Design and FPGA implementation of a wireless hyperchaotic communication system for secure realtime image transmission
 Said Sadoudi^{1},
 Camel Tanougast^{2}Email author,
 Mohamed Salah Azzaz^{1} and
 Abbas Dandache^{2}
DOI: 10.1186/16875281201343
© Sadoudi et al.; licensee Springer. 2013
Received: 29 January 2013
Accepted: 26 June 2013
Published: 29 July 2013
Abstract
In this paper, we propose and demonstrate experimentally a new wireless digital encryption hyperchaotic communication system based on radio frequency (RF) communication protocols for secure realtime data or image transmission. A reconfigurable hardware architecture is developed to ensure the interconnection between two field programmable gate array development platforms through XBee RF modules. To ensure the synchronization and encryption of data between the transmitter and the receiver, a feedback masking hyperchaotic synchronization technique based on a dynamic feedback modulation has been implemented to digitally synchronize the encrypter hyperchaotic systems. The obtained experimental results show the relevance of the idea of combining XBee (Zigbee or Wireless Fidelity) protocol, known for its high noise immunity, to secure hyperchaotic communications. In fact, we have recovered the information data or image correctly after realtime encrypted data or image transmission tests at a maximum distance (indoor range) of more than 30 m and with maximum digital modulation rate of 625,000 baud allowing a wireless encrypted video transmission rate of 25 images per second with a spatial resolution of 128 × 128 pixels. The obtained performance of the communication system is suitable for secure data or image transmissions in wireless sensor networks.
Introduction
Over the past decades, the confidentiality of multimedia communications such as audio, images, and video has become increasingly important since communications of digital products over the network (wired/wireless) occur more frequently [1, 2]. Therefore, the need for secure data and transmission is increasing dramatically and defined by the required levels of security depending on the purpose of communication. To meet these requirements, a wide variety of cryptographic algorithms have been proposed.
In this context, the main challenge of stream cipher cryptography relates to the generation of long unpredictable key sequences. More precisely, the sequence has to be random, its period must be large, and the various patterns of a given length must be uniformly distributed over the sequence.
Traditional ciphers like DES, 3DES, IDEA, RSA, or AES are less efficient for realtime secure multimedia data encryption systems and exhibit some drawbacks and weakness in the high stream data encryption [3, 4]. Indeed, the increase and availability of a highpower computation machine allow a force brute attack against these ciphers. Moreover, for some applications which require a highlevel computation and where a large computational time and high computing power are needed (for example, encryption of large digital images), these cryptosystems suffer from lowlevel efficiency [5]. Consequently, these encryption schemes are not suitable for many highspeed applications due to their slow speed in realtime processing and some other issues such as in the handling of various data formatting.
Over the recent years, considerable researches have been taken to develop new chaotic or hyperchaotic systems and for their promising applications in realtime encryption and communication [6–8]. In fact, it has been shown that chaotic systems are good candidates for designing cryptosystems with desired properties [9]. The most prominent is sensitivity dependence on initial conditions and system parameters, and unpredictable trajectories.
Furthermore, chaosbased and other dynamical systembased algorithms have many important properties such as the pseudorandom properties, ergodicity and nonperiodicity. These properties meet some requirements such as sensitivity to keys, diffusion, and mixing in the cryptographic context. Therefore, chaotic dynamics is expected to provide a fast and easy way for building superior performance cryptosystems, and the properties of chaotic maps such as sensitivity to initial conditions and randomlike behavior have attracted the attention to develop data encryption algorithms suitable for secure multimedia communications. Until recently, chaotic communication has been a subject of major interest in the field of wireless communications. Many techniques based on chaos have been proposed such as additive chaos masking (ACM) [10], where the analog message signal is added to the output of the chaos generator within the transmitter. In [11], chaos shift keying is used where the binary message signal selects the carrier signal from two or more different chaotic attractors. Authors in [12] use chaotic modulation where the message information modulates a parameter of the chaotic generator. Chaos control methods [13, 14] rely on the fact that small perturbations cause the symbolic dynamics of a chaotic system to track a prescribed symbol sequence. In [15], the receiver system is designed in an inverse manner to ensure the recovery of the encryption signal. An impulsive synchronization scheme [16] is employed to synchronize chaotic transmitters and receivers. However, all of these techniques do not provide a real and practical solution to the challenging issue of chaotic communication which is based on extreme sensitivity of chaotic synchronization to both the additive channel noise and parameter mismatches. Precisely, since chaos is sensitive to small variations of its initial conditions and parameters, it is very difficult to synchronize two chaotic systems in a communication scheme. Some proposed synchronization techniques have improved the robustness to parameter mismatches as reported in [16, 17], where impulsive chaotic synchronization and an openloopclosedloopbased coupling scheme are proposed, respectively. Other authors proposed to improve the robustness of chaotic synchronization to channel noise [18], where a coupled lattice instead of coupled single maps is used to decrease the masterslave synchronization error. In [19], symbolic dynamicsbased noise reduction and coding are proposed. Some research into equalization algorithms for chaotic communication systems are also proposed [20]. For other related results in the literature, see [21–23]. However, none of them were tested through a real channel under real transmission conditions. Digital synchronization can overcome the failed attempts to realize experimentally a performed chaotic communication system. In particular, when techniques exhibit any difference between the master/transmitter and slave/receiver systems, it is due to additive information or noise channel (disturbed chaotic dynamics) which breaks the symmetry between the two systems, leading to an accurate nonrecovery of the transmitted information signal at the receiver. In [24], an original solution to the hard problem of chaotic synchronization high sensibility to channel noise has been proposed. This solution, based on a controlled digital regenerated chaotic signal at the receiver, has been tested and validated experimentally in a real channel noise environment through a realized wireless digital chaotic communication system based on zonal intercommunication globalstandard, where battery life was long, which was economical to deploy and which exhibited efficient use of resources, known as the ZigBee protocol. However, this synchronization technique becomes sensible to high channel noise from a higher transmission rate of 115 kbps, limiting