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It should be noted that this will consume memory on the coordinator which may impact on other services such as packet buffers, so it is not advised to simply set this to the maximum value. The custom firmware from Zigbee2MQTT opens new window can also be used, and has been reported working by some users. The required dependencies can be installed with sudo apt install build-essential libusb The firmware can be flashed with.
Change the path to the firmware accordingly. For flashing the dongle using windows you need the TI Flash Programmer opens new window version 1, not version 2 and the Cebal drivers from this TI site opens new window available in section Software. In the Windows device manager update the device driver with the Cebal drivers.
Now the TI Flash Programmer should show your device. Select the firmware file, flash and verify your dongle firmware. Note that there are generally two versions of the Ember NCP firmware in use. If you are programming your own stick e.
If the usb dongle is not recognized, it might be necessary to make the dongle's device id known to the CPx driver by Silicon Labs:. Other XBee S2C devices should also be supported. The following devices have been tested by openHAB users with the binding. The absence of a device in this list does not mean it will not work - if the device is a standard ZigBee device similar to ones on this list, then it should work. Note 2: The Hue Dimmer can be integrated but needs additional rule-configuration to work properly.
See below for example. Note 3: The illuminance channel value is being reported incorrectly. Discovery is performed by putting the binding into join mode by starting an inbox search , and then putting the device into join mode.
Generally, it is best to reset the device to do this. Resetting the device ensures that it is no longer joined to a previous network, will ensure it is awake if it is a battery device, and will restart any channel and network search that the device may perform. Once the binding is installed, and an adapter is added, it automatically reads all devices that are set up on the ZigBee controller and puts them in the Inbox. When the binding is put into discovery mode via the user interface, the network will have join enabled for 60 seconds.
The binding will store the list of devices that have joined the network locally between restarts to allow them to be found again later. A ZigBee coordinator does not store a list of known devices, so rediscovery of devices following a restart may not be seemless if the dongle is moved to another system.
When a ZigBee device restarts e. Battery devices often have a button that may also perform this function. Note: Currently only Ember coordinators support Zigbee 3. ZigBee 3. This must be added to the binding before the discovery starts. Install codes should be printed on the box the device came in, or possibly on the device itself. Note that there is no standard format for how these codes may be displayed on the device or its packaging. You may need to use a QR reader to read the code - again these are not standard in their format, although you should be able to find the address and install code in the displayed text.
The install code must be entered into the coordinator settings before starting the discovery process. Note that the last four characters in the install code are the checksum and may be provided separately. When a thing is deleted, the binding will attempt to remove the device from the network by sending the leave command on the network.
It is not advised for force remove the Thing as this may cause an incomplete removal, and the device may be immediately added back to the Inbox. The binding will attempt to automatically detect new devices, giving them a type based on the information they report, and will read their supported clusters to define the supported channels.
A set of channels will be created depending on what clusters and endpoints a device supports. Channels are loosely linked to clusters in that for the majority of channels, a single cluster is used. However, some channels may utilise more than one cluster to provide the required functionality. The binding will attempt to configure a connection with the device to receive automatic and instantaneous reports when the device status changes.
Should this configuration fail, the binding will resort to using a fast polling note that "fast" is approximately 30 seconds at this time. The syntax for the command strings is as in the examples above, where the possible values for type , useStrobe , warningMode , squawkMode , sirenLevel , squawkLevel , and duration are as follows:. These channels are set as Triggers and will generate output in the events.
To utilize these events, no new Item is required and the rule can be used to directly trigger off of this event. The Channel that should be used can be copied directly from PaperUI under the Channels-section of the Thing or can be read from the events.
ZigBee has a standard way of configuring how a device sends status reports to the binding - this is called Reporting. Reporting is configured using three pieces of information Polling may be used by the binding to request data from the device. Polling is normally only used if reporting doesn't work for some reason.
Open source can extend to both hardware and software. In software, this includes all lines of code; in hardware, this typically includes documents describing the implementation. Open source hardware is a more recent development than open source software. Open source not only allows designers to create their own devices and systems from existing proven designs, but enables companies to sell complete open source systems to consumers.
This allows for the consumer to design and improve the system as they see fit, but giving them a concrete standard and working starting point, typically known as a COTS commercial off-the-shelf system.
One important aspect that needs to be considered is the use of open source technologies to implement the solutions required for the Internet of Things. Open source technologies and systems have a number of advantages and disadvantages [ 10 ]. One of the major advantages is that the system is generally designed with the wider community in mind; this means that compatibility issues are less likely.
