Generalized switching table.
\r\n\t2) The divergence between the levels of reliability required (twelve-9’s are not uncommon requirements) and the ability to identify or test failure modes that are increasingly unknown and unknowable
\r\n\t3) The divergence between the vulnerability of critical systems and the amount of damage that an individual ‘bad actor’ is able to inflict.
\r\n\t
\r\n\tThe book examines pioneering work to address these challenges and to ensure the timely arrival of antifragile critical systems into a world that currently sees humanity at the edge of a precipice.
Advanced control of electrical machines requires an independent control of magnetic flux and torque. For that reason it was not surprising that the DC machine played an important role in the early days of high-performance electrical drive systems, since the magnetic flux and torque are easily controlled by the stator and rotor current, respectively. The introduction of field oriented control meant a huge turn in the field of electrical drives, since with this type of control the robust induction machine can be controlled with a high performance. Later in the 1980s, a new control method for induction machines was introduced: The direct torque control (DTC) method. It was proposed by Takahashi and Depenbrock [1, 2]. It bases on the direct selecting of the switching states to control the voltage source inverter (VSI) through a switching look-up table. Due to the limits of the conventional DTC strategy, especially the high torque and flux ripples problem, various control structures are presented to improve the performances of control, [3, 4]. The constant switching frequency DTC using the space vector modulation (DTC-SVM) is a well discussed solution; in order to improve the DTC-SVM performances, hysteresis comparators of electromagnetic torque and stator flux have been replaced by PI controllers, [5, 6]. The main drawbacks of DTC-SVM using PI controllers are the sensitivity of the performances to the system-parameter variations and the inadequate rejection of external disturbances and load changes [7, 8, 9, 10, 11]. To cope with this disadvantage, it is suggested to replace the conventional regulators used for the speed control, flux, and electromagnetic torque by intelligent controllers by fuzzy logic to make the controls more robust against the disturbances of the parameters of the machine. The aim of this chapter is to design and compare three strategies for the direct torque control (DTC) of induction motor (IM). The first method is a conventional direct torque control (C-DTC) where the torque and the flux are regulated by the hysteresis controllers. The second one is direct torque control by space vector modulation strategy (SVM-DTC) where the torque and flux are regulated by PI controllers. The third one is fuzzy SVM-DTC with adaptive fuzzy-PI speed controller where the torque and flux are regulated by fuzzy logic controllers. The main feature of the proposed (SVM-DTC) strategy is the reduction of torque and flux ripples.
The mathematical model of induction motor can be described in the stator fixed reference frame (α,β) (stationary frame) by assuming the rotor and the stator flux as state variables:
with
Rs, Rr are stator and rotor resistance.
The rotor motion can be described by:
where J is the motor inertia, Tem is the electromagnetic torque, TL is the load torque, and f is the friction coefficient.
Two-level three-phase voltage source inverter (VSI) is considered as a mature technology and becoming an industrial standard for the demand for energy saving. The output phase voltages are produced by the rectifier (Vdc) is delivered to the inverter input, which, thanks to controlled transistor switches, converts this voltage to three-phase AC voltage signal with wide range variable voltage amplitude and frequency.
The type of the used switches depends on the power of the inverter and switching frequency. In the most applications, IGBT transistors with antiparallel diodes are so helpful.
The model of two-level voltage inverter is shown in Figure 1.
Three-phase VSI fed star-connected induction machine.
Figure 1 shows the two-level three-phase voltage source inverter (VSI) with six transistor switches, S1–S6, and a dc constant voltage source Vdc connecting a three-phase load.
The voltage vector is generated by the following equation:
where Sa, Sb, and Sc are three-phase inverter switching functions, which can take a logical value of either 0 or 1.
Direct torque control principle was introduced in the late 1980s by [1, 2]. It achieves a decoupled control of the stator flux and the electromagnetic torque in the stationary frame (α, β), and it allows induction machines to have an accurate and fast electromagnetic torque response. It uses a switching table for the selection of an appropriate voltage vector. The selection of the switching states is related directly to the variation of the stator flux and the torque of the machine. Hence, the selection is made by restricting the flux and torque magnitudes within two hysteresis bands. Those controllers ensure a separated regulation of both of these quantities [12, 13, 14]. The inputs of hysteresis controllers are the flux and the torque errors as well as their outputs determine the appropriate voltage vector for each commutation period.
Basing on the induction motor model in stationary frame, the stator flux equation can be expressed as follows [15, 16, 17, 18, 19]:
Considering that the control of the switches of the inverter is done by control period (or sampling)
A vector inscription of this expression can be given by:
We can neglect the stator resistance voltage drop compared to Vs for high speed regions. Then Eq. (6) can be written as:
Eq. (7) means that the stator flux can be changed by the application of stator voltage during a time k. The stator flux vector’s extremity moves in direction given by the voltage vector and making a circular trajectory.
