Design and Application Spaces for 6LoWPANs

This section lists a fundamental set of LoWPAN application scenarios in terms of system design. A complete list of practical use cases is not the objective of this document.

LoWPAN applications for industrial monitoring can be associated with a broad range of methods to increase productivity, energy efficiency, and safety of industrial operations in engineering facilities and manufacturing plants. Many companies currently use time-consuming and expensive manual monitoring to predict failures and to schedule maintenance or replacements in order to avoid costly manufacturing downtime. LoWPANs can be inexpensively installed to provide more frequent and more reliable data. The deployment of LoWPANs can reduce equipment downtime and eliminate manual equipment monitoring that is costly to be carried out. Additionally, data analysis functionality can be placed into the network, eliminating the need for manual data transfer and analysis. Industrial monitoring can be largely split into the following application fields: Process Monitoring and Control: combining advanced energy metering and sub-metering technologies with wireless sensor networking in order to optimize factory operations, reduce peak demand, ultimately lower costs for energy, avoid machine downtimes, and increase operation safety. A plant's monitoring boundary often does not cover the entire facility but only those areas considered critical to the process. Easy to install wireless connectivity extends this line to include peripheral areas and process measurements that were previously infeasible or impractical to reach with wired connections. Machine Surveillance: ensuring product quality and efficient and safe equipment operation. Critical equipment parameters such as vibration, temperature, and electrical signature are analyzed for abnormalities that are suggestive of impending equipment failure (see ). Supply Chain Management and Asset Tracking: with the retail industry being legally responsible for the quality of sold goods, early detection of inadequate storage conditions with respect to temperature will reduce risk and cost to remove products from the sales channel. Examples include container shipping, product identification, cargo monitoring, distribution and logistics. Storage Monitoring: sensor systems designed to prevent releases of regulated substances to ground water, surface water and soil. This application field may also include theft/tampering prevention systems for storage facilities or other infrastructure, such as pipelines.

Example: Hospital Storage Rooms In a hospital, maintenance of the right temperature in storage rooms is very critical. Red blood cells need to be stored at 2 to 6 degrees Celsius, blood platelets at 20 to 24 C, and blood plasma below -18 C. For anti-cancer medicine, maintaining a humidity of 45% to 55% is required. Storage rooms have temperature sensors and humidity sensors every 25m to 100m, based on the floor plan and the location of shelves, as indoor obstacles distort the radio signals. At each blood pack a sensor tag can be installed to track the temperature during delivery. A LoWPAN node is installed in each container of a set of blood packs. In this case, highly dense networks must be managed. All nodes are statically deployed and manually configured with either a single- or multi-hop connection. Different types of LoWPAN nodes are configured based on the service and network requirements. Especially, LCs play a role in aggregation of the sensed data from blood packs. In the extended networks, more than one LoWPAN LCs can be installed in a storage room. In the case that the sensed data from an individual node is urgent event-driven data such as outrange of temparature or humidity, it will not be accumulated (and further delayed) by the LCs but immediately relayed. All LoWPAN nodes do not move unless the blood packs or a container of blood packs is moved. Moving nodes get connected by logical attachment to a new LoWPAN. When containers of blood packs are transferred to another place of the hospital or by ambulance, the LoWPAN nodes on the containers associate to a new LoWPAN. This type of application works based on both periodic and event-driven notifications. Periodic data is used for monitoring the temperature and humidity in the storage rooms. The data over or under a pre-defined threshold is meaningful to report. Blood cannot be used if it is exposed to the wrong environment for about 30 minutes. Thus, event-driven data sensed on abnormal occurrences is time-critical and requires secure and reliable transmission. LoWPANs must be provided with low installation and management costs, and for the transportation of blood containers, precise location tracking of containers is important. The hospital network manager or staff can be provided with an early warning of possible chain ruptures, for example by conveniently accessing comprehensive online reports and data management systems. Dominant parameters in industrial monitoring scenarios: Deployment: pre-planned, manually attached Mobility: no (except for asset tracking) Network Size: medium to large size, high node density Power Source: most of the time battery-operated Security Level: business-critical. Secure transmission must be guaranteed. Multi-hop communication: multi-hop networking Connectivity: always on for crucial processes QoS: important for time-critical event-driven data Traffic Pattern: P2P (actuator control), MP2P (data collection) Other Issues: Sensor network management, location tracking, real-time early warning