the use of the ZigBee and Wireless Fidelity (WiFi) protocols which permit wireless transmissions up to 250 kbps and 65 Mbps [25, 26], respectively. Consequently, no reliable commercial chaosbased communication system is used to date to the best of our knowledge. Therefore, there are still plentiful issues to be resolved before chaosbased systems can be put into practical use. To overcome these drawbacks, we propose in this paper a digital feedback hyperchaotic synchronization and suggest the use of advanced wireless communication technologies, characterized by high noise immunity, to exploit digital hyperchaotic modulation advantages for robust secure data transmissions. In this context, as results of the rapid growth of communication technologies, in terms of reliability and resistance to channel noise, an interesting communication protocol for wireless personal area networks (WPANs, i.e., ZigBee or ZigBee Pro LowRateWPAN protocols) and wireless local area network (WLAN, i.e., WiFi protocol WLAN) is developed. These protocols are identified by the IEEE 802.15.4 and IEEE 802.11 standards and known under the name ZigBee and WiFi communication protocols, respectively [25]. These protocols are designed to communicate data through hostile Radio Frequency (RF) environments and to provide an easytouse wireless data solution characterized by secure, lowpower, and reliable wireless network architectures. These properties are very attractive for resolving the problems of chaotic communications especially the high noise immunity property. Hence, our idea is to associate chaotic communication with the WLAN or WPAN communication protocols. However, this association needs a numerical generation of the chaotic behavior since the XBee protocol is based on digital communications. In the hardware area, advanced modern digital signal processing devices, such as field programmable gate array (FPGA), have been widely used to generate numerically the chaotic dynamics or the encryption keys [27–31]. The advantage of these techniques is that the parameter mismatch problem does not exist contrary to the analog techniques. In addition, they offer a large possible integration of chaotic systems in the most recent digital communication technologies such as the ZigBee communication protocol. In this paper, a wireless hyperchaotic communication system based on dynamic feedback modulation and RF XBee protocols is investigated and realized experimentally. The transmitter and the receiver are implemented separately on two Xilinx VirtexII Pro circuits [32] and connected with the XBee RF module based on the WiFi or ZigBee protocols [26, 33]. To ensure and maintain this connection, we have developed a VHSIC (very high speed integrated circuit) hardware description language (VHDL)based hardware architecture to adapt the implemented hyperchaotic generators, at the transmitter and receiver, to the XBee communication protocol. Note that the XBee modules interface to a host device through a logiclevel asynchronous serial port. Through its serial port, the module can communicate with any logic and voltagecompatible Universal Asynchronous Receiver/Transmitter (UART) [33]. The used hyperchaotic generator is the wellknown and the most investigated hyperchaotic Lorenz system [34]. This hyperchaotic key generator is implemented on FPGA technology using an extension of the technique developed in [27–29] for threedimensional (3D) chaotic systems. This technique is optimal since it uses directly VHDL description of a numerical resolution method of continuous chaotic system models. A number of transmission tests are carried out for different distances between the transmitter and receiver. The realtime results obtained validate the proposed hardware architecture. Furthermore, it demonstrates the efficiency of the proposed solution consisting on the association of wireless protocols to hyperchaotic modulation in order to build a reliable digital encrypted data or image hyperchaotic communication system.
The remainder of this paper is organized as follows: the ‘Hyperchaotic synchronization and encryption technique’ section proposes an adapted feedback hyperchaotic synchronization based on a dynamic feedback modulation. This section details the proposed synchronization and data masking principle by considering hyperchaotic systems. The ‘FPGA implementation of hyperchaotic Lorenz generator’ section briefly introduces continuous Lorenz hyperchaotic system, which is used as key stream generators of the proposed digital encryption hyperchaotic modulation. This section then details the hardware architecture used for implementing the Lorenz hyperchaotic generator. A register transfer level (RTL) architecture for embedded hardware implementation of the considered key stream generator is also given in this section. The ‘Experimental framework’ section presents our experimental framework used to realize and validate the wireless hyperchaotic communication scheme. This section gives details of the transmitter and the receiver blocks with a short description of the XBee RF modules. The ‘Wireless realtime data or image transmission tests and results’ section presents different realtime results proving that the proposed system is suitable for efficient secure realtime data or image transmissions of wireless sensor networks. Performance analysis through real wireless data transmission tests is also discussed in this section. The ‘Security analysis’ section gives the statistical analysis of the proposed image encryption scheme, which increases the complexity of the random bit generation and hence making it difficult for an intruder to extract information about the proposed encryption/decryption hyperchaotic modulation. Finally, the ‘Conclusions’ section draws appropriate conclusions.
Hyperchaotic synchronization and encryption technique
Therefore, the slave/receiver will generate a hyperchaotic behavior identical to that of the master/transmitter allowing to recover correctly the information signal after the demodulation operation. The advantage of this technique is that the information signal d(t) does not perturb the hyperchaotic generator dynamics, contrary to the ACMbased techniques of [10] and [36], because d(t) is injected at both the master/transmitter and slave/receiver after the additive hyperchaotic masking (Figure 1). Thus, for small values of information magnitude [35], the information will be recovered correctly. It should be noted that we have already confirmed this advantage by testing experimentally the HSDFM technique performances for synchronizing hyperchaotic systems (fourdimensional (4D) continuous chaotic systems) in the case of wired connection between two VirtexII Pro development platforms. After many experimental tests and from the obtained realtime results, we concluded that the HSDFM is very suitable for wired digital chaotic communication systems. However, in the present work, one of the objectives is to test and study the performances of the HSDFM technique in the presence of channel noise through realtime wireless communication tests as it is shown in Figure 1. To perform the proposed approach, a digital implementation of the master and slave hyperchaotic systems is required. Therefore, we investigate the hardware implementation of the proposed FHSDFM technique between two Lorenz hyperchaotic generators using FPGA. To achieve this objective, we propose the following details of the proposed architecture.
FPGA implementation of hyperchaotic Lorenz generator
System model and numerical resolution
Hardware architecture
The operating principle of the developed MSM (Figure 5) is as follows:

