Power Aware Simulation Framework for Spice Compatible Battery Model of Wireless Sensor Networks

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Power Aware Simulation Framework for Spice Compatible Battery Model of Wireless Sensor Networks
Salah-ddine Krit, Jalal Laassiri, and Said El Hajji
Abstract—This paper targets heterogeneous low power communication circuits and Systems that will be used in the future generations’hand-held devices (PDA’s, mobile phones). Those platforms will probably contain a few studies have emerged and considerable amount of on-chip memories. An optimized communication architecture will be required to interconnect them efficiently. Many communication architectures have been proposed in the literature: shared buses, bridged buses, segmented buses and more recently, Networks-on-Chip. Being battery-powered devices, the energy consumption of the platform is a critical issue. However, with the exception of buses, power consumption has been mostly neglected in interconnection networks. Only very recently have a few studies emerged in that domain. The Power Aware Wireless Sensors (PAWiS) simulation framework becomes an essential tool to evaluate design models of Wireless Sensor Networks (WSNs) including Soft-Ware (SW) and Hard-Ware (HW) platforms. PAWiS is an OMNeT++ based discrete event simulator written in C++. It captures the node internals (modules) as well as the node surroundings (network, environment) and provides specific features critical to WSNs like capturing power consumption at various levels of granularity, support for mobility, and environmental dynamics as well as the simulation of timing effects. The design of integrated low-power wireless sensor nodes involves the convergence of many technologies and disciplines. Submicron complementary metal oxide semiconductor (CMOS) devices, micro-electro-mechanical system filters, on and off chip electromagnetic elements, sensors and dc-dc converters are some of the technologies that will enable pervasive systems such as wireless sensor networks. High system complexity requires the use of many simulation environments during design: algorithm simulators, behavioural and transistorlevel circuit simulators, electromagnetic (EM) simulators and network simulators. It is shown that highly integrated, selfcontained systems require multiple-domain simulations to uncover complex interactions between domains. In this paper we present a flexible and extensible simulation framework to estimate power consumption of sensor network applications for arbitrary HW platforms. Specific examples of block and system level design methodologies used in low-power wireless systems are presented here.

Index Terms— Power Aware Wireless Sensors, Wireless Sensor Networks, Soft-Ware, Hard-Ware, power consumption, low-power wireless systems. —————————— ——————————

1 INTRODUCTION
tion and timing issues at the desired level of granularity. The proposed PAWiS simulation framework [1, 2] assists in developing, modeling, simulating, and optimizing WSN nodes and networking protocols. It particularly supports detailed power reporting and modeling of wireless environments. A typical WSN node may comprise various types of sensors (e.g., power consumption, humidity, strain gage, pressure), a central processing unit (CPU) with peripherals, and a radio transceiver. The simulation covers the internal structure of these nodes as well as communication among them. Sensor nodes forming a network communicate with each other via an ad hoc multi hop network. The range of applications that can be simulated covers many domains such as building automation, car-interior devices, car-tocar communication systems, container monitoring and tracking, and environmental surveillance. The PAWiS simulation framework provides a way to reduce the overall power consumption by carefully optimizing various design aspects within the context of the application. Enhancing energy performance could propel many new applications since the lack of sufficient battery lifetime or limited energy ———————————————— scavenging systems is still the main cause for the slowly • S. Krit Author is with the University Ibn Zohr, Polydisciplinary Faculty of spreading number of WSN applications. Ouarzazate Morocco • J. Laassiri is with the University Ibn Tofeil, Faculty of Sciences Depparte- In previous research performed at the Vienna University ment of Informatics, kenetra, Morocco. of Technology [3], several weaknesses of current WSN • S. El Hajji is with the University Mohamed V, Faculty of Sciences Dep- nodes were identified. These include the wakeup problem He advances in distributed computing and microelectronic systems have fueled the development of smart environments powered by wireless sensor networks (WSNs). WSNs face challenges like limited energy, memory, and processing power and require detailed study before deploying them in the real world. Analytical techniques, simulations, and test beds can be used to study WSNs. Though analytical modeling provides a quick insight to study WSNs, it fails to give realistic results because of WSN-specific constraints like limited energy and the sheer number of sensor nodes. Real world implementations and test beds are the most accurate method to verify the concepts but are restricted by costs, effort, and time factors. Simulations provide a good approximation to verify different schemes and applications developed for WSNs at low cost and in less time. The available simulation frameworks are either general purpose or WSN specific. The general purpose network simulators do not address WSN specific unique characteristics while WSN specific simulators mostly lack the capability of capturing and analyzing the power consumppartement of Mathematics Informatics, Rabat, Morocco.