The network configuration of the above use case can differ substantially by system design. As illustrated in , the simplest way is to build a star topology inside of each storage room. Based on the layout and size of the storage room, the LoWPAN can be configured in a different way of mesh topology as shown in . Each LoWPAN node may reach the LBR by a predefined routing/forwarding mechanism. Each LoWPAN node configures its link-local address and obtains a prefix from its LBR by an 6LoWPAN ND procedure . LoWPAN nodes need to build a multi-hop connection to reach the LCs and LBR. Secure data transmission and authentication is crucial in a hospital scenario to prevent personal information to be retrieved by an adversary. Confidential data must be encrypted not only in transmission, but also when stored on nodes, because nodes can potentially be stolen. The data volume is usually not so large in this case, but is sensitive to delay. Data aggregators can be installed for each storage room, or just one data aggregator can collect all data. To make a light transmission, UDP is likely to be chosen, but secure transmission and security mechanism must be added. To increase security, link-layer mechanisms and/or additional security mechanisms should be used. Because a failure of a LoWPAN node can critically affect the storage of the blood packs, network management is important in this use-case. A light-weight management mechanism must be provided for the management. The service quality of this case is highly related to effective handling of event-driven data which is delay intolerant and mission critical. The event of wrong humidity and temperature needs to be detected as quickly and reliable as possible. It is important to provide efficient resource usage for such data with consideration of minimal usage of energy. Energy aware QoS support in wireless sensor networks is a challenging issue . It can be considered to provide appropriate data aggreation for minimizing the delay, maximizing the accuracy of the delivery by using power-affluent nodes, or aided by middleware or other types of network elements. When a container is moved out from the storage room, and connected to the other hospital system (if the hospital buildings are fully or partly covered with LoWPANs), a mechanism to rebind to a new parent node and a new LoWPAN must be supported. In the case that it is moved by an ambulance, it will be connected to an LBR in the vehicle. This type of mobility is supported by 6LoWPAN ND and routing mechanism. LoWPANs must be provided with low installation and management costs, providing benefits such as reduced inventory, and precise location tracking of containers, and mobile equipment (moving beds at the hospital or ambulances).

Intelligent monitoring in facility management can make safety checks and periodic monitoring of the architecture status highly efficient. Mains-powered nodes can be included in the design phase of a construction or battery-equipped nodes can be added afterwards. All nodes are static and manually deployed. Some data is not critical for security protection (such as periodic or query-driven notification of normal room temperature), but event-driven emergency data (such as a fire alarm) must be handled in a very critical manner.

Example: Bridge Safety Monitoring A 1000m long concrete bridge with 10 pillars is described. Each pillar and the bridge body contain 5 sensors to measure the water level, and 5 vibration sensors are used to monitor its structural health. The LoWPAN nodes are deployed to have 100m line-of-sight distance from each other. All nodes are placed statically and manually configured with a single-hop connection to the local coordinator. All LoWPAN nodes are immobile while the service is provided. Except from the pillars, there are no special obstacles of attenuation to the node signals, but careful configuration is needed to prevent signal interference between LoWPAN nodes. The physical network topology is changed in case of node failure. On the top part of each pillar, a sink node is placed to collect the sensed data. The sink nodes of each pillar become data gathering point of the LoWPAN hosts at the pillar and act as local coordinators. This use case can be extended to medium or large size sensor networks to monitor a building or for instance the safety status of highways and tunnels. Larger networks of the same kind still have similar characteristics such as static node placement, manual deployment and dependent on the blue print of the structure, mesh topologies will be built with mains-powered relay points. Periodic, query-driven, and event-driven real-time data gathering is performed and the emergency event-driven data must be delivered without delay. Dominant parameters in structural monitoring applications: Deployment: static, organized, pre-planned Mobility: none Network Size: small (dozens of nodes) to large Power Source: mains-powered nodes are mixed with battery powered (mains-power nodes will be used for local coordination or relays). Security Level: safety-critical. Secure transmission must be guaranteed. Only authenticated users must be able to access and handle the data. Multi-hop communication: multi-hop mesh networking is recommended to be supported. Connectivity: always connected or intermittent by sleeping mode scheduling. QoS: Emergency notification (fire, over-threshold vibrations, water level, etc.) is required to have priority of delivery and must be transmitted in a highly reliable manner. Traffic Pattern: MP2P (data collection), P2P (localized querying) Other Issues: accurate sensing and reliable transmission are important. In addition, sensor status reports should be maintained in a reliable monitoring system.

