**4.1. Industrial wireless sensing and communications architecture**

The deployment of heterogeneous wireless communications in industrial environments presents significant challenges [2]. On one hand, industrial environments are usually characterized by challenging propagation conditions (obstructions, interferences, etc.) that difficult the establishment of robust wireless links [3]. On the other hand, hybrid network architectures pose significant challenges to design a system platform efficiently managing data, in particular when real-time connectivity needs to be ensured across multiple wireless technologies [4] to support the reliable risk management. However, ubiquitously monitoring the worker's conditions requires a reliable mobile sensing and communications platform that ensures the wireless connectivity among the Wireless Sensor Network (WSN) nodes. FASyS has designed an end-to-end heterogeneous wireless solution that enables the continuous sensing of the working environment and the worker's health and physiological conditions in order to detect in advance any potential risks. Fig. 6. depicts FASyS's heterogeneous communications architecture for industrial environments.

To transmit the sensed data to a control centre, a wireless backhaul including medium range technologies for communications within the factory and long range technologies for the transfer of the aggregated data to the control centre has been proposed. The medium range technologies (IEEE 802.11/WiFi and IEEE 802.16/WiMAX) transmit locally sensed data (including video) from different areas of the factory towards a factory's gateway. The gateway can then transmit the received data using WiMAX and/or cellular HSDPA to a

remote control centre. This architecture efficiently and reliably satisfies the requirements imposed by the industrial environment in general, and by the identified FASyS hazards in particular. The proposed architecture takes into account, not only radio propagation and communication aspects, but also semantics and security planes.

Integrated and Personalised Risk Management in the Sensing Enterprise 295

**Figure 7.** GORATU's main factory of machine tools.

and locations must be carefully selected.

An example, the results of one of the experiments conducted to analyse the connectivity between a TX mobile sensing mote (e.g. a mote attached to a worker or industrial vehicle) and a stationary RX base station using the MEMSIC Iris WSN motes are presented in Figure 7. In this experiment, the base station was strategically deployed with relatively good propagation conditions with the different areas of the factory (the RX base station was located at position RX in Figure 7.with an antenna height of hRX=5m). During the experiments, the TX mobile node (antenna height of hTX=1.2m) moved across different areas of the factory at pedestrian speed. This node was configured to periodically transmit a data packet every *T* seconds with a payload of 50bytes excluding headers, emulating the data transmissions of a body sensor device. Along its path, the TX mobile node experienced different propagation conditions with the RX fixed node: LOS (Line of Sight) with reduced obstructions (Z1); partial NLOS (Non LOS) due to cranes, pillars, and machinery (Z2 and Z3); NLOS due to multiple obstructing elements and high distance (Z4); and NLOS and heavy obstruction (Z5, the warehouse). The Figure below depicts the PER (Packet Error Rate) levels measured as the mobile TX mote moves around the factory. The figure shows the average PER levels experienced during time intervals of *Tp*=5s and the distance between the TX and RX nodes along the path; this figure differentiates the different zones of the factory (Z1, Z2,…, Z5). Additional experiments were conducted with different transceivers, antenna heights and transmission powers. The obtained results show that IEEE 802.15.4/Zigbee can provide the connectivity requirements of industrial applications, even for mobile applications. However, the transceiver, deployment conditions

(a) Plan of GORATU's main factory (axis in meters) (b) View of one of the factory's corridors (Z1)

**Figure 6.** FASyS heterogeneous communications architecture

## **4.2. Industrial wireless communications and sensing connectivity**

To evaluate the performance and connectivity levels of mobile IEEE 802.15.4/ZigBee [3] sensing communications, as well as the quality of service that IEEE 802.11/WiFi, IEEE 802.16/WiMAX and HSDPA technologies [4] can provide in industrial environments, a large field testing campaign has been conducted. This field testing campaign was conducted in GORATU covering a surface area of more than 10.000m2 – see Figure 7a. As illustrated in Figure 7b, the plant is characterized by the presence of a large number of potential metallic obstacles that influence the radio propagation and thereby the wireless connectivity.

communication aspects, but also semantics and security planes.

