Technologies and Methodologies Enabling Reliable Real-Time Wireless Automation

Wireless   automation   is   an   emerging   field   of   research,   engineering  and   industrial development  that  aims  at  significant  savings  in  installation  times  and  costs  of  cabling  in automation   systems,   while   providing   a   new   level   of   flexibility   for   system   design, reconfiguration,  and  agility.  It  is  applicable  to  both  new  automation  systems  and  retrofit applications. The use of wireless technologies is rather common in consumer applications, consider, for example, cellular phones, cordless desktops, etc., but the strict real-timeliness and   reliability  requirements   of   automation   systems   have   limited   the   use   of   wireless technology in industrial environments. There are, however, a variety of existing industrial applications  of Bluetooth,  ZigBee  and  WLAN  networks,  but  very  rarely  these  are  used  in time-critical  applications.  The  bulletproof  wireless  technologies  are  few,  and  hence  the technologies have not yet spread to wide use in industrial automation. The main concerns in this respect are related to the problem of how the reliability and real-timeliness of wireless communications could be guaranteed.Wireless automation considers a wide range of technologies that are used in an automation system to enable wireless communications on one or several levels of the system, including factory, automation system, and field device levels. The wireless communications are used to  deliver  measurements  and  control  values,  device  configuration  information  and  other process  data  between  the  devices,  control  rooms  and  servers.  This  chapter  reviews  the wireless  automation  standards  and  related  technologies,  but  also  certain  medium  access control  (MAC)  and  routing  protocols  and  control  design  approaches  are  discussed  that together   could   solve   the   problem   of   achieving   a   reliably   working   real-time   wireless automation system. We will also consider simulation of wireless automation systems, which is essential for the network and automation system co-design validation, and evaluate some candidate system designs with a suitable simulator. The focus is on the field device level, and  hence  the  technologies  used  for  device  to  device  wireless  communication  for  control purposes are addressed.

The  main  contributions  of  this  chapter  include  a  review  of  current  technologies  used  in wireless automation. We also discuss and propose a new MAC and mesh routing protocol (limited  broadcast  protocol,  LBP)  to  enhance  the  reliability  and  real-time  performance  of wireless automation systems. We will also demonstrate how the modelling and simulation of  packet  drops  could  be  done  and  integrated  in  the  co-design  procedure  of  wireless automation  systems.  Finally,  we  will  investigate  some  practical  control  solutions  and designs for wireless automation with a full-scale simulator to validate the proposed designs.

Wireless automation

Wireless automation and the related field of wireless sensor networks (WSN) are currently attracting researchers and industry world-wide. The progress of development has emerged into  two  industrial  wireless  automation  standards,  namely  WirelessHART  (2007)  and ISA100.11a   (2009).   In  the   current   phase,   these  standards   consider  mainly   monitoring applications without guarantees of real-time performance. However, in automation systems, it is possible to handle the missing data and variable time-delays induced by the somewhat non-deterministic wireless communications by proper design of the system, especially via appropriate control design.The  primary  benefit  of  wireless  control  technology  is  the  reduced  installation  cost,  as  a considerable  investment  is  made  in  the  wiring  of  factories,  both  financially  and  in  labor (Brooks,  2001).  The  use  of  wireless  technology  is  not  only  a  replacement  of  cables;  the benefits go beyond that. With wireless devices, increased flexibility is gained, as sensors can be   placed   more   freely,   even   on   rotating   machines.   Robustness   is   increased,   as   the communication can be done over several paths in a mesh network and failure of cables is eliminated (TSMP, 2010). Finally, there are the opportunities for new applications that are enabled by wireless control. Some existing or emerging applications are remote control of devices,  for  example  cranes  or  dexterous  and  mobile  robots,  mobile  applications,  and wireless  monitoring  of  large  plants  for  fault  detection,  maintenance,  production  quality monitoring, and compliance to environmental regulations (Gungor & Hancke, 2009).There is a strong aim (Moyne & Tilbury, 2007) to develop and deploy wireless networked control systems (WiNCS), where a control system communicates over a wireless network, in factory and home automation. In a related field, sensor network applications have as well received   much   attention   (Akyildiz   et   al.,   2002;   Baronti   et   al.,   2007).   Today,   wireless automation  technologies  are  mostly used  in  monitoring  applications,  because  in  these applications the operational requirements can be satisfied with the current technology. The industry  is  cautious  in  applying  wireless  to  closed-loop  control,  due  to  the  inherent unreliability of wireless communications. Next we summarize the main research activities on WiNCS to enable wireless automation.

Research on wireless networked control systems

A networked control system (NCS) is a distributed real-time control system consisting of the plant,   sensors,   controllers,   actuators,   and   a   shared   data   network   that   is   used   for communication  between  the  components  of  the  system  (Antsaklis  &  Baillieul,  2004).  A general  NCS  layout  is  depicted  in  Fig.  1.  In  WiNCS,  wireless  networks  are  used  for transferring data.One  of  the  benefits  of  NCS  is  reduced  cabling  cost  (Neumann,  2007),  which  is  removed completely by the introduction of wireless devices. Other advantages include ease of adding field   devices,   introducing   two-way   communication   with   field   devices   for   remote configuration  and  device  health  monitoring,  and  more  advanced  control  strategies  are possible  because  of  improved  availability  of  field  level  data  (Gungor  &  Hancke,  2009; Neumann, 2007). Wireless control systems deliver more benefits than NCSs as there are no wires,  but  also  more  problems,  mainly  because  of  the  unreliable  shared  communication medium.   The   main   concern   against   deploying   wireless   networks   for   control   is   the uncertainty of communication and inability to guarantee a sufficient quality of service for the  control  system.  The  network  must  provide  real-time  and  continuous  operation,  as controlling  a  physical  system  is  a  continuous  task  (Moyne  &  Tilbury,  2007).  This  real- timeliness  may  not  always  be  guaranteed,  which  causes  problems  for  the  control  system design (Lian et al., 2006). Soft real-time operation is enough, if it is taken into account in the control  design  as  shown  later  on.  Another  concern  hindering  the  adoption  of  wireless technologies   is   security,   since   the   wireless   medium   is   open   for   eavesdropping   and interference (Mustard, 2006). The security issue is solved by authentication and encryption methods (Karlof et al., 2004).