This model does lend itself to abuse, and all open source software and hardware should be thoroughly tested before being applied in highly sensitive applications.
Currently, the Internet of Things is an attractive idea for industrial applications [ 11 , 12 ]. Allowing data acquisition across multiple facets of an industrial life cycle could greatly increase efficiency, as well as allow the implementer a greater deal of insight and control over their industrial activities [ 13 ].
A possible application scenario would involve a great deal of embedded devices in a single environment. The major concerns for industrial implementations of the Internet of Things are: capability, reliability and cost [ 14 ]. With the increasing following and emerging standards for wireless communication, learning from and improving upon old technologies will allow for the creation of platforms that will still be developmentally relevant in the future.
This will allow for developer communities to build and establish code bases and standards on which future platforms would be based. This would go a long way in being able to establish hardware and software standards for the Internet of Things. More inexpensive technology could be used, such as radio frequency identification RFID , but apart from identification and data capture, this does not truly Open Hardware Zigbee Tutorial realize an intelligent and connected device.
Implementations of the Internet of Things designed for a specific application also appear to have seen little movement forward in terms of design updates. In this paper, we focus on the possibility that the development of supporting technologies for the Internet of Things could be based on generic and open source technologies that can be molded to suit the required task.
It is our view that this approach would more rapidly increase the number of devices within the Internet of Things. Raspberry Pi is an example of such a device that could potentially be used in a number of applications [ 15 , 16 ]. The ease with which new technologies, such as Raspberry Pi, Beaglebone and the Arduino board, can be expanded, through modular expansion boards, and built upon will allow for an easy to implement and robust application using these devices.
The application of modern, modular technologies will allow for an increase in the flexibility of the Internet of Things [ 17 ]. These technologies are aiming first and foremost to be generic, allowing for a more standardized approach to the Internet of Things. Designing generic technologies, which follow clearly predefined standards, will allow for a large degree Open Source Zigbee Hardware Group of interoperability between all technologies. Industrial and consumer electronics have also reached a stage where the designer can easily and quickly use COTS modules and reference designs to build advanced embedded devices, which arguably provide comparable or improved performance in terms of power consumption, communication and processing resources when compared to built-for-purpose devices.
We believe that these advantages weigh heavily in the case for the application of open source technology to the Internet of Things. In a building automation example, the developer could deploy a relatively inexpensive open source COTS base system Raspberry Pi with a suitable modular communication capability ZigBee and easily design his own application to run on this system.
As new communication technologies become available through the years, a simple upgrade to the communication module of the application is possible without completely redesigning the application or changing the already deployed base system [ 18 ]. This makes the application more flexible, cost effective and capable over the long term. Keeping these advantages in mind, we have decided to examine the possibility of taking open source Internet of Things technologies and applying them to wireless sensor networks [ 19 ].
Due to these advantages, one of the most appealing examples of the combination of the two separate paradigms is in the application of WSN and Internet of Things technology to the application of smart grids [ 20 ]. The focus is not on creating a hardened technology for important tasks within a factory environment. The idea is to introduce the Internet of Things technology gradually by initially using it in non-critical systems within a wireless sensor network.
Using this design approach, we believe the open source concept is future proof, uses an advanced open source operating system, is inexpensive and as good as built-for-purpose components. We also created a testbed allowing us to benchmark our open source choices against the industry built-for-purpose devices that are currently available.
The final aim of our paper is to provide an overview of all of the different technologies that are available and, through our testbed, give a blueprint to how these technologies can be used together. The paper details a cross-section of the most promising options that are available for Internet of Things applications.
Additionally, these options are compared to some of the devices that were part of the first generation of sensor network technologies. This was done to act as a gauge of the progress that has been achieved through the development of the technologies related to the Internet of Things. We also created a device from COTS components, in order to provide a simple guide to better show the possibilities available when implementing and designing Internet of Things applications, especially when creating it for specific application areas.
A generic implementation of the Internet of Things can be seen in Figure 1. The generic implementation shows a simple view of an embedded Internet of Things implementation. The Internet of Things device cloud is a traditional wireless sensor network.
In this simple view, the gateway device would communicate with one of the many platform options that are available through standard technologies allowing for Internet connectivity [ 21 ]. In the image, two distinct networking zones can be seen.