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(i.e., how to wakeup a sleeping node), the voltage matching and power supply problem, the fairly long oscillator startup time, and other hardware related problems. However, overall efficiency also strongly depends upon the application and its interaction with other nodes and the environment. Here, communication protocols play an important role, but considering the different layers of protocols independently and not taking into account adjacent layers as well as the hardware and environment, improvements can only be suboptimal [4]. PAWiS explicitly supports crosslayer design to exploit the synergy between layers. Several aspects regarding power aware wireless sensors are emphasized and directly supported by the PAWiS framework. The PAWiS framework hence helps to capture the whole system in one simulation and extracts power consumption figures from software and hardware modules uncovering leakages in early design stages. The main contributions of this work are as follows. (i) One of the main contributions is to equip the user to program models of a wide variety of abstraction, (ii) Another contribution is to model the internals of WSN nodes as well as the communication between them. The framework distinguishes between software and hardware tasks, yet it is easy to change the hardware/software partitioning, (iii) one of the main contributions also is an elaborate power simulation with any level of accuracy which can still be balanced with complexity. The simulated power consumption can depend on the supply voltage, for example, for a nearly empty battery when supplying a microcontroller that operates at very low voltages. In WSN nodes, components with different supply voltages are combined resulting in the need for low dropout regulators (LDOs) and DC/DC converters. The PAWiS framework allows modeling this hierarchical supply structure as well as the efficiency factor of the converters. (iv) Powerful analysis and visualization techniques are provided to evaluate the simulation results and derive a path to optimization. (v) The RF communication is modelled according to real world wave propagation phenomena while still maintaining an efficient simulation. It includes interferers, noise, and attenuation due to distance to influence the bit error ratio of communication links. No preset topology is required because the packets are transmittedto all nodes within reach. The topology of network communication itself originates from the link quality and the routing algorithm. With this approach any routing protocol, especially ad hoc protocols, can be implemented. The transmission model implementation is entirely independent of the underlying modulation for-

mat enabling the simulation of any type of modulation. Multiple participants can utilize the RF channel by multiple access schemes and are separated by space, time, frequency, and code.

2 STRUCTURE OF PAWIS
The PAWiS framework is based on the OMNeT++ [5] discrete event simulator and the C++ programming language. Figure 1 depicts the structure of the framework from the users view. The model programmer mostlyinteracts with the framework and C++. Additionally basic knowledge of concepts of OMNeT is required to comprehend the simulation process. The node compositions as well as the network layout are specified in configuration files. Completed models can be compiled (optionally with a GUI based on Tcl/Tk) to an executable simulator. With the optional GUI the

Fig. 1. Structure of the PAWiS simulation framework.

workflow and communication of the model can be observed during the simulation. Additionally a log file with power and timing profile is generated for post-simulation analysis.

3

DESIGN TOOLS

There exist numerous possibilities for reduction of the low power communication circuits and systems in wireless networks. To have credible results through simulation, the choice of models and the simulation environment is very important. Key properties for WSN simulators must include a way to capture energy consumption at any level of abstraction, powerful scripting language, graphical user interface (GUI) support to animate, trace, and debug, and the ease to integrate new modules. Some of these key properties are discussed in [6]. NS-2 simulator: NS-2 [3], [8], [11], perhaps, is the most popular general purpose network simulator. NS-2 supports simulation for widely used IP network protocols. These include TCP, routing and multicasting protocols for conventional wired and wireless networks. NS-2 has a highly extensible object-oriented architecture with discrete-event engine. Its object-oriented model allows extension of simulation functionality by adding customers components and libraries. The simulation in NS-2 environment is based on a combination of C++ and OTcl [7]

