Wireless sensor networks (WSNs) represent one of the most promising technologies for precision farming. Over the next few years, a significant increase in the use of such systems on commercial farms is expected. WSNs present a number of problems, regarding scalability, interoperability, communications, connectivity with databases and data processing. Different Internet of Things middleware is appearing to overcome these challenges. This paper checks whether one of these middleware, FIWARE, is suitable for the development of agricultural applications. To the authors’ knowledge, there are no works that show how to use FIWARE in precision agriculture and study its appropriateness, its scalability and its efficiency for this kind of applications. To do this, a testbed has been designed and implemented to simulate different deployments and load conditions. The testbed is a typical FIWARE application, complete, yet simple and comprehensible enough to show the main features and components of FIWARE, as well as the complexity of using this technology. Although the testbed has been deployed in a laboratory environment, its design is based on the analysis of an Internet of Things use case scenario in the domain of precision agriculture. The purpose of precision agriculture (PA) is to increase farm productivity by capturing and interpreting data concerning the climate, weather, terrain, water quality and crop status. In recent years, farmers have begun using information systems to improve crop management and increase productivity. These systems include sensors for monitoring the crops and for tracing the products from crops to shelves. As a result, agriculture is becoming a data-intensive industry. In this sense, precision agriculture poses many of the same problems as the other areas where IoT is being developed , as well as others of its own domain, such as those described in ,2: (1) oThe biggest challenge for PA is capturing enough high-quality data to make the right decisions. In open spaces, most of the data comes from aerial photographs taken by satellites and aircraft and from sensors and cameras mounted on agricultural machinery and, in recent times, even drones are being used. Precision agriculture calls for a wide array of technologies to capture data, contextualise them in time and space, statistically characterise them, save and merge them and finally analyse them for decision-making purposes. In this scenario, data processing is often based on statistical techniques and artificial vision where no particular attention is paid to latency.The authors’ research group has quite a lot of experience in wireless sensor networks , one of Currently, in situ wireless sensor networks (WSN) are not widespread due to the high price of the sensors and the problem of powering them in places where there are no electrical lines. In the absence of external power sources from which sensor can harvest energy, sensor activity (capturing and transmitting data) must be kept to a minimum However, these limitations may change significantly in coming years thanks to advances in the design of power systems and batteries and price reductions resulting from the mass production of miniaturised sensors. It is therefore possible to imagine a scenario in which many sensors measure parameters related to soil (moisture and nutrient levels), water  and plants (water stress) are deployed. Capturing, transmitting, storing and processing this volume of information collected by the WSN presents a number of challenges, especially regarding integration technologies, communications, databases and computing. Fortunately there are a number of middleware platforms capable of doing most of the work although only some of these are geared for the IoT. One such middleware technology is called FIWARE  whose arFIWARE is the technology promoted by the European Commission to make possible the IoT in the context of the Future Internet  in collaThe aims of this paper are to provide such comprehensible use case, and to check whether FIWARE can be scaled to the same extent as agricultural applications. The answers are important because the selection of a particular platform implies a long-term commitment. We have created a testbed to achieve this goal.The rest of this paper is structured as follows: Future Internet [The Internet of Things is the vision of a set of technologies, systems and design principles for connecting things, based on the physical environment, via the Internet. As machine to machine communication is increasing, a new category of applications is emerging that demands evolutionary changes in network infrastructure. The original Internet architecture was not conceived to support communications between billions of devices such as sensors with very limited computational resources. That is why we must embark upon the path towards the Internet  through Internet ,13 such In the future there will be an even greater deal of heterogeneity in devices connected to the IoT, both intelligent as well as inert . Moreover, the vast majority of smart devices lack the computing resources needed to implement the IP protocol stack and this situation will continue in the future for many of the devices used in IoT applications . This isShort range , Local Area Networks (LAN) and wireless Metropolitan Area Networks(MAN)) mesh networks, RFID, Wi-Fi, etc;Long range  and pseudo-long-range): Global System for Mobile communications (GSM), combination of Worldwide Interoperability for Microwave Access and Code division multiple access (WiMax CDMA), Wideband Code Division Multiple Access (WCDMA) and other networks, and satellite communication.The challenge of heterogeneity is also present in communications. The basic infrastructure of the IoT is supported by wired and wireless technologies. The biggest challenges are found in wireless technologies which are classified as:Zigbee) and only communicate within their local network.Another challenge is the unknown and dynamic topology of WSN due to the variable number of devices connected at any given time. WSN typically use short-range wireless mesh networks as their fundamental communication technique  than the other. In most cases, the loss of individual messages is tolerable. Lastly and most importantly, traditional peer-to-peer IP exchanges block much of the potential wealth of the Internet of Things. Large data streams will be flowing, many of which will be unknown and unplanned. Only a publish/subscribe architecture is capable of taking advantage of this knowledge by discovering interesting relationships and data flows. And only a publish/subscribe network can scale to the enormous size of the Internet of Things .Identifying things presents a particular challenge. Today devices can be identified through different maps . In order to find information and services related to things, it is necessary to have a resolution infrastructure which links identifiers of things to other devices on the network. Probably the clearest example of such infrastructure is the so called Object Name Service (ONS) in the Electronic Product Code (EPC) global network .Web of Things, WoT) making all sensors and sensor data repositories discoverable, accessible and, where applicable, controllable through the World Wide Web.Beyond identifying things, different applications must be able to access and use the information coming from things. This goal is difficult to achieve due to the heterogeneity of data representation formats. There are a number of initiatives and standardisation bodies in the field of WSN. IEEE 1451  is a setBluetooth and Zigbee).To meet that challenge, the OGC and the World Wide Web Consortium (W3C) [Another obstacle standing in the way to interoperability is the lack of interconnection standards between wireless networks   have beeum (W3C) . The terum (W3C)  is an apum (W3C) . SpecifiThe European Telecommunications Standards Institute M2M Technical Committee .International Telecommunication Union—Telecommunication sector view .Internet Engineering Task Force architecture fragments .Open Geospatial Consortium architecture .Internet of Things-Architecture (IoT-A) .The multiplicity of solutions to address each of the challenges posed provides an indication of the current landscape of standardisation. Currently, the main IoT architectures are:Architectural Reference Model) enabling the design of any IoT application. IoT-A and FIWARE are both European initiatives. Though they are in many aspects independent to each other, FIWARE usually follows the model defined by the IoT-A. Despite these efforts, there is still no architecture for IoT systems, nor is there a widely recognised universal set of standards.The IoT-A differs from the rest in that it creates a reference architecture consisting of a horizontal ARM , protocols and infrastructure services to support the swift and accurate development of services and distributed applications of Future Internet services.In light of their importance, many research projects have focused on IoT middleware, the latter being studied, compared and classified by different authors ,34. In mThe current trend in middleware development is the use of a Service Oriented Architecture (SOA) services oriented approach and the implementation of Web standards (SOAP and RESTful) ,36. By e2 [Farm Management Systems from monolithic systems to open systems capable to operate over the Internet. This paper shows a broad range of possible scenarios in the precision farming domain, where FIWARE technology can be applied, and it also offers a general description of FIWARE technology. However, it does not describe in detail how to use the FIWARE components to develop precision farming applications, or the performance and scalability of the resulting applications. Reference [WSO2 , PubNub 2  and FIWA2  are clea2  and of I2 . Referen2  describeeference  complemeeference . This paThe cloud computing paradigm is one of the most positive aspects of ICT development for the IoT insofar as it allows independent and virtualized execution environments to host multiple applications in an isolated fashion on the same hardware platform, typically in large data centres. Additionally, cloud computing has the advantage of providing interconnection to different businesses if they are running on the same platform.Cloud computing is also a key enabler when moving from a product-centred to a services-centred approach due to its elasticity that allows companies a “pay-as-you-grow” offer.Big Data which refers to the growing number and size of data sets available to businesses and individuals for compilation and analysis. New paradigms have emerged in the field of Big Data. MapReduce [Closely related to the issue of data centres, intelligence software and data processing will play an increasingly important role in IoT solutions. A popular concept today is apReduce  is one tFuture Internet Public Private Partnership programme [The FIWARE project is being developed as part of the rogramme  launchedFuture Internet covering all application domains. Therefore, FIWARE architecture is very extensive and is broken down into seven completely open technical chapters [Generic Enablers [FIWARE is designed to be the core of the chapters  based onEnablers  which ofThis section reviews those aspects of FIWARE that facilitate the understanding of this work. We will begin by introducing concepts related to modelling and information management. Subsequently, an overview of FIWARE’s IoT architecture will be given.FIWARE specification is based on the Open Mobile Alliance Next Generation Service Interfaces (OMA NGSI) Context Management  standardentities. Entities may refer to physical objects , to hardware and software  and to high level abstractions