A two-level hysteresis comparator is used for flux regulation. It allows to drop easily the flux vector extremity within the limits of the two concentric circles with close radius. The choice of the hysteresis bandwidth depends on the switching frequency of the inverter Figures 2 and 3.
Evolution of stator flux vector in the complex plan.
Two-level hysteresis comparator for stator flux control.
The logical outputs of the flux controller are defined as:
where
The stator flux error is defined by the difference between the reference value of flux and the actual estimated value:
During one sampling period, the rotor flux vector is supposed invariant. The rotor and stator flux vectors are linked by the following relation:
The angle between these two vectors is given by:
Finally, between the modules of the two flux vectors, we have the following relation:
The general expression of electromagnetic torque is given by:
where:
δ angle between the stator and rotor flux vectors.
From expression (14), it is clear that the electromagnetic torque is controlled by the stator and rotor flux amplitudes. If those quantities are maintaining constant, the torque can be controlled by adjusting the load angle δ.
The torque regulation can be realized using three-level hysteresis comparator.
(Figure 4).
Three-level hysteresis comparator for electromagnetic torque control.
The logical outputs of the torque controller are defined as:
where
The torque error is defined by the difference between the references values of the torque and the actual estimated values:
The amplitude of the stator flux is estimated from its two-phase components
Or
The stator voltage components
The stator currents components
The produced electromagnetic torque of the induction motor can be determined using the cross product of the stator quantities (i.e., stator flux and stator currents). The torque formula is expressed as the following:
To maintain a decoupled control, a pair of hysteresis comparators receives the stator flux and torque errors as inputs. Then, the comparators outputs determine the appropriate voltage vector selection. However, the choice of voltage vector is not only depending on the output of hysteresis controllers but on the position of stator flux vector also. Thus, the circular stator flux vector trajectory will be divided into six symmetrical sectors (Table 1).
Increases | Decreases | |
---|---|---|
Vi−1 and Vi+1 | Vi+2 and Vi−2 | |
Vi+1 and Vi+2 | Vi−1 and Vi−2 |
Generalized switching table.
For each sector, the vectors (Vi and V3+i) are not considered because both of them can increase or decrease the torque in the same sector according to the position of flux vector on the first or the second sector. If the zero vectors V0 and V7 are selected, the stator flux will stop moving, its magnitude will not change, and the electromagnetic torque will decrease, but not as much as when the active voltage vectors are selected. The resulting look-up table for DTC which was proposed by Takahashi is presented in Table 2.
Flux | Torque | 1 | 2 | 3 | 4 | 5 | 6 | Comparator |
---|---|---|---|---|---|---|---|---|
Cflx = 1 | Ctrq = 1 | V2 | V3 | V4 | V5 | V6 | V1 | Two-level |
Ctrq = 0 | V7 | V0 | V7 | V0 | V7 | V0 | ||
Ctrq = −1 | V6 | V1 | V2 | V3 | V4 | V5 | Three-level | |
Cflx = 0 | Ctrq = 1 | V3 | V4 | V5 | V6 | V1 | V2 | Two-level |
Ctrq = 0 | V0 | V7 | V0 | V7 | V0 | V7 | ||
Ctrq = −1 | V5 | V6 | V1 | V2 | V3 | V4 | Three-level |
Look-up table for basic direct torque control.
The global control scheme of conventional direct torque control strategy is shown in Figure 5. It is composed of speed regulation loop; the proportional-integral (PI) controller is used for the regulation. It is performed by comparing the speed reference signal to the actual measured speed value. Then the comparison error becomes the input of the PI controller. The pole placement method is used to determine the controller gains. The used PI controller in our work in the outer speed loop is the anti-windup controller. It allows to enhance speed control performance by canceling the windup phenomenon which is caused by the saturation of the pure integrator [20]. Figure 6 shows the speed anti-windup PI controller diagram block.
Global control scheme of basic direct torque control.
Speed anti-windup PI controller.
This strategy consists on the correction of the integral action based on the difference between the control signal and the saturation limit. The difference value is passed through a gain block (tracking time constant Ti) before arriving as feedback to the integrator. As well flux and torque hysteresis controllers, look-up switching table, an association of VSI-Induction motor, voltage and current calculation blocks with 3/2 (Concordia) transformation and flux/torque estimators with position/sector determination.