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languages where protocols are implemented in C++. OTcl is used as a scripting language to describe and control the simulation process. The complexity of NS-2 objectoriented model creates substantial dependencies and execution overheads. It makes impossible to scale simulation for a large number of network units, which is inherent to WSNs. While object-oriented model is advantageous in terms of extensibility, it is a restriction for scalability and performance. Besides, NS-2 does not provide representation for the HW network components. OMNet++ simulator: Like NS-2, OMNet++ [6] provides deep analysis of network activities at the packet layer. Besides, OMNet++ provides a GUI front-end for simulation and debugging processes. It has component based architecture with a discreteevent simulation kernel. It exploits modules and channels to implement and connect simulation components, where components are connected in a hierarchical fashion via generic interfaces (gates). OMNet++ has extension for sensor network simulation, called SenSim [8], [22], [23]. It represents sensor node as modular hierarchical structure of simple OMNet++ components. This simulator provides more scalability and runs faster than NS-2. However, despite the apparent benefits of OMNet++ and SenSim, there is no precise and accurate HW model of sensor node. It, in turn, does not allow to study sensor networks from an energy perspective. TOSSIM: TOSSIM simulation environment is included in the TinyOS [11] framework. TinyOS has gained general acceptance as a standard operating system for WSN applications. It has a component-oriented programming model, based on the nesC language [3]. A TinyOS program is presented as set of components, where each component is an independent computational entity. The TinyOS framework includes a simple FIFO task scheduler and hardware independent drivers for abstract HW components. The inter-component communications occur through command-event mechanism. By changing a small number of TinyOS components, TOSSIM simulate test the behavior of the low-level hardware. It includes models for CPUs, analog-to-digital converters (ADCs), clocks, timers, flash memories and radio components. The network communication over the wireless channel is abstracted as a directed graph, where vertexes and edges represent nodes and links between them, respectively. TOSSIM simulation architecture provides high level of scalability and execution speed for the networks with large number of sensor nodes. However, the abstract HW model of TOSSIM does not capture low-level details of timings and interrupts, which can be important for precise power analysis. In addition, simulation is supported only for the single HW platform (Micaz [2]). Obviously, it largely restricts the applying scope. VIPTOS and Visual Sense: VIPTOS (Visual Ptolemy and TinyOS) is a graphical development and simulation environment for TinyOS based WSN applications [10]. VIPTOS bridges together the Visual Sense [11] simulator and the TinyOS framework. Visual Sense is a Ptolemy II [9]

based graphical simulation environment designed for WSNs. It exploits the actor-oriented computational model of Ptolemy II, a general modeling framework for heterogeneous embedded systems. Visual Sense defines actororiented models for sensor node subsystems and communication channels. However, VIPTOS does not provide accurate HW representation of sensor node. Substantially, it focuses more on algorithmic and application domains. Additionally, VIPTOS has been integrated only with the first version of TinyOS, which is not currently supported. AVRORA: AVRORA [12], like TOSSIM, is one of the widely used WSN simulation tool. It exploits cycle accurate instructionby- instruction manner to run code. AVRORA runs actual applications without the need to specially adapt it for simulation. AVRORA represents each HW component as corresponding object classes thus as classes of CPUs, Timers, flash memories, ADCs and off-chip components such as sensors. The HW model of a single sensor node is the combination of such objects in a hierarchical manner. The CPU object contains the simulation engine with the event queue for the entire node. This architecture allows node replication for network simulation, where each node is run as independent computational entity. However, AVRORA supports solely AVR MCU [1] cores and does not