Ethernet networks are becoming regularly used in control applications (Moyne & Tilbury,2007).  Similarly  the  “Industrial  Ethernets”  (Neumann,  2007),  which  allow  for  real-time operation, where an operation is guaranteed to be executed in a given time, are gradually being applied. The same benefits are also available by means of wireless technology, such as WLAN, with the addition of accessing the data wirelessly using a handheld device, enabling in-situ  inspection  of  the  process  (Brooks,  2001).  An  overview  of  NCSs  can  be  found  in (Antsaklis & Baillieul, 2004) and (Hespanha et al., 2006). The   field   of   WiNCS   is   multidisciplinary:   the   network,   the   control   system   and   their interactions need to be taken into account. Traditionally, either the network or the control system has been studied separately. As such there has been relatively little research focusing on both aspects at the same time. Current wireless control system research has its roots in networked control system theory, as the issues of a shared communication medium are the same, mainly related to variable communication time-delays and packet losses, and system architecture design, see (Willig, 2008). Both fields deal with network protocols (Akkaya & Younis,    2005;    Marco    et    al.,    2010),    transmission    scheduling    (Weiss    et    al.    2009), communication and control co-scheduling (Samii et al., 2009), traffic reduction (Lian et al.,2002), congestion control (Velasco et al., 2004), estimation (Nebot et al., 1999), LQG control with  packet  drop  (Gupta  et  al.,  2007),  Kalman  filtering  (Sinopoli  et  al.,  2004),  controller tuning (Eriksson, 2008), control performance (Lian et al., 2001), and control stability (Cervin et al., 2004; Gupta et al., 2007; Kao & Lincoln, 2004; Weiss et al., 2009). The main difference between NCSs and WiNCSs is that wireless communication is less deterministic because of interference   and   finite   communication   range,   but   problems   with   wiring   and   failing connectors are eliminated.

Technologies in wireless automation–A review

The physical properties of the radio, such as frequency, antenna and modulation, determine the  range  and  bit  rate  of  the  network.  The  medium  access  control  (MAC)  determines  the delay a message experiences before the transmitter gains access to the wireless medium and the  message  can  be  transmitted.  In  a  large  control  system,  there  are  many  small  packets, containing, for instance, measurements from a sensor or control values to actuators, to be transmitted in a short timeframe. The particular communication attributes and the need for a  real-time  WiNCS  set  special  requirements  on  the  MAC  protocol,  which  among  other things, affects the packet delay and collision probability. The MAC protocol is thus one of the most important network design issues in WiNCS.

Medium access and networking protocol

The  main  categorization  of  MAC  protocols  is  between  deterministic  (contention  free)  and random  access  (contention  based).  In  random  access  MACs,  no  guarantees  that  a  certain node  gets  access  to  the  medium  in a  given  time  can,  in  general,  be  given.  Most  of  the random  access  MACs  used  in  wireless  sensor  networks  are  based  on  the  carrier  sense multiple  access  (CSMA)  protocol.  In  deterministic  MACs,  a  communication  slot  (either  in frequency, time or with code division, or a combination of them) is assigned to each node. This assignment has the advantage that the communication is deterministic in the sense that the access to the medium can be guaranteed in a predetermined time. Networks using time- scheduled MAC are typically controlled by a centralized network manager, and they require tight time-synchronization of the whole network. The advantage of random access MAC is that in low traffic conditions (low sampling rate), any node can transmit immediately if the medium is idle. In high traffic conditions (large number of control loops or high sampling frequency), a packet might experience collisions and  random  back-off  times.  The  non-deterministic  exponential  back-off  is  not  suitable  for wireless  control  applications,  since  the  communication  delay,  which  is  important  for  the control stability (Cervin et al., 2004), cannot be bounded and packet drop due to congestion decreases the performance (Liu & Goldsmith, 2003). Therefore, deterministic MAC protocols are   often   desired   in   control   applications   to   overcome   the   problem   of   variability   in transmission  times.  Although  deterministic  MAC  protocols  might  not  always  provide  the optimal use of resources, they are used in WiNCS as one solution to support real-timeliness. In  current  wireless  automation  standards,  the  trend  is  also  to  use  deterministic  MAC protocols, which enable a reliable communication schedule for periodic measurements and control packets.

The original IEEE 802.15.4 standard defines two operation modes. In the beacon mode, it is possible  to  utilize  both  reserved  time  slots  (scheduled  MAC),  and  contention  based  slots (random  access).  However,  most  of  the  shipped  radios  nowadays  come  only  with  CSMA random access MAC, which is due to the complexity of the beacon mode protocol and its known    performance    problems    (Werb    et    al.,    2005).    The    WirelessHART    protocol (WirelessHART,  2010),  aimed  at  industrial  applications,  defines  both  token  passing  and scheduled  MAC.  The  protocol  allows  frequency-time  slots  to  be  dedicated  to  links.  Some slots  can  also  be  reserved  for  contention  based  access  using  CSMA.  Another  industrial wireless   automation   standard   is   ISA100.11a,   which   uses   a   similar   MAC   protocol   to WirelessHART. These industrial standards are further discussed in Section 3.As  in  any  realistic  large-scale  sensor  network,  not  all  the  nodes  are  able  to  communicate directly  with  each  other  due  to  the  limited  radio  range  and  dynamic  nature  and  other challenges posed by the environment. Hence routing protocols are needed to ensure that the data could be transmitted from any node to any other node at all times in the network. The aim of a routing protocol is to setup the routes in the network in an energy-efficient manner and  to  reliably  relay  the  data  from  the  source  to  the  sink  node (Akkaya  &  Younis,  2005). Typically in monitoring applications, the data are eventually collected into one of the nodes, i.e. the sink node, having the capability to store the data for offline analysis and/or visualize the data for the system user.Many  of  the  proposed  routing  protocols  can  be  divided  into  data-centric,  hierarchical  or location-based, but some of them also consider e.g. network quality of service (QoS). For a more detailed review of routing protocols in WSN, see (Akkaya & Younis, 2005). It should be noticed that especially in networks having real-timeliness constraints, the MAC and routing protocols are tightly coupled. For example, an intermediate node on the route should be able to forward a message once the node receives it as fast as possible to the next node  along  the  path  to  reduce  the  end-to-end  communication  delay.  In  WirelessHART networks the routes are either predetermined by the network manager (graph routing)  or determined by the nodes themselves (source routing for ad hoc communications) (Song et al., 2008).