ST0. In the initial state, all outputs are initialized to 0, and the state variables are initialized by the initial condition values x = x _{0}, y = y _{0}, z = z _{0}, and w = w _{0}. The process then passes unconditionally to the next state ST1.

ST1. Compute the initial slopes k _{0}, m _{0}, l _{0}, and p _{0}, and the first intermediate points u _{1}, v _{1}, r _{1}, and e _{1} defined by the following equations (see Figure 2):$\begin{array}{l}{u}_{1}={x}_{j1}+{k}_{0}/2\end{array}$(6)$\begin{array}{l}{v}_{1}={y}_{j1}+{m}_{0}/2\end{array}$(7)$\begin{array}{l}{r}_{1}={z}_{j1}+{l}_{0}/2\end{array}$(8)$\begin{array}{l}{e}_{1}={w}_{j1}+{p}_{0}/2\end{array}$(9)
At the next clock cycle, the machine passes unconditionally to the next state, ST2.

ST2. Assign the values of the first intermediate points u _{1}, v _{1}, r _{1}, and e _{1} to the variables α, β, γ, and θ, respectively. The use of these variables permits to optimize our architecture. Indeed, we use the same module to calculate all the slopes of the RK4 method, and the same module is used for the calculation of the intermediate points. At the next clock cycle, the machine passes unconditionally to the next state, ST3.

ST3. Compute the slopes k _{1}, m _{1}, l _{1}, and p _{1} and the second intermediate points u _{2}, v _{2}, r _{2}, and e _{2}.${u}_{2}={x}_{j1}+{k}_{1}/2$(10)${v}_{2}={y}_{j1}+{m}_{1}/2$(11)${r}_{2}={z}_{j1}+{l}_{1}/2$(12)${e}_{2}={w}_{j1}+{p}_{1}/2$(13)
At the next clock cycle, the machine passes unconditionally to the next state, ST4.

ST4, ST6. Use the same instructions as stated in ST2; update the variables α, β, γ, and θ, and at the next clock cycle, the process passes unconditionally to the next state.

ST5. Compute the slopes k _{2}, m _{2}, l _{2}, and p _{2} and the last intermediate points u _{3}, v _{3}, r _{3}, and e _{3} defined by the following equations:${u}_{3}={x}_{j1}+{k}_{2}$(14)${v}_{3}={y}_{j1}+{m}_{2}$(15)${r}_{3}={z}_{j1}+{l}_{2}$(16)${e}_{3}={w}_{j1}+{p}_{2}$(17)
At the next clock cycle, the process passes unconditionally to the following state.

ST7. Compute the slopes k _{3}, m _{3}, l _{3}, and p _{3} and then the solutions x, y, z, and w (see Figure 2).

ST8. Assign the hyperchaotic solution values x, y, z, and w to the outputs S _{0}, S _{1}, S _{2}, and S _{3}, respectively. At the next clock cycle, the process passes unconditionally to the final state, ST9.

ST9. In the final state, the actual solution values x, y, z, and w are assigned to the variables α, β, γ, and θ, respectively, to compute the next hyperchaotic solution values. At the next clock cycle, if the counter value, cp, is equal to a defined integer value N, the process goes back again to the first state, ST1, and then the process is revived again for calculating the next solution values. Otherwise, the process stays waiting at state ST9. The value of N is chosen to ensure synchronization between the embedded hyperchaotic generator and an external connected device, permitting the control of the throughput of the embedded hyperchaotic generator.
Synthesis results and performance analysis
Synthesis results
Virtex II  Virtex V  Virtex VI  Virtex VII  

(2vp30ff8967)  (5vlx30ff6763)  (6vcx75tff7842)  (7vx330tffg11573)  
Selected device  
Slices LUTs  2,067 (15%)  4,721 (24%)  3,238 (6%)  3,238 (1%) 
Slice registers  1,150 (4%)  1,105 (5%)  1,100 (1%)  1,100 (< 1%) 
IOBs  130 (23%)  130 (32%)  130 (36%)  130 (21%) 
MULT18X18s  36 (26%)       
DSP48Es    20 (62%)  60 (20%)  60 (5%) 
GCLKs  1 (6%)  1 (3%)  1 (3%)  1 (3%) 
Maximum frequency (MHz)  25.364  36.271  28.507  35.842 
To evaluate the performance of the proposed hardware implementation, the throughput rate and time latency metrics are used. In our case, the throughput rate (defined as the number of bits per unit of time) corresponds to 32 bits of wordlength for ten operating clock cycles (for number of MSM states, see Figure 5) after the initialization phase at the output of the FPGA circuit.
Latency is defined as the time required to generate one single wordlength signal after the start of the generator. Therefore, a minimum and maximum throughput of 81.16 and 116 Mbps with a maximum and minimum time latency of 394.25 and 275.7 ns are obtained for Virtex II and Virtex V technologies, respectively. The results prove that our hardware architecture can be implemented in the recent FPGA devices with a significant amelioration of its performances in terms of throughput and logic area cost.
Experimental framework
XBee RF modules
Transmitter architecture