The division between these two networking zones exist because of the requirements of the devices that inhabit these areas of the architecture. One method of allowing these different networking zones to communicate is to make use of a gateway routing device.
The device cloud is the implementation of a traditional wireless sensor network WSN. These wireless sensors typically have two major functions: they allow for the sensing of the environment, and sometimes, they allow for the WSN to act on the environment through an actuator.
The Internet of Things device cloud will consist of nodes that have at least the following capabilities:. Self organizing, allowing for the determination of routes, as well as handling failed nodes;.
Some of the embedded protocols that these devices make use of are proprietary, and the gateway devices that will be used will need to be able to communicate across the chosen set of protocols.
This section of the architecture for the Internet of Things typically involves the use of a gateway device to communicate and, in some cases, translate the device cloud communications onto the wider Internet, thereby allowing any Internet-capable device to communicate with the sensor and actuator device cloud [ 22 , 23 ]. Many options exist for the gateway devices.
These gateway devices will have the capabilities of allowing for the translation between the Internet of Things device cloud and the cloud-based platform. These gateway devices will typically spend most of their time performing tasks similar to a routing device.
The application of security features, such as encryption, to the data from the device cloud to the platform will also fall upon this gateway device. These gateway devices must be able to communicate with conventional network devices, as well as the low-power network devices embedded within the device cloud.
The back-end server devices are generally deployed in two separate ways: either the servers are deployed in-house or the developers use a platform to manage the server devices.
These platforms are typically deployed on the cloud. This makes the deployment more elastic and, in some circumstances, more reliable. Some of the cloud platforms that are currently deployed are gaining more and more acceptance worldwide and becoming the standard method for creating Internet of Things applications. The two approaches both have their advantages and disadvantages.
A number of articles have been written regarding this proposed structure [ 24 ]. The purpose of this area is to collect, analyze and present the data in a method that the end user can both understand and make use of.
This area is therefore primarily concerned with the presentation of the data to the client. Many different approaches have arisen to creating an Internet of Things application. Some of the current ranges of embedded enabling devices for the Internet of Things device cloud are introduced below [ 25 ].
We noticed that the divide between open source and closed source seems to be that the newer devices follow an open source approach, whereas the older versions follow a closed source approach.
MICAz and Imote2 are closed source devices. A few of the devices mentioned as open source are not completely open source due to external problems and the inability to use the advantages of being an open source device. A good example of this is the TelosB device: the ability to advance or add attachments to the device is limited because of the design.
Therefore, although being an open source device, due to external complications, it does not gain the advantages of other open source devices and is therefore very similar to a closed source device. Waspmote Pro is a new open source, wireless sensing node from cooking hacks by Libelium, improving on their initial Waspmote node [ 26 ].
Waspmote Pro can be used in various industries, such as gas and events monitoring, smart metering, agriculture and radiation detection, owing to a number of sensor boards available for use with the node. This node falls under our built-for-purpose devices, as it has been designed for a specific task and contains specific sensors and equipment. It is fitted with a The Lotus is well suited for applications requiring acoustic, video, vibration and other high-speed sensor data, condition-based maintenance, industrial monitoring and analysis and seismic and vibration monitoring.
The Sprouts node is a developing node from Queens University [ 28 ]. Developed as part of industry-related research, the node has attracted attention in the oil and gas mining, steel production and power grid monitoring sectors as an event monitoring node.
This is one of the nodes that falls under our open source options with many expansion capabilities. Developed at Carnegie Mellon University, the Firefly 2. The newer options that have been discussed above are not the only options that are available.
There are a number of other options that could be discussed, and a complete list of node options are available [ 25 ]. Many of these examples are closed source. We have therefore decided to make use of these three devices as representative of the older devices that are available. The oldest versions are well represented by TelosB and MICAz two of the first iterations of the Internet of Things , and through the years, the upgrades can all be represented through Imote2.
These decisions were made after carefully studying the components used to create the different iterations of all of these available devices. Alternative options to the ones listed do exist. One of the major options that we have is to create our own device by making use of COTS components [ 30 ].
This allows us to personalize our devices to perfectly suit our requirements. The advantage gained from being able to create a node that specifically addresses your requirements must be done with the other devices in mind. The huge range of technologies used to create the Internet of Things often means that, within a single application, a large range of technologies are used.
For example the Industrial production lines using the more mature WiFi protocol also exist [ 31 ]. WiFi technology is based on the The The standard has evolved, and the current implementation, These are a few of the considerations that must be taken into account when developing your own node.