**Figure 6.** FASyS heterogeneous communications architecture

**4.2. Industrial wireless communications and sensing connectivity** 

To evaluate the performance and connectivity levels of mobile IEEE 802.15.4/ZigBee [3] sensing communications, as well as the quality of service that IEEE 802.11/WiFi, IEEE 802.16/WiMAX and HSDPA technologies [4] can provide in industrial environments, a large field testing campaign has been conducted. This field testing campaign was conducted in GORATU covering a surface area of more than 10.000m2 – see Figure 7a. As illustrated in Figure 7b, the plant is characterized by the presence of a large number of potential metallic

obstacles that influence the radio propagation and thereby the wireless connectivity.

remote control centre. This architecture efficiently and reliably satisfies the requirements imposed by the industrial environment in general, and by the identified FASyS hazards in particular. The proposed architecture takes into account, not only radio propagation and

(a) Plan of GORATU's main factory (axis in meters) (b) View of one of the factory's corridors (Z1)

#### **Figure 7.** GORATU's main factory of machine tools.

An example, the results of one of the experiments conducted to analyse the connectivity between a TX mobile sensing mote (e.g. a mote attached to a worker or industrial vehicle) and a stationary RX base station using the MEMSIC Iris WSN motes are presented in Figure 7. In this experiment, the base station was strategically deployed with relatively good propagation conditions with the different areas of the factory (the RX base station was located at position RX in Figure 7.with an antenna height of hRX=5m). During the experiments, the TX mobile node (antenna height of hTX=1.2m) moved across different areas of the factory at pedestrian speed. This node was configured to periodically transmit a data packet every *T* seconds with a payload of 50bytes excluding headers, emulating the data transmissions of a body sensor device. Along its path, the TX mobile node experienced different propagation conditions with the RX fixed node: LOS (Line of Sight) with reduced obstructions (Z1); partial NLOS (Non LOS) due to cranes, pillars, and machinery (Z2 and Z3); NLOS due to multiple obstructing elements and high distance (Z4); and NLOS and heavy obstruction (Z5, the warehouse). The Figure below depicts the PER (Packet Error Rate) levels measured as the mobile TX mote moves around the factory. The figure shows the average PER levels experienced during time intervals of *Tp*=5s and the distance between the TX and RX nodes along the path; this figure differentiates the different zones of the factory (Z1, Z2,…, Z5). Additional experiments were conducted with different transceivers, antenna heights and transmission powers. The obtained results show that IEEE 802.15.4/Zigbee can provide the connectivity requirements of industrial applications, even for mobile applications. However, the transceiver, deployment conditions and locations must be carefully selected.

Integrated and Personalised Risk Management in the Sensing Enterprise 297

The technology used in FASYS has been standardized by the OGC [6] and has been extended and specially applied to factory automation by the research team [10][7]. The concept of SSW, is based on the use of a special type of information infrastructure for web-centric collection, modelling, storage, subsequent withdrawal, sharing, manipulation, analysis and visualization of information on sensors and observation of phenomena from them. The definition of SSW by OGC is: "*Networks of sensors and sensor data storage accessible via the web, which can be discovered and accessed using protocols and application interfaces standards*"[6]. The standard and components of the SWE SOA are: Observations & Measurements (O&M); Sensor Model Language (SensorML); Transducer Model Language (TransducerML or TML); Sensor Observation Service (SOS); Sensor Planning Service (SPS); Sensor Alert Service (SAS) and Web Notification Services (WNS) [8]. FASYS considers the use of different SOS located in strategic points in the Communication Architecture in order to provide homogeneous access in the heterogeneous

FASYS semantic sensor system is based in the use of SOS and Sensor ML, it provides access to the data generated by the sensors so as the metadata to configure and customize each individual component (sensor) of the network. The main benefits of using semantic sensor networks in FASYS are: (i) Platform independence as practically any sensor or modelling system can be supported (even simulated sensors); (ii) easy development of services allowing dynamic connectivity between resources; (iii) Liaison with semantic environments, adding semantic information to the basic SWE paradigm; (iv) Traceability and support to the implementation and management of real-time measurements; (v) Flexibility in implementation: container capacity and existing sensors, implementing and processing services; and (vi)

Scalability from a single sensor to a collection, individual, group or cluster of sensors. [9]

register with the SOS from each gateway to have controlled all data sources [10][11].

to exchange information and to use the information that has been exchanged. [12].