4

DESIGN METHOLOGY

To design a WSN and its nodes, the functional specification is defined. For a full optimization, the work flow is a cyclic application of the following steps. (a) Every node typically consists of multiple sub modules. In this step, the node structure is defined and for every module type a certain implementation is chosen (composition). Initially, the modules only need to meet minimum functional requirements. For instance, for the aforementioned TPMS node, the user chooses a CPU, a pressure sensor, an AD converter, a timer, an RF transmitter, and memory. Additionally, software modules like the network stack and sensor handling are required. (b) The modules chosen in the previous step are integrated. Their interfaces have to be adopted and their functions must be coordinated. For example, the chosen pressure sensor might require special treatment for its power-on sequence, which must be implemented accordingly in the module which operates it. (c) The modules are configured, that include setting values for the clock frequency of the CPU, the resolution of analog-to-digital converters (ADCs), and so forth. (d) In the previous steps, a fully functional model of a node and of the network was set up and is simulated in the current step. (e) The simulation results are evaluated. This includes the verification of the function, analysis of the power consumption and timing, and detection of potential for further optimization. (f) The issues identified in the previous step are considered for a refinement of the models as well as the design. Examples are increasing the detailedness and accuracy of power consumption and timing, dividing the

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functionality into more elaborate modules, configuration changes, and even a modification of the node composition by exchanging module implementations. The chosen pressure sensor might consume too much energy in every measure cycle due to its long-lasting power-up sequence, and hence should be exchanged by a sensor with faster startup. Another example is the physical layer model (including the RF transceiver) which might need a refinement of the power consumption reporting during intermediate states (e.g., when switching from transmit to receive mode). With the refined node implementation, the procedure is started over again, until the optimization goal is achieved. These refinement cycles are the main track to enhance the development and design [3]. After completing the optimization process, the final outcome comprises the verified function, the architecture of the node, the implementation details, and the power specification of every module. The module library is particularly intended for the composition and integration of modules to a node. It provides a collection of multiple module implementations for every module type which can be combined in numerous ways. The integration effort is minimized because these modules conform to the interface specification.

Fig. 2. Basic Structure of the Sensor Node in our Simulator Structure

6

CASE STUDY

In this section we describe the case study simulation of an abstract WSN application mapped to a mobile phone platform. In particular, we have studied the influence of parameters of the real device and application behavior on the power consumption.

5 SIMULATION DESIGN
This section presents the architecture of a sensor node and the overall design of our new simulator [21][22][23]. The topology of the Sensor Network field in our simulations is derived from the Simple and Compound Module concept of the OMNeT++ framework. As shown in Figure 1, layers of a node behave as Simple Modules and a Sensor Node behaves as a Compound Module and all these Sensor Nodes constitute the Sensor Network depicted as System Module. The architecture of a Sensor Node is depicted in Figure 4. Each layer of the sensor node is represented as a Simple Module of OMNeT++. The layers communicate with each other through gates and each of the layers has a reference to the Coordinator. The structure of a layer is represented as in Figure 3. These Simple Modules are connected according to the layered architecture of a Sensor Node. The different layers of the Sensor Node have gates to the other layers of the Sensor Node to form the Sensor Node stack. A simple module with Wireless Channel functionality is used to communicate with these compound modules (Sensor Nodes) through multiple gates. The functionalities provided by each Module are described below with Radio, CPU and Battery Module forming the Hardware model of Sensor Node.

6.1 The Application A typical application for WSN can be divided into several standard functional stages in which it first gathers some controlled quantity while sensing the environment, processes this data and forms the packets that are then sent to a base station. The case study presented in this paper follows this standard scenario. It performs a periodical sensing of the environmental temperature, with the period equal to one second. An on-board ADC is used to convert the analog value into its digital representation. When the converted data are ready, the application turns on the transceiver (radio), composes the network packet, waits till the radio is in the transmission mode and sends the data to an abstract base station. After the data transmission is over, the radio is switched to the Power Down state where it stays until it is reawaken in the following period triggered by means of a timer. The interrupt handler of the timer used to trigger the ADC conversion is as follows: void _interrupt Timer_ICR(void* ptr){ SysTop* pSys = (SysTop*)ptr; pSys->Adc.RunConvertion(); } Here, “pSys” represents a particular HW platform that includes the “ADC” component providing the “RunConvertion()” service. The communication algorithm (a network protocol) is abstracted from the real MAC and routing protocol implementation by annotating the functionality of a network stack with time needed to perform abstract operations with data packets. Basically we focus on the HW state transitions during the execution process. The current consumption waveform we obtained is presented as a sequence of pulses. An example of a zoomed in single pulse is depicted in Figure 4.