Clock_Generator. This module generates and provides the clock (clk_out) and reset signals to the two other modules. The frequency of the clock signal is imposed either by the serial interface data rate of the XBee modules (see Table 2) or the implemented logic blocks in the FPGA. Depending on the maximum data rate allowed, the clk_out signal frequency is defined for driving the transmitter architecture. To obtain this frequency, we have implemented a clock divider architecture to derive it from the frequency of the 100 MHz global clock signal (clk_sys) provide by the Xilinx VirtexII Pro XC2VP30 FPGA.

Lorenz_Generator. This module represents the main module of the proposed transmitter architecture. It is based on the hardware architecture already detailed and presented in the ‘FPGA implementation of hyperchaotic Lorenz generator’ section for implementing the hyperchaotic Lorenz system but with some modifications introduced in the MSM of Figure 5 in order to adapt with the hyperchaotic generator data output S to the XBee module data input R _{ x }. The adaptation includes introducing a 2bit control signal (cmd) in the hyperchaotic generator architecture which will control the operation of the parallel/serial converter module (PISO) to provide data frames according to the XBee communication protocols (Zigbee or WiFi) [26, 33]. Therefore, the changes in the Lorenz_Generator architecture are as follows :
At the initial state ST0, we set cmd = ‘11’, and the process follows the same steps as in the ‘FPGA implementation of hyperchaotic Lorenz generator’ section until reaching state ST7. However, to realize our solution, we have added an 11th state to the MSM, and then from the state ST8, the next steps are as follows:
ST8. We realize the modulation operation using additive chaos masking of [10] and [36], i.e., the information signal samples d are masked by those of the hyperchaotic signal x and then the result is assigned to the output of the module S _{0} = x + d. At the same time, we put cmd = ‘10’, and the process passes unconditionally to state ST9.

ST9. At this state, we realize the dynamic feedback operation, used by the FHSDFM technique, by assigning the additive hyperchaos masking results S _{0} to the variable α. Hence, the actual sample S _{0} is injected to the master dynamics, and it is used to generate the next hyperchaotic sample x. We put cmd =‘00’, and the passage to the next state, ST10, is controlled by the parameter value T of the counter ct. More details are given in the next paragraph.

ST10. This tenth state is added to control the eventual distance between two successive data frames which can imposed by the XBee communication protocols [26, 33]. This is ensured by the parameter value N of the counter cp as shown in Figure 9. In this last state, cmd is set to the value 11.


PISO. This module is a binary parallel/serial converter. Under the command signal cmd, the module converts the parallel data samples S _{0} (coded in 32 bits), received from the Lorenz_Generator module after the modulation operation, to a serial data. The command signal values are presented in Table 3. The transmitted data frame is formed by four successive data frames of 8 bits that started and ended by a start bit (‘0’) and a stop bit (‘1’), respectively. Therefore, the data frame wordlength is T = 40 bits. Note that this data format is imposed by the XBee RF module’s communication protocols [26, 33].
Wireless performance and data or image transmission results
Protocol  Zigbee (WPAN)  WiFi (WLAN) 

Maximum distance (indoor/urban range)  Up to 100 ft (30 m)  Up to 120 ft (32 m) 
Transmit power output of Xbee modules  1 mW (0 dBm)  >15 dBm 
Maximum data bit rate (modulation rate)  250 Kbps (6,250 baud)  25.364 Mbps (634,100 baud) 
Maximum RF data rate  250 Kbps  54 Mbps 
Supply voltage  2.8 to 3.4 V  3.1 to 3.6 V 
Operating frequency modulation  ISM 2.4 GHz  ISM 2.4 to 2.5 GHz 
PISO commands
Value  Command 

10  Form the serial data frame 
00  Start serial transmission 
11  Wait and put the line at the high level 
Receiver architecture

SIPO. This module is a binary serialtoparallel converter. Once the start bit is detected at the receiver by the SIPO module, the serial/parallel conversion of the received data begins. At the same time, the module generates a clock signal (clk_lz) at the same frequency as the clk_out clock signal generated by the Clock_Generator module. This means that the generation of the clk_lz clock signal is triggered at each start bit detection. This constitutes our interesting solution to overcome the problem of shifting data frames at the XBee RF module output (T _{ x }). Indeed, this solution permits to adapt and to synchronize the execution of the implemented receiver architecture to the cadence of the XBee RF module data output (T _{ x }). However, to synchronize the SIPO and the Lorenz_Generator modules, we use a 1bit command signal (cm). Initially, this last one is set to ‘0’, and it is set to ‘1’ during one clock period only when the converted 32bit parallel data S _{ r } are available at the output of the SIPO module, i.e., the serialtoparallel conversion is finished.

