensor nodes.

Number of nodes

49

The performance function

Size of data message

512 bytes

Communication range

we regulate the range where each node just can communicate with 4 neighbors 20s 10.2s

B. Scenario Investigation We present the selected scenarios as follows, assuming that the topology of the network is fixed in a grid fashion. z Triggered event. Assign a certain node in the network to send the traffic to the sink) the simulation parameters will be listed as follows.

Simulating time Time of source code sending data

The WSN performance modeling we have proposed has several flaws, the metrics such as scalability of the network or the robustness of the network which we do not take into consideration. In the future, we should model other important metrics into our performance function to make our optimization system more accurate. The simulation tool

z

Periodic event, which occurs in the same network topology. We still define a certain node in the network as the source which periodically sends data to the sink node. Parameters Number of nodes Size of data message Communication range

Simulating time Time of source code sending data The interval between each report

z

VALUE 49 512 BYTES we regulate the range where each node just can communicate with 4 neighbors 20s 1.1s 2s

Tracking event, we assign a certain node moving around in the deployed network range, and sending message to the sink nodes. This simulation is still under development. VI. FUTURE WORK

This paper consists of a short literature review of WSNs, a discussion of our proposed optimization system for WSN performance and some simple simulations for future work. The following issues will be addressed in the future.

Although NS2 is widely used in the WSN simulation experiments, the drawbacks are inevitable when our aim is the configuration of the practical nodes. In the future, a study on using TISSOM as our simulator to fulfill the optimization process will be carried out, since TISSOM has the ability to interact with the hardware of the node [13]. REFERENCES [1]

I.F.Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey”, computer networks 38, 2002, pp 393-422.

[2]

M. Gen and R. Cheng, “Genetic Algorithms and Engineering Optimization”, Wiley-Interscience Publication, New York, December 1999.

[3]

Holger Karl and Andreas Willig. ”A short survey of wireless sensor networks” ˈ TKN Technical Report TKN-03-018, Technical University Berlin, October 2003.

[4]

Wei Ye, John Heidemann and Deborah Estrin, ”An Energy-Efficient MAC Protocol for Wireless Sensor Networks,” Proc.of 12th IEEE International Conference on Computer Networks,INFOCOM 2002, New York, NY, USA, June 2002.

[5]

Wei Ye and John Heidemann, Medium Access Control in Wireless Sensor Networks, Wireless Sensor Networks, Chapter 4, Taieb Znati, Krishna M. Sivalingam and Cauligi Raghavendra (eds.), Kluwer AcademicPublishers,May2004.

[6]

G. Lu, B. Krishnamachari, and C. Raghavendra, “Performance Evaluation of the IEEE 802.15.4 MAC for Low-Rate LowPower“ Wireless Networks, Workshop on Energy-Efficient Wireless Communications and Networks (EWCN '04), April 2004

[7]

Jamal N. Al-Karaki & Ahmed E. Kamal, “Routing Techniques in Sensor Networks: A survey”, IEEE communications, Volume 11, No. 6, Dec. 2004, pp. 6--28.

[8]

Qinru Qiu, Qing Wu, Daniel Burns, Douglas Holzhauer: Distributed genetic algorithm for energy-efficient resource management in sensor networks. GECCO 2006: 1425-1426

[9]

Damien B. Jourdan, Olivier L. de Weck “Multi-objective genetic algorithm for the automated planning of a wireless sensor network to monitor a critical facility “, Proceedings of SPIE -- Volume 5403 Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III, Edward M. Carapezza, Editor, September 2004, pp. 565-575

Add more modules to NS2 As we mentioned in section 4, limited protocols are available for sensor networks in NS2. We aim to add more protocols modules to enlarge the set of protocols. As a result, we will increase the number of solutions (chromosomes) to achieve the fairness of the genetic algorithm. At the same time, the protocols we add to NS-2 need to be investigated to work out how to obtain the performance metrics we need from the trace files. Establish an automatic interface to carry out the optimization procedure

[10] Ian Downard, “Simulating Sensor Networks in NS-2”, Naval Research Laboratory” NRL Formal Report 5522-04-10, 2004. [11] David Gay, Phil Levis, Rob von Behren, Matt Welsh, Eric Brewer, and David Culler “The nesC Language: A Holistic Approach to Networked Embedded Systems”, In Proceedings of Programming Language Design and Implementation (PLDI) 2003, June 2003 [12] The Network Simulator - NS-2. http://www.isi.edu/nsnam/ns/; last visited at 15:29 24,April 25, 2007 [13] Philip Levis , Nelson Lee , Matt Welsh , David Culler, TOSSIM: accurate and scalable simulation of entire tinyOS applications, Proceedings of the 1st international conference on Embedded networked sensor systems, November 05-07, 2003, Los Angeles, California, USA