The conventional direct torque control has several disadvantages, among which the variable switching frequency and the high level of ripples. Consequently, they lead to high-current harmonics and an acoustical noise and they degrade the control performance especially at low speed values. The ripples are affected proportionally by the width of the hysteresis band. However, even with choosing a reduced bandwidth values, the ripples are still important due to the discrete nature of the hysteresis controllers. Moreover, the very small values of bandwidths increase inverter switching frequency. In order to overcome these drawbacks, most of the studies presented in the literature have been oriented towards modification in the conventional DTC method by the introduction of a vector modulator [21, 22]. The vector PWM technique (SVM) is used to apply a voltage vector with a fixed switching frequency. The control system consists of replacing the switching table and the hysteresis comparators with proportional and integrating controllers (PI) for controlling the stator flux and the electromagnetic torque, [6, 23, 24, 25, 26, 27]. The main drawbacks of DTC-SVM using PI controllers are the sensitivity of the performances to the system-parameter variations and the inadequate rejection of external disturbances and load changes [28, 29]. To cope with this disadvantage, it is suggested to replace the conventional regulators used for the speed control, flux, and electromagnetic torque by intelligent controllers by adaptive fuzzy-PI and fuzzy logic to make the control more robust against the disturbances of the parameters of the machine.
This technique is much requested in the field of control in that the reference voltages are given by a global control vector approximated over a modulation period Tz. The principle of SVM is the prediction of inverter voltage vector by the projection of the reference vector
Diagram of voltage space vector.
The application time for each vector can be obtained by vector calculations, and the rest of the time period will be spent by applying the null vector.
When the reference voltage is in sector 1 (Figure 8), it can be synthesized by using the vectors V1, V2, and V0 (zero vector).
Reference vector as a combination of adjacent vectors at sector 1.
The determination of times T1 and T2 corresponding to voltage vectors are obtained by simple projections (Figure 9).
Switching times of sector 1.
where Vdc is the DC bus voltage.
T1, T2, and T0 are the corresponding application times of the voltage vectors, respectively. Tz is the sampling time.
Figure 10 shows the global block diagram of DTC with SVM.
Global control scheme of SVM-direct torque control with PI controller.
The complete block diagram DTC-SVM improvement of induction motor drive with fuzzy logic controller is shown in Figure 11. The practical difficulty with PI controllers has been addressed in the previous section. The PI controllers are being replaced by fuzzy logic controllers that generates the module and the voltage vector angle in order to bring the stator flux and the electromagnetic torque to references optimally; this vector is used by a PWM control vector to generate the pulses for the control of the switches of the inverter, and PI speed controller is replaced by the adaptive fuzzy-PI speed controller to offer a good insensitivity to parameter variations, to get better response in external disturbance rejection and fast dynamics.
Global control scheme of SVM-direct torque control with fuzzy logic controllers and adaptive fuzzy-PI speed controller.
The position of the reference voltage vector with respect to the stator flux vector must be chosen so as to maintain the stator flux and the electromagnetic torque in an optimal error band around their reference value. The errors of torque and flux are multiplied by “scales factors” to obtain standardized sizes and functions. These values are used by the fuzzification block to be transformed into fuzzy values. These are used by the block fuzzy control rules after defuzzification; the value of (ψ) which must be added to the angle of the stator flux [30, 31, 32] (Figure 12).
Controller structure for estimating the angle (ψ).
The voltage vector module must be selected to minimize the error of torque and flux. A fuzzy logic controller is designed to generate the appropriate voltage vector magnitude (Figure 13).
Controller structure for voltage vector module estimation.
The voltage vector obtained from the characteristic comes to the vector modulation
Calculate the biphasic components of the desired voltage vector using the following equations [30, 31, 32]:
Calculation of the area where the desired voltage vector is.
Get the switching vectors and their operating cycle. Then calculate the operating cycle of the null switching vector
Calculation of the relative position of the clock (PRH) in the sampling time by using the following equations:
In what follows, we show the synthesis and description of the adaptation of the PI controller by a fuzzy system method:
The fuzzy inference mechanism adjusts the PI parameters and generates new parameters during the process control. It enlarges the operating area of the linear controller (PI) so that it also works with a nonlinear system [33, 34].
The inputs of the fuzzy adapter are the error (e) and the derivative of error (
The normalization PI parameters are given by:
The parameters
where Ai, Bi, Ci, and Di are fuzzy sets on corresponding supporting sets.
The associated fuzzy sets involved in the fuzzy control rules are defined as follows:
PB | Positive big | NB | Negative big | B | Big |
PM | Positive medium | NM | Negative medium | ZE | Zero |
PS | Positive small | NS | Negative small | S | Small |
The membership functions for the fuzzy sets corresponding to the error e and
Membership functions e and Δe.
Membership functions kp′ and ki′.
By using the membership functions shown in Figure 15, we satisfy the following condition.