Radios for wireless automation

Wireless networks for control applications are currently envisioned to use existing standard wireless technologies, such as Bluetooth, ZigBee (based on IEEE 802.15.4 radio) (Baronti et al.,  2007),  and  WLAN  (IEEE  802.11).  Nevertheless,  it  should  be  noted  that  the  traditional computer networks, such as Ethernet and WLAN, use CSMA type medium access control with  exponential  back-off  in  case  of  collisions,  which  is  not  always  applicable  in  wireless control  as  already  discussed  above.  The  wireless  network  design  problems  are  further discussed, for instance, in (Kumar, 2001).Wireless networks are already used in control. The preferred solution is to use deterministic networks, using polling (e.g. Bluetooth) or scheduling (WirelessHART and ISA100.11a), but standard  wireless  networks  are  also used  in control  applications.  Using  standard  wireless hardware  for  automation  is  considered  in  (Pellegrini  et  al.,  2006),  where  two  application layer  protocols  suitable  for  real-time  control  are  designed  and  evaluated.  Some  early adoptions of wireless devices as cable replacements are listed in (Koumpis et al., 2005). The first wireless deployments have been mostly cable replacements using Bluetooth. Bluetooth has,  however,  given  way  to  ZigBee,  as  ZigBee  has  lower  power  consumption  and  more flexible networking.

An  overview  of  ZigBee/IEEE  802.15.4  can  be  found  in  (Baronti  et  al.,  2007).  ZigBee  has rightfully   been   criticized   for   being   unreliable,   lacking   techniques   to   mitigate   the communication  problems,  and  unsuitable  for  industrial  control  (Lennvall  et  al.,  2008). ZigBee is more suitable for small applications, and there are separate industrial standards for wireless automation. ZigBee is used, for example, for home and building automation as an enabling technology to create smart home and smart energy applications. WLAN  networks  provide  such  high  data  throughputs  that  they  could  be  well  used  in automation systems from the data rate point of view. The problem with WLAN is the high energy consumption of the radio. Obviously, a standard WLAN network does not support real-time  communications,  but  the  radio  technology  itself  is  useful  for  creating  high-bandwidth  real-time  systems.  Nevertheless,  it  is  challenging  to  create  purely  wireless WLAN  networks  for  automation  due  to  the  high energy  consumption  of  the  nodes.  For cable replacement, though, if power is available from the process, the WLAN radios can be well used for single hop wireless communication.

The use of heterogeneous networks spanning the whole automation system from low level devices to high level functions, such as production monitoring, is considered in (Moyne & Tilbury, 2007; Neumann, 2007), where the applicability of different networks at the different levels and tasks are evaluated. For the higher level functions, such as plant monitoring and production planning, trend analysis, or gathering of batch information, real-time operation is  not  necessary,  and  office  grade  wireless  networks  are  suitable  for  these  tasks.  In  the current  wireless  automation  standards  only  field  device  level  wireless  networks,  where sensor devices report their measured values and possible health data to a gateway and the rest of the automation system, are considered. The network is thus used only at the lowest device level in the whole automation system (Steigmann & Endresen, 2006). In practice, also plant  wide  wireless  networks  with  proprietary  protocols  based  on  the  office  grade  IEEE 802.11 standard are used.

Standards and applications

Currently, there are two standards for industrial wireless automation: WirelessHART and ISA100.11a. Both industrial standards are based on the IEEE 802.15.4 radio (ZigBee, 2006). The  IEEE  802.15.4  standard  is  suitable  for  building  automation  (Kintner-Meyer,  2005), industrial monitoring, and control applications (Wheeler, 2007). The main characteristics are low   bit   rate   and   low   power   consumption.   The   WirelessHART   standard   and   some implementation  details  are  discussed  in  (Song  et  al., 2008).  ISA100.11a  is  in  practice  very similar to WirelessHART, as both have similar design goals, but the two standards are not compatible. Furthermore, it should be noted that the WISA system (Scheible et al., 2007) is a complete solution for a reliable wireless cell in industrial manufacturing.

The  architecture  of  both  industrial  wireless  automation  standards  includes  sensor  nodes, wireless routers communicating with each other, and a gateway, which is connected to the automation fieldbus and the rest of the automation system. Mesh networking is possible for reliability, but all communication between devices in the wireless network is routed via the gateway. This routing constraint makes the network scheduling and routing easier. WirelessHART  is  by  now  in  use  (WirelessHART,  2010)  and  several  manufacturers  have released devices for WirelessHART. The ISA100.11a standard (ISA100, 2010) was published in September 2009, and is submitted for ANSI and IEC standard approvals. Hence, the field of  industrial  wireless  control  has  taken  its  first  steps.  The  standards  are  designed  for determinism, such that traditional control can readily be applied. Although determinism is the  main  design  goal,  this  is  never  fully  assured.  Wireless  networks  are  inherently  non- deterministic, and no network design can make it fully dependable, because of interference in the open communication media.