that model things and group both physical objects and resources.All information handled by applications is modelled through the definition of a Context Information Model composed of contextual elements which we will refer to as attributes, characterised by a triplet . And each attribute can have optional semantic data (metadata) also characterised by triplets <name_metadata, type, value>. The values of the attributes become the system’s contextual information. A fundamental concept is that entities are not linked to a specific representation formalism. For example, they can be represented as an XML document  or as entries in a NoSQL database (MongoDB). An important advantage of this conceptual model is its compatibility with IoT formats (SensorML) while also allowing additional extensions.Entities are represented by the generic data structures shown in events refer to something that has happened in the system. Changes in contextual information, , are considered events. FIWARE is an event-driven architecture and Generic Enablers are defined taking this fact into account [In FIWARE-based IoT systems,  account .registerContext to register the entities that publish their data and discoverContextAvailability to discover them. The NGSI-10 API enables the contextual information exchange itself. To do that it offers, among others, updateContext operations to update contextual information and subscribeContext to create subscriptions to contextual information.The OMA NGSI-9 and NGSI-10 interfaces are HTTP RESTful APIs . The FIWThings are a special type of entity. Things are abstractions of the real world and often pool resources according to their location and attribute type.association [An ociation  is an orattribute domain is a set of closely related context attributes. Each attribute has a name and belongs to a single domain. Domains in an NGSI operation are very useful because the attributes in that domain are always requested, provided, updated or stored at the same time. This implies that the creation and updating of data within a domain are atomic and that context data associated with their attributes are always consistent.An FIWARE IoT architecture  is the rdevice is a hardware component that measures properties of its environment or acts on that environment. Therefore, sensors and actuators are devices. On the other hand, IoT resources are computational elements (software) that allow devices to carry out detection or operational tasks. Generally, IoT resources are hosted on the device. There are two types of devices for FIWARE depending on whether or not they use FIWARE NGSI middleware. Devices that are not equipped with FIWARE NGSI middleware do not have the capacity to accommodate the computational resources required to provide NGSI services.A Gateways concentrate information from end devices and channel it to consumers. This is hardware generally found near the end devices (sensors/actuators). Gateways also provide computational resources for devices within their area of influence. Just as with devices, a distinction must be drawn between two types of gateways depending on whether or not they host FIWARE middleware. The gateways that do not host FIWARE can be integrated into the system by connecting to FIWARE gateways. FIWARE gateways host two GEs: Protocol Adapter and Data Handling ,50. The associations between devices/resources and the things of an IoT system. The IoT Broker interacts with the Publish/Subscribe Context Broker GE  such as Ultralight 2.0 over Hypertext Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT) or OMA LWM2M/IETF Constrained Application Protocol (CoAP), among others. The IoT Backend communicates with end devices using standard protocols  and the state of the vineyards . The actuators are installed in crop irrigation systems. The system has to be scalable: if the number of sensors and actuators is increased, only computational resources would need to be increased without implying greater complexity or system redesign. To address this challenge of scalability, the implementation of the Farm Management System (FMS) illustrated in We are assuming a distributed scenario in the field of precision agriculture/viticulture . Hypothetically, this is a complex IoT scenario as it involves managing a large number of Future Internet by relying on the IoT FIWARE architecture  and the things which are defined explicitly from the application. In FIWARE, resources and things represent different levels of abstraction, as also shown in We now turn our attention to the opposite end of 5.associations between entities that model things and those that model system resources.Mapping between resources and things is achieved in IoT Discovery which records Subscription to the Information Generated by the Entities6.subscribeContext operation from the Context Broker. In our case the FMS application creates entities and subscribes to them, and Cosmos, which is a storage system for FIWARE Big Data, have also been subscribed to them. The Cosmos connection is not direct but rather through another FIWARE component, the injector Cygnus-NGSI. Note that there is a chain of subscriptions and brokers . The most important is the Context Broker which makes things and resources accessible to applications and is the reference implementation for the system’s other brokers. The Context Broker subscribes to the context information of the IoT Broker which mediates between the Context Broker and different Data Handling associated with different areas or management zones of the crop field. The IoT Broker uses the discoverContextAvailability service of IoT Discovery in order to discover the resources associated with the things with which the Context Broker works. For our case study, all subscriptions are onChange: only events are generated and therefore notifications are sent to subscribers when changes occur in entity attributes.Once entities have been defined, whether these be resources or things, any application can subscribe to the information that these entities generate by invoking the Information Notification7.updateContext. Data Handling then notifies (notifyContext) the IoT Broker which, in turn, notifies the Context Broker which, in turn, passes it on to its subscribers, in our case the FMS and Cygnus-NGSI application.Crop data is acquired by IoT devices and resources that transmit this information to the IoT Gateway where they are received by the Protocol Adapter. In 8.The case of the IoT Broker is special because it must interact with the IoT Discovery component to translate notifications at resource level to the corresponding notifications at thing level sent to the Context Broker. The Cygnus-NGSI injector is another special case which is explained below.9.Finally, the FMS application implements two Web services from the information it is supplied by the Context Broker and Cosmos. In the first service, information is published in real time as are specialised reports on the environment and status of vineyards  thus facilitating the taking of decision concerning irrigation, fertilisation and pest control. The second Web service is a human machine interface (HMI) enabling the remote activation of the control elements installed in crop irrigation systems. In this case, information travels in the opposite direction, the first step being the sending of control commands to the Context Broker.knowledge extraction. This knowledge facilitates decision-making and the consequent sending of activation commands to the actuators. This is possible thanks to the orchestration of IoT services provided by FIWARE allowing resources associated with devices to become searchable, accessible and usable thus maximising their interaction with the application of FMS. Thus, in the architecture . In this case the deployment of a Backend Device Management instance  deployed, number and characteristics of Generic Enablers instantiated and parameters of the Cygnus-NGSI injector.Test settings and autostart against FIWARE instances.The following requirements had to be met in the design phase of the testbed:The testbed designed  takes thBroker) or a final NGSI node  of the Universidad Politécnica de Cartagena. This application simulates the generation of context information from an IoT node network by implementing RESTful (NGSI) clients. One design criterion is that each IoT node simulates a producer of context that can refer to an intermediate node  extracts the Flume events from the channel and uses the API WebHDFS/HttpFS [Hadoop Distributed File System [MapReduce tasks associated with new generation mining and analysis techniques (reasoning models) that process historical context information stored in Cosmos. The resulting perceptions are extracted through HiveQL language.The Http source of the Cygnus-NSGI injector is responsible for receiving events from the e events  and injeS/HttpFS  to achiee System  set up ie System  for the Orion Context Broker (version 1.2.1), version 3.0.12 of a MongoDB database and version 0.13.0 of the Cygnus-NGSI injector. The VM has the following characteristics: 4 VCPU, 8 GB of RAM and 80 GB disk. This more than meets the hardware requirements of Orion and Cygnus-NGSI. An account was created in the global instance of Cosmos whose initial quota of 5 GB of storage is expandable. DSIE laboratory machines have the same characteristics:Hardware: HP Compaq Business Desktop dc7700p .Software: Windows 7 Home Premium operating system, Luna Eclipse development environment SR2 (4.4.2) and context generator application.There is a community account in FILAB’s Spain2 cloud infrastructure enabling the deployment of a virtual machine (VM) hosting an instance of ponents: (1) MongoDB; (2) Orion and (3) Cygnus-NGSI. The most important aspects of configuration are as follows:From a performance point of view, FIWARE recommendations were taken into account to refine the following testbed comTransparent Huge Pages were disabled (THP) [First, we used version 3.0.12 of MongoDB, recommended for intensive update scenarios. In addition, the adjustments limiting use to deployed VM resources were checked and ed (THP) .reqPoolSize, -dbPoolSize and -notificationModethreadPool:q:n) [Second, Orion parameters were adjusted for scenarios with high update/notification rates (-ool:q:n) . These pOrionRestHandler was configured to convert NGSI events from Orion into Flume events. Concerning channel design, we chose Memory Channel because of its performance when implemented directly in memory. This type of channel is ideal for flows that require increased performance and are willing to lose data in the case of Flume agent errors. Finally, we chose an Orion HDFS Sink whose performance could be enhanced by using the batching mechanism to reduce the number of writes in the HDFS storage configured in Cosmos. This mechanism is configured in each simulation through batch size and batch_timeout parameters [Third, Cygnus-NGSI components were adjusted to increase performance and avoid potential bottlenecks in order to properly assess Orion. To achieve this we opted for an HTTP source where the rameters .java.net API and its functionality is based on the HttpURLConnection class. This class only allows blocking connections. Thus, in each simulation the same number of persistent HTTP connections as simulated IoT nodes are opened.In each test, the application developed offers users a choice between two ways of generating NGSI traffic. The first method (blocking method) simulates intermediate IoT node NGSI traffic generation. Its implementation is supported by the SocketChannel class of the java.nio API was used to create non-blocking connections. Therefore, in each simulation the same number of connections as NGSI requests generated are opened.The second