Regarding interoperability of systems and applications, the use of SOS provides syntactic and semantic interoperability. Interoperability is a property referring to the ability of diverse systems and organizations to work together (inter-operate). The term is often used in a technical systems engineering sense, or alternatively in a broad sense, taking into account social, political, and organizational factors that impact system to system performance. Interoperability may also be understood as the ability of two or more systems or components

Regarding the design architecture, it is important to consider the location of the SOS within the network. Basically, there are two main approaches. The first approach considers locating the SOS in the coordinator node, as near as possible to the physical sensor. The second approach considers locating the SOS in the gateway node, as near as possible to the control center. In order to determine the most appropriate place for the SOS, it is important to consider the data flows that are envisioned between sensors and the SOS and between applications and the SOS in order to minimize data traffic. As the FASyS system uses a Complex Event Processing (CEP) system as key component of the Control Center that continuously issues requests to the SOS, the data flow is considered to be significantly higher than the data between sensors and the SOS. Therefore, the second approach has been selected in FASyS. Once the SOS has been set up in the gateway node, all sensors have to

network to the different control applications.

**Figure 8.** PER performance as a Memsic IRIS WSN mote with Pt=3dBm moves around the factory (hTX=1.2m, hRX=5m, T=200ms, payload=50Bytes).

#### **4.3. Smart object semantic representation**

Regarding semantics, a traversal control plane has been included in the architecture. Semantics are based in the Semantic Sensor Web paradigm, [5] and on a specific abstraction for virtual objects. Semantics, interoperability and exchange of relevant sensor configuration and information are based on Service Oriented Architecture. The key components of the FASYS semantic sensor environment are the Sensor Observation Server (SOS), a standard from OGC [6], and the HMI located in the command and control location of the risk management architecture.

The concept of semantic sensor network is used to organize, manage, interrogate, understand and control the different components of the data gathering process (i.e. network, sensors and the resulting data using high-level specifications). If semantics are introduced in the reasoning process of a FASyS subsystem, it is important to design properly the various steps of communication and interfaces if sensors and sensor networks are involved, as they impose various kinds of restrictions and limitations, such as power constraints, finite and limited memory, unreliable communication network and the quality and variability of data received.

The Semantic Sensor Web (SSW) or the Semantic Sensor Networks (SSN) base their operation on the existence of a sensor network that implements a physical layer (PHY), a sub-level medium access (MAC) and network layer (NET), usually implemented by standard protocols (e.g. Zigbee and 6LowPAN), but considering mechanisms and proprietary systems. The contribution of this type of mechanism is the addition to the data measured / generated by sensors in the form of metadata annotations of semantic information of a temporal, spatial and thematic, accessible through a Service Oriented Architecture.

The technology used in FASYS has been standardized by the OGC [6] and has been extended and specially applied to factory automation by the research team [10][7]. The concept of SSW, is based on the use of a special type of information infrastructure for web-centric collection, modelling, storage, subsequent withdrawal, sharing, manipulation, analysis and visualization of information on sensors and observation of phenomena from them. The definition of SSW by OGC is: "*Networks of sensors and sensor data storage accessible via the web, which can be discovered and accessed using protocols and application interfaces standards*"[6]. The standard and components of the SWE SOA are: Observations & Measurements (O&M); Sensor Model Language (SensorML); Transducer Model Language (TransducerML or TML); Sensor Observation Service (SOS); Sensor Planning Service (SPS); Sensor Alert Service (SAS) and Web Notification Services (WNS) [8]. FASYS considers the use of different SOS located in strategic points in the Communication Architecture in order to provide homogeneous access in the heterogeneous network to the different control applications.

296 Risk Management – Current Issues and Challenges

(hTX=1.2m, hRX=5m, T=200ms, payload=50Bytes).

PER [%]

management architecture.

received.

Architecture.

**4.3. Smart object semantic representation** 

**Figure 8.** PER performance as a Memsic IRIS WSN mote with Pt=3dBm moves around the factory

0 100 200 300 400 500 600 700

Elapsed time [s]

PER (average 5s) Distance TX-RX [m] Z1 Z2 Z3 Z4 Z3 Z5 Z3

Distance TX-RX [m]

Regarding semantics, a traversal control plane has been included in the architecture. Semantics are based in the Semantic Sensor Web paradigm, [5] and on a specific abstraction for virtual objects. Semantics, interoperability and exchange of relevant sensor configuration and information are based on Service Oriented Architecture. The key components of the FASYS semantic sensor environment are the Sensor Observation Server (SOS), a standard from OGC [6], and the HMI located in the command and control location of the risk

The concept of semantic sensor network is used to organize, manage, interrogate, understand and control the different components of the data gathering process (i.e. network, sensors and the resulting data using high-level specifications). If semantics are introduced in the reasoning process of a FASyS subsystem, it is important to design properly the various steps of communication and interfaces if sensors and sensor networks are involved, as they impose various kinds of restrictions and limitations, such as power constraints, finite and limited memory, unreliable communication network and the quality and variability of data