Creation of your own node requires only a basic understanding of electronics, communication interfaces and embedded programming. To illustrate the feasibility of rapidly prototyping a self-built solution, we created a simple node to test alongside the nodes discussed above. The node has full communication capabilities based on the IEEE The developed node can be seen in Figure 2 , and the functional diagram is given in Figure 3.
This shows the simple design of the implemented node and the range of communication options available to the node. The main chip was chosen to ensure a wide range of compatibility with as many of the available Internet of Things operating systems as possible. The other components were chosen to ensure that the device remained as cost effective as possible, but could still perform the required operations.
An advantage of making use of such a design is to ensure that the device meets one's individual requirements and is able to meet the goals of one's application, but remain easy to both replace and upgrade.
This ensures that the entire application remains robust, inexpensive and flexible, all of which ensure a future-proof design. This node that we have created is capable of performing all of the functions that the other devices can achieve and when compared against a number of the available options and at a much lower cost, even when considering that we were ordering small quantities.
The primary comparison of the devices will look at their basic functionality and capability [ 28 ]. The initial testing will look at the power consumption of each of the devices. The list of devices above can be broken down into two separate categories: traditional wireless sensor network nodes and non-traditional devices that were not originally created to perform the role of a wireless sensor network node.
These devices have not been developed to use the minimal amount of power, etc. This means that these devices perform very well in certain aspects of our benchmarking. For the time that the nodes are not operational, the device spends its time in sleep mode, attempting to conserve as much energy as possible. The most economical of all of the devices is the Sprouts device.
Some of the other economical devices are Waspmote, which achieves a maximum of 15 mAh in full active mode. The older devices that have been compared here all perform very similarly.
The Firefly node achieves a similar current requirement of These devices are followed closely by Imote2 and Lotus platform, each achieving 44 mAh and 66 mAh, respectively, for continuous operation. Using these graphs and by knowing the power limitations of the application being designed for, a suitable node can be chosen beforehand.
This allows for adequate planning and can help prepare a suitable budget when deciding on which node to use for the Internet of Things application. As can be seen from the graphs shown above, our rapidly prototyped COTS node has only slightly higher power requirements, mostly due to the more powerful processor that is included on the board, than a cluster of current sensor node options.
The sleep mode current is high for the device, but the operational current is similar to what is required for the other nodes. The sleep and non-sleep of these nodes is defined differently from the sleep and non-sleep of the traditional nodes. A traditional WSN node sleep typically involves a state in which most power consuming hardware is off and waiting for a wake command, either from the onboard processor or another device in the network.
The sleep for Raspberry Pi and Beaglebone is defined as a state of rest, where little to no processing is occurring an expansion board for Raspberry Pi can be added, which will enable a WSN node, like sleep mode. The traditional sleep for WSN without the expansion board can be achieved, but it is very difficult to wake the device, and it takes a significant length of time longer than the more traditional nodes.
As can be seen from Figure 5 , Raspberry Pi consumes 44 mAh for continuous operation, and Beaglebone consumes mAh for continuous operation.
In the current world of computing, more cost typically gives you greater capability. A comparison was completed between the cost of the nodes and the processing capability that is received for the cost. Processing capability is not the only performance-based metric that can be used. This metric was chosen, as it is generally the one most linked to the cost of the node and potentially the one with the most impact on actual performance.
The cost of the Firefly device is currently not known, as it is still under development. The Sprouts device is open source, and although the cost of the completed device is not available, the bill of materials cost was used, as a capable individual could in theory create one of these devices themselves.
Surprisingly, in this comparison Figure 6 the most powerful devices are also the least expensive. The Sprouts node is the least expensive complete WSN node, with our own rapid prototype device coming in as being one of the least expensive available, as well. Although both Sprouts and the CSIR-made device are low on processing and power, depending on the application, this should not be a drawback from the implementation within a wireless sensor network. The low power requirement will allow for long-term deployments within Internet of Things applications, such as those within embedded environment monitoring applications, and with a careful design, the low processing power will not negatively affect the performance of the application.
Comparison of processing speed against cost for WSN nodes commercial off-the-shelf COTS and open platforms indicated in green and red, respectively. The next two least expensive are Raspberry Pi and Beaglebone. Both of these devices will require a XBee module or similar for wireless communication.