The network configuration of this use case can be done by simple topologies, however, there are many extended use cases for more complex structures. The example bridge monitoring case may be the simplest case (an example topology is illustrated in ). The LoWPAN Nodes are installed on the place after manual optimization of their location. As the communication of the leaf LoWPAN nodes may be limited to the data gathering points, both 16-bit and 64-bit can be used for IPv6 link-local addresses . Each pillar might have one LC for data collection from each pillar. Communication schedules should be set up between leaf nodes and their LC to efficiently gather the different types of sensed data. Each data packet may include meta-information about its data, or the type of sensors could be encoded in its address during the address allocation. This type of application works based on periodic, query-driven and event-driven notifications. The data over or under a pre-defined threshold is meaningful to report. Event-driven data sensed on abnormal occurrences is time-critical and requires secure and reliable transmission. Conflictly, for energy conservation, all nodes may have periodic and long sleep modes but wake up on certain events. To ensure the reliability of such emergency event-driven data, such data is immediately relayed to a power-affluet or mains-power node which usually takes a LoWPAN router role, and does not go into a long sleep status. The data gathering entity can be programmed to trigger actuators installed in the infrastructure, when a certain threshold value has been reached. Due to the safety-critical data of the structure, authentication and security are important issues here. Only authenticated users must be allowed to access the data. Additional security should be provided at the LBR for restricting the access from outside of the LoWPAN. The LBR may take charge of authentication of LoWPAN nodes. Reliable and secure data transmission must be guaranteed.

The "Connected" Home or "Smart" home is with no doubt an area where LoWPANs can be used to support an increasing number of services: Home safety/security Home Automation and Control Healthcare (see above section) Smart appliances and home entertainment systems In home environments LoWPAN networks typically comprise a few dozen and probably in the near future a few hundreds of nodes of various nature: sensors, actuators and connected objects.

Example: Home Automation The home automation and control system LoWPAN offers a wide range of services: local or remote access from the Internet (via a secured edge router) to monitor the home (temperature, humidity, activation of remote video surveillance, status of the doors (locked or open), etc.) but also for home control (activate the air conditioning/heating, door locks, sprinkler systems, etc.). Fairly sophisticated systems can also optimize the level of energy consumption thanks to a wide range of input from various sensors connected to the LoWPAN: light sensors, presence detection, temperature, etc. in order to control electric window shades, chillers, air flow control, air conditioning and heating with the objective to optimize energy consumption. With the emergence of “Smart Grid” applications, the LoWPAN may also have direct interactions with the Grid itself via the Internet to report the amount of KWatts that could be load shed (Home to Grid) and to receive dynamic load shedding information if/when required (Grid to home): this application is also referred to as Demand-Response application. Another service known as Demand Side Management (DSM) could be provided by utilities to monitor and report to the user its energy consumption with a fine granularity (on a per device basis). Other inputs such as dynamic pricing can also be received by the user from the utility that can then turn on and off some appliances according to its local policy in order to reduce its energy bill. In terms of home safety and security, the LoWPAN is made of motion- and audio-sensors, sensors at doors and windows, and video cameras to which additional sensors can be added for safety (gas, water, CO, Radon, smoke detection). The LoWPAN typically comprises a few dozen nodes forming an ad-hoc network with multi-hop routing since the nodes may not be in direct range. It is worth mentioning that the number of devices tends to grow considering the number of new applications for the home. In its most simple form, all nodes are static and communicate with a central control module but more sophisticated scenarios may also involve inter-device communication. For example, a motion/presence sensor may send a multicast message to a group of lights to be switched on, or a video camera will be activated sending a video stream to a gateway that can be received on a cell phone. Ergonomics in Connected Homes is a key and the LoWPAN must be self-managed and easy to install. Traffic patterns may greatly vary depending on the applicability and so does the level of reliability and QoS expected from the LoWPAN. Humidity sensing is typically not critical and requires no immediate action whereas tele-assistance or gas leak detection is critical and requires a high degree of reliability. Furthermore, although some actions may not involve critical data, still the response time and network delays must be on the order of a few hundreds of milliseconds to preserve the user experience (e.g. use a remote control to switch a light on). A minority of nodes are mobile (with slow motion). With the emergence of energy related applications it becomes crucial to preserve data confidentiality. Connected Home LoWPAN usually do not require multi-topology or QoS routing. Fairly simple QoS mechanisms are enough for handling emergency data. It can be programmed to alarm by actuators or to operate sprinklers. Dominant parameters for home automation applications: Deployment: multi-hop topologies Mobility: some degree of mobility Network Size: medium number of nodes, potentially high density Power Source: mix of battery and mains-powered devices Security Level: authentication and encryption required Multi-hop communication: no requirement for multi-topology or QoS routing Connectivity: intermittent (usage-dependent sleep modes) QoS: support of limited QoS for emergency data (alarm) Traffic Pattern: P2P (inter-device), P2MP and MP2P (polling)