The fuzzy outputs
where
and
Once the values of
e | |||||||
---|---|---|---|---|---|---|---|
NB | NM | NS | ZE | PS | PM | PB | |
NB | B | B | B | B | B | B | B |
NM | S | B | B | B | B | B | S |
NS | S | S | B | B | B | S | S |
ZE | S | S | S | B | S | S | S |
PS | S | S | B | B | B | S | S |
PM | S | B | B | B | B | B | S |
PB | B | B | B | B | B | B | B |
Fuzzy rule base for computing
e | |||||||
---|---|---|---|---|---|---|---|
NB | NM | NS | ZE | PS | PM | PB | |
NB | B | B | B | B | B | B | B |
NM | B | S | S | S | S | S | B |
NS | B | B | S | S | S | B | B |
ZE | B | B | B | S | B | B | B |
PS | B | B | S | S | S | B | B |
PM | B | S | S | S | S | S | B |
PB | B | B | B | B | B | B | B |
Fuzzy rules base for computing
The DTC control algorithms have been simulated by MATLAB/Simulink software. A comparative study between the three strategies for the direct torque control (DTC) of induction motor (IM) is presented. The first method is a conventional direct torque control (C-DTC) where the torque and the flux are regulated by the hysteresis controllers. The second one is direct torque control by space vector modulation strategy (SVM-DTC) where the torque and flux are regulated by PI controllers. The third one is fuzzy SVM-DTC with adaptive fuzzy-PI speed controller where the torque and flux are regulated by fuzzy logic controllers is presented. The simulation has been conducted for a three-phase 1.5 kW squirrel-cage induction motor with characteristics given in the appendix. The starting up and the steady states of the controlled motor with load introduction are presented. For the classical DTC, the chosen bandwidths of the hysteresis controllers are ±0.01 Wb for flux and ±0.1 Nm for torque.
This section presents the starting up state of the induction motor according to speed step reference of 1000 rpm. Then, a load of 10 Nm is suddenly applied between (t = 1 s) and (t = 2 s).
Figures 16 and 17 show, respectively, rotor speed, torque, stator phase current isa, flux magnitude, and the circular trajectory.
Simulation results of the classical DTC control applied to IM.
Simulation results of the SVM-DTC control applied to IM. (a) SVM-DTC-PI. (b) SVM-DTC- Fuzzy.
Figures 16 and 17(a) illustrate the comparison between speed responses of conventional DTC and SVM-DTC-PI, according to the speed reference step of 1000 rpm. The load disturbance has been introduced between (t = 1 s) and (t = 2 s). The results of Figure 16 show that the conventional DTC technique gives a good dynamic at starting up. We can notice that the speed regulation loop rejects the applied load disturbance quickly. The SVM-DTC-PI in Figure 17(a) kept the same fast speed response of DTC strategy. Since the same PI speed controller is used for both schemes, there is no difference in the transient response.
Then, the results illustrate the torque responses with load application. The figures show that at the beginning the speed controller (PI anti-windup) operates the system at the physical limit. It can be seen clearly that the constant switching frequency-based DTC strategy in Figure 17(a) has a reducer ripples level owing to the use of SVM compared to the conventional DTC in Figure 16, where it is observed that the high torque ripples exceed the hysteresis boundary. Next, the stator phase current with zoom is presented. The conventional DTC in Figure 16 shows a chopped sinusoid waveform of current which indicates a high harmonic level, while SVM-DTC in Figure 17(a) shows a smoother sinusoid waveform. After that, the results exhibit the magnitude of stator flux evolution and circular trajectory. It is clear that the flux ripples of the conventional DTC have exceeded the hysteresis boundary. The magnitude and the trajectory illustrate that the flux takes a few steps before reaching the reference value (1.2 Wb) at the starting stage due to the zone’s changing.
The simulation in Figure 17(b) shows that the SVM-DTC-fuzzy has better performance than those obtained by both other DTC strategies (conventional and SVM-PI). There is an appreciable decrease in the start-up response time; we can notice that the speed regulation loop rejects the applied load disturbance very quickly which proves the performance of adaptive fuzzy-PI controller as well as a significant attenuation of the ripples of the torque and of the sinusoidal current without any ripple in the steady state.