The Semantic Sensor Web (SSW) or the Semantic Sensor Networks (SSN) base their operation on the existence of a sensor network that implements a physical layer (PHY), a sub-level medium access (MAC) and network layer (NET), usually implemented by standard protocols (e.g. Zigbee and 6LowPAN), but considering mechanisms and proprietary systems. The contribution of this type of mechanism is the addition to the data measured / generated by sensors in the form of metadata annotations of semantic information of a temporal, spatial and thematic, accessible through a Service Oriented FASYS semantic sensor system is based in the use of SOS and Sensor ML, it provides access to the data generated by the sensors so as the metadata to configure and customize each individual component (sensor) of the network. The main benefits of using semantic sensor networks in FASYS are: (i) Platform independence as practically any sensor or modelling system can be supported (even simulated sensors); (ii) easy development of services allowing dynamic connectivity between resources; (iii) Liaison with semantic environments, adding semantic information to the basic SWE paradigm; (iv) Traceability and support to the implementation and management of real-time measurements; (v) Flexibility in implementation: container capacity and existing sensors, implementing and processing services; and (vi) Scalability from a single sensor to a collection, individual, group or cluster of sensors. [9]

Regarding the design architecture, it is important to consider the location of the SOS within the network. Basically, there are two main approaches. The first approach considers locating the SOS in the coordinator node, as near as possible to the physical sensor. The second approach considers locating the SOS in the gateway node, as near as possible to the control center. In order to determine the most appropriate place for the SOS, it is important to consider the data flows that are envisioned between sensors and the SOS and between applications and the SOS in order to minimize data traffic. As the FASyS system uses a Complex Event Processing (CEP) system as key component of the Control Center that continuously issues requests to the SOS, the data flow is considered to be significantly higher than the data between sensors and the SOS. Therefore, the second approach has been selected in FASyS. Once the SOS has been set up in the gateway node, all sensors have to register with the SOS from each gateway to have controlled all data sources [10][11].

Regarding interoperability of systems and applications, the use of SOS provides syntactic and semantic interoperability. Interoperability is a property referring to the ability of diverse systems and organizations to work together (inter-operate). The term is often used in a technical systems engineering sense, or alternatively in a broad sense, taking into account social, political, and organizational factors that impact system to system performance. Interoperability may also be understood as the ability of two or more systems or components to exchange information and to use the information that has been exchanged. [12].

Regarding Interoperability we can distinguish two different possibilities syntactical and semantics interoperability [13]. FASYS provides both kinds of interoperability starting from the correct use of a SOS as support for merging and concentrating the information generated by sensors and distributed devices. Though strictly speaking the SOS provides syntactical interoperability, it is relatively easy to incorporate simple semantic support as temporal, spatial and thematic filtering are natively supported by SensorML and O&M, the two standard interfaces used by the SOS. Additionally, SensorML supports extensibility through annotations. If such annotations are part of a semantic vocabulary, then more complex semantic operations can be supported. SOS has been extended with a database based on a specific FASYS data model that includes some specific features not included in the OGC standard and are required to integrate the SSN in the FASYS HMI.

Integrated and Personalised Risk Management in the Sensing Enterprise 299

A second example of mobile sensing application leveraged by FASyS is an innovative monitoring platform built up by up to six sensors following the HealthAlliance interface that arranges the worker physiological follow up in two strategies: (a) **intensive monitoring**, for those workers on health risk approaching minimum invasiveness as well as maximum ergonomics. (b) **preventive monitoring**, to check periodically main health indicators. FASyS is capable of integrating data coming from different sensors, working with different communication protocols and interfaces. This is done implementing Health Alliance 11073 standard logic communication and manufacturer protocol for the sensing devices. After the acknowledgment, the values are packed into HL7 standard messages and transmitted through secure pathways to the factory DPC (data processing centre), where the personalized health management module can access to it – see next Section on Occupational

Most developed countries include within their basic welfare policies the right of citiziens, as workers, to be protected from sickness, disease and injury arising from their employment. Despite this intention, the International Labour Organization (ILO) estimates that 160 million workers are victims of occupational accidents and diseases every year [14] and over two million of people lose their lives from work-related accidents and diseases. The standards on occupational safety and health provide necessary tools for governments, employers, and workers to establish such practices and to provide for full safety at work. In 2003, ILO assumed a global strategy to improve occupational safety and health, which included the introduction of a preventive safety and health culture, the promotion and