method (non-blocking method) simulates final IoT node NGSI traffic generation. Its implementation is focused on mechanisms that allow concurrent traffic generation . In thisAnother implementation detail to keep in mind is that each NGSI client (blocking or not) simulates the behaviour of a virtual IoT node in a separate thread.payload that determines the attribute domain of the virtual IoT system entities. The master process also sends updateContext requests to Orion to create entities. After starting Cygnus-NGSI, also implement onchange type subscribeContext request sender and the generation and broadcast transmission of a stream of test parameters through the network.Initially, Orion is started as a system service. Then, the context generating application is started executing 1 master process and up to 12 slave processes. The master process launches a graphic user interface (GUI) that automates the configuration and startup of the test. Test parameters are introduced in the GUI, i.e., number of IoT nodes, frequency of NGSI traffic generation and entity is updated, an event is generated and a notification is sent to Cygnus-NGSI through the TCP/5050 port.Each slave process decodes the steam, synchronizes with the master process and executes one of the context generation methods in a certain number of NGSI clients. As a result, M virtual IoT nodes send context information to Orion through the TCP/1026 port. When an attribute of an Orion database RStudio development environment. The Hive client allows queries of historical information stored in Cosmos through the TCP/10000 port.Finally, the Cygnus-NGSI injector handles information persistence in Cosmos through the TCP/14000 port. At the conclusion of the simulation, the master process compiles all the measurements from the slave processes and performs additional operations to obtain performance statistics for the experiment. These statistics are presented to the user through the GUI and stored in a text file formatted for uploading to the payload of the updateContext requests sent to Orion, four types of tests were run . Each of these tests is divided into two categories depending on the context generation method used (blocking or non-blocking). The first category includes performance tests employing the blocking method to measure throughput parameters (expressed in requests per second or kilobytes per second) and round-trip time (in milliseconds). The second category includes performance tests employing the non-blocking method to measure the throughput parameter (expressed in requests per second or kilobytes per second). 20 simulations were launched per category on different days and in different time slots. The test plan, therefore, involves more than 400 simulations. The following section shows the representative results of each type of test.Considering the The following procedure was followed in the tests. The IoT nodes are modelled by threads associated with processes. In the initial simulations, we started with 12 single-threaded processes, each executed on a different machine. When the number of IoT nodes exceeds 12, new threads are started in the processes. To simplify testing, multiples of the number of processes started for the tests are simulated, generally 12, in order to equitably distribute the simulation load among the processes. In certain cases, to refine the results, we worked with fewer machines and therefore fewer processes . That is why the graphs show measurements with a number of nodes that is not a multiple of 12. A simulation concludes when each virtual IoT node has tried to send 200 requests to the Orion Context Broker. The throughput and round-trip time values of the simulation are calculated from the average of the 200 measurements obtained.Orion RESTful server numerous simulations were made. The realization of these simulations was a very time-demanding task due to network latency. The results obtained are not exact. They do, however, offer a realistic view of the performance of this Generic Enabler under different deployments and load conditions.In order to evaluate the performance of the Orion Context Broker, throughput and latency were plotted graphically depending on the degree of concurrency simulated in each type of test. To study the behaviour of the payload established in the test plan . A new message is sent as soon as the previous one has been completely delivered. The highest rate of data transfer was 2963 kB/s obtained when simulating a payload of 1 MB and 20 NGSI clients see . RTT in The second best result was 2846 kB/s obtained when simulating a payload of 100 kB and 24 NGSI clients. However, RTT was 430 ms, clearly much lower than the preceding case.The next throughput measurement in descending order was 2520 kB/s corresponding to the simulated payload of 10 kB and 24 NGSI clients. Although the same number of active entities as in the previous case was used, RTT was lower (83 ms).Finally, the lowest data transfer rate (978 kB/s) was obtained with the simulation of a payload of 1 kB and 72 NGSI clients. We should note that a much higher number of active entities was needed in comparison with the other simulations. Also, RTT (72 ms) is the lowest value of the cases included in this analysis.To analyse the performance of Orion in greater detail, we studied the maximum throughput values obtained in each test type. This parameter can be evaluated from two perspectives: Data transferred and requests processed. From the first perspective  at a payload of 10 kB is substantially better.Finally, the measure of throughput obtained (978 req/s) in the simulation of a 1 kB payload is far superior to the other cases in the study.In other words, the system performs better when faced with high loads generated by few nodes. The increase in the number of nodes causes a very sharp decrease in the volume of data that can be transferred. From the second perspective  that exceed the capacity of the Orion server. The purpose of this test is to measure maximum throughput in terms of requests served per second or kilobytes per second for both type of requests, blocking and non-blocking. The tests were performed with a payload of 1 kB, i.e., that which allowed simulation of the highest degree of concurrency with the blocking method. Thus, blocking and non-blocking simulations were conducted with the optimum load parameter for the blocking case. This facilitates comparisons.updateContext request once the previous one is completed. Clients remain in sync with the server and the rate of requests generated never exceeds the capacity of the Orion server.If we compare the maximum throughput measured when traffic is generated by means of the two testbed methods, we see that the value obtained with the non-blocking method is significantly lower than that obtained with the blocking method. We would note that in the blocking context generation method, each NGSI client generates an This paper has shown a use case of FIWARE with enough detail level to provide an accurate view of how a FIWARE application is developed. It is a complex technology as it is designed to adapt to the variability of a wide range of applications, which demand different interaction schemes. At the same time, FIWARE assumes applications driven by events, which may show an asynchronous behaviour. All these features inevitably imply the use of many components with interdependencies. The FIWARE architecture, which defines these dependencies, is based on common design patterns in the communications software, mainly the use of a broker. The use of common design patterns makes both the understanding of FIWARE technology and its use easier for application developers. However, in our opinion, the documentation is not well organized. Furthermore, it does not always offer enough detail level to give a specific answer to an implementation problem. The deficiencies in the documentation involve investing a lot of time and effort into solving relatively simple problems.In this regard, we believe that, this work, without going into coding details, provides a useful guide to identify the components needed to develop a FIWARE application as well as the interfaces used to connect them.Regarding the performance of the resulting applications and based on the results obtained in the testbed, it is safe to say that the choice of payload is critical in the performance of the Orion Context Broker. As described in Precision agriculture applications, like many other IoT applications, are characterised by two types of situations: (1) very frequent exchanges of small amounts of data from low capacity sensors; (2) mass transmission of off-line data, i.e., data obtained from a drone or a camera mounted on an agricultural vehicle. FIWARE performs well in both cases.For all of the above, we conclude that the FIWARE platform meets the requirements of today’s IoT applications in the sphere of precision agriculture.In other domains there are applications that require the transmission of massive volumes of data in real time  accompanied by the processing of such data online as they are generated. Apart from the Orion Context Broker, FIWARE also supports other Generic Enablers  as Kurenentities. In this case, the critical (scarce) resource is the database (DB), pointing to the need to scale the MongoDB layer. The usual procedure to scale MongoDB is through the use of shards described in the official documentation of MongoDB.Scalability in the number of Scalability in operation requests to manage entities. In this case, additional Orion Context Broker instances can be used (each running on a different VM), plus an additional VM ahead of them running the software load balancer  to distrIn the case of future IoT scenarios, As for data persistence in Cosmos through Cygnus-NGSI, in the same way that an Orion processes farm can be created, additional Cygnus-NGSI processes responsible for receiving Orion farm notifications can be added. To do that, it is advisable to define a mapping strategy for system entities. Thus, subscriptions are established corresponding to a set A of entities that will notify the Cygnus-NGSI A process, another set of entities B which will notify the Cygnus B process, etc.Data/Context Management, would alleviate traffic sent to Cygnus-NGSI.Another important feature of Generic Enabler Data Handling is the optimisation of traffic sent to the IoT Backend. This functionality helps achieve greater efficiency and reliability by using CEP techniques. In short, it is possible to increase system efficiency by injecting preprocessed context information (more relevant for the IoT application) into the backend. Thus the CEP, also discussed in the Chapter on Summarising, it can be concluded that the combination of: (1) optimal performance tuning of FIWARE components, (2) the mechanisms provided by each component, (3) the horizontal scalability of each instance in the cloud and (4) the vertical scalability of the platform, makes the development of agricultural applications in futuristic scenarios as described in Although the testbed is oriented towards measurement of performance and data storage capacity, it is extendible to include other properties. For this reason, it is possible to further the study of the FIWARE platform  or conduct a wide variety of studies focusing on current IoT middleware. In addition, all the software infrastructure that simulates the sensors and the different ways in which they generate the data  can be easily used to test other IoT middleware. In doing so, we will be able to compare the results obtained with FIWARE with other middleware platforms, such as WSO2, PubNub… opening up a future avenue of research through its use with other platforms enabling comparative studies.