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Fig. 3. Example of a zoomed pulse

7 THE PROPOSED SPICE COMPATIBLE BATTERY MODEL
In our work we have implemented the electrical battery model proposed by Chen [18] using PSpice [19]. The charging device can be either a charger with a low–output impedance regulated or non-regulated voltage source of 20V absolute maximum or a device plugged in the USB wall outlet(figure 5). The device plugged in the USB wall outlet can beeither a USB driver with a low-output impedance dc voltage source from 4.4V to 5.25V or a carkit with a low-output impedance dc voltage source from 4.75V to 5.25V. Two external PMOS power transistors, driven by ICTLAC2 and ICTLUSB2 of the TWL3029 device, control the choice between the charger input and the USB input. Their role is also to prevent reverse leakage current from the main battery in case of one of the two charging devices is connected to the mobile phone without delivering any voltage at its output. Two external PMOS power transistors driven by ICTLAC1 and ICTLUSB1 of the TWL3029 device, control the current flowing from the charging device to the main battery. The main function of the battery charger interface is the charging control of both 1–cell Li–Ion battery or 3–series Ni– MH/Ni–Cd cell battery with the support of an external µC. The external µC enables the charge by forcing the programming bit CHEN to 1. The external µC select the charging device either by forcing the programming bit ACPATHEN to 1 for a charge with a charger or by forcing the programming bit USBPATHEN to 1 for a charge with the USB. The hardware forbidden having ACPATHEN and USBPATHEN forced to 1 in the same time. The charging scheme for the Li–Ion battery is constant current first (typical current is 1xC) followed by constant voltage charging once a certain voltage threshold is reached (4.2 V typical). Charging is stopped when the charging current at constant voltage has decreased down to C/20 (typical). Because the BCI works in the linear mode, the power dissipation around the external components must be taken into account. The charging scheme for the Ni–MH/Ni–Cd is constant current only. Charging is stopped when _V across battery terminals versus time inverts from positive to slightly negative (typically, a few mV per cell) or by any other

criteria involving battery voltage or battery temperature. Ni–MH/Ni–Cd 3–cell battery voltage can reach 5.5 V at the end of a charge cycle. In case of power dissipation problems around the external components, pulsed charge with a charger is also supported. The external µC enables the pulsed charge by forcing the programming bit PWMEN to 1. The hardware forbidden having the pulsed charge and the linear charge enable in the same time. A Pulsed Width-Modulated signal controls the externals PMOS transistors driven by ICTLAC1 and ICTLAC2 of the TWL3029 device. The frequency of the PWM signal is 1Hz typical. In addition to the above charging schemes, the pre-charge is systematically applied when a battery charger or a carkit is connected to a switched–off mobile phone: a constant charging current (typically C/20) is applied automatically to the battery from PCHGAC or PCHGUSB of the TWL3029 device when the battery voltage is lower than 3.6 V. A specific pre-charge mode enables a full charge with the charger when the battery voltage is higher than 2.0V. The BCI can also supply active portable accessory devices without any own supply by forcing the programming bit ACCSUPEN to 1. In this mode, the two external PMOS transistors driven by ICTLAC1 and ICTLAC2 works like two closed switches that connect the battery voltage to the device plugged into the charger wall outlet. The hardware forbiddenhaving the accessory supply mode and the charge or pre-charge mode enable in the same time. When a charger is plug, the hardware disables automatically the accessory supply mode. A watch-dog timer, using the CK32K clock, allows to automatically stop the battery charge after a programmable delay, see register BCIWDOG. The watch-dog protection is automatically enabled when CHEN is forced to 1. In this case, the delay is set to 4.0s. The software can

Fig. 4. Battery Charger Block Diagram

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disable this protection by forcing WEN to 0.