This will not change the ranking of these two devices on the scale. This graph shows that some of the least powerful Open Hardware Zigbee 5000 devices are in fact the most expensive. The built-for-purpose devices end up being expensive, possibly due to the ease of plug and play capability.
The additional cost for Raspberry Pi and Beaglebone is getting the devices to communicate successfully with the network through the general purpose input output GPIO serial ports. Due to the open source nature, many tutorials and online examples exist to assist with this problem. This comparison looks specifically at the hardware and software capabilities of the individual devices costs shown in the table are rough guides, as large discounts can be achieved when ordering large quantities.
As can be seen, the features of the devices differ greatly. One important feature is the operating system of the node chosen. The operating system has an impact on the stability, capability and security of the device.
A well-developed and continuously-updated operating system can allow for new powerful features to be deployed to the nodes without making any hardware changes. Raspberry Pi and Beaglebone both have many options available, and many of these options are well supported in terms of continuous updates. TinyOS and Contiki are both open source, with active communities driving development.
Mote Runner is a set of tools to aid the running of the node from IBM [ 34 ]. Table 1 shows that the features of the devices can differ greatly. However, it also shows that the current communication choice for Internet of Things embedded nodes is the ZigBee protocol. This is due to the large number of devices that support this communication standard, as can be seen from Table 1.
A number of the devices also include built-in onboard sensors that can be used for sensing applications, giving a reduction of the cost for certain applications when these onboard sensors are used. Some of the devices also include a range of GPIO ports available on the devices. These devices allow for advanced features to be added to the application. As can be seen, a range of features is available to the nodes.
It can be seen from the table that our node, although being a rapid implementation, has similar capabilities to the other nodes tested. This shows that the use of the circuits available and a simple working knowledge of general electronic skills can allow one to create powerful and inexpensive devices.
The node is also significantly less expensive than many of the built-for-purpose devices. Table 1 shows as many of the capabilities of each of the individual devices. Some of the additional expansion capabilities are included in footnotes at the bottom of the page, and these contain links to developer web pages, showing a large list of expansion capabilities available for Raspberry Pi and Beaglebone.
When considering the performance capabilities of gateway devices in an industrial implementation, the most important characteristic is communication performance. To prevent data bottlenecks and slow transmission when measuring performance, three key areas must be considered: reliability, throughput and response time [ 12 ]. Reliability measures the confidence in a transmitted packet being received by the cloud platform.
For a gateway in an industrial application, high reliability is required. This enables the application implementer to be sure that transmitted data are received by the cloud platform. High reliability will ensure that data transfer rates are not negatively affected by retransmission of data across a low resource network.
Typically, networks based on the This means that reliable transmission is not guaranteed by the communication protocol. A high reliability will allow for a greater degree of confidence that transmitted data have reached the destination. Reliability must also take into account the order in which the packets are received. In a time-sensitive implementation, which has limited resources, any additional processing must be avoided [ 36 ]. Throughput is a measure of the amount of data that the system can process per second.
The gateway implementation will need to take data from one communication technology and retransmit on the other end. The higher the throughput of the device, the better for the implementation. Another measure of the throughput is the latency of the device. Latency can also be called response time. This is a measure of the time it takes for another device to respond. A low latency is preferred, as this means that the gateway is operating more efficiently.
Due to the nature of the information that will be transferred across the Internet by Internet of Things applications, the additional process of adding security to the communication needs to be benchmarked [ 37 ]. The three major requirements for the Internet of Things security is confidentiality, integrity and authentication [ 38 , 39 ].
These three requirements are known as the primitive security objectives. The processing requirements of these primitive objectives are extensive and will be a good benchmarking tool for the devices, but these devices will also be required to implement these objectives in communication in order to secure the communication [ 40 ]. Confidentiality is the ability to hide the data that are transmitted. Integrity is the ability to ensure that the data that are transmitted are the same as the data that are received.
Authentication is used to ensure that the individuals communicating are the people that are expected to be part of the communication. Using all three of these objectives allows us to ensure data security when communicating. To complete the benchmarking of the possible devices, two approaches could be used: built-in toolkits for benchmarking; or the creation of scripts that perform the required benchmarking.
Due to the increasing community interest in Raspberry Pi, the device has undergone a large amount of testing and benchmarking already. Another device to consider for implementation is Beaglebone. This device has been benchmarked, but is not considered as an easy alternative to Raspberry Pi.