The WirelessHART protocol is designed for deterministic communication and interference resistance, satisfying the real-time requirements of a wireless control system. WirelessHART uses  a  combination  of  time  division  multiple  access  (TDMA)  and  frequency  division multiple access (FDMA) MAC protocol. The TDMA slot length is 10 ms, in which the data packet with sensor or control information and an acknowledgement are exchanged between two  nodes.  The  network  and  transport  layers  are  based  on  the  Time  Synchronized  Mesh

Protocol  (TSMP)  originally  developed  by  Dust  Networks  (TSMP,  2010).  Each  node  pair  is assigned a unique time/frequency slot for contention free communication by a centralized network manager. Some slots can be reserved for contention based access using CSMA, for communicating  rare  event  messages.  Additionally  frequency  hopping  is  used  to  mitigate interference on some channels. A more detailed presentation of WirelessHART can be found in (Song et al., 2008). The benefits of WirelessHART and how to accommodate the control system to the wireless network,  and  meet  the  required  control  performance,  is  discussed  in  (Nixon  et  al.,  2008). ISA100.11a uses similar techniques and both network standards can be applied where the application  can  tolerate  a  delay  jitter  in  the  order of  100  ms.  The  delay  jitter  stems  from packet drop due to interference. In laboratory setting, the TSMP combined with frequency hopping  over  the  16  available  channels  of  the  IEEE  802.15.4  radio  has  been  reported  to achieve  carrier  grade  reliability  for  a  low  data  rate  wireless  sensor  network  (Werb  et  al.,2005).

Due  to  the  determinism  of  the  TDMA  approach  with  a  pre-determined  schedule,  fixed bounds on the communication can be advertized, although not guaranteed. In the case of packet drops due to interference or fading, retransmission is needed, which may cause the information to exceed the delay bound. Retransmission slots must thus be incorporated into the schedule, which reduces the bandwidth usage and unavoidably introduces delay jitter. Retransmission  can  take  place  on  the  slots  allocated  for  random  access,  or  on  extra  slots allocated   in   the   schedule.  Current   research  related   to  the   standards   is,   for   instance, communication  and  controller  scheduling  (Samii  et  al.,  2009)  and  the  optimality  of  the time/frequency-slot scheduling and routing (Weiss et al., 2009).Despite  the  wireless  communication,  the  devices  may  still  have  wired  power,  because  of large power requirements of the sensor or, usually, the actuator. For truly wireless devices, the power source must be local. A battery contains a finite amount of energy, and thus either the  device  lifetime  is  limited,  or  energy  must  be  gathered  during  operation  from  the environment   with   energy   harvesting   techniques.   Sources   of   auxiliary   energy   are,   for example,  electromagnetic  waves,  light,  vibration,  or  temperature  differences  (Paradiso  & Starner,   2005).   Another  solution   to   completely   get   rid   of   cables   is   wireless   power transportation. An existing solution is inductive power transfer to devices located inside a cage  by  the  ABB  WISA  system  (Scheible  et  al.,  2007).  The  cage  walls  induce  a  rotating magnetic  field  that  solenoids  in  the  devices  convert  to  current.  Typical  power  transfer ranges from 10 to 100 mW (Steigmann & Endresen, 2006).

Design challenges and solutions

The  wireless  roadmap,  with  the  needed  technological  and  social  development  for  the adoption of wireless technology in automation, is summarized in (Koumpis et al., 2005). A comprehensive  overview  of  current  technologies,  future  issues,  and  research  topics  of wireless  industrial  networking  is  given  in  (Gungor  &  Hancke,  2009)  and  (Willig,  2008). Several wireless standards are presented and the anticipated promising research topics are introduced.  Some  of  them  are:  network  architecture  and  scalability,  network  standards, quality of service measures, provisioning and analysis of wireless industrial networks, real- time and reliability, security, and energy efficiency.There are many other papers giving an overview of the current wireless technologies and networks for control, e.g. (Gungor & Hancke, 2009; Pellegrini et al., 2006), and (Willig et al.,2005). Gungor & Hancke (2009) review the challenges, design goals, and technical solutions for  industrial  wireless  sensor  networks.  Willig  et  al.  (2005)  discuss  several  properties  and challenges of using wireless in real-time control applications. Some of the network related issues  are:  interference,  path  loss,  timing  and  timeliness,  co-existence  of  other  wireless networks,   and   connection   to   an   existing   wired   automation   system.   Pellegrini   (2006) discusses  the  requirements  and  features  for  using  wireless  at  the  device  level  in  an automation  system,  including  power  consumption,  security,  and  connection  to  the  wired control system. The necessity of wireless protocols aimed specifically at control applications is also pointed out.

Radio environment challenges in wireless automation

The  use  of  wireless  technologies  in  automation  also  introduces  new  challenges,  as  cable replacement does not simply mean unplugging a wire and using a wireless device instead. The  radio  channel  is  a  shared  medium,  and  thus  subject  to  interference  from  co-channel transmissions. Wireless communication is usually less reliable than wired solutions. Radio propagation conditions in industrial setting can be harsh. Measurement results in factories indicate   that   the   channel   is   subject   to   frequency   selective   fading   due   to   multipath propagation.  Furthermore,  errors  tend  to  appear  in  bursts  in  which  several  consecutive packets are lost (Willig & Mitschke, 2006). The unreliability and interference problems of the wireless  networks  can  be  addressed  in  the  different  protocol  layers.  These  design  choices have an impact on the used control methods, which also need to be redesigned to cope with the problems of wireless communication.In the current wireless automation applications, the radios typically operate in the open 2.4 GHz  ISM  frequency  band.  The  ISM  band  is  quite  crowded,  as  also  the  office  networks (WLAN, Bluetooth) operate at the same frequencies. The existence of other networks in the same  band  does  not  mean  that  the  wireless  automation  network  could  not  be  used.  The reliability  of  communications  depends  then  on  the  activity  of  the  interfering  network.  To avoid  collisions  and  interference,  frequency  hopping  and  channel  blacklisting  are  useful techniques.  In  the  future,  a  separate  frequency  band  should  be  reserved  world-wide exclusively   for   industrial   automation   applications,   to   enable   proper,   interference   free wireless control operation.