8

BATTERY MODEL VERIFICATION

To validate the NiMH battery model developed in PSpice we have applied a constant current charge and discharge, and two various pulse discharge experiments both for the model and a real battery.In our experiment, we used a 150-mAh, 5.5 V NiMH Coop rechargeable battery (5 V prismatic type). This battery type has the cut-off discharge voltage at 7.0 V level (the gap is 3.6 V) that is more convenient for battery model verification in comparison to 1.0 V cut-off discharge level of 1.8 V battery type (the gap is 0.5 V). The first experiment aimed to discharge the battery with a constant 23 mA current which is the typical current consumption of TelosB sensor nodes with the radio turned on. The end-ofcharge battery voltage is 9 V. To record the battery voltage we used (in all cases) an Agilent TLC035 digital multimeter. The discharge profile of the real battery and of battery model is depicted in Figure 6. The second case is a continuous battery charging with 0.1C current, where C is a nominal battery capacity. The last two cases refer to battery discharging with 0.1C and 0.2C current pulses (see Figure 4 and 5 respectively). 0.1C current pulses have a 1000 s pulse width for a 1200 s pulse period. 0.2C current pulses, in turn, have a 2000 s pulse width for a 2500 s pulse period. The pulses were generated by an HP 33220A programmable pulse generator which handled the Schrack RA 200006 relay to close and break the loop.

Fig. 7. Block Diagram Battery Charger Block Diagram

9

IMPLIMENTATION

The Energy Framework was originally implemented and tested in the Mobility Framework [24]. Two modules, CSMA and IEEE 802.11 NIC, have been modified to send draw messages to the SimpleBattery. The integration of the two frameworks is shown in Figure 2. The SimpleBattery module uses the Mobility Framework’s publishsubscribe BlackBoard to send HostState notifications. When the residual capacity becomes negative, a HostState notification is published. The NIC’s SnrEval module and the Application Layer module both subscribe to Host-State notifications. The former deletes all frames arriving at the NIC. The latter stops generating new application frames. It is not currently possible to enforce that independently developed Mobility Framework modules subscribe and respond properly to a change in Host State. This problem is fully addressed in the MiXiM port.

Fig. 5. Diagram Battery discharging with continuous 23 mA current

Fig. 8. Mobility framework: IEEE 802.11 NIC

10
Fig. 6. Continuous battery charging (0.1C)

PERFORMANCE

The Energy Framework is intended to be highly extensible and uses OMNeT++ message passing to inform the battery module of changes in the current being drawn. We have ex-plored some of the advantages of this ap-

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proach, but it is also important to consider the overhead compared to more integrated approaches. We therefore compare the performance of the Energy Framework to that of BatteryModule2.0 [25], another contributed module for the Mobility Framework. BatteryModule2.0 uses the Mobility Framework’s Black-Board to snoop on radio state changes and decrements the residual battery capacity according to a linear model like that used in SimpleBattery. This integrated strategy means that it is not possible to include other sources of energy consumption, nor does BatteryModule2.0 handle host failure. BatteryModule2.0 was easily reimplemented in the Energy Framework by creating a new Device module that also uses the BlackBoard to detect radio state changes, but sends Draw messages to the Energy Framework’s SimpleBattery module. A new BatteryStatistics module was also implemented to collect data from the SimpleBattery and write it to perhost log files, as in BatteryModule2.0. It was confirmed that the two variants obtained the same results. The performance comparison used the Mobility Framework’s bursty traffic application module and IEEE 802.11 NIC, with no mobility. The performance metric is the number of simulated seconds per second (simsec/sec), as sampled by the OMNeT++ engine every 50000 events, taking the harmonic mean rate over the simulation lifetime. The first and last samples were discarded to avoid initialization and finalization effects. Figure 9 shows the results. Ten random topologies were generated for networks of 50 and 100 simulated hosts. For the smaller network, a BatteryModule2.0 simulation runs, on average, 1.3% simsec/sec faster than an equivalent simulation in the Energy Framework. As the number of hosts (node density) doubles, the difference is just under 1%. Measured CPU time gives similar results. The data suggest that the Energy Framework is reasonably performant compared to the more integrated solution.