In the home automation use case, the network topology is made of a mix of a battery operated and mains-powered nodes that both communication with each other and a LBR provides connectivity to the outside of world for control management (). In home network, installation and management must be extremely simple for the user. Link local IPv6 addresses can be used by nodes with no external communication and the LBR allocates routable addresses to communicate with other LoWPAN nodes not reachable over a single radio transmission.

In some scenarios, the traffic will be sent to a LC for processing that may in turn decide of local actions (switch a light on, …). In other scenarios, all devices will send their data to the LCs that may also act as the LBR for data processing and potential relay of data to outside of the LoWPAN. It does not mean that every device gets through the LC and LBR for communicating each other. For the sake of illustration, some of the data may be processed to trigger local action (e.g. switch off an appliance), simply store and sent once enough data has been accumulated (e.g. energy consumption for the past 6 hours for a set of appliances) or could trigger an alarm immediately sent to a datacenter (e.g. gas leak detection). Although in the majority of cases nodes within the LoWPAN will be in direct range, some nodes will reach the LBR/LC with a 2-3 hops path (with the emergence of several low-power media such as low-power PLC) in which case LoWPAN routers will be deployed in the home to interconnect the various IPv6 links. The home LoWPAN must be able to provide extremely reliable communication in support of some specific application (e.g. fire, gas leak detection, health monitoring) whereas other application may not be critical (e.g humidity monitoring). Such emergency data has the same QoS issues with the event-driven data in the other applications, and can be delivered by pre-defined paths through mains-powered node without being stored in intermidiate nodes such as LCs. Similarly some information may require the use of security mechanisms for authentication, confidentiality.

LoWPANs are envisioned to be heavily used in healthcare environments. They have a big potential to ease the deployment of new services by getting rid of cumbersome wires and simplify patient care in hospitals and for home care. In healthcare environments, delayed or lost information may be a matter of life or death. Various systems, ranging from simple wearable remote controls for tele-assistance or intermediate systems with wearable sensor nodes monitoring various metrics to more complex systems for studying life dynamics, can be supported by LoWPANs. In the latter category, a large amount of data from various LoWPAN nodes can be collected: movement pattern observation, checks that medicaments have been taken, object tracking, and more. An example of such a deployment is described in using the concept of Personal Networks.

