A Decision-Support Tool for Wireless Sensor Networks

A Decision-Support Tool for Wireless Sensor Networks C´edric Ramassamy

Hac`ene Fouchal

LAMIA Universit´e des Antilles et de la Guyane France [email protected]

Universit´e de Reims Champagne-Ardenne France [email protected]

Abstract—The design of efficient Wireless Sensor Networks (WSN) is a very hot topic nowadays. The efficiency could be observed at many levels, from the application layer to the physical part. In order to get better performances over WSN, many parameters could be considered: the application type, the routing protocol, the MAC protocol, the physical protocol, the radio range of nodes, the topology of the networks. In this paper 1 , 2 , we present a support-tool that we have developed in order to manage all these parameters in order to help designers to choose the most appropriate value for each parameter involved in the design of efficient solutions based on WSN. This tool has used a learning step where intensive simulations have been conducted. The results of this step allow the platform to suggest the most appropriate parameter for any situation chosen by the designer. The tool could be handled easily with adequate GUI. In addition, it is also able to generate NS-2 scripts in order to check the efficiency of the proposed configurations.

I. I NTRODUCTION Wireless sensor networks (WSN) is a hot topic in the area of communicating systems. Indeed, these networks could be deployed for many application domains as environment monitoring [1], observation of rare species or people [2] and [3], home or industrial monitoring [4], optimization of diagnosis for the patients [5], etc. We find several methods to classify WSN [6]. For each domain, WSN have different features. They can distinguish themselves by the communication model, data transmission model, the mobility in the network, etc. The industrial actors involved in the field of WSN, must be capable of developing quickly reliable solutions. The companies which design and deploy WSN have to make it as fast as possible to face the competitors. The economic stakes related to the conception of WSN is very important. Besides the economic stakes related to WSN design, sensors are small components with low capacity of storage and computing and they are powered by batteries. So that WSNs remain autonomously during a reduced duration (few months up to few years) without human intervention, the energy consumption becomes the fundamental problem. For traditional wireless networks, this issue is not problematic 1 This work has been partially supported by the EC TEN Project SCOOP@F-Part1 2 This work has been partially supported by the project ”Projet Incitatif en Amont de la Rgion Champagne-Ardenne-2014, France”

since we can always load batteries of wireless devices such as mobile phones or laptops. But in WSN, it is very difficult to change batteries [1]. Also for under-water WSN deployment, we cannot send somebody every week in order to change batteries. Even with such weakness, WSN are more and more used in various areas. Many projects in Europe and the United States are developed and are deployed using a high number of sensors. As we know we can find various protocols for each layer but the manner to choose one protocol instead another has not investigated in any research study. The combination of all layers makes the process of choosing the appropriate protocols more and more complex. In this paper, we suggest to solve this issue: we design a tool able to suggest the most adequate parameter value for each layer in any kind of situation. Various features have considered: the application layer, routing protocols, MAC layer, Physical layer, radio range and topology of the network. This paper is organized as follows. Section 2 is dedicated to related works. Section 3 presents our decision-support tool. In section 4, we conclude the study and give some perspectives. II. R ELATED WORKS The design of communications protocols is a domain of aside from research. Many tools and methods were developed and bring obvious helps to research and industry community during the design of new products. Depending on the target objectives, we may use specification and formal validation tools [7], in other cases we use tools dedicated to network and protocol simulation [8], and in some specific cases we use real prototypes [9]. Each kind of tool could bring various advantages according to user needs and to required abstract level. We worked on several evaluations to determine the impact of some parameters [10], [11] such as: the network topology, the radio coverage and the network size on the performances of a wireless sensor networks. We observed many performance criteria as lost application packets and energy-consumption. Then, we have introduced a classification model allowing a configuration of any WSN which provides best parameters for a reliable configuration and a longer lifetime [12]. The contribution of [13] is about energy-efficient communication systems of the sensor nodes and requirements for their effective functioning such as a strategy for mea-