The focus of the paper is to supply a current state-of-the-art for the implementation of an Internet of Things application. Beaglebone, although being a very capable device, has not seen the interest and development from third party individuals that Raspberry Pi has enjoyed. Beaglebone has a less well-supported operating system, known as Angstrom Linux. Although the operating system on both devices can be changed, the paper looks at the default design of the devices, and Raspberry Pi's Raspbian operating system has more support and software packages due to its Debian heritage.
During the benchmarking of the devices, it was shown that Beaglebone and Raspberry Pi perform on similar levels. Beaglebone lacks some of the easy expansion capabilities of Raspberry Pi.
It was therefore decided that Raspberry Pi would represent the open source implementation of a gateway device. The proprietary implementation of the gateway devices involves the use of the Digi Connectport devices acting as the gateway for the embedded Internet of Things sensors to the wider Internet. As discussed previously, the two devices that are tested are the Connectport X2 and the Connectport X4.
Unlike Raspberry Pi, these devices have a very limited set of customization options available. They both run the Digi-implemented operating software that has a specific focus on operating requirements [ 42 , 43 ]. A set of experiments were conducted that showed the capabilities of Raspberry Pi with regards to the requirements for an open source gateway device, namely reliability, throughput and security. The aim of these experiments was to determine whether or not Raspberry Pi is a device capable of acting as a gateway for the Internet of Things in an industrial application.
Tests were done to show each of the requirements and to measure Raspberry Pi's capabilities with regards to these requirements. Each of the tests will be discussed in the sections below. Due to the interest in Raspberry Pi, many benchmarking tests have already been completed. Although being a good performance comparison between the different devices available, it does not provide a real-world comparison of the devices. The benchmarking tools allow for uniform data to be worked upon in a structured and controlled loop only utilizing high-speed sections of onboard hardware RAM.
This is the ideal operating environment for benchmarking, but does not provide a real-world view of operational capabilities. A real-world implementation involves more operational calls to external input output devices and interrupts from other devices. This is why when completing the implementation, these external features were simulated within the code, allowing for a more realistic view of the real-world operational capabilities of the device.
The first set of tests were done to confirm the reliability of Raspberry Pi. Reliability testing was done using a widely-known network benchmarking tool.
This is used to ensure that the packets sent and received are reliably delivered. It should be noted that our definition of reliability is somewhat limited. We have decided to only focus on the successful delivery of packets between the receiver and transmitter. This was to provide us with a solid starting point for comparison against the two security protocols tested later in the section. Although being a limited approach, it provides us with a good basis for testing the security protocols.
The reliability could be affected by nodes moving in or out of the network range, as well as other possible faults that could arise. Raspberry Pi hosted a WiFi access point and was also connected to a desktop computer via the onboard Ethernet capabilities.
Each of these separate networks was setup in a subnet. Making use of the Iperf benchmarking suite, the performance was measured [ 44 ]. When measuring the reliability of the wireless communication, the distance of the transmitter from the receiver can play an important role. The tests were run at three distance intervals, with the laptop moving away from Raspberry Pi. The Connectport devices are designed for reliability, as they are created to be deployed in an industrial environment.
The Connectport devices have a similar setup structured for the reliability testing. Due to the wired nature of their communication, distance testing was not required. The throughput testing was completed using two separate tests. The raw data bandwidth was measured using the Iperf benchmarking suite for data of different sizes.
Additionally, the Iperf benchmarking suite has the capability of simulating the communication of up to one hundred devices. A test was run that simulated the communication of a large number of devices across the WiFi channel to Raspberry Pi and onto the server connected via Ethernet. This simulation was run three times, and the average results were calculated. The latency of the communication was also measured by pinging the laptop and desktop computer through Raspberry Pi.
The Python script built the required command and then called the command using the built-in set of OpenSSL command line functions on Raspberry Pi. The command was executed on a file that was greater than MB, and the time to complete the required security objective was measured. The authentication of the system is not included in the results below, as the times were consistently less than one second.
This leads to unreliable results, so it is assumed that a key is used that is encrypted with the message as authentication. These tests were run a number of times, and the average throughput calculated.
The tests were completed using a block size of 8, bytes. These tests assumed that the entire file has the required security objective applied and is then transmitted as an entire package by some underlying protocol, for example a TCP-based unencrypted communication.
The underlying protocol will then break the file into packets as required. Once received, the entire file could be decrypted using a pre-shared key.
For the integrity tests, the secure communication of the signature of the file was assumed to exist.
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