Lorenz_Generator. This module is similar to the hyperchaotic Lorenz generator used at the transmitter. This means that it generates the hyperchaotic keys with the same values of the parameters and initial conditions at the transmitter in order to allow the hyperchaotic synchronization as proposed in the ‘Hyperchaotic synchronization and encryption technique’ section. Under the command signal cm and the rhythm of the clk_lz clock signal, the module regenerates identical hyperchaotic samples (x _{ r }) to that of the transmitter. It synchronizes them with the received and parallel converted data samples S _{ r } and recovers the information data d after a demodulation operation based on the arithmetic subtraction. Finally, it controls the paralleltoserial conversion operation of the PISO module using the 2bit command signal cd. The cd commands are the same as those of cmd command signal presented in Table 3. As the transmitter architecture, the Lorenz_Generator module uses the same state machine presented in Figure 5, but with the modifications introduced and presented in Figure 11.
At the first clk_lz clock cycle, the module starts the execution of the state machine instructions from ST0 to ST7. Therefore,
At the synchronization step ST8, we assign the generated hyperchaotic samples x to the output x _{ r } and the received and parallel converted data S _{ r } to the output S _{ r } (Figure 11). Note that we have used the same parameter name S _{ r } for the parallel converted data at the input and the output of the Lorenz_Generator module because no change is made for this data at this module. Therefore, if the command signal cm is set to 1, i.e., the received data S _{ r } are available at the output of the SIPO module, then the process passes to the next state, ST9. Otherwise, it stays waiting at the current state.

At the demodulation step ST9, after the synchronization of the x _{ r } and S _{ r } samples at the previous state, the information data d is recovered using the subtraction arithmetic operation d = S _{ r }  x _{ r }, and then the command signal cd takes the value 10. Finally, the process passes unconditionally to the next state, ST10.

At the last state ST10, the command signal cd takes the value 00, and the received data sample S _{ r } is assigned to the parameter α, which is used to generate the next hyperchaotic sample x, instead of the actual generated hyperchaotic sample x. This instruction permits to realize the hyperchaotic synchronization principle presented in Figure 6, in which the received signal S(t) is injected to the dynamic of the hyperchaotic generator of the receiver. Finally, after N clock cycles, the process passes to state ST1. The value of N is fixed by the time needed by the PISO module to finish the paralleltoserial conversion of the hyperchaotic samples x _{ r }. Then, in our experiments, N = 33 clock cycles, considering that the samples x _{ r } are encoded on 32 bits.

PISO. This module is similar to that in the transmitter block. Under the command signal cd, it converts the 32bit parallel outputs of the Lorenz_Generator module, i.e., the regenerated hyperchaotic samples x _{ r }, the received data samples S _{ r }, and the recovered data information d, to 32bit serial frames. This module is introduced in the architecture just for validating the proposed approach by realtime viewing and comparing these serial data frames on a digital oscilloscope.

Wireless realtime data or image transmission tests and results
The main considered application is the security of wireless sensor networks (WSNs) which are becoming more and more important, and they can gain advantage of reconfigurable technology, in terms of flexibility, energy consumption, and sensor lifetime. This is particularly true for the networks of data diffusion based on embedded systems, which can be used for the protocol communication. Indeed, a WSN provides different aspects in the sharing of information by deploying a system that is able to execute wireless exchange of data, image, or video [41] according to transmission rate performance. Subsequently, we considered the wireless data or image transmission with the Zigbee and WiFi protocols in a WSN context.
The ciphered data or image is transmitted through a public and unsecure channel. Using selfsynchronization technique at the receiver side [32], the key can be recovered at the receiver and the decryption operation from the transmitted scrambled image with the regenerated key, allowing us to recover the plain image.
In the experimental transmission tests, we use binary data encoded on 32bit fixedpoint format with hexadecimal representation as information signal. At the transmitter, these information data are masked by the hyperchaotic samples x of the Lorenz_generator encoded also on 32bit fixedpoint data format. The encrypted signal samples S _{ i }(t) are then converted to serial data format by the PISO module, sent to its associate XBee Pro RF module according to the considered protocol (Zigbee or WiFi), and then transmitted to the receiver. At the receiver, the associated XBee Pro RF module transmits the received data to the SIPO module according to the asynchronous serial communication protocol to regenerate the received encrypted signal sample format S _{ r }(t), allowing for the hyperchaotic synchronization and recovery of the masked information d(t) according the proposed scheme depicted in Figure 1.
Table 2 summarizes experimentally the performance and results in terms of digital transmission rate (symbol rate or modulation rate), maximum distance, and frequency modulation according to the considered wireless protocols by the Xbee RF modules and Virtex II technology. For these measurements, we have placed the transmitter and the receiver at two neighboring rooms at the distance about 20 m with a received signal strength indicator (RSSI) of 2 dBm. With this disposition, we obtain a packet error rate (PER) of 0% at the receiver. The maximum distance that we can obtain experimentally between the transmitter and the receiver (indoor range) is more than 30 and 32 m for Zigbee and WiFi protocols, respectively. Therefore, the indoor/urban ranges of the XBee RF modules used are up to 30 m (see Table 2), and the sensitivity of the XBee RF module receivers reaches 92 dBm with a PER of 1% [33]. The XBee modules offer the advantage to realize a wireless communication application without errors (PER = 0%) according to environment application, distance, disposition, and channel chosen as is shown in [42].
The maximum bit rate of the proposed system is limited either by the RF modules or implemented hardware logic blocks. Indeed, the hardware FPGA implementations allow parallel/serial and serial/parallel converters with minimum and maximum rates of 25.36 and 36.27 Mbps obtained with Virtex II and Virtex V technologies, respectively (see Table 1). However, for the considered Zigbee protocol, the maximum bit rate of the proposed system is limited by the RF modules. The hardware FPGA implementation performance (for working clock frequency, see Table 1) of the proposed system (at the transmitter and receiver) is considered, and it is larger than the serial interface data rate and better than the bit rate, which is allowed by the considered Zigbee RF modules. Consequently, the limitation is imposed by the transmission rate of the parallel/serial and serial/parallel converters toward Zigbee Xbee RF modules while FPGA implementations allow to provide transmission rates to at least 25 Mbps. Thereby, for a null distance frame value (N = 0), the obtained data bit rate of the serial communication is 250 kbps (corresponding to a modulation rate of 625 symbols per second or baud, due to the maximum serial interface data rate of the Zigbee protocol based XBee Pro modules [33], and with an operating frequency modulation of 2.4 GHz. In the case of the WiFi protocol, the maximum bit rate of the proposed system is limited by the work frequency of the implemented hardware modules. Although the FPGA implementation of the Lorenz generator allows to provide a throughput of 80 Mbps (maximal frequency of 25 MHz is allowed by the considered Virtex II platform with an encoded 32bit encrypted data), the maximum transmission bit rate of the proposed system is limited by the hyperchaotic key generators up to 25 Mbps (for N = 0) and by a corresponding modulation rate of 625,000 baud. Therefore, the parallel/serial and serial/parallel converters to WiFi Xbee RF modules limit the transmission rate up to the maximum work frequency, depending on the considered FPGA technology. Indeed, each symbol of the data transmission system carries 40 bits according to the data frame wordlength allowed by the serial interface Xbee modules. This digital modulation rate operates with a frequency modulation range between 2.4 and 2.5 GHz [26]. In summary, considering a synchronous system at the maximum work frequency allowed between the key stream generators and parallel/serial or serial/parallel converters, the limitations with respect to 54Mbps and 25kbps bit rates of the WiFi and Zigbee RF modules are due to the work frequencies of hyperchaotic Lorenz generators and the serial interface data rate, respectively.
In Figure 13, we present the first three successive recovered information data frames for an RSSI of 70 dBm and a PER of about 1%. These results show that the information data are totally lost because of the PER of 1%. This confirms the extreme sensibility of chaotic synchronization to small channel perturbation.
Security analysis
To test the robustness of the proposed scheme, security analysis including statistical analysis and differential analysis was performed. This evaluation of the quality of randomness is carried out to demonstrate the satisfactory security of the new proposed chaosbased cryptosystem.
NIST statistical analysis
NIST tests results
Statistical tests  P valueT  Proportion 