The main objective of this chapter is the improvement of the performance of an induction motor drive controlled by DTC. The objective of this improvement is to minimize the ripples of the couple and the flux of the IM on the one hand and the decrease of the switching frequency of the inverter on the other hand. In this context, a comparative analysis between different DTC strategies has been presented. This chapter began by explaining the principle of the conventional DTC, SVM-DTC-PI, and SVM-DTC-fuzzy with adaptive Fuzzy-PI speed controller. The chapter presents later a discussion based on the simulation results presented in the same work. The synthesis of this simulation study reveals advantages of SVM-DTC-fuzzy scheme compared to the two strategies: conventional DTC and SVM-DTC-PI. It has been observed by comparing the torque, speed, and stator flux characteristics that the method SVM-DTC-fuzzy is better. It is clear that the current is sinusoidal without any ripple in the steady state and torque ripples are reduced. In order to improve the SVM-DTC-fuzzy to have better performances, this method has been associated to the adaptive fuzzy-PI speed controller. This association makes the induction motor-based DTC perform more and more stable; there is an appreciable decrease in the start-up response time; we can notice that the speed regulation loop rejects the applied load disturbance very quickly.
Item | Symbol | Data |
---|---|---|
IM mechanical power | PW | 1.5 kw |
Nominal speed | 1420 rpm | |
Nominal frequency | f | 50 Hz |
Pole pair number | P | 2 |
Stator resistance | Rs | 4.85 Ω |
Rotor resistance | Rr | 3.805 Ω |
Stator self-inductance | Ls | 274 mH |
Rotor self-inductance | Lr | 274 mH |
Mutual inductance | Lm | 258 mH |
Moment of inertia | J | 0.031 kg m2 |
Friction coefficient | F | 0.00114 kg m2/s |
The chapter is devoted to the discussion of the telecommunications development strategy. Communication specialists all around the world are facing the problem: how to shift from circuit switching to packet switching. The same problem is the main challenge for the U.S. Department of Defense.
“The DoD today still has analog, fixed, premises-based, time-division multiplexing (TDM) and even asynchronous transfer mode (ATM) infrastructure,”- is the AT&T view [1]. Really, the DoD has one aging network based on circuit switching point-to-point circuits. This “old” technology requires an expensive support of hardware and additional upgrades with difficulties carried on in the IP era.
Cyber threats are another hard obstacle in a move to IP world. In October of 2018, the Government Accounting Office (GAO) has reported [2], the United States weapons systems developed between 2012 and 2017 have severe, even “mission critical” cyber vulnerabilities. DoD weapon systems nowadays are more and more software dependent (Figure 1). We observe the weapons, from ships to aircrafts; use more software than even before. For example, the aircraft F-35 Lighting II software contains eight million lines of code [3].
Software and information technology systems in aircraft (shown for classification reasons) [2].
The rest of paper is as follows. Sections 2 and 3 are about DoD’s strategies “Joint Vision 2010” and “Joint Vision 2020,” respectively. In Sections 4 and 5, we consider the target DISN infrastructure and Joint regional security stacks. In Section 6, the up-to-date JEDI Cloud Strategy and Artificial Intelligence Initiative have given in short. In the concluding Section 7, we point out rather unsuccessful US Army Regulator fights for IP technology. It is exampled by Defense Red Switch Network using 40 years old ISDN technology.
The Defense Information Systems Network (DISN) is a global network. It provides the transfer of various types of information (speech, data, video, multimedia). Its purpose is to provide the effective and secure control of troops, communications, reconnaissance, and electronic warfare.
The new DoD Doctrine [4] had issued by General J. Shalikashvili in 1995. This is the keystone document for Command, Control, Communications, and Computer (C4) systems up to now. At that time, “Joint Vision 2010” doctrine met a strong criticism from the US GAO side [5]. The GAO pointed out that the military services are operating as many as 87 independent networks. DISA initiated a similar data call after GAO survey and identified much more - 153 networks throughout Defense.
General J. Shalikashvili had met the technological uncertainty and the controversial requirements. Under these conditions, DISA (Defense Information Systems Agency) has made a very important decision - to use the “open architecture” and commercial-off-the-shelf (COTS) products only for military communication networks. The decision was – to use widely tested developments of Bell Labs, namely, the telephone signaling protocol SS7 and the Advanced Intelligent Network (AIN). These products were rather ‘old’ at that time: SS7 protocols had developed at Bell Labs since 1975 and defined as ITU standards in 1981.
The details regarding the transition to SS7 and AIN we found in a paper [6] from Lockheed Martin Missiles & Space – the well-known Defense contractor.
SS7 is an architecture for performing out-of-band signaling. In supports the call establishment, routing, and information exchange functions as well as enables network performance. In own order, the Advanced Intelligent Network was originally designed as a critical tool to offer sophisticated services such as “800” calls and directory assistance. The functional structure of the SS7 makes it possible to create the AIN by putting together functional parts: Service Control Point, Service Switching Point, the Service Creation Environment, Service Management System, Intelligent Peripheral, Adjunct, and the Network Access Point. Figure 2 describes the AIN components that operate in the worldwide military telecommunication network, as well as how they are deployed in SS7 backbone, the space Wide Area Network (WAN), circuit switched voice network and the packet switched terrestrial WAN.