In the vertebrate CNS, afferent sensory inputs are targeted to specific depths or layers of their target neuropil. This patterning exists ab initio, from the very beginning, and therefore has been considered an activity-independent process. However, here we report that, during circuit development, the subcellular segregation of the visual and mechanosensory inputs to specific regions of tectal neuron dendrites in the tadpole optic tectum requires NMDA receptor activity. Blocking NMDARs during the formation of these sensory circuits, or removing the visual set of inputs, leads to less defined segregation, and suggests a correlation-based mechanism in which correlated inputs wire to common regions of dendrites. This can account for how two sets of inputs form synapses onto different regions of the same dendrite. Blocking NMDA receptors during later stages of circuit development did not disrupt segregation, indicating a critical period for activity-dependent shaping of patterns of innervation.DOI:http://dx.doi.org/10.7554/eLife.20502.001 In most regions of the vertebrate CNS, afferent axonal inputs form topographic maps within specific layers of their target region. This laminar patterning of maps allows for multiple sets of afferent inputs to coexist across the same two dimensional plane of a common target, conducive to integration. This patterning motif is found in many brain structures that receive multiple sets of afferent input. For example, the different sensory inputs that are received and processed by the optic tecta of birds , fish X, and ampXenopus tadpole optic tectum as a model system, a relatively simple and well-studied multisensory integration center. The optic tectum receives direct visual input from the retinal ganglion cells in the eye and mechanosensory input that enters the tectum via the brainstem. Both sets of afferent sensory inputs form topographic maps parallel with the surface of the neuropil,  region of the laminar axis while the non-visual mechanosensory inputs that enter the tectum via the hindbrain (HB), and so called ‘HB inputs’, target the proximal  region ; Figure functional pattern of synaptic input. To address this, we used a modified whole brain preparation that allows direct access to the entire distal-proximal axis of the neuropil while, like the traditional whole brain preparation, retaining both the RGC and HB inputs. It was previously established that the pattern and strength of RGC and HB-evoked responses recorded from tectal neurons of the modified preparation do not differ from that recorded in the traditional preparation, indicating that RGC and HB inputs are not severed in the modified brain preparation  into the intact eye and DiI (red) into both sides of the HB. As predicted by the CSD and FP data, we observed that HB axonal inputs in the DCR tecta appeared less focused in space and extended into the vacant dendritic region that is normally occupied by RGC axons, whereas the control tectum showed a typical confined band of HB axons along the proximal part of the laminar axis . Figure It has been established that blocking NMDARs during the formation of the retinotectal projection prevents refinement of the topographic map  and the Chronic NMDAR blockade caused both sets of afferent inputs to shift closer to one another across the laminar axis, with a portion of the inputs moving outside the boundaries of their lamina and forming synapses where they normally would not. Overall, this resulted in a greater amount of spatial overlap of inputs at the border region. Besides appearing less confined to a specific lamina, the spatial pattern of synaptic connections displayed by both sets of inputs appeared less defined, lacking an obvious peak region of synaptic input.maintenance of this organization is also NMDAR-dependent. To address this, tadpoles were exposed to MK-801 at a later stage in development (stage 45), after both the visual and non-visual inputs have established their postsynaptic targets. Once tadpoles reached stage 49, the same set of experiments as previously described were carried out to characterize the degree of functional and structural segregation. The RGC- and HB-evoked spatial profile of peak FP amplitudes of the MK-801-exposed tecta resemble control profiles  versus late (stage 45) NMDAR blockade on the spatial pattern of functional synaptic input made by the RGC and HB inputs  is mirrored by the pattern of axonal terminations across the neuropil, DiD (green) and DiI (red) was injected into the eyes and HBs, respectively, of control and MK-801-reared tadpoles to visualize RGC and HB axon terminals in the neuropil. In addition to measuring the distance between the peak fluorescence of RGC and HB axons, the amount of overlap between inputs was also calculated by measuring the area of neuropil that was common to both inputs. We found that early and late NMDAR blockade significantly increased the total amount of overlap of RGC and HB axons compared to control  Similarly, chronic blockade of NMDA receptors during circuit development desegregates the two different sets of sensory inputs, showing that the subcellular targeting of the different afferent inputs to specific regions of a common tectal dendrite requires NMDAR activation (The major findings of this study are (1) During the development of the tivation . (3) BloWe observed that removing the contralateral RGC inputs before they reached the tectum caused the entire spatial pattern of mechanosensory (HB) synaptic input to shift distally. A portion of the HB inputs extended beyond their normal proximal lamina into a more distal lamina, and formed synapses onto more distal regions of dendrite that are normally synaptic targets for RGC axons, suggesting that the sensory-dependent segregation of afferent inputs across the neuropil, and in this case, across the distal-proximal axis of individual dendrites, requires their physical presence. If the input is absent during the development of the circuit, its target region becomes ectopically innervated by the remaining neighboring afferent input. Similar monocular enucleation studies in the axolotl  and the only in an EphrinA2/A5 double knockout mouse. The shift was not observed in wild type mice because Ephrin expression between the LGN and MGN inhibits the remaining input from crossing over into vacant territory. Therefore, because we observe a shift in mechanosensory inputs to the more distant dendrites by just removing the RGC input indicates that, in this case, segregation is not under the sole control of molecular cues. Nevertheless, the possibility that the absence of the RGC input somehow altered the expression pattern of some putative molecular signal cannot be ruled out, and it is interesting to note that a differential pattern of ephrin expression across the laminar axis of both the Rana pipiens  MK-801 exposure was not observed to alter the spatial position of peak field potential amplitudes, peak CSD sinks, nor peak HB and RGC axon fluorescence, it did appear to significantly increase the overall amount of overlap between these two sets of axons. This increase in overlap in response to late MK801 exposure could reflect a subpopulation of relatively immature axons that have more recently entered the tectum and are still undergoing NMDAR-dependent correlation-based processes. In addition, the HB axons, specifically, appeared to be more affected by late MK-801 exposure than the RGC axons .Why may the segregation of inputs involve an activity-dependent component while many other examples of lamination appear to be hard wired? One important aspect of the tadpole optic tectum that sets it apart from most other integration centers is that the tectal neuron dendrites span the entire laminar axis and receive input from both sensory modalities. Thus, in the tadpole optic tectum, inputs are segregated to the distal and proximal regions of a common dendrite, while in zebrafish, and chick, inputs form onto specific cell types . The subThis study does not address the function of subcellular segregation of inputs. We have not yet been able to address this because the chronic blockade of NMDARs – which disrupts the segregation – also disrupts the refinement of the RGC topographic map . In ordeDuring development of many sensory circuits in the vertebrate CNS, molecular cues guide sensory inputs to form a course topographic map across the 2-dimensional surface of their target regions. The course topographic map is refined via NMDAR-dependent fire-together-wire-together rules . Our resXenopus laevis tadpoles were raised in Steinberg’s solution at 25°C on a 12:12 hr light-dark cycle, and developmental stages were identified according to 2, 3 MgCl2, 5 HEPES, and 10 glucose, pH 7.25, osmolarity 255 mosM). Next, the skin overlying the brain was peeled away and the brain and brainstem were filleted open along the dorsal midline, and the most lateral 1/4th of the tectum  is excised. Next, the preparation is pinned onto the side of a piece of submerged silicone, positioned such that the sliced side is on the surface and readily accessible for recording, and also so that bipolar stimulation electrodes can be placed onto the optic chiasm and hindbrain to stimulate the visual (RGC) and non-visual (HB) inputs, respectively . Wildtype ectively . TypicalMonocular enucleations were done at stage 34 using a sterile 26-gauge syringe needle. Tadpoles were first anesthetized using 0.02% MS-222 and the entire eye, from lens to choroid, was removed. The animals