sured values processing, network topology, transmission media access, routing, coding and modulation. In [14], the authors describes a system for recognition of objects in traffic scene from multiplemoving vehicles in the vehicular ad hoc network (VANET). The proposed system is based on query in which a user can explain what objects to find in an image captured by a camera mounted in a vehicle along with other sensors. The study of [15] is dedicated to traffic safety. Indeed, they deal with the problem of car intersections with blocked view crossing. A special wireless sensor network (WSN) for car detection taking advantage of ultra-low-power TI products, microcontroller MSP430F2232, 868MHz RF transceiver CC1101 and LDO voltage regulator TPS7033, was developed due to this reason. The WSN consist of four network nodes supplied with the special safety lightings which serve the function of intelligent traffic safety mirror There exists in the literature studies about how to provide optimal performances and reliable networks. In [1], the authors propose a design and an architecture network for environmental monitoring such as soil moisture and rain. They show how to choose antenna types, batteries and other configurations in order to provide the best performances and robustness results on the network to design. In [16], authors propose a fault tolerant location based service discovery protocol for vehicular networks which can work well under service provider failures, communication link and roadside routers failures. [17] presents TWIST, a scalable and reconfigurable testbed architecture for indoor deployment of wireless sensor networks. The design of TWIST is based on an analysis of typical and desirable use-cases. It provides basic services like node configuration, network-wide programming, out-ofband extraction of debug data and gathering of application data. TWIST supports experiments with heterogeneous node platforms. it also supports active power supply control of the nodes. This enables easy transition between USB-powered and battery-powered experiments, dynamic selection of topologies as well as controlled injection of node failures into the system. Thirdly, TWIST supports creation of both at and hierarchical sensor networks. The self-configuration capability, the use of hardware with standardized interfaces and open-source software makes the TWIST architecture scalable, affordable, and easily replicable. In the literature, we found many comparative studies of two most important WSN layer: routing protocols [18], [19] and [20] and MAC layer [21] and [22], [23]. In [18] and [19] authors discuss widely properties of many routing protocols and propose different categories for these ones. In [20], authors show a survey about almost all routing protocols we can find for ad hoc networks. In [21] authors deal with two categories of MAC techniques: contention based and schedule based. They give an unique performance analysis and comparison of benefits and limitation of each protocol. They show that random topology contention based approach may be helpful and also schedule based approach may be more energy efficient if the deployment is not random. In [22], [23], the authors focus on a new methods to transmit data efficiently based on X-MAC

protocol [24] in order to reduce the energy consumption. In all these studies, there is a lack of mixing all layers to consider WSN performances and the Quality of Service they are able to provide. Our tool can consider all layers to provide mechanisms to tailor with precision the choice of best parameters in order to provide more efficient WSN networks. III. D ECISION - SUPPORT TOOL The design of our tool is composed of three main parts: a learning part, choosing parameter part and a result analysis part. Before observing all parts, we have based our tool on an extensive set of simulations. A. Part 1: Learning Our model of classification is based on the results obtained from a large number of simulations achieved with all layers involved in usual WSNs. We have handled most of the well known protocols deployed on WSNs. We have used the well know NS-2 simulator [8] to design our classification scheme. In addition to observe protocol behaviors, we checked the impact of radio range and network sizes. For this reason, we conducted simulations with various network sizes: 25 to 300 nodes. We have used Peer-to-Peer topology with one PAN coordinator and we have used the AODV, AOMDV, DSDV and DSR routing protocols in non-beacon mode and in beaconenabled mode of IEEE 802.15.4 MAC protocol. All details and results are collected in [12]. In order design our decision-support tool, we performed approximately 2000 simulations to provide a broad set of various situations and we stored all results in a database. In the meantime, we know that WSN can be used for many applications in various domains. We divided applications into three classes: â Regular applications: which characterize those which communicate with low-data within large intervals. â High rate applications: which characterize applications which have large streams of data for communication. â Burst based applications: which characterize applications which send a high amount of data in a reduced period and sleep in the rest of the time. At the end of all simulations, we have analyzed many performance criteria as the rate of lost application packets, the energy-consumption, the rate of lost MAC packets. All these criteria allow us to provide a large cross-table which contains a summary of all these informations. Finally this part allows us to teach the system all different situations we extracted from the classification. At any moment, a user may check what has been learned by the toolbox. This database can evolve with an extension with other simulation results could be achieved with ease. The extension is expected in the case where new protocols have not been handled into the tool or in the case where some specific configurations have not been considered. For example, if we need to add a new routing protocol or to change the maximum number of nodes or to add a new parameter, we will be able to do so. But we must have results of the performance criteria

as the rate of lost application packets and the rate of energyconsumption. The database can can be improved either from simulation studies or from real environment studies.