Advanced intelligent network military service architecture [6].
To illustrate the current DISN architecture (Figure 3) we refer to the certification of Avaya PBX by DISA Joint Interoperability Test Command in 2012 [7]. The SS7 network is some kind of the nervous system of DISN up to the resent time. It connects the channel mode MFS (MultiFunctional Switches) and many others network components. That is, within the DISN network, the connections have established by means of SS7 signaling. All new terminal equipment what appears is largely IP type, nevertheless SS7 network retains its central place.
The simplified DISN view: The current state [7].
Just a few years later as “Joint Vision 2010” had introduced, namely, in 2007 the next Pentagon strategy “Joint Vision 2020” appeared. Pentagon published a fundamental program [8]. There we find the most important point: DISN have been built on basis of IP protocol (Figure 4). IP protocol should be the only means of communication between the network’s transport layer and all available applications. The following 10 years have shown it is an extremely hard challenge.
Joint vision 2020: Each warfare object has own IP address.
To implement Joint Vision 2020, the most important step is the replacing of channel switching electronic Multifunctional switches (MFS) by packet switching routers. The transition to IP protocol has based on the use of Multifunctional SoftSwiches (MFSS) and new signaling protocol AS-SIP (Assured Services Session Initiation Protocol). MFSS operates as a media gateway (MG) between TDM circuits switching and IP packet switching components. During the transition phase, MFSS operates under the control of the media gateway controller (MGC). Communications control protocol H.248 has used between MG and MGC. As shown in Figure 5, MFSS should be pure packet switch besides DRSN ‘island’ using ISDN protocol.
Reference model for multifunction SoftSwitch [9].
A few words about SIP signaling. The SIP protocol widely used now for internet telephony is not able to provide secrecy during transmission (under cyber warfare conditions) and to provide priority calls. Therefore, the Department of Defense ordered to develop one new secure AS-SIP protocol [10]. The AS-SIP protocol turned out to be extremely difficult. AS-SIP uses the services of almost 200 different RFC standards while ordinary SIP uses only 11 RFC standards.
The aim of “Joint Vision 2020” concept is to implement unified services based on Unified Capabilities concept. Army Unified Capabilities (UC) have defined as the integration of voice, video, and/or data services. These services have delivered across secure and highly available network infrastructure [11].
The following are the basic Voice Features and Capabilities:
Call Forwarding (selective, on busy line, etc.)
Multi-Level Precedence and Preemption (MLPP)
Precedence Call Waiting (Busy with higher precedence call, busy with Equal precedence call, etc.)
Call Transfer (at different precedence levels)
Call Hold and Three-Way Calling and many others.
The Unified Capabilities services are covering a plenty of communication capabilities: from point-to-point to multipoint, voice-only to rich-media, multiple devices to a single device, wired to wireless, non-real time to real time, etc. A collection of services include email and calendaring, instant messaging and chat, unified messaging, video conferencing, voice conferencing, web conferencing (Figure 6).
Rich information services surrounding a soldier: not too much?
The target DISN infrastructure contains two level switching nodes: Tier0 and Tier1 (Figure 7). Top level Tier0 nodes interconnect as geographic cluster and a cluster typically contains at least three Tier0 SoftSwitches. The distance between the clustered SoftSwitches must planned so that the return transmission time does not exceed 40 ms. As propagation delay equals 6 μs/km thus the distance between Tier0 should not exceed 6600 km. The classified signaling environment uses a mix of protocols including the vendor-based H.323 and the AS-SIP signaling. The use of H.323 has allowed only during the transition period to all IP protocol based DISN CVVoIP (Classified VoIP and Video). Classified VVoIP interfaces to the TDM Defense RED Switch Network (DRSN) via a proprietary ISDN PRI as a temporary exception.
DISN classified VoIP and video signaling design [12].
In October 2010, the US Army Cyber Command had set up. USCYBERCOM is now a part of the Strategic Command along with strategic nuclear forces, missile defense and space forces [13]. One of Cyber Command key tasks is to build Joint Information Environment (JIE) and to implement Single Security Architecture (SSA).
It is worth noting the US Cyber Command activities significantly slow down the transition to IP world. Cyber Command shall receive UC network situational awareness from all network agents including DoD Network Operations Security Centers (NOSCs), and the DISA Network Operation Center (NOC) infrastructure (Figure 8). Thus, DISA and the other DoD Components shall be responsible for end-to-end UC network management providing the strong cybersecurity requirements. The solution of cyber defense tasks radically changes the all DISN network modernization plans.
Operational construct for unified capabilities network operations [12].