focuses on extensibility and reusability by outlining a protocol architecture and provides module library. The results show that the performance (execution time) of the PAWiS simulation framework is comparable with other frameworks and appropriate for the targeted field of application. The case studies showed that the modularization of OMNeT++ models combined with the abstract component concept of the PAWiS framework generally results in a reduced design-debug cycle. The framework in its current version handles already much of the functionality and effects that are important for simulating wireless sensor networks. However, there are still some extensions and features that need to be enhanced or included. Performance of the simulation framework needs to be further enhanced, as the current results show scalability issues for larger networks. Furthermore, an important aspect of the simulation framework is the postprocessing tool chain. The visualization tool will be extended by additional functions for analyzing the output of the simulation application. Currently, the output comprises power consumption, fired events, and module occupaion. Additionally, analysis tools will be included to allow the comparison of nodes regarding various properties gained from the simulation and features like significant power peak detection, min-max over sliding window, integration features.

12 REFERENCES
[1] D. Weber, J. Glaser, and S. Mahlknecht, “Discrete event simulation framework for power aware wireless sensor networks,”in Proceedings of the 5th IEEE International Conference on Industrial Informatics (INDIN ’07), vol. 1, pp. 335–340, Vienna, Austria, June 2007. [2] T. Kempf, M. Doerper, R. Leupers, et al., “A modular simulation framework for spatial and temporal task mapping onto multiprocessor SoC platforms,” in Proceedings of the Design, Automation and Test in Europe Conference (DATE ’05), vol. 2, pp. 876–881, Munich, Germany, March 2005. [3] Roundy, S., Steingart, D., Frechette, L., Wright, P., and Rabaey, J.: ‘Power sources for wireless sensor networks’, Lecture Notes Comput. Sci., 2004, 2940, pp. 1–17 [4] Shah, R.C., Wietholter, S., Wolisz, A., and Rabaey, J.:‘Modeling and analysis of opportunistic routing in low traffic scenarios’. IEEE Third Int. Symp. on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, (WIOPT), 2005 [5] Shah, R.C., Wietholter, S., Wolisz, A., and Rabaey, J.:‘When does opportunistic routing make sense?’. IEEE Third Int. Conf. on Pervasive Computing and Communications Workshops (PerCom), 2005 [6] Varga, A.: ‘The OMNeTþþ discrete event simulation system’. European Simulation Multiconf., June 2001 [7] Kopke, A., Willig, A., and Karl, H.: ‘Chaotic maps as parsimonious bit error models of wireless channel’. IEEE Twenty-Second Annual Joint Conf. IEEE Computer and Communications Societies. (IEEE InfoCom), March 2003. [8] Boser, B.E., and Wooley, B.: ‘The design of sigma-delta analog to digital converters’, IEEE J. Solid-State Circuits, 1988, 23, (6), pp. 1298– 1308 [9] Ruby, R., Bradley, P., Larson III, J., Oshmyansky, Y., and Figueredo, D.: ‘Ultra-miniature high-Q filters and duplexers using FBAR technology’. IEEE ISSCC, 2001, pp. 120–121 [10] Kwok, K., and Luong, H.: ‘Ultra-low-voltage highperformance CMOS VCOs using transformer feedback’, IEEE J. Solid State Circuits, 2005, 40, pp. 652–660

Figure 9: Comparing the Energy Framework with an integrated solution, BatteryModule2.0. The performance difference is negligible; the data points generally overlap.

11 CONCLUSION
In this paper, we have presented a power aware discrete event simulation framework for wireless sensor networks and nodes. The realization of true Ambient Intelligence is achievable only if advances are made in the hardware implementation of self contained wireless sensing. The modelling and simulation of the batteries, BCI (Battery Charger Interface) components, RF transceivers, network algorithms and low energy analog circuitry have been described. Based on OMNeT++, it provides additional features to capture energy consumption (at any desired level), introducing a model for the RF communication (enabling complex topologies) and environmental effects with scripting capabilities. It provides a visualization tool to analyze and visualize energy usage. The framework