There  are  several  studies  of  the  performance  of  IEEE  802.11  networks,  e.g.  (Prasad  et  al.,2001), where the network design is also discussed. In industrial or factory environments the radio  propagation  signal  deviates  considerably  from  the  ideal  free  space  propagation models used in most network simulator models. Besides the free space model there exists many  other  fading  models  for  wireless  communication  (Goldsmith,  2004).  Metal  and obstacles, typically present in a factory, cause shadowing and multipath effects that amplify or attenuate the radio signal strength. The radio environment in a factory can be harsh with motors  radiating  interfering  electromagnetic  waves  and  moving  machinery  temporarily blocking links of the wireless network. Reflections of radio waves, causing multipath fading, can in these environments be an advantage, because shadowed locations can obtain a strong signal through reflections.

There are some reports on studies of measurements done in industrial  environments. The received signal strength in a chemical pulp factory, cable factory and a nuclear power plant was  measured  with  an  IEEE  802.11  network  at  the  2.4  GHz  ISM  radio  band  (Kjesbu  & Brunsvik, 2000). The conclusions of the experiments were that the radio environment is not as harsh as initially thought; multipath improves the signal strength in shadow areas. While many  locations  are  improved  by  multipath  fading,  communication  in  some  locations  is impossible,  due  to  no  signal  or  destructive  interference,  even  if  the  distance  is  short (Björkbom et al., 2010). Another study presents measurements of the bit-error-rate and more importantly,  the  error  pattern,  of  an  IEEE  802.11  network  in  an  industrial  environment (Willig  et  al.,  2002).  Interesting  findings  were  that  the  packet  losses  are  correlated,  error burst  and  packet  loss  burst  lengths  fluctuate  several  orders  of  magnitude  with  time.  This means that the outages due to consecutive packet drops may be long in some instants and hard to eliminate, for various physical reasons caused by the environment and the radio. On the other hand, error free periods also vary and can be long. Packet loss rates vary from the high 80 % to less than 10 % in generous situations.

Next,   we   present   a   method   for   modelling   packet   drop   probability   in   an   industrial environment,  such  that  the  models  could  be  used  in  wireless  automation  network  and control  design.  The  measurements  done  in  a  real  industrial  hall  show  the  challenge  of wireless   communication   in   harsh   environments.   When   packet   drop   occurs,   feedback information  for  the  controller  is  not  available  and  the  real-time  operation  of  a  wireless automation  network  is  endangered.  Suitable  control  design  to  cope  with  this  problem  is presented in Section 5.

Measurements and models for radio environments

In order to study and design WiNCS, realistic packet drop models of networks are needed, since information loss affects the control performance. The physical properties of an existing radio environment can be assessed by carrying out actual measurements at the target site, as described  next.  Our  interest  is  to  use  IEEE  802.15.4  radios  in  a  particular  industrial environment  and  hence  the  tests  are  performed  with  such  radios.  To  efficiently  collect packet drop data from an environment of interest, we propose the following hardware setup to  be  used  for  the  tests.  The  transmitter  device  is  connected  to  two  monopole  WLAN antennas, with a spatial separation of 12.5 cm.  Similarly four receivers are arranged in an array, placed 6.25 cm from each other, which is half the wavelength at 2.4 GHz. The purpose of  having  multiple  antennas  at  the  transmitter  and  receiver  is  to  implement  and  test diversity techniques (both temporal and spatial diversity).The   sensor   nodes   are   equipped   with   Texas   Instruments   CC2431   radio   modules. Transmission  power  is  set  to  0  dBm and  measurements  are  taken  for  several  different distances  and  locations.  The  transmitter  switches  between  the  two  antennas  for  every consecutive packet, thus eight different signal paths are recorded. A total of 15000 packets of size 119 bytes are transmitted for each location at an interval of 0.1 seconds. In the tests, we use the channel 26 of the IEEE 802.15.4 radio, which has the least frequency overlap with the IEEE 802.11 radio, to mitigate packet drop due to WLAN interference and other devices. Packets  are  recorded  with  their  RSSI  value  (Received  Signal  Strength  Indicator)  and  an indication  if  the  packet  was  correctly  received  with  no  bit  errors,  or  dropped.  These measurements  differ  from  other  similar  measurements,  e.g.  (Kjesbu  &  Brunsvik,  2000),  as the  packet  reception  is  measured,  not  only  the  received  signal  strength.  Here,  the  same hardware  as  would  be  used  in  a  real  application  is  used,  not  a  specialized  measurement device, which could differ significantly from the signal reception capabilities of the actual device.

As  an  example,  measurements  performed  in an industrial assembly  hall are  presented. In the  industrial  hall  there  are  machines,  racks  of tools,  and  open  spaces.  Measurements  are made in different parts of the hall, which can be categorized as light: open space, medium: mostly open with machines standing on the floor, and heavy: racks of tools obstructing the line-of-sight.   The   distances   between   the   transmitter   and   receiver   for   the   different measurements are in the range of 25-35 m.