Fig. 1.

C. Part 3: result Analysis In this part, we give the opportunity to the user to show the results of each simulation, where we can observe of some sensible parameters as the evolution of the lost packets rate and the rate of remaining energy during the simulation. The observed results could be used by the user to adapt his configuration by changing a routing protocol by another one since the observed results are not as expected by the user. The results shown to the user are those provided by the set of simulations. Nevertheless, we have considered the case where learned results could come from another simulator or from real environment studies.

Result screen from the learning part

B. Part 2: Parameter choices The second part is dedicated to propose to the user the input his actual configuration. Based on the cross table produced by the learning part, the toolbox suggests the most appropriate parameters to select in order to have the most efficient WSN. The efficiency is calculated according to the user requirements (latency, security, lost packet rate). We can choose the routing protocol, the kind of application, the number of nodes, the type of MAC layer, the topology type and the radio coverage. This part is our main contribution. As far as we know, such a tool has not been proposed elsewhere. This toolbox is able to help any designer for an application over WSNs to have the opportunity to select an adequate protocol in each layer with the most adapted parameter value. This tool could be included in the software engineering cycle for applications over WSNs. Here we have an exploration of the database provided in the first part. This exploration builds the best configuration with a optimal function. We intend to upgrade our function to include equipment cost.

Fig. 3.

Evolution of consumption energy rate

Fig. 4.

Fig. 2.

Decision part after the configuration input

Evolution of lost packet rate

The tool offers the opportunity to view more relevant results of all simulations according to the number of nodes and the kind of applications. When we choose the application type and the number of nodes, the tool displays some results where the nodes turn on and off their radio to preserve energy. We use two modes for medium access of IEEE 802.15.4 MAC layer: â Non-beacon mode purely based on the CSMA/CA (Carrier Sense Multiple Access) /(Collision Avoidance) mechanism. â Beacon mode, where nodes have an active and an inactive time to save energy. In this mode The PAN coordinator operates with a Superframe. It starts the

Superframe with beacon for node synchronization. The Superframe contains an active and an inactive portion where nodes may move to the sleeping status and then save energy. The active portion contains fixed size slots which represent two period: a Contention Access Period (CAP) where nodes use CSMA/CA mechanism, and a Contention Free Period for large packets or timecritical data deliveries assigned by the PAN coordinator. Synchronization and sending (non GTS) operations are executed in the CAP period. Information for pending delivery are in the beacon frame. The choice of the best parameters is performed by the user because real production costs will limit the user to choose one configuration rather another. IV. G ENERATING NS-2 SCRIPT A. Manual script In order to make our decision-support tool able to provide mechanisms to tailor with precision the choice of best parameters, we must achieve many simulations. In each simulation, we changed some MAC parameters of the 802.15.4 MAC layer such as BO and SO values to change inactive and active period of nodes. We changed also some routing protocol parameters, radio range values, application type, number of nodes and topology. But with all of these changes, we achieved nearly 2000 simulations, so we provide manual scripts to launch simulations. When we have started simulation with NS2 we noticed that is hard to execute many simulation file with different parameters. So we have written a script with some parameters in order to reduce simulation files. In a script file, we can find nodes number, routing protocols, BO and SO values, application type, timing of application, radio range and topology. However a simulation file is composed about nearly 400 code lines. For this reason, we have designed an application to provide simulation files as easiest possible. B. Automatic script We make a generator of script NS-2 in three views:

Fig. 5.