recovered from the operation in Steinberg’s solution. Post-surgery, tadpoles displayed normal swimming and schooling behaviors, similar to controls. Electrophysiological experiments and axonal dye fills were performed approximately ten days after the operation.To chronically block NMDA receptors, tadpoles were reared in Steinberg’s solution containing 25 µM (+)-MK-801 (Tocris) starting at stage 39 for 8–10 days, and the drug-containing media was refreshed every three days . Electro+-gluconate internal recording saline . Electrophysiological recordings were carried out using an Axon Instruments 700B Multipatch amplifier , digitized at 10 kHz using a Digidata 1322A digitizer, and recorded using pCLAMP software.Tectal neurons were visualized using a Zeiss light microscope equipped with a 60X water-immersion objective and a Hamamatsu infrared charge-coupled device camera. For whole-cell recordings, we used glass micropipettes with 8–12 MΩ resistance filled with KAll field potential (FP) recordings were done as described in FP amplitudes reflect the peak deflection that occurred within 4 ms of the time of stimulus since the monosynaptic response is known to occur within this time frame . Peak FPTo reveal more precisely the spatial pattern of the major sites of synaptic activity, we calculated the current-source density (CSD) profile of visual and mechanosensory inputs along the neuropil as described in To image axons, DiI  and DiD  were dissolved into 5% ethanol for injection. Xenopus tadpoles were anesthetized in 0.01% MS-222, DiI was injected into both sides of the hindbrain, and DiD was injected into both eyes, then tadpoles were fixed in 4% paraformaldehyde (PFA) for six days. Prior to imaging, samples were rinsed three times in 0.1 M phosphate buffer (PB: pH 7.4), mounted onto a customized slide for in vivo imaging, and then imaged using a laser scanning confocal microscope (Carl Zeiss LSM 710).ImageJ software was used to quantify the pixel intensity of RGC and HB axon terminations in the laminar axis. For this, a single line was drawn parallel to the laminar axis at a point along the rostrocaudal axis where the neuropil is the widest. The fluorescent profile along this line was generated. We identified the distance from the distal edge of the laminar axis at which the peak pixel intensity, corresponding to the bulk of axonal input, of RGC and HB axons occurred and measured the distance (i.e. the gap) between the two peaks. The merged RGC and HB axon fluorescent profiles were also used to measure the amount of overlap between the two sets of axons by quantifying the area of each profile that was common to both inputs. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, one of whom, Ronald Calabrese, is a member of our Board of Reviewing Editors and the evaluation has been overseen by Eve Marder as the Senior Editor.Thank you for submitting your article "An NMDA receptor-dependent mechanism for subcellular segregation of sensory inputs in the tadpole optic tectum" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:Xenopus tadpoles. The study confirms that these inputs are segregated onto the more distal and more proximal portions respectively of the same dendrites of tectal neurons. It is particularly clearly shown that they form functional synapses on these specific portions of the dendrites. The authors then show that elimination of the visual input causes spread of the somatosensory inputs and that normal segregation is compromised if the tadpoles are reared from the stage of initial synapse formation in the presence of NMDR blocker (MK-801) but not if the blockade is initiated at a later stage. Because the inputs are segregated initially during development, the results suggest a progression of activity-independent, then -dependent mechanisms that together result in the segregation of inputs across tectal neuron dendrites. Activity – specifically NMDAR-dependent activity – segregates the different inputs, implicating a correlation-based mechanism. This work thus adds to our knowledge about how inputs are segregated onto dendrites during development.This study combines neuronal labeling and electrophysiology  to study the segregation of visual and somatosensory inputs in the tectum of Essential revisions:There are some concerns that must be addressed, but overall this appears to be a strong study of general significance.1) The examples of anatomical data presented seem to support the conclusions, but the results must be quantified and comparisons made across a number of animals to test whether the differences seen in the examples presented do indeed stand up. Such analysis is necessary for the data in 2) The analysis of CSD sinks should be more thorough or this measure dropped and the physiological analysis restricted LFP. This concern is fleshed out more in the attached comments of reviewer #2.Xenopus compared to other species. This makes the findings interesting, but also means that the cellular mechanism is an important one to hash out.3) A weakness of the paper is that the MK801 effect is the only direct evidence implicating NMDARs. Without evidence showing that disrupting NMDAR function specifically in tectal cells is sufficient to replicate the MK801 results the exact site of action is not clear. There are several reports of presynaptic NMDARs playing important roles, so the experiments provided do not exclude other NMDAR-dependent mechanisms being at play. Furthermore, in the discussion much attention is given to the fact that the data suggest a fundamentally different mechanism for the sorting of visual/non-visual input in At the minimum this should be addressed in the discussion as a limitation of the preparation and something that should be addressed in the future. This would also be a good opportunity to provide any evidence (references or unpublished data) supporting a model in which NMDAR activity in tectal neurons is, in fact, the cellular mechanism mediating this phenomenon.Reviewer #2:While the interpretation is interesting, the data analysis is incomplete, decreasing confidence in the conclusions.There are two principal concerns with the data analysis:1) The anatomical pattern of innervation from the retina and the HB to the tectum, and the effect of eye enucleation and pharmacological treatment, are described anecdotally (a couple of examples). It is possible and probably essential to quantify (and summarize) the effect of enucleation and MK-801 on tectal innervation using some kind of laminar area measure or segregation measure . This would put on much more solid ground the apparent effect of enucleation or MK-801 on HB projection 'segregation'.2) The presentation of CSD and FP is a little confusing. Both are presented in the figures. It is argued that the CSD shows the site of sources and sinks (concentrated synaptic contacts occur at sinks if the structure is laminar in nature). However, only the FP amplitude is quantified, not the amplitude of the CSD sinks. In my opinion, the authors should also quantify the location (and spread as a result of enucleation) of the CSD sinks. If this is altered by the enucleation and/or MK-801, it would strengthen their arguments about the NMDA-receptor dependent 'competitive' segregation process. If quantification of the CSD pattern (and it's changes) is not possible, then this should be explained (and probably the CSD data should be de-emphasized or removed).eLife. Your article has been reviewed by one peer reviewer, and the evaluation has been overseen by a Reviewing Editor and Eve Marder as the Senior Editor. The reviewer has opted to remain anonymous.Thank you for submitting your article "An NMDA receptor-dependent mechanism for subcellular segregation of sensory inputs in the tadpole optic tectum" for consideration by The reviewer has discussed the reviews with the Reviewing Editor and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:The revision is very complete with most points forthrightly addressed. One more point remains. This revision should be quick and will not necessitate re-review. See comments of Reviewer #2Reviewer #2:The authors nicely included a quantification of the anatomical patterns of projection from the retina and HB to the tectum. This was done primarily by identifying the location of the peak of flourescence of the HB or retinal inputs to the tectum. While this measure does show a shift that is consistent with the interpretation of the authors, it would have been better to also include a measure of OVERLAP of the HB and retinal input to the tectum. That is, to what degree do the 'red' and 'green' signals from the retinal and HB axons overlap in the tectum with and without manipulation . Such an OVERLAP index (instead of a measure of the location of the peak flourescence) more closely conforms to the interpretation of the anatomical and functional data the authors are presenting. I don't think this is an essential omission, but including an OVERLAP index would have been better. If an OVERLAP index does not show an effect consistent with their interpretation, perhaps the authors can comment on this in the discussion. Essential revisions:There are some concerns that must be addressed, but overall this appears to be a strong study of general significance.1) The examples of anatomical data presented seem to support the conclusions, but the results must be quantified and comparisons made across a number of animals to test whether the differences seen in the examples presented do indeed stand up. Such