The packet drop results from the industrial hall case are shown in Fig. 3. The packet drop probability  varies  from  location  to  location  and  there  is  significant  variation  between  the antenna pairs. This implies that the signal strength is very sensitive to the antenna location, due to multipath fading.A  common  way  to  model  a  network  with  packet  drops  is  the  Gilbert-Elliott  (G-E)  model (Elliott, 1963; Gilbert, 1960), which is based on the Markov-chain. The G-E model has two states:  one  corresponding  to  good  (G)  and  the  other  to  bad  (B)  conditions,  with  separate packet  drop  probabilities  in  the  good  and  bad  state,

P(drop|G) = dG
P(drop|B) = dB , respectively. The transitions between the states follow a two-state Markov model. The state- transition matrix is given by

Enhancements to MAC and routing protocols for real-time communication

There are several methods to design the network protocols such that real-time requirements of the control application can be met. In general, to enable real-time operation, the handling of packet drop should be such that the retransmission has the highest probability to succeed. This is achieved with diversity: if the packet drop was caused by interference, it is likely that interference will continue and therefore retransmission is wise to do on another frequency. In general, the following diversity methods are available: frequency (channel hopping), time (retransmission), code, spatial (send to different node or with different antenna), which all should be optimally used.

Reliability and real-timeliness of wireless communication can be improved in many ways.Reliable  hardware  with  powerful  enough  radio  is  the  base  for  all  wireless  technology. However, reliable hardware itself does not guarantee reliable nor real-time communication,

and instead the communication must be controlled and organized for optimal performance. The  interest  in  using  wireless  sensor  networks  for  real-time  applications,  such  as  process control, has grown during the recent years. Therefore, considerable research effort has been put   into   developing   reliable   real-time   routing   protocols,   which   also   consider   the computational  limitations  and  energy  constraints  of  the  wireless  devices.  Some  protocols, such  as  SPEED  (He  et  al.,  2003)  and  MM-SPEED  (Felemban  et  al.,  2006),  are  based  on geographic  routing  mechanism.  Both  the  protocols  rely  on  position  information  of  the nodes,  which  they  receive  via  GPS.  Optimal  route  is  determined  based  on  the  distance between  the  source  and  destination  and  the  time  left  to  deliver  the  packet.  In  indoor situations, the location information should obviously be obtained by other means than the GPS.  RTLD  (Adel  &  Norsheila,  2008)  takes  a  similar  approach,  but  the  localization  of  the nodes  is  done  based  on  the  RSSI-values  and  a  path  loss  model  of  the  environment.  Yet, another similar protocol is proposed in (Abinash et al., 2006), the main difference being  a prioritized MAC, where real-time packets have shorter backoffs and inter frame spaces (IFS) than  other  packets.  Real-time  and  non-real-time  packets  also  have  different  transmission queues and the real-time packets are sorted by an urgency factor.

These sorts of solutions are suitable for large scale WSN, where the nodes are spread over a large area and distances between the nodes are long.Another  way  to  approach  the  problem  is  to  make  modifications  on  the MAC  layer.  Black Burst  (BB)  contention  has  been  studied  in  (Sheu  et  al.,  2004; Sobrinho  &  Krishnakumar,1999). In BB contention the nodes jam the channel with pulses of energy, the duration of the pulse is relative to the time the nodes have been waiting for the access to the channel. The node which has been waiting for the longest time transmits a longer pulse than any of the other  nodes  and  thus  gains  access  to  the  channel.  In  (Sheu  et  al.,  2004),  additional modifications have been made to the MAC to deal with situations where several nodes have packets with same priority. After the BB contention a unique ID is given to each node and then the nodes transmit in a round robin manner based on the IDs. This sort of solution can be easily implemented on top of CSMA by substituting random backoffs with BB. However, the downside is that both energy and time are wasted for contending over the channel. Both ODMRP (Lee et al., 2002) and AMRoute (Xie et al., 2002) take advantage of a multicast scheme. They are designed to be robust and reliable communication protocols for wireless ad hoc networks. In addition to multicast communication, both protocols utilize a mesh topology instead of a conventional tree type of network for improved reliability. Improved reliability is a result from the fact that even if the network topology changes, a mesh is more likely going to maintain some functional links between the nodes unlike a tree, where even one broken link will  obstruct  the  communication.  However,  neither  of  the  two  protocols  focuses  on  the network latency or real-timeliness and is therefore not suitable for control applications. Since wireless control networks are typically limited in the number of nodes and the amount of  data  needed  to  be  transmitted,  the  communication  protocols  need  not  to  be  ultimately scalable  nor  support  high  data  rates.  From  this  perspective,  we  have  developed  a  robust communication   protocol   for   real-time   control   applications   called   Limited   Broadcast Protocol, which will be presented next. It has been implemented and tested on IEEE 802.15.4 compatible  wireless  sensor  nodes,  but it could  also  be applied  on  different  radio  network platforms after some adjustment of communication parameters.

Limited Broadcast Protocol

Limited  Broadcast Protocol  (LBP) is  a  wireless communication  protocol designed  for  real- time control applications with limited size networks. It is designed for a scenario, where the gateway needs to repeatedly collect new measurements from the nodes of a sensor network in a predefined time, which could be in the order of 50-500 ms. LBP takes advantage of mesh networking  and  broadcasting.  In  a  dynamic  environment  a  mesh  network  is  relatively robust, since there could be several links between the different nodes. If one link fails then alternative  routes  can  be  used  for  delivering  packets  with  minimal  additional  delay.  All transmissions in LBP are broadcasted and in an ideal case every node is capable of receiving and  forwarding  any  packet  in  the  network.  In  this  way,  occasional  link  failures  do  not deteriorate   the   performance   of   the   network.   To   meet   the   real-time   requirements   all transmissions  are  scheduled  in  time  in  order  to  avoid  packet  collisions  and  unnecessary retransmissions.  Reliability  is  achieved  by  a  smart  retransmission  system  in  addition  to packet forwarding, piggybacking and multi-hop communication. The protocol uses dedicated time slots for communication (i.e., TDMA). All communications between  the  nodes  occur  during  a  superframe  (Fig.  5).  The  length  of  the  superframe  is determined by the maximum delay that can be tolerated in the system, i.e. the time in which all sensor values need to be collected by the gateway node. The superframe further consists of  several  repeating  frames.  Consequently,  the  length  of  a  frame  is  determined  by  the number of nodes in the network and the length of the slot reserved for each of them. At the end of each frame, one extra slot is reserved for receiving late replies possibly generated by the CSMA as described below. A frame is repeated, within a superframe, as many times as necessary to receive a reply from all the nodes. However, if a superframe expires before all nodes have replied, there will be no more retransmissions and the data are lost. When all the nodes have replied a new superframe can be initiated.