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[11] Li, S.-S., Lin, Y.-W., Xie, Y., Ren, Z., and Nguyen, C.T.-C.: ‘Micromechanical “hollow-disk” ring resonators’. Technical Digest, IEEE MEMS Conf., 2004, Maastricht, The Netherlands, pp. 821–824 [12] Lakin, K.M.: ‘Thin film resonators and filters’. Proc. IEEE Ultrasonics Symp., October 1999, pp. 895–906 [13] Que´vy, E.P., Bhave, S.A., Takeuchi, H., King, T.-J., and Howe, R.T.: ‘Poly-SiGe high frequency resonators based on lithographic definition of nano-gap lateral transducers’. 2004 Solid State Sensor, Actuator and Microsystems Workshop (Hilton Head 2004), Hilton Head Island, South Carolina, June 2004, pp. 360–363 [14] J. Adam Butts and Gurindar S. Sohi. A static power modelfor architects. In MICRO, pages 191–201. 2000. [15] Ali Keshavarzi, Kaushik Roy, and Charles F. Hawkins. Intrinsic Leakage in Low-Power Deep Submicron CMOS ICs. In ITC, pages 146– 155. IEEE Computer Society, 1997. ISBN 0-7803-4209-7. [16] S. Mukhopadhyay K. Roy and H. Mahmoodi-Meimand. Leakage current mechanisms and leakage reduction techniques in deepubmicron CMOS circuits. In Proceedings of the IEEE. 2003. [17] T. Sakurai. Perspectives on Power-Aware Electronics, Plenary Talk 1.2,. In Proc. ISSCC. 2003, San- Francisco,CA, pp. 26-29. 2003. [18] M. Chen and G. A. Rincon-Mora. Accurate electrical battery model capable of predicting runtime and iv performance. IEEE transactions on energy conversion, 21(2):504– 511, June 2006. [19] OrCAD. OrCAD PSpice A/D. Online Manual, USA, October 1998. [20] Rahul Kumar and C. P. Ravikumar. Leakage Power Estimation for Deep Submicron Circuits in an ASIC Design Environment. In ASPDAC, pages 45–50. IEEE, 2002. ISBN 0-7695-1299-2. [21] LSU SensorSimulator (LSU SenSim-Version1, January 2005) User Manual. Authors: LSU Research Group, Dept. Of Computer Science, Louisiana State University, Baton Rouge, Louisiana – 70803. [22] “SenSim: A Wireless Sensor Network Simulation Template” Project by Shivakumar Basavaraju, Department Of Computer Science, Louisiana State University, Baton Rouge, LA –70808. [23] “SensorSimulator: A Simulation Framework for SensorNetworks” Thesis by Cariappa Mallanda, Department of Computer Science, Louisiana State University, Baton Rouge, LA – 70808. [24] W. Drytkiewicz, S. Sroka, V. Handziski, A. Koepke, and H. Karl. A mobility framework for OMNeT++. In 3rd Int’l OMNeT++ Workshop, Jan. 2003. [25] A.F¨orster.BatteryModule2.0.http: //www.inf.unisi.ch/phd/foerster/downloads.html. Salah-ddine Krit received the B.S. and Ph.D degrees in Microectronics Engineering from Sidi Mohammed Ben Abdellah university, Fez, Morroco. Institute in 2004 and 2009, respectively. During 2002-2008, he is also an engineer Team leader in audio and power management Integrated Circuits (ICs) Research. Design, simulation and layout of analog and digital blocks dedicated for mobile phone and satellite communication systems using CMOS technology. He is currently a professor of informatics with Polydisciplinary Faculty of Ouarzazate, Ibn Zohr university, Agadir, Morroco. His research interests include wireless sensor Networks (Software and Hardware), computer engineering and wireless communications.

Jalal Laassiri received his Bachelor’s degree (License es Sciences) in Mathematics and Informatics in 2001 and his Master’s degree (DESA) in computer sciences and engineering from the faculty of sciences, university Mohammed V, Rabat, Morocco, in 2005, and he developed He received his Ph.D. degree in computer sciences and engineering from University of Mohammed V, Rabat, Morocco, in Juin, 2010. He was a visiting scientific with the Imperial College London, in London, U.K. He is Member of the International Association of Engineers (IAENG), He joined the Faculty of Sciences of Kénitra, Department of Computer Science , Ibn Tofail University, Morocco, as an Professor in October 2010, His current research interests include Software and Systems Engineering, UML-OCL, B-Method, ..

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