analysis is necessary for the data in See reviewer 2 #1 below.2) The analysis of CSD sinks should be more thorough or this measure dropped and the physiological analysis restricted LFP. This concern is fleshed out more in the attached comments of reviewer #2.See reviewer 2 #2 below.3) A weakness of the paper is that the MK801 effect is the only direct evidence implicating NMDARs. Without evidence showing that disrupting NMDAR function specifically in tectal cells is sufficient to replicate the MK801 results the exact site of action is not clear. There are several reports of presynaptic NMDARs playing important roles, so the experiments provided do not exclude other NMDAR-dependent mechanisms being at play. Furthermore, in the discussion much attention is given to the fact that the data suggest a fundamentally different mechanism for the sorting of visual/non-visual input in Xenopus compared to other species. This makes the findings interesting, but also means that the cellular mechanism is an important one to hash out.At the minimum this should be addressed in the discussion as a limitation of the preparation and something that should be addressed in the future. This would also be a good opportunity to provide any evidence (references or unpublished data) supporting a model in which NMDAR activity in tectal neurons is, in fact, the cellular mechanism mediating this phenomenon.Whether segregation of visual and mechanosensory inputs relies specifically on postsynaptic NMDA receptors, presynaptic NMDA receptors, or both is not determined in this study, but will be addressed in the future. The following has been added to the Discussion:“Exposing tadpoles to MK-801 blocks all NMDARs, so the possibility that presynaptic NMDARs could play a role in axon segregation cannot be ruled out. […] In addition, this will be an especially interesting experiment given that postsynaptic activation of CaMKII in cultured cortical neurons has been shown to remodel inputs such that the strongest inputs are kept and strengthened while other inputs are completely lost .”We do not suggest a different mechanism in tadpole compared to other species, but a different situation. In the tadpole tectum it is well established that single tectal neurons receive visual and mechanosensory input. Thus, our findings suggest a classic NMDAR-dependent correlation-driven mechanism underlies this subcellular segregation of inputs. This does not mean that NMDAR-dependent mechanisms are not involved in axon segregation in the optic tectum of Zebrafish or chick where axons are segregated, to the best of our knowledge, at the cellular (not subcellular) level. Indeed, NMDARs have been shown to be required for the proper segregation of RGC input onto different cells of the LGN, and for refinement of many circuits .Reviewer #2:While the interpretation is interesting, the data analysis is incomplete, decreasing confidence in the conclusions.There are two principal concerns with the data analysis:1) The anatomical pattern of innervation from the retina and the HB to the tectum, and the effect of eye enucleation and pharmacological treatment, are described anecdotally (a couple of examples). It is possible and probably essential to quantify (and summarize) the effect of enucleation and MK-801 on tectal innervation using some kind of laminar area measure or segregation measure . This would put on much more solid ground the apparent effect of enucleation or MK-801 on HB projection 'segregation'.The manuscript now includes analysis of the spatial fluorescence intensity profiles of the HB and RGC axon projections across the tectum. ImageJ was used to generate the fluorescence intensity profiles . For the enucleation experiment, the peak fluorescence of HB inputs – which presumably reflects the peak in axon density – was measured and reported as the distance from the distal edge of the tectum. For the MK801 experiments, the distance from distal edge of peak fluorescence for both HB and RGC axons was measured as well as the distance (gap) separating these peaks. These data have been added to the figures and the text. The anatomical data are (strikingly) consistent with the FP and CSD data.2) The presentation of CSD and FP is a little confusing. Both are presented in the figures. It is argued that the CSD shows the site of sources and sinks (concentrated synaptic contacts occur at sinks if the structure is laminar in nature). However, only the FP amplitude is quantified, not the amplitude of the CSD sinks. In my opinion, the authors should also quantify the location (and spread as a result of enucleation) of the CSD sinks. If this is altered by the enucleation and/or MK-801, it would strengthen their arguments about the NMDA-receptor dependent 'competitive' segregation process. If quantification of the CSD pattern (and it's changes) is not possible, then this should be explained (and probably the CSD data should be de-emphasized or removed).The location of the major CSD sink along the laminar axis has been quantified and this data has been added to the figures and text. The CSD data is in accordance with the FP data. The amount of shift observed in the major CSD sink due to NMDAR blockade is the same amount of shift observed with the FPs. Identifying the major CSD sink was straightforward. We did not include the quantification of the amount of spread of the CSD sinks, however, as we did not observe such a pattern, probably because the CSDs reflect only δ. So for example if there was a big string of depolarization across the axis (i.e. a spread of depolarization) – this would not be reflected in the CSDs since a persistent depolarization (across space) would not be detected as a change in potential.Reviewer #2:The authors nicely included a quantification of the anatomical patterns of projection from the retina and HB to the tectum. This was done primarily by identifying the location of the peak of flourescence of the HB or retinal inputs to the tectum. While this measure does show a shift that is consistent with the interpretation of the authors, it would have been better to also include a measure of OVERLAP of the HB and retinal input to the tectum. That is, to what degree do the 'red' and 'green' signals from the retinal and HB axons overlap in the tectum with and without manipulation . Such an OVERLAP index (instead of a measure of the location of the peak flourescence) more closely conforms to the interpretation of the anatomical and functional data the authors are presenting. I don't think this is an essential omission, but including an OVERLAP index would have been better. If an OVERLAP index does not show an effect consistent with their interpretation, perhaps the authors can comment on this in the discussion.The amount of OVERLAP between the RGC and HB axons was calculated by measuring the area of the spatial fluorescent profile that was common to both sets of axons. These data are summarized in


As the largest ellipsoid (LE) data fusion algorithm can only be applied to two-sensor system, in this contribution, parallel fusion structure is proposed to introduce the LE algorithm into a multisensor system with unknown cross-covariances, and three parallel fusion structures based on different estimate pairing methods are presented and analyzed. In order to assess the influence of fusion structure on fusion performance, two fusion performance assessment parameters are defined as Fusion Distance and Fusion Index. Moreover, the formula for calculating the upper bounds of actual fused error covariances of the presented multisensor LE fusers is also provided. Demonstrated with simulation examples, the Fusion Index indicates fuser’s actual fused accuracy and its sensitivity to the sensor orders, as well as its robustness to the accuracy of newly added sensors. Compared to the LE fuser with sequential structure, the LE fusers with proposed parallel structures not only significantly improve their properties in these aspects, but also embrace better performances in consistency and computation efficiency. The presented multisensor LE fusers generally have better accuracies than covariance intersection (CI) fusion algorithm and are consistent when the local estimates are weakly correlated. Multiple sensors have been widely employed in various systems, such as the integrated navigation system of driverless cars. Multisensor data fusion aims to achieve an accurate, robust and reliable representative of the target of interest by combining the information from different used sensors. The data (estimate) fusion algorithms of multisensor system can be generally classified to centralized fusion algorithms and distributed fusion algorithms. The centralized fusion algorithms can obtain the globally optimal estimate by directly combining sensor outputs to an augmented measurement. However, such fusion architecture leads to a heavy computational burden; furthermore, the fused estimate will be easily corrupted if any sensor degenerates. The distributed fusion algorithms can reduce the computational burden and facilitate fault detection or isolation more conveniently through combining the local estimates from sensors by weighting matrices. In the distributed data fusion of multisensor system, once the cross-covariances among local estimates are known exactly, globally optimal or suboptimal estimates can be obtained by using optimal fusion algorithms, such as the two-sensor Bar-Shalom and Campo (BC) algorithm  or its vThe LE algorithm is a two-sensor fusion algorithm