receive  all  the  information  available  in  the  network.  Thus  within  the  limits  of  maximum packet size, all available data that have not yet reached the gateway can be forwarded in the same packet by any node. This type of packet will be referred to as a status packet. If a node misses the first command packet from the gateway then any of the received status packets can be used as a reference (Fig. 6). All the replies are scheduled in time in order to minimize the possibility of collisions and the resultant packet drops. A node, including the gateway, is always either in receiving state or transmitting a packet. This ensures that critical data are not lost unless there are serious connectivity problems. The final time slot in each frame is reserved  for  contention  based  communications.  This  slot  can  be  used  by  a  node  that  has missed its own time slot during the frame, e.g., if it has no direct link to the gateway. Such nodes may transmit in this slot, and hence they do not need to wait for the next frame and their own slot in that.

During  the  test  period,  the  protocol  was  run  for  approximately  750000  superframes.  The gateway was able to receive the measurements from all the nodes in >99.91 % of the cases in <100  ms.  Furthermore,  it  could  receive  all  the  data  in  >99.96  %  of  the  cases  in  <500  ms. During  the  whole  test  period,  over  6  %  of  all  measurements  from  one  of  the  nodes  were transmitted  through  other  nodes  because  of  the  lack  of  direct  connectivity.  Clearly  the proposed  protocol  contributes  to  the  reliability  and  real-timeliness  required  in  wireless control systems, but further developments, such as integration of temporal, frequency and spatial diversity techniques, are needed to attain 100 % reliability.

Control design for wireless automation

Because of the networking challenges described previously, wireless real-time control is not straight-forward.   In   this   section   we   describe   some   methods   to   compensate   for   the deficiencies of the network at the control layer. The main problem is to guarantee stability of the   control   system,   even   if   the   real-time   operation   of   the   network   is   occasionally compromised. The  schedule  and  retransmissions  of  the  network  used  in  wireless  automation  determine when information is available to the control system, and hence affect the control operation. There  exists  work  where  the  actual  network  MAC  protocol  and  related  functions  such  as duty-cycle (Marco et al., 2010), or routing and schedule (Samii et al., 2009; Weiss et al., 2009) are taken into account in the control stability proof. These rely on a predetermined schedule, whereupon  the  controller  stability  is  proven.  Wireless  communication  is  to  some  degree

stochastic, so control stability proofs for randomly varying feedback delay are needed, such as the jitter margin presented next.


Control with packet drops and varying delay stemming from a network is a complex case to be  analyzed,  because  of  the  stochastic  and  time-varying  nature  of  the  problem.  Ensuring stability of NCS has been under much research lately (Hespanha et al., 2006). Some results deal with optimal control (Lincoln & Bernhardsson, 2000), jump-linear Markov models (Xiao et al., 2000) and the jitter margin (Cervin et al., 2004; Kao & Lincoln, 2004). The jitter margin defines the amount of additional delay that a control system can tolerate without becoming unstable. The delay may vary in any way, provided that it is bounded by the jitter margin δmax. By selecting a tuning of a conventional controller such that the control loop has a positive jitter margin, the control loop is stable for network induced delay jitter and packet drop given by the jitter margin. The  jitter  margin  theorem  states  that  in  the  continuous-time  case,  the  closed-loop  system

with process G(s) and controller Gc(s) is stable for any additional delay  0 ≤ δ (t) ≤ δmax   in the loop, if (Kao & Lincoln, 2004)

The  jitter  margin  can  be  used  in  controller  tuning  to  guarantee  stability  of  a  control  loop with  a  varying  delay.  The  above  condition  may  be  considered  as  a  constraint  in  the controller  parameter  optimization  problem,  when  the  optimal  controller  parameters  must satisfy  the  given  jitter  margin  requirement,  or  then  the  jitter  margin  theorem  can  be formulated  as an  objective function,  which leads  to the  maximization  of  the  jitter  margin. Alternatively, the controller tuning may be based on some known process parameters, and certain fixed tuning rules may be applied, but then it is important to study the obtained jitter margin with the specific tuning. Examples of such tuning rules and methods for the widely used PID controller are given in Table 1, based on the work in (Eriksson, 2008). It should be noted  that  many  of  these  tuning  rules  and  methods  have  the  preferred  property  that the

desired jitter margin is an input to the rules. The tuning adapts the PID gains to ensure a stable control system with individual packet drops, as far as the length of losses is less than δmax  . By increasing the jitter margin, the control becomes generally more conservative. The

tuning   rules   allow   the   delay   to   vary   in   any   way,   which   might   introduce   some conservativeness to the control design.

Co-design validation by simulation

In addition to the theoretical results, the simulation of WiNCSs is important and necessary for  several  reasons.  Little  is  said  in  the  literature  about  the  practical  implementation, behaviour, and performance of the wireless control systems. Simulations are a feasible way to test and evaluate the practical benefits of the developed theory and algorithms, where the critical properties and behavior of the network, and the impact on the control system can be analyzed.  These  issues,  in  particular  the  protocol  specific  ones,  are  hard  to  approach analytically. Simulation studies will, hopefully, unravel these matters and lead to a coherent theory, best practices knowledge, and design expertise of WiNCSs. To enable the simulation study of WiNCS, the network and control co-simulator PiccSIM has been developed. In the following  section  PiccSIM  is  described  in  more  detail  and  some  simulation  cases  are presented,  that  show  the  capabilities  of  PiccSIM  and  the  benefits  of  co-simulation  for WiNCSs  design.  The  simulation  cases  involve  multiple  control  loops,  which  cannot  be studied without co-simulation.