One of the European objectives set for 2020 is the 25% reduction in the number of industrial accidents [15][16]. In order to reduce accidents it is essential to pay attention to the workers, their single workplaces and to their working conditions. In addition, favourable environments make workers feel more comfortable while they are in the factories, and thus the efficiency is increased. As a consequence, it is possible to obtain the maximum efficiency in the factory as a whole, which also produces economic benefit for the company. From a healthcare point of view, factories lack normally in an amount of enough information to allow a holistic care of the worker. Health data stored by companies are only a small amount of data, usually stored once a year, and referred to the physical condition of a

For these reason, future factories and enterprises need to do an effort in focusing resources and strategic planning towards making the workplace safer, healthier and to significantly reduce the number of accidents and the work related diseases in their population. In order

*4.4.2. Continuous physiological monitoring module* 

**5. Occupational Personal Health Systems (O-PHS)** 

development of relevant instruments, and technical assistance [14].

**5.1. Proactive paradigm for Occupational Health Systems** 

person just in a particular moment [17].

Personal Health Systems.

#### **4.4. Mobile sensing applications**

FASyS advanced IoT networking paves the ground for advanced monitoring functions that can be exploited by other parts of the FASyS risk management system to perform advanced health and safety prevention. Mobile sensing applications facilitated by FASyS include among other collision avoidance and continuous physiological monitoring.

#### *4.4.1. Collision avoidance*

Collisions between workers and fork-lift trucks, or between any type of vehicles, have been identified as one of the most common accidents in factories. Such collisions could be prevented if workers and vehicles would be equipped with WSN (Wireless Sensor Network) motes so that they can dynamically exchange information about their position and speed in real time. With this information, they could be able to detect in advance, and avoid, potential dangerous situations, such as the intersection shown in the Figure below. Robust and reliable wireless communication links should be established between any two nodes with a risk of collision, despite the potentially challenging propagation conditions, represented in the intersection by a wall, and large metallic machinery and obstructing elements placed within a large wood container at the intersection.

**Figure 9.** Collision avoidance use case: testing intersection

### *4.4.2. Continuous physiological monitoring module*

298 Risk Management – Current Issues and Challenges

**4.4. Mobile sensing applications** 

*4.4.1. Collision avoidance* 

Regarding Interoperability we can distinguish two different possibilities syntactical and semantics interoperability [13]. FASYS provides both kinds of interoperability starting from the correct use of a SOS as support for merging and concentrating the information generated by sensors and distributed devices. Though strictly speaking the SOS provides syntactical interoperability, it is relatively easy to incorporate simple semantic support as temporal, spatial and thematic filtering are natively supported by SensorML and O&M, the two standard interfaces used by the SOS. Additionally, SensorML supports extensibility through annotations. If such annotations are part of a semantic vocabulary, then more complex semantic operations can be supported. SOS has been extended with a database based on a specific FASYS data model that includes some specific features not included in the OGC

FASyS advanced IoT networking paves the ground for advanced monitoring functions that can be exploited by other parts of the FASyS risk management system to perform advanced health and safety prevention. Mobile sensing applications facilitated by FASyS include

Collisions between workers and fork-lift trucks, or between any type of vehicles, have been identified as one of the most common accidents in factories. Such collisions could be prevented if workers and vehicles would be equipped with WSN (Wireless Sensor Network) motes so that they can dynamically exchange information about their position and speed in real time. With this information, they could be able to detect in advance, and avoid, potential dangerous situations, such as the intersection shown in the Figure below. Robust and reliable wireless communication links should be established between any two nodes with a risk of collision, despite the potentially challenging propagation conditions, represented in the intersection by a wall, and large metallic machinery and obstructing

standard and are required to integrate the SSN in the FASYS HMI.

elements placed within a large wood container at the intersection.

**Figure 9.** Collision avoidance use case: testing intersection

among other collision avoidance and continuous physiological monitoring.

A second example of mobile sensing application leveraged by FASyS is an innovative monitoring platform built up by up to six sensors following the HealthAlliance interface that arranges the worker physiological follow up in two strategies: (a) **intensive monitoring**, for those workers on health risk approaching minimum invasiveness as well as maximum ergonomics. (b) **preventive monitoring**, to check periodically main health indicators. FASyS is capable of integrating data coming from different sensors, working with different communication protocols and interfaces. This is done implementing Health Alliance 11073 standard logic communication and manufacturer protocol for the sensing devices. After the acknowledgment, the values are packed into HL7 standard messages and transmitted through secure pathways to the factory DPC (data processing centre), where the personalized health management module can access to it – see next Section on Occupational Personal Health Systems.