Frequency  0.644060  0.9800 
Block frequency  0.023545  0.9833 
Cumulative sum up  0.671779  0.9900 
Cumulative sum down  0.699313  0.9933 
Runs  0.117661  0.9933 
Longest run  0.630178  0.9967 
Rank  0.840081  0.9867 
Discrete Fourier transform  0.100508  1.0000 
Nonoverlapping templates  0.588652  0.9833 
Overlapping templates  0.547637  1.0000 
Universal  0.568055  0.9833 
Approximate entropy  0.162606  0.9933 
Random excursions  0.249991  0.9947 
Random excursion variant  0.711827  0.9840 
Serial 1  0.706149  0.9833 
Serial 2  0.110952  0.9833 
LempelZiv value  0.071177  0.9900 
Linear complexity  0.487885  0.9967 
Histogram and differential analysis
To avoid the knownplaintext attack and the chosenplaintext attack, the changes in the ciphered image should be significant even with a small change in the original one. According to the proposed encryption process, this small difference should be diffused to the whole ciphered data, with respect to diffusion and confusion. Consequently, this differential attack would become very inefficient and practically useless. Generally, the differential analysis can be reflected by the number of pixels’ change rate (NPCR) and unified average changing intensity (UACI) evaluations [44]. NPCR stands for the number of pixels’ change rate while one pixel of plain image changed. The more NPCR gets close to 100%, the more sensitive the cryptosystem to the changing of plain image is and then the more effective for the cryptosystem to resist plaintext attack. UACI measures the average intensity of differences between two encrypted images. Currently, the bigger the UACI, the more effective is the cryptosystem to resist at a differential attack.
Results of NPCR and UACI tests
Plain image  NPCR (%)  UACI (%) 