PiccSIM  stands  for  Platform  for  integrated  communications  and  control  design,  simulation, implementation and modelling (Nethi et al., 2007) and is developed at Aalto University School of Science and Technology (PiccSIM, 2010). PiccSIM integrates two simulators to achieve an accurate  and  versatile  simulation  system  at  both  the  communication  and  control  level  for WiNCSs. It  has  the  unique  feature  of  delivering  a  whole  chain  of  tools  for  network  and  control modelling  and  design,  integrated  into  one  package  with  communication  and  control  co- simulation capabilities (Kohtamäki et al., 2009). The tools in PiccSIM range from the design of  the  system,  through  simulation  and  system  testing,  to  implementation  of  a  wireless control system. The PiccSIM simulator is an integration of Matlab/Simulink where the dynamic system is simulated, including the control system, and ns-2 (ns-2, 2010) where the network simulation is  done.  The  PiccSIM  Toolchain  (Kohtamäki  et  al.,  2009),  is  a  graphical  user  interface  for network and control design, realised in Matlab. It is a front-end for the PiccSIM simulator and delivers the user access to all the PiccSIM modelling, simulation and implementation tools.There are several reasons to build a co-simulation platform consisting of Matlab and ns-2. Matlab   and   Simulink   are   widely   employed   research   tools   used   in   dynamic   system simulation, providing efficient tools for control design. Control engineers are accustomed to working  in  this  environment.  Ns-2,  on  the  other  hand,  is the  de  facto  standard  tool  for network simulation in the communication research community. Ns-2 simulates the network on a per packet basis, with models for physical layer, MAC, routing and transport protocols.

Picc SIMarchitecture

The PiccSIM simulator consists basically of two computers on a local area network (LAN): the Simulink computer for system simulation, including plant dynamics, signal processing and control algorithms, and the ns-2 computer for network simulation. For further details see  (Nethi  et  al.,  2007),  where  the  integration  of  ns-2  and  Simulink  is  reported,  and (Kohtamäki et al., 2009; Pohjola et al., 2005).

Packets  sent  over  the  simulated  network  are  routed  through  the  ns-2  computer,  which simulates   the   network   in   ns-2   according   to   any   TCL   script   specification   generated automatically   by   a   network   configuration   tool.   Simulation   time-synchronization   is performed  between  the  computers.  To  close  the  gap  between  the  simulators,  a  data exchange mechanism is implemented, which can pass information from one simulator to the other.   This   enables   the   simulation   of   cross-layer   protocols   that   take   advantage   of information  from  the  other  application  layers.  An  example  where  the  data  exchange mechanism  can  be  used  is  with  mobile  scenarios,  where  the  location  of  the  nodes  and further   the   network   topology   depends   on   the   application   operation,   for   instance,   in applications of robotics and moving machinery.

Crane control simulation case

In this section the communication and control co-simulation is demonstrated with PiccSIM, where the impact of the network protocol on the control performance is shown. The benefits of the LBP protocol presented above are demonstrated in a real-time control case, where we simulate an operator driving a trolley crane. The operator gives the velocity reference for the crane with a wireless handheld device. The control messages are routed over a local wireless IEEE 802.15.4 network installed on the crane. The laboratory scale crane model presented in (Eriksson et al., 2006) is scaled up by a factor of ten and used in the simulation cases. The crane control system consists of PID controllers for the trolley and hoist motors, which operate the actuators based on the velocity reference given  by  the  operator  through  the  wireless handheld  device.  For  simulation  purposes  the operator is represented by PID controllers for the vertical and horizontal movement of the load. For load swing compensation, the human transfer function identified in (Tervo et al.,2009) is used. The load of the crane is moved according to a predefined trajectory, given as reference to the “operator controllers”. In the case of packet drop, the velocity reference is set to zero at the receiving side. The above presented Gilbert-Elliot packet drop models that have  been  identified  based  on  extensive  tests  in  a  real  industrial  hall  are used  in  the simulations. To  assess  the  impact  of  the  network  QoS  on  the  control  performance,  simulations  with different   network   QoS   parameters   are   made.   Several   load   movement   trajectories   are simulated  with  different  Gilbert-Elliott  network  model  parameters.  The  resulting  control performances, each averaged over ten runs, are shown in Fig. 8. Obviously, at high packet drop rates the control performance is significantly decreased, but the results also show that it  is  extremely  difficult  to  predict  the  effects  of  packet  drops  on  the  control  performance without extensive simulations that include the network and dynamical system models.


In  this  chapter,  we  have  discussed  the  different  technologies  and  methodologies  enabling reliable  and  real-time  wireless  automation.  The  industrial  environment  is  difficult  for  the use of wireless technologies, but there are currently many serious efforts in trying to achieve the level of reliability of wired communications by wireless networks. Some of these efforts have  emerged  recently  into  standards  of  wireless  automation,  e.g.  WirelessHART  and ISA100.11a. Besides the technology review, we proposed a communication protocol LBP for real-time  networking  in  limited  size  wireless  networks.  The  protocol  can  effectively  take advantage  of  mesh  networking  and  hence  change  the  routing  dynamically  upon  link failures. This is a prerequisite when operating in harsh industrial environments with real- time  applications.  In  the  end  of  the  chapter,  we  presented  the  PiccSIM  co-simulation platform  and  demonstrated  the  benefits  and  capabilities  of  such  a  simulator.  In  WiNCS, analytical  results  are  rarely  available  regarding  the  stability  of  a  large-scale  system  and hence co-simulation may reveal the problems in the design of either networking or control. Furthermore,   via   simulation   greater   insight   into   the   behaviour   and   interactions   of communications and control could be obtained.

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