The essence of the Joint Information Environment concept is to create a common military infrastructure, provide corporate services and a unified security architecture. The very concept of JIE is extremely complex, and the requirements of cybersecurity make it even more difficult. According to SSA, Joint regional security stacks (JRSS) are the main components of the JIE environment providing a unified approach to the structure of cybersecurity as well as protecting computers and information networks everywhere in military organizations.
JRSS performs many functions as a typical IP-router providing cybersecurity: firewall functions, intrusion detection and prevention, and a lot other network security capabilities. JRSS equipment contains a complex set of cyber-protection software. For example, the typical NIPR JRSS stack is comprised physically of as many as 20 racks containing cyber-protection software and in real time testing information streams. Currently, JRSS stacks have installed for the NIPRNet (Non-classified Internet Protocol Router Network). It has planned also to install the stacks for the SIPRNet (Secret Internet Protocol Router Network). In 2014, 11 JRSS stacks had installed in the United States, 3 stacks in the Middle East and one in Germany. The total amount of works includes the installation of 23 JRSS stacks on the NIPRNet service network and 25 JRSS stacks on the secret SIPRNet network (Figure 9). By 2019, it has planned to transfer to these stacks all cybersecurity programs. In nowadays, these programs are located in more than 400 places over the world [13].
JRSS current and planned deployments [14].
The DISN and DoD Component enclaves provide the two main network transport elements of the DODIN (Department of Defense Information Network) with the interconnecting JRSS role as shown in Figure 10.
The leading role of JRSS in DODIN transport [15].
On June 2012, Lockheed Martin won the largest tender for managing the DISN network - Global Services Management-Operations (GSM-O) project. The essence of the GSM-O contract was to modernize DISN management system taking into account the USCYBERCOM security requirements. The cost of work was 4.6 billion dollars for 7 years.
In 2013, the GSM-O team began to study the current state of the DISN management. There are four management centers: two centers in the US - at the AB Scott (Illinois) and Hickam (Hawaii) and two more - in Bahrain and Germany. They are responsible for the maintenance and uninterrupted operation of all Pentagon computer networks. The work is very laborious: there are 8100 computer systems in more than 460 locations in the world, which in turn have connected by 46,000 cables. The first deal was to consolidate the operating centers - from four to two, namely, to expand the US centers by closing the centers in Bahrain and Germany.
In 2015, the telecommunications world had shocked by the news: Lockheed Martin is not coping with GSM-O project, not able to upgrade of the DISN network management. Lockheed Martin has sold its division “LM Information and Global Solutions” to the competing firm Leidos. One can assume that the failure of the work was most likely due to the inability to recruit developers. New generation of software makers are not familiar with the ‘old’ circuit switching equipment and are not capable to combine it with the latest packet switching systems. The more, they should take into account the never cybersecurity requirements [16].
This failure is much more scandalous. During several last years, the GAO criticized Pentagon’s budget, particularly paying attention to JRSS budget. Many tests regarding JRSS effectiveness were unsuccessful, they were not able to reduce the number of cyber threats [17].
Despite the strong GAO critics, DoD continues the JRSS initiative. DOD stood up 14 of the 25 security stacks planned across the network in the U.S., Europe, and Pacific and southwest regions in Asia. The final security stack has planned for completing by the end of 2019 [18].
Could be fulfilled this Pentagon’s grandiose JRSS plan? The complexity of the task, in particular, characterizes the set of requirements for potential JRSS developers, named in the invitations to work for Leidos. The requirement list includes work experience of 12–14 years and knowledge of at least two or more products from ArcSight, TippingPoint, Sourcefire, Argus, Bro, Fidelis XPS, and other companies. In reality, it is extremely hard work to combine all these software complexes for cyber defense. The more, these high-level software developers should work in top-secret environment.
It turned out that the project has a significant critical flaw: JRSS equipment is too S-L-O-W, the time for information stream processing is too long. It sounds like a sentence on the fate of the JRSS project [19]. Despite of that, the JRSS is going on.
On October 2018, the Defense Information Systems Agency has released a final solicitation for the potential 10-year 6.52 billion dollars project Global Solutions Management-Operations (GSM-O II). The contract winner is Leidos. GSM-O II is a single award contract designed to provide a full global operations and sustainment solution to support DODIN/DISN [20].
The key GSM-O II attributes include the cybersecurity defense of the DISA enterprise infrastructure and Joint Regional Security Stacks aids in the support to enhance the mission (?).
Now we are looking for Leidos success (or failure). It is yet unclear and 10-year period, of course, is a rather long time. Could Leidos cope with GSM-O II?