Cameraman  99.586  34.77 
In summary, this security analysis proves that encrypted and synchronized generated signal is nonperiodic and has a flat spectrum which is suitable for encryption image scheme by showing a robustness against plaintext attacks.
Conclusions
This paper proposes an experimental demonstration of a wireless hyperchaotic communication based on wireless communication protocols suitable for secure realtime data or image transmissions in wireless sensor networks. We have proposed a new digital synchronized modulation based on FHS through a DFM technique between two hyperchaotic generators. The choice of a DFM principle for implementing the FHS between two identical hyperchaotic systems of Lorenz in FPGA shows more robustness than the classic chaotic masking while allowing high transmission rates. In practice, we have associated and adapted the hyperchaotic communication principle with the XBee RF modules by developing a reconfigurable VHDLbased hardware architecture implemented on FPGA technology. Indeed, the proposed system can be used as hard key generator in a hyperchaotic synchronized data or image stream cipher/decipher, and it can be used for synchronizing any fourdimensional continuous chaotic system (such as hyperchaotic Lorenz system) where the master chaotic system is embedded in the proposed FPGA transmitter side and the slave chaotic system is embedded in the FPGA receiver side. Many realtime transmission tests are carried out between two distanced Virtex IIPro Xilinx FPGA platforms. The obtained realtime results show the efficiency of the proposed idea consisting on associating the hyperchaotic communication, and the ZigBee or WiFi communication protocols characterized by high immunity against channel noise. Indeed, we could recover correctly the information data on the distance about 20 m using the XBee RF modules at 2 dBm with a PER of 0%. Note that these performances can be improved using the most recent XBee modules. Experimental results applied to image encryption have demonstrated that our approach exhibits attractive performances and is useful in the field of realtime secure wireless data communications. The proposed technique may make it more applicable in such field (video, image, internet, etc.) and for all type of wireless network. Indeed, thorough experimental tests have been carried out with detailed numerical analysis, demonstrating the high security of the new data or image encryption scheme. More precisely, the proposed approach used to design a secure symmetric image encryption increases its resistance to various attacks such as statistical and key analysis attacks and can avoid the hidden security attacks in realtime applications. Finally, our perspective for the presented work consists on developing and realizing a secure wireless hyperchaotic communication network using the proposed modulation system.
Declarations
Authors’ Affiliations
References
 Spanos GA, Maples TB: Performance study of a selective encryption scheme for the security of networked, realtime video. In Proceedings of the 4th International Conference on Computer Communications and Networks, Las Vegas, 2023 Sept 1995. IEEE, Piscataway; 1995:210.Google Scholar
 Yang T: A survey of chaotic secure communication systems. Int. J. Comput. Cogn 2004, 2(2):81130.Google Scholar
 Schneier B: Applied Cryptography: Protocols, Algorithms, and Source Code in C. Wiley & Sons, New York; 1996.Google Scholar
 Zambreno J, Nguyen D, Choudhary AN: Exploring area/delay tradeoffs in an AES FPGA implementation. Field Programmable Logic and Applications, ed. by J Becker, M Platzner, S Vernalde. Proceedings of the 14th International Conference, FPL 2004, Leuven, 30 August1 September 2004. Lecture Notes in Computer Science, vol. 3203. (Springer, Heidelberg, 2004), pp. 575–585
 Yi X, Tan CH, SC K, Syed MR: Fast encryption for multimedia. IEEE Trans. Consum. Electron 2001, 47(1):101107. 10.1109/30.920426View ArticleGoogle Scholar
 Kennedy MP, Kolumban G: Digital communication using chaos. Signal Process 2000, 80: 13071320. 10.1016/S01651684(00)000384View ArticleGoogle Scholar
 Alvarez G, Li S: Some basic cryptographic requirements for chaosbased cryptosystems. Int. J. Bifurcation Chaos 2006, 44: 21292151.MathSciNetView ArticleGoogle Scholar
 Lin JS, Liao TL, Huang CF, Yan J: Design and implementation of digital secure communication based on synchronized chaotic systems. Digit. Signal Process 2010, 20: 229237. 10.1016/j.dsp.2009.04.006View ArticleGoogle Scholar
 Chang W: Digital secure communication via chaotic systems. Digit. Signal Process 2008, 19: 693699.View ArticleGoogle Scholar
 Cuomo KM, Oppenheim AV, Trogatz SHS: Synchronization of Lorenzbased chaotic circuits with applications to communications. IEEE Trans. Circuits Syst. II: Analog Digit. Signal Process 1993, 40(10):626633. 10.1109/82.246163View ArticleGoogle Scholar
 Dedieu H, Kennedy MP, Hasler M: Chaos shift keying: modulation and demodulation of a chaotic carrier using selfsynchronizing Chua’s circuits. IEEE Trans. Circuits Syst. II: (Special Issue on Chaos in Nonlinear Electronic Circuits Part C: Applications) 1993, 40: 634642.Google Scholar
 Halle KS, Wah WC, Itoh M, Chua LO: Spread spectrum communication through modulation of chaos. Int. J. Bifurcation Chaos 1993, 3: 469477. 10.1142/S0218127493000374View ArticleGoogle Scholar
 Hayes S, Grebogi C, Ott E: Communicating with chaos. Phys. Rev. Lett 1993, 70: 30313034. 10.1103/PhysRevLett.70.3031View ArticleGoogle Scholar