Example: healthcare at home by tele-assistance A senior citizen who lives alone wears one to few wearable LoWPAN nodes to measure heartbeat, pulse rate, etc. Dozens of LoWPAN nodes are densely installed at home for movement detection. A LBR at home will send the sensed information to a connected healthcare center. Portable base stations with LCDs may be used to check the data at home, as well. The different roles of devices have different duty-cycles, which affect node management. Multipath interference may often occur due to the mobility of the patients at home, where there are many walls and obstacles. Even during sleeping, the change of the body position may affect the radio propagation. Data is gathered both periodically and event-driven. In this application, event-driven data can be very time-critical. Thus, real-time and reliable transmission must be guaranteed. Privacy also becomes an serious issue in this case, as the sensed data is very personal. A small set of secret keys can be shared within the sensor nodes during bootstapping procedures in order to build a secure link without using much of memory and energy. In addition, different data will be provided to the hospital system from what is given to a patient's family members. Role-based access control is needed to support such services, thus support of authorization and authentication is important. Dominant parameters in healthcare applications: Deployment: pre-planned Mobility: moderate (patient's mobility) Network Size: small, high node density Power Source: hybrid Security Level: Data privacy and security must be provided. Encryption is required. Role based access control is required to be supported by light weight authentication mechanism Multi-hop communication: multi-hop for homecare devices, star topology on patients body. Multipath interference due to walls and obstacles at home must be considered. Connectivity: always on QoS: high level of reliability support (life and death implication), role-based Traffic Pattern: MP2P/P2MP (data collection), P2P (local diagnostic) Other issues: Plug-and-play configuration is required for mainly non-technical end-users. Real-time data acquisition and analysis are important. Efficient data management is needed for various devices which have different duty-cycles, and for role-based data control. Reliability and robustness of the network are also essential.

In this use case, the local network size is rather small (less than 10s of nodes). The home care system is statically configured with multi-hop paths and the patient’s body network can be built as a star topology. The LBR at home is the sink node in the routing path from sources on the patient's body. A plug-and-play configuration is required. As the communication of the system is limited to a home environment, both 16-bit and 64-bit can be used for IPv6 link-local addresses . An example topology is provided in . The patient’s body network can be simply configured as a star topology with a LC dealing with data aggregation and dynamic network attachment when the patient moves around at home. As multipath interference may often occur due to the patients' mobility at home, the deployment of LoWPAN nodes and transmission paths should be well considered. At home, some nodes can be installed with power-affluence status, and those LoWPAN nodes can be used for relaying points or data aggregation points. The sensed information must be maintained with the identification of the patient no matter if the patient visits the connected hospital or stays at home. If the patient's LoWPAN uses globally unique IPv6 address, the address can be used for the identification. However, it causes cost for privacy and security. The hospital LoWPAN where the patient's information is transferring needs to operate additional identification system together with strong authority and authentication mechanism. The connection between the LBR at home and the LBR at Hospital must be reliable and secure, as the data is privacy-critical. To achieve this, additional policy for security is recommended between the two LoWPAN.

LoWPANs play an important role in intelligent transportation systems. Incorporated in roads, vehicles, and traffic signals, they contribute to the improvement of safety of transporting systems. Through traffic or air-quality monitoring, they increase the possibilities in terms of traffic flow optimization and help reducing road congestion.

Example: Telematics As shown in , LoWPAN Nodes are included in roads during their construction for motion monitoring. When a car passes over these nodes, the possibility is then given to track the trajectory and velocity of cars for safety purposes. The lifetime of the LoWPAN Nodes incorporated into roads is expected to be as long as the life time of the roads (about 10 years). Multi-hop communication is possible between LoWPAN Nodes, and the network should be able to cope with the deterioration over time of the node density due to power fails. Sink nodes placed at the side of road are most likely mains-powered, LoWPAN Nodes in the roads run on battery. Power saving schemes might intermittently disconnect the nodes. A rough estimate of 4 nodes per square meter is needed. Other applications may involve car-to-car communication for increased road safety. Dominant parameters in vehicle telematics applications: Deployment: pre-planned (road, vehicle) Mobility: none (road infrastructure), high (vehicle) Network Size: large (road infrastructure), small (vehicle) Power Source: hybrid Security Level: handling physical damage and link failure Multi-hop communication: multi-hop, especially ad-hoc Connectivity: intermittent Traffic Pattern: mostly Multi-Point-to-Point (MP2P), Point-to-Multi-Point (P2MP)

For this use case, the network topology includes fixed LBRs that are mains-powered and have a connection to high speed networks (e.g., Internet) in order to reach the transportation control center (). These LBRs may be logically combined with LC as a data sink to gather sensed data from a number of LoWPAN Nodes inserted in the tarmac of the road. In the road infrastructure, a LoWPAN with one LBR forms a fixed network and the LoWPAN nodes are installed by manual optimization of their location.