that obtains a new estimate from two local estimates based on a series of matrix transformations. Instead of computing a tightest fused error covariance ellipsoid which encloses the intersection region of the covariance ellipsoids of local estimates in the CI algorithm, the LE algorithm computes the largest ellipsoid contained within that intersection region, which leads to a tighter fused error covariance ellipsoid. Besides not requiring optimization of a cost function, the LE algorithm has many other advantages. It comparatively has better consistency performance than the CC algorithm and has better actual fused accuracy than the EI algorithm and CI algorithm in general. The two shortcomings of the LE algorithm are that the LE algorithm is limited to two-sensor applications and its consistency can not be unconditionally satisfied for correlated local estimates. However, although the cross-covariances are hard to be known exactly, some information about the dependency properties among local estimates might be possible to be obtained for users, such as the correlation level . When thThis paper proposes a largest ellipsoid fusion Kalman filtering with parallel fusion structure for the data fusion of multisensor system with unknown cross-covariances among local estimates, which realizes the multisensor fusion as a tree form with each level consisting of one or a series of parallel LE fusions. With parallel fusion structure, the data processing task of the proposed filtering is amenable to multiprocessor implementation. Three different estimate pairing methods for constructing the parallel fusion structure are given, and two fusion performance assessment parameters of Fusion Distance and Fusion Index for assessing the influence of fusion structure on fusion performance are defined. The attributes of the presented fusers in Fusion Distance, Fusion Index, and accuracy relation based on covariance ellipsoid, as well as the formula for calculating the upper bounds of actual fused error covariances of the presented fusers regardless of unknown cross-covariances, are provided. In order to verify the effectiveness of the proposed filtering, simulation examples are carried out to compare the performances of CI algorithm, optimal distributed Kalman fuser weighted by matrices, LE fuser with sequential structure and the LE fusers with the proposed parallel structures.An estimate of stochastic state s P¯e≤Pe ,13, the matrix C , the supConsider the discrete time-invariant linear stochastic system with multiple sensorsilter as (2)x^i is given by (6)x^(L)The LE algorithm obtains a new estimate from two local estimates based on a series of matrix transformations. Given two local estimates After these transformations, we obtain two new estimates in the new Euclidean space The fused estimate of LE algorithm in the original Euclidean space From (16)–(19), we know that both the fused state and fused error covariance of LE algorithm are the linear estimates. The covariance ellipsoid of x-axis and the y-axis represent the first and second dimension of the state, respectively; the covariance ellipse of There are mainly two drawbacks for the LE algorithm. The first one is that the LE algorithm can only handle two sensors at a time. The second one is that the LE algorithm can’t guarantee its consistency, which implies that rding to , for anyHowever, the covariance ellipsoid corresponding to the actual fused error covariance accounting for the dependence of local estimates will become smaller as the dependence becomes weaker , which mOne way to extend the application of LE algorithm from a two-sensor case to a multisensor case is applying the sequential processing method as the SCI algorithm proposed in ; here, wThe SLE fuser has a recursive sequential form asAs we can see from Step 1: In the first fusion level, all of the local estimates received from local steady-state Kalman filters are fused in pairs using the LE algorithm. When the number of local estimates is even, we can get Step 2: As Step 1, all the estimates received from the upper fusion level are fused in pairs using the LE algorithm, and the obtained new fused estimates are passed to the next fusion level.The PLE fuser contains ⋮Step N: There are only two estimates received from the upper fusion level in the fusion level Method 1:In the Method 2:The fusion levels of this type of PLE fuser alternately pair their received estimates from Method 3:In the As shown in In the following part of this paper, the notations PLE1, PLE2 and PLE3 denote the PLE fusers, respectively, with estimate pairing Method 1, Method 2 and Method 3. For the multisensor system (1) consisting of five sensors, the fusion schemes of PLE1, PLE2 and PLE3 fusers are shown schematically in Similarly to the SLE fuser, the PLE fuser can also be formulated in linear expressions as follows:Definition 1.The Fusion Distanceindicates the number of the LE fusions that theth local estimate encounters in its fusion route in fuser.Remark 1.When, we haveand. For any certain, we have,,, andDefinition 2.The Fusion Indexshows to what extent the Fusion Distances of all the local estimates differ from each other in fuser. It is defined asRemark 2.When, we have. For any certain, we have,,Comparing (18) to (7), we can find that the state estimation weighting matrices of the LE algorithm will deviate from the optimal weighting matrices, which are the weighting matrices of the optimal distributed Kalman fuser having the same sensors as the LE algorithm, on account of the inaccurate error covariances of local estimates and the presence of unknown cross-covariances. Taking this point into account, the state estimation weighting matrices of SLE fuser and PLE fuser, respectively expressed in (23) and (25) can be rewritten intoBecause the fused error covariance ellipsoid of LE algorithm is contained within the intersection region of the covariance ellipsoids of local estimates, we can easily come to the conclusion that rding to , for anyFor the SLE fuser and PLE fuser, we obtainBy taking the minimization of error covariance trace as the optimization target, we get the optimization model asApplying the Lagrange multiplier method, we introduce the Lagrange function defined byReplacing (33) into (30), we obtainConsider a dynamic example of the multisensor system (1) with five sensors asx-axis and the y-axis indicate how far the covariance ellipses extend in the directions of the first and second dimension of the state from the center, respectively.The local steady-state Kalman estimates, local filtering error covariances and cross-covariances can be obtained according to (2)–(5), and then the fused estimates of the optimal distributed Kalman fuser weighted by matrices, the CI algorithm, the SLE fuser and PLE fusers can be computed. The fusion schemes of PLE1, PLE2 and PLE3 fusers are shown in From In order to verify the above theoretical results on the accuracy relation, the Monte Carlo method is applied to compute mean square error (MSE). The MSE value at time godicity , we haveFor the dynamic example (35)–(36), 1000 Monte Carlo runs are performed, and the statistical results of local estimates and the fused estimates of optimal fuser, SLE fuser and PLE fusers in the Monte Carlo simulation are shown in In x-axis represents all of the permutations of five used sensors, 120 in total; the y-axis represents the traces of As shown in x-axis represents all of the permutations of nine used sensors, 362,880 in total; and the legends of this figure are the same as those in The accuracies of SLE fuser and PLE fusers with respect to different sensor orders for the expanded multisensor system with nine sensors are shown in From x-axis not only represents the In practice, the number of used sensors may vary in different periods. For the subsystems individually with sensors x-axis represents the 30 simulated random overall error covariances, and the legends of this figure are the same as these in In the simulation cases above, we have specified the characteristics of the sensors. Without such specifications, we give a further study on the performances of the SLE fuser and PLE fusers in the multisensor systems with arbitrary overall error covariances. Because the overall error covariance For any two local estimates on model :(41)PijFrom In summary, according to above simulation results, whether in the actual fused accuracy as well as its sensitivity to the sensor orders and its robustness to the accuracy of a newly added sensor, or in consistency and in computation efficiency, the PLE fusers have better performances than SLE fuser, and PLE3 fuser outperforms PLE2 fuser, which performs better than the PLE1 fuser.In order to obtain a fused estimate with high actual fused accuracy in a multisensor LE fuser, the weights of the state estimation weighting matrices should be assigned in accordance with the accuracies of sensors. As shown in (23) and (25), both Therefore, longer Fusion Distance means less weight and greater deviation; the Fusion Distance differences in the fuser affect the balances of weight assignment and deviation among local estimates and thus have a significant influence on the fuser’s actual fused accuracy performance. Owing to the sequential structure, the Fusion Distances of each local estimate in the SLE fuser are remarkably different from each other, which leads to the fuser structure severely degrading the dependency of the actual fused accuracy on the accuracies of sensors. Comparatively, the differences of the