 Lai YC, Bollt E, Grebogi C: Communicating with chaos using twodimensional symbolic dynamics. Phys. Lett. A 1999, 255: 7581. 10.1016/S03759601(99)001759View ArticleGoogle Scholar
 Feldmann U, Hasler M, Schwarz W: Communication by chaotic signals: the inverse system approach. Int. J. Circuit Theory Appl 1996, 24: 551579. 10.1002/(SICI)1097007X(199609/10)24:5<551::AIDCTA936>3.0.CO;2HView ArticleGoogle Scholar
 Yang T: A survey of chaotic secure communication systems. Int. J. Comput. Cogn 2004, 2(2):81130.Google Scholar
 Grosu I, Padmanaban E, Roy PK, Dana SK: Designing coupling for synchronization and amplification of chaos. Phy. Rev. Lett 2008, 100: 234102.View ArticleGoogle Scholar
 Eisencraft M, Batista AM: Discretetime chaotic systems synchronization performance under additive noise. IEEE Trans. Circuits Syst. II: Analog Digit. Signal Process 2011, 91: 21272131.Google Scholar
 Schweizer J, Schimming T: Symbolic dynamics for processing chaotic signalsI: noise reduction of chaotic sequences. IEEE Trans. Circuits Syst. I 2001, 48: 12691282. 10.1109/81.964416MathSciNetView ArticleGoogle Scholar
 Ciftci M, Williams DB: Optimal estimation sequential channel equalization algorithms for chaotic communications systems. EURASIP J. Appl. Signal Process 2001, 4: 249256.View ArticleGoogle Scholar
 Wang X, Wang Z: A robust demodulation application communication using chaotic signals. Int. J. Bifurcation Chaos 2003, 13: 227231. 10.1142/S0218127403006479View ArticleGoogle Scholar
 Murali K: Heterogeneous chaotic systems based cryptography. Phys. Lett. A 2000, 272: 184192. 10.1016/S03759601(00)004205MathSciNetView ArticleGoogle Scholar
 Li S, Alvarez G, Chen G, Mou X: Breaking a chaosnoisebased secure communication scheme. Chaos 2005, 15: 013703. 10.1063/1.1856711View ArticleGoogle Scholar
 Sadoudi S, Tanougast C, Azzaz MS: First experimental solution for channel noise sensibility in digital chaotic communications. Prog. Electromagnetics Res. C 2012, 32: 181196.View ArticleGoogle Scholar
 Ergen SC: ZigBee/IEEE 802.15.4 Summary. IEEE, Piscataway; 2004.Google Scholar
 Digi International Inc: XBee WiFi RF module, Product Manual v1.xEx  802.11 Protocol. Digi International Inc., Minnetonka; 2011.Google Scholar
 Sadoudi S, Tanougast C, Azzaz MS, Dandache A, Bouridane A: Embedded GenesioTesi chaotic generator for ciphering communications. 7th International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), Newcastle, 2123 July 2010. IEEE, Piscataway; 2010.Google Scholar
 Sadoudi S, Tanougast C, Azzaz MS, Dandache A, Bouridane A: Realtime FPGA implementation of Lü’s chaotic generator for cipher embedded system. International Symposium on Signals, Circuits and Systems (ISSCS), Iasi, 910 July 2009. IEEE, Piscataway; 2009.Google Scholar
 Sadoudi S, Azzaz MS, Djeddou M, Bensalah M: An FPGA realtime implementation of the Chen’s chaotic system for chaotic communications. Int. J. Nonlinear Sci 2009, 7(4):467474.MathSciNetGoogle Scholar
 Sobhy MI, Aseeri MA, Shehata AER: Real time implementation of continuous (Chua and Lorenz) chaotic generator models using digital hardware. In Proceedings of the Third International Symposium on Communication System Networks and Digital Processing (CSNDSP), Staffordshire, 1517 July 2002. IEEE, Piscataway; 2002:3841.Google Scholar
 Indrusiak LS, Junior ECDS, Glesner M: Advantages of the LinzSprott weak nonlinearity on the FPGA implementation of chaotic systems: a comparative analysis. Proceedings of the Int. Symp. Signals, Circuits and Sys 2005, 2: 753756.Google Scholar
 Xilinx Inc.: Xilinx University Program VirtexII Pro Development System, UG69 (v1.1). Xilinx Inc., San Jose; 2008.Google Scholar
 Digi International Inc: Product Manual v1.xEx  802.15.4 Protocol. Digi International Inc., Minnetonka; 2009.Google Scholar
 Barboza R: Dynamics of a hyperchaotic Lorenz system. Int. J. Bifurcation Chaos 2007, 17(12):42854294. 10.1142/S0218127407019950MathSciNetView ArticleGoogle Scholar
 Milanovic V, Zaghloul ME: Improved masking algorithm for chaotic communications systems. Elec. Lett 1996, 32: 1112. 10.1049/el:19960004View ArticleGoogle Scholar
 Kocarev KML, Halle KS, Eckert K, Chua LO: Experimental demonstration of secure communications via chaotic synchronization. Int. J. Bifurcation Chaos 1992, 2: 709713. 10.1142/S0218127492000823View ArticleGoogle Scholar
 Xilinx Inc: Virtex 5 FPGAs datasheet. Xilinx Inc., San Jose; 2008.Google Scholar
 Xilinx Inc: Virtex 6 series fpgas configurable logic block. Xilinx Inc., San Jose; 2012.Google Scholar
 Xilinx Inc: Virtex 7 FPGAs datasheet. Xilinx Inc., San Jose; 2013.Google Scholar
 Analog Devices: AC’97 SoundMAX Codec, AD1881A datasheet. Analog Devices, Norwood; 2000.Google Scholar
 Yick J, Mukherjee B, Ghosal D: Wireless sensor network survey. Comput. Netw 2008, 52(12):22922330. 10.1016/j.comnet.2008.04.002View ArticleGoogle Scholar
 Centeno A, Alford N: Measurement of ZigBee wireless communications in modestirred and modetuned reverberation chamber. Prog. Electromagnetics Res. M 2011, 18: 171178.View ArticleGoogle Scholar
 Rukhin A, Soto J, Nechvatal J, Smid M, Barker E, Leigh S, Levenson M, Vangel M, Banks D, Heckert A, Dray J, Vo S: A statistical test suite for random and pseudorandom number generators for cryptographic applications. Technical report, NIST Spec. Publication 80022 Revision 1a, NIST, Gaithersburg, 2010Google Scholar
 Wu Y, Noonan J, Agaian S: NPCR and UACI randomness tests for image encryption. Cyber Journals: Multidisciplinary Journals in Science and Technology. J. Selected Areas Telecommunications 2011, 1(4):April 2011 Edition, 3138.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.