Given the fact that nodes are incorporated in the road, tampering with sensors is difficult for an adversary. However, the application must be robust against possible attacks and node failures. Sensed data should thus be used primarily for monitoring purposes, not to instruct (and potentially mislead) traffic participants.

Accurate temporal and spatial monitoring can significantly increase agricultural productivity. Due to natural limitations, such as a farmers' inability to check the crop at all times of day or inadequate measurement tools, luck often plays a too large role in the success of harvests. Using a network of strategically placed sensors, indicators such as temperature, humidity, and soil condition can be automatically monitored without labor intensive field measurements. For example, sensor networks could provide precise information about crops in real time, enabling businesses to reduce water, energy, and pesticide usage and enhancing environment protection. The sensing data can be used to find optimal environments for the plants. In addition, the data on the planting condition can be saved by sensor tags, which can be used in supply chain management.

Example: Automated Vineyard In a vineyard with medium to large geographical size, a number of 50 to 100 LC nodes are manually deployed in order to provide full signal coverage over the study area. An additional number of 100 to 1000 leaf nodes with (possibly heterogeneous) specialized sensors (i.e., humidity, temperature, soil condition, sunlight) are attached to the LCs in local wireless star topologies, periodically reporting measurements to the associated LCs. For example, in a 20-acre vineyard with 8 parcels of land, 10 LoWPAN Nodes are placed within each parcel to provide readings on temperature and soil moisture. The LoWPAN Nodes are able to support a multi-hop forwarding/routing scheme to enable data transmission to a sink node at the edge of the vineyard. Each of the 8 parcels contains one data aggregator to collect the sensed data. Localization is important for this type of LoWPAN where installed in a geographically large area, for pinning down where an event occurred, and for combining gathered data with their actual position. Using manual deployment, device addresses can be used for identifying the position and localization. For randomly deployed nodes, a localization algorithm needs to be applied. There might be various types of sensor devices deployed in a single LoWPAN, each providing raw data with different semantics. Thus, an additional method is required to correctly interpret sensor readings. Each data packet may include meta-information about its data, or a type of a sensor could be encoded in its address during address allocation. Dominant parameters in agricultural monitoring: Deployment: pre-planned The nodes are installed outdoors or in a greenhouse with high exposure to water, soil, dust, in dynamic environments of moving people and machinery, with growing crop and foliage. LoWPAN nodes can be deployed in a pre-defined manner, considering the harsh environment. Mobility: all static Network Size: medium to large, low to medium density Power Source: all nodes are battery-powered except the sink, or energy harvesting Security Level: depending on business-purpose. Light-weight security or a simple shared key management can be used depending on the business purpose. Multi-hop communication: mesh topology with local star connections. Connectivity: intermittent (many sleeping nodes) Traffic Pattern: Mainly MP2P/P2MP. P2P actuator triggering. Other issues: Time synchronization among sensors are required, but the traffic interval may not be frequent (e.g. once in 30 minutes to 1 hour).

The network configuration in this use case might, in the most simple case, look like illustrated in . This static scenario consists of one or more fixed LBR that are mains-powered and have a high-bandwidth connection to a backbone link, which might be placed in a control center, or connect to the Internet. The LBRs are strategically located at the border of vineyard parcels, acting as data sinks. A number of LCs are placed along a row of plants with individual LoWPAN nodes spread around them. While the LBRs implement the IPv6 Neighbor Discovery protocol (RFC 4861 ) to connect the outside of the LoWPAN, the LoWPAN Nodes operate a more energy-considering ND described in , which includes basic bootstrapping and address assignment. Each LBR can have predefined forward management information to a central data aggregation point, if necessary. LoWPAN nodes may send event-driven notifications when readings exceed certain thresholds, such as low soil humidity; which may automatically trigger a water sprinkler in the local environment. For increased energy efficiency, all LoWPAN Nodes are in periodic sleep state. However, the LCs need to be aware of sudden events from the leaf nodes. Their sleep periods should therefore be set to shorter intervals. Communication schedules must be set up between master and leaf nodes, and time synchronization is needed to account for clock drift. Also, the result of data collection may activate actuators. Context-awareness, node identification and data collection on the application level are necessary.