local estimate Fusion Distances in PLE fuser are significantly reduced, which means that the actual fused accuracy of PLE fuser is more dependent on the accuracies of sensors and hence is better than that of the SLE fuser. The Fusion Index of the multisensor LE fuser not only represents the fuser’s actual fused accuracy, but also indicates the sensitivity of the actual fused accuracy to the sensor orders and the robustness of the actual fused accuracy to the accuracy of the newly added sensor. A smaller Fusion Index means that the fuser has better performances in these aspects. In the simulation examples, when the multisensor system has five sensors, we have Apparently, compared to the SLE fuser, another one of the advantages of the PLE fuser is that the PLE fuser can make full use of the multiprocessor to fuse multiple sensors in parallel and more efficiently. Because each LE fusion can reduce the estimates to be fused by one in the SLE fuser and PLE fuser, it can be concluded that both the SLE fuser and the PLE fuser for the multisensor system (1) contain For the multisensor system with unknown cross-covariances, this paper proposes a largest ellipsoid fusion Kalman filtering with parallel structure and gives three different estimate pairing methods to construct the parallel fusion structure. Two fusion performance assessment parameters of Fusion Distance and Fusion Index are defined, and the attributes of the SLE fuser and PLE fusers in Fusion Distance, Fusion Index and accuracy relation are given. Verified with examples, if a local estimate has a longer Fusion Distance, its weighting matrix will tend to be lighter in weight and of greater deviation. The Fusion Index reflects the influence of fusion structure on fusion performance and indicates a fuser’s actual fused accuracy performance. A smaller Fusion Index implies that the actual fused accuracy of the fuser is generally higher, and is less sensitive to the sensor orders and more robust to the accuracy of newly added sensors. The presented multisensor LE fusers can achieve consistent fused results when the local estimates are weakly correlated but become inconsistent when the local estimates are strongly correlated; under such strong correlations, the upper bounds of actual fused error covariances of the presented multisensor LE fusers can be obtained by the provided formula to limit the uncertainties of the fused results. Compared to SLE fusers, the proposed PLE fusers not only can operate in parallel and more efficiently, but also get better performances in regards to accuracy and consistency.


There is an increasing interest in using ultrasonic guided waves to assess the structural degradation of above-ground storage tank floors. This is a non-invasive and economically viable means of assessing structural degradation. Above-ground storage tank floors are ageing assets which need to be inspected periodically to avoid structural failure. At present, normal-stress type transducers are bonded to the tank annular chime to generate a force field in the thickness direction of the floor and excite fundamental symmetric and asymmetric Lamb modes. However, the majority of above-ground storage tanks in use have no annular chime due to a simplified design and/or have a degraded chime due to corrosion. This means that transducers cannot be mounted on the chime to assess structural health according to the present technology, and the market share of structural health monitoring of above-ground storage tank floors using ultrasonic guided wave is thus limited. Therefore, the present study investigates the potential of using the tank wall to bond the transducer instead of the tank annular chime. Both normal and shear type transducers were investigated numerically, and results were validated using a 4.1 m diameter above-ground storage tank. The study results show shear mode type transducers bonded to the tank wall can be used to assess the structural health of the above-ground tank floors using an ultrasonic guided wave. It is also shown that for the cases studied there is a 7.4 dB signal-to-noise ratio improvement at 45 kHz for the guided wave excitation on the tank wall using shear mode transducers. Structural Health Monitoring (SHM) of engineering structures using ultrasonic guided waves is a rising area of interest. Much research has been conducted on the use of ultrasonic guided waves to inspect elongated engineering structures, i.e., pipes, plates, rails, and cables, because of their inherent long range propagation . WorltonMuch research has been conducted on the inspection of above-ground storage tank floors using guided waves. The main difficulty with SHM using guided waves is the level of attenuation due to long-distance propagation requirements, as well as reflection and mode conversion of waves at boundaries. The fundamental symmetric Lamb mode has been used as the principal mode of interest due to low energy losses compared to the fundamental asymmetric Lamb mode . The funHeavy corrosion of the tank annular chime limits the market share for SHM of tanks using guided waves as the transducer bonding process becomes challenging. Therefore, this study investigates the potential for using the tank wall to bond transducers for SHM of tank floors using guided waves. Both normal and shear loads were considered in this study. A previously validated finite element method has been used to investigate the guided wave propagation across a tank floor . The modOver the years, numerous Non-Destructive Testing (NDT) techniques have been used to inspect the condition of storage tanks, e.g., penetrant testing, magnetic particle testing, radiography, eddy current, thermography, acoustic emission, and conventional ultrasonic testing . InspectDue to its layout, guided wave inspection of tank floor is complicated. A tank floor comprises a large number of plates (dependent on the tank diameter) of 6–8 mm thickness joined with lap welds. There are 3 fundamental wave modes in the guided wave operating frequency range (20–100 kHz) for plate inspection  was performed to study the potential for guided wave excitation from the tank wall to assess the structural health of the tank floor. The FEA was performed in two separate parts; guided wave excitation from the tank floor to study the conventional operating conditions, and guided wave excitation from the tank wall as the proposed technique. A 3D model was built for the numerical analysis using ABAQUS/EXPLICIT version 6.13 ,26. A so follows ,(2)Mü=PThe acceleration is integrated over time using the central difference rule to calculate a change in velocity, which is then added to the velocity from the middle of the previous time increment to determine the velocity at the middle of the current time increment as follows,Similarly, the velocity is integrated over time and added to the displacement A 4.1 m diameter steel tank was modelled with a 50 mm annular chime and tank wall height of 1 m. The thickness of the tank wall and floor was 6.25 mm. Material properties are given in the previous section refer . Figure h0) were in the range of 3.125–3.13 mm and calculated as follows,The layout of the Finite Element (FE) model and the main cases studied in the FEA are illustrated in The ABAQUS element type C3D8R (linear eight node brick elements with reduced integration) was used to achieve efficient computation time, and the mesh refinement was such that there were at least eight elements to represent the smallest possible wavelength in the main lobe of the frequency bandwidth. This level of mesh refinement was validated in previous studies ,27. The This is a complex structure to be analyzed, and numerical results for waves propagating at different time increments for the normal mode excitation on the tank floor are illustrated in The Von-Mises FEA results for both studies  are illustrated in Experiments were conducted on a steel tank to validate the FEA results in The Signal-to-Noise Ratio (SNR) can be used to compare the transducer performance for the studied cases, calculated as,SNR for a range of frequencies is summarized in SHM of above-ground tank floor inspection using guided waves is a growing area of interest. It is a non-invasive and economically viable means of assessing structural degradation. Currently, normal-stress type (elongated type) transducers are attached to the tank annular chime to excite guided waves to assess the structural health of the tank floor. The present study investigates the potential of using the tank wall to excite guided waves as an alternative to the annular chime, which is desirable because, over time, corrosion of the annular chime can impede transducer installation; it is also desirable for accommodating new tank designs. Both normal and shear type transducers were considered in the current study. It was demonstrated that the normal mode transducers cannot be applied to excite guided waves from the tank wall to inspect the floor, and in this case the elongated type of transducer is not applicable. Shear type transducers have shown merit in exciting guided waves from the wall. At 45 kHz, there is a 7.4 dB SNR improvement compared to the guided wave excitation from the floor. Furthermore, based on the ToA comparison of theoretical, numerical, and experimental results of the mode of interest, there is only 5% error. Further work needs to be conducted to construct a tomographic image of the tank floor using the transducers attached to the wall and also to study the defect sensitivity.