The Defense Department’s never initiative concerns the cloud strategy. The foundation of cloud initiative is the general-purpose Joint Enterprise Defense Infrastructure (JEDI) [21]. The strategy emphasizes a cloud hierarchy at DOD, with JEDI on top. Many fit-for-purpose military clouds, which include MilCloud 2.0 run by DISA, will be secondary to the JEDI general-purpose cloud.
On April 10, 2019, the Department of Defense confirms that Amazon and Microsoft are the cloud contract winners. The competitors Oracle and IBM are officially out of the race for a key 10 billion dollars defense cloud contract.
Could be the JEDI Cloud Strategy successful? A key technological difficulty for the JEDI project is interoperability of clouds (Figure 11). The Pentagon’s JEDI cloud strategy leaves a series of unanswered questions that could be reasons for disasters in the future [22].
DoD pathfinder to hybrid cloud environments [21].
For internal interoperability, the strategy lays out the correct goal, common data and application standards. There are the 500+ clouds already used within the Pentagon. They have own data formats. Now they need to migrate and interoperate onto the unique JEDI platform.
The next unanswered question regards the JEDI cloud’s external interoperability. It concerns a future conflict situation. Would America’s allies need to use the same cloud provider (e.g., Microsoft) and the same data-formatting practices as the DoD? The strategy does not discuss these long-term issues.
The cloud strategy has started in 2015 by establishing the Defense Innovation Unit (DIU). This DoD organization has founded to help the US military make easier and faster use of innovative commercial technologies. The organization has headquartered in Silicon Valley (California) with offices in Boston, Austin, and some more. The next step – the establishing of Joint Artificial Intelligence Center as a focal point of the DoD Artificial Intelligence Strategy [23].
Taking into account the potential magnitude of Artificial Intelligence’s impact on the whole of society, and the urgency of this emerging technology international race, President Trump signed the executive order “Maintaining American Leadership in Artificial Intelligence” on February 11, 2019. That document has launched the American AI Initiative. This was immediately followed by the release of DoD’s first-ever AI strategy [24].
Artificial intelligence - this is really one great idea, if it happens be successful. Could it have more success than JRSS initiative?
US Army Regulator fights for IP technology but, honesty speaking, unsuccessfully. The Army regulator recognizes in 2017 [25] that there is ‘old’ equipment on the network: time-division multiplex equipment, integrated services digital networking, channel switching video telecommunication services. According to the document [25], all these services will use IP technology, at least, in the nearest future. As an example, name the instructive claim regarding DRSN:
4–2.d. Commands that have requirements to purchase or replace existing Multilevel Secure Voice (previously known as Defense Red Switched Network (DRSN)) switches will provide a detailed justification and impact statement to the CIO/G–6 review authority.
In conditions of cyberwar, no reason to be surprised that the Defense Red Switch Network (DRSN) will use 40 years old ISDN technology for long time yet, the more – in conditions of cyberwar. DRSN is a dedicated telephone network, which provides global secure communication services for the command and control structure of both the United States Armed Forces and the NATO Allies (Figure 12). The network has maintained by DISA and has secured for communications up to the level of Top Secret.
Secure terminal equipment; note slot in front for crypto PC card (left). The DRSN architecture (right) [25].
“Red Phone” (Secure Terminal Equipment, STE) uses ISDN line for connections to the network. “Red Phone” operates at a speed of 128 kbps. There is the slot at the bottom right serving for a crypto-card and four buttons at the top - to select the priority of communications. The STE is the primary device for enabling security. It may be used for secure voice, data, video, or facsimile services.
As we have mentioned above citing the AT&T view [1], the DoD today still has analog, fixed, premises-based, time-division multiplexing and seems could remain for unpredictable period according to the well-known software developers slogan: “Don’t touch what works”. In conditions of cyberwar, the very transition to internet technologies in telecommunications seems doubtful. Thus, we conclude that the long-term channel-packet coexistence seems inevitable, especially in the face of growing cyber threats.
AI | artificial intelligence |
AIN | advanced intelligent network |
AS-SIP | assured services session initiation protocol |
CS | capability set |
DISA | defense information systems agency |
DISN | defense information systems network |
DoD | department of defense |
DODIN | department of defense information network |
DRSN | defense red switched network |
GAO | Government Accounting Office |
IP | internet protocol |
ISDN | integrated services digital network |
JEDI | joint enterprise defense infrastructure |
JIE | joint information environment |
JRSS | joint regional security stack |
MFS | multifunctional switch |
MFSS | multifunctional softswich |
MG | media gateway |
MGC | media gateway control |
NIPRNet | non-classified internet protocol router network |
RFC | request for comments |
SIP | session initiation protocol |
SIPRNet | secret internet protocol router network |
SS7 | signaling system protocol #7 |
SSA | single security architecture |
UC | unified capabilities |
TDM | time division multiplexing |
Supporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.
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