Basic options in NS-2

In the first view (Fig. 5), we can see basic option of a script in NS-2. The channel leads to specify if we use a wired channel, wireless channel or satellite channel between nodes. The propagation model is the model of propagation waves for wireless channel, there is two models Two ray Ground or Shadowing. For network interface, we can select the type of interface, 802.11, 802.15.4 and Satellite interface, it is the same manner for MAC layer. Queues model the output buffers attached to a link in a ”real” router in a network. They represent locations where packets may be held or dropped. Packet scheduling refers to the decision process used to choose which packets should be serviced or dropped. The Link Layer is responsible for simulating the data link protocols. This is for example packet fragmentation/ reassembly and reliable link protocol. And we have packet size maximum number, number of nodes, routing protocol and the name of NAM and Trace file.

Fig. 6.

Nodes options

In the second view (Fig. 6) we have the topology and node options such as logs about movement of nodes, packets received or sent or dropped in MAC layer, packets of application layer and routing packets.

Fig. 7.

Application options

In the third view, we can configure an application. An application is a set of traffic between nodes. So we can set a source, a destination, a kind of packets and timing constraints to configure the application. The kind of packets depends if we need a constant traffic like CBR or a Poisson stream for burst. V. C ONCLUSION In this paper, we have proposed an original tool for WSN application design. Indeed, actually when a user intends to develop an application over a WSN, he has to fix the radio range, the number of nodes, the adequate MAC protocol and the most appropriate routing protocol. Our tool provides an easy fashion to select the adequate parameters in order to propose the most efficient use of the application over the WSN. The choice of the parameters has been done thanks to a large number of simulations (nearly 5000 simulations) done within part one of the tool. All what we have learned have been modeled in the proposed tool. As an output of tool, we are able to provide the NS-2 script according to the user needs. This part helps beginners who study the simulator tool NS-2. For future works, we intend to improve the update mechanism of the application from other simulators or real test environments. This is done in order to have finer data to the database. In addition, we will improve the choice of best parameters. Indeed, we will offer some possibilities to the user to choose a configuration based on costs of proposed solutions. An optimal solution (based on performance criteria) can cost more expensive, but another solution can be correct enough and have costs and assembly significantly lower than the optimal solution. R EFERENCES [1] R. Cardell-oliver, K. Smettem, M. Kranz, and K. Mayer, “Field testing a wireless sensor network for reactive environmental monitoring,” in In International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2004, pp. 14–17. [2] H. Gros-Desormeaux, P. Hunel, and N. Vidot, “Counting birds with wireless sensor networks,” in IWCMC, M. Guizani, P. M¨uller, K.-P. F¨ahnrich, A. V. Vasilakos, Y. Zhang, and J. Zhang, Eds. ACM, 2009, pp. 1163–1167. [3] A. Rozyyez and H. Hasbullah, “Comparison of routing protocols for child tracking in wsn,” in 4th International Symposium on Information Technology 2010 (ITSim’10), June 2010, pp. 1238–1243. [Online]. Available: http://eprints.utp.edu.my/4564/ [4] N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin, “A wireless sensor network for structural monitoring,” in Proceedings of the 2nd international conference on Embedded networked sensor systems, ser. SenSys ’04. New York, NY, USA: ACM, 2004, pp. 13–24. [Online]. Available: http://doi.acm.org/10.1145/1031495.1031498 [5] N. Dessart, H. Fouchal, P. Hunel, and N. Vidot, “On using a distributed approach for help in medical diagnosis with wireless sensor networks,” in IICS, ser. LNI, G. Eichler, A. K¨upper, V. Schau, H. Fouchal, and H. Unger, Eds., vol. P-186. GI, 2011, pp. 70–81. [6] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Comput. Netw., vol. 52, pp. 2292–2330, August 2008. [Online]. Available: http://dl.acm.org/citation.cfm?id=1389582.1389832 [7] J. Bengtsson and F. Larsson, UPPAAL - a Tool Suite for Symbolic and Compositional Verification of Real-Time Systems (Draft). [8] “The network simulator ns-2,” http://www.isi.edu/nsnam/ns/, 2001.

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