The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. The Internet of things (IoT) describes physical objects (or groups of such objects) with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks.
The increasing miniaturization of electronics has enabled tiny sensors and processors to be integrated into everyday objects, making them ‘‘smart’’, such as smartwatches, fitness monitoring products, food items, home appliances, plant control systems, equipment monitoring and maintenance sensors and industrial robots. By means of wireless and wired connections, they are able to interact and cooperate with each other to create new applications/services in order to reach common goals. By 2025, it is predicted that there can be as many as 100 billion connected IoT devices or networks of everyday objects as well as sensors that will be infused with intelligence and computing capability.
The Internet of Things has evolved due to the convergence of multiple technologies, including ubiquitous computing, real-time analytics, machine learning, commodity sensors, and embedded systems. Traditional fields of embedded systems, wireless sensor networks, control systems, automation (including home and building automation), and others all contribute to enabling the Internet of Things.
Sensors and Actuators
Sensors are devices that respond to inputs from the physical world and then take those inputs and display them, transmit them for additional processing, or use them in conjunction with artificial intelligence to make decisions or adjust operating conditions.
Sensors are designed to respond to specific types of conditions in the physical world, and then generate a signal (usually electrical) that can represent the magnitude of the condition being monitored. Those conditions may be light, heat, sound, distance, pressure, or some other more specific situation, such as the presence or absence of a gas or liquid.
The common IoT sensors include:
Temperature sensors detect the temperature of the air or a physical object and convert that temperature level into an electrical signal that can be calibrated accurately to reflect the measured temperature.
Pressure sensors measure the pressure or force per unit area applied to the sensor and can detect things such as atmospheric pressure, the pressure of a stored gas or liquid in a sealed system such as tank or pressure vessel, or the weight of an object.
Motion sensors or detectors can sense the movement of a physical object by using any one of several technologies, including passive infrared (PIR), microwave detection, or ultrasonic, which uses sound to detect objects. These sensors can be used in security and intrusion detection systems, but can also be used to automate the control of doors, sinks, air conditioning and heating, or other systems.
Level sensors translate the level of a liquid relative to a benchmark normal value into a signal. Fuel gauges display the level of fuel in a vehicle’s tank, as an example, which provides a continuous level reading.
Image sensors function to capture images to be digitally stored for processing. License plate readers are an example, as well as facial recognition systems. Automated production lines can use image sensors to detect issues with quality such as how well a surface is painted after leaving the spray booth.
Proximity sensors can detect the presence or absence of objects that approach the sensor through a variety of different technology designs.
These approaches include: Inductive technologies which are useful for the detection of metal objects; Capacitive technologies, which function on the basis of objects having a different dielectric constant than that of air; Photoelectric technologies, which rely on a beam of light to illuminate and reflect back from an object, or Ultrasonic technologies, which use a sound signal to detect an object nearing the sensor
Water quality sensors sense and measure parameters around water quality. Some examples of what is sensed and monitored include:
chemical presence (such as chlorine levels or fluoride levels)
oxygen levels (which may impact the growth of algae and bacteria)
electrical conductivity (which can indicate the level of ions present in water)
pH level (a reflection of the relative acidity or alkalinity of the water)
turbidity levels (a measurement of the number of suspended solids in water)
Chemical sensors are designed to detect the presence of specific chemical substances which may have inadvertently leaked from their containers into spaces that are occupied by personnel and are useful in controlling industrial process conditions.
Gas sensors: Related to chemical sensors, gas sensors are tuned to detect the presence of combustible, toxic, or flammable gas in the vicinity of the sensor. Examples of specific gases that can be detected include: Bromine (Br2), Carbon Monoxide (CO), Chlorine (Cl2), Chlorine Dioxide (ClO2), Ethylene (C2H4), Ethylene Oxide (C2H4O), Formaldehyde (HCHO), Hydrazine(s):
(H2NNH2, CH3NHNH2, [CH3]2NNH2), Hydrogen (H2) etc.c..
Smoke sensors or detectors pick up the presence of smoke conditions which could be an indication of a fire typically using optical sensors (photoelectric detection) or ionization detection.
Infrared (IR) sensors: Infrared sensor technologies detect infrared radiation that is emitted by objects. Non-contact thermometers make use of these types of sensors as a way of measuring the temperature of an object without having to directly place a probe or sensor on that object. They find use in analyzing the heat signature of electronics and detecting blood flow or blood pressure in patients.
Acceleration sensors: While motion sensors detect movement of an object, acceleration sensors, or accelerometers as they are also known, detect the rate of change of velocity of an object. This change may be due to a free-fall condition, a sudden vibration that is causing movement with speed changes, or rotational motion (a directional change). One of several technologies that are employed in acceleration sensors include:
Hall-effect sensors (which rely on measuring changes in magnetic fields)
Capacitive sensors (which depend on measuring changes in voltage from two surfaces)
Piezoelectric sensors (which generate a voltage that changes based on pressure from distortion of the sensor)
Gyroscopic sensors: Gyroscopes or gyroscopic sensors are used to measure the rotation of an object and determine the rate of its movement called the angular velocity, using a 3-axis system. These sensors enable the determination of the object’s orientation without having to visibly observe it.
Humidity sensors: Humidity sensors can detect the relative humidity of the air or other gas, which is a measure of the amount of water vapor contained in that gas. Controlling environmental conditions is critical in the production processes of materials and humidity sensors enable readings to be taken and changes made to mitigate increasing or decreasing levels. A common application is in HVAC systems to maintain desired comfort levels.
Optical sensors respond to light that is reflected off of an object and generate a corresponding electrical signal for use in detecting or measuring a condition. These sensors work by either sensing the interruption of a beam of light or its reflection caused by the presence of the object. The types of optical sensors include:
Through-beam sensors (which detect objects by the interruption of a light beam as the object crosses the path between a transmitter and remote receiver)
Retro-reflective sensors (which combine transmitter and receiver into a single unit and use a separate reflective surface to bounce the light back to the device)
Diffuse reflection sensors (which operate similarly to retro-reflective sensors except that the object being detected serves as the reflective surface)
Sensors are essential components of MIoT and shall include sensors like mobile phone sensors, chemical/biosensors, EO/infrared sensors, environment sensors, Chemical and Biological sensors, medical sensors, Radar, Sonar, and RFID. Applications may also include navigational sensors like GPS, accelerometers, gyroscopes, and IMUs.
The actuators, which induce motion, may be electric, hydraulic, and pneumatic type. Guns, tanks may also be considered type of actuators in for integrated fire control applications.
The sensors and actuators should be small size, low in cost, and generally have limited memory and computing capability. The advances in MEMS, Nanotechnology and Biotechnology need to be leveraged to develop nanoscale and energy efficient sensors and actuators. There is requirement for software such as operating systems that are deployable in low-power IoT devices and support device connectivity.
Sensor technology is evolving fast. EO/IR sensors, radar, sonars, motion or sound detectors have their capabilities augmented as the technology they incorporate improves. For example, EO/IR can see further, at much tougher climatic and atmospheric conditions, whether it is day or night, compared to just a few years ago.
Phased-array radars can multi-task, simultaneously collecting intelligence in the land, maritime or air domains without losing range coverage or accuracy. Moreover, subcomponent technology allows those sensors to be manufactured in miniature, allowing their integration in a multitude of platforms.
Therefore, developments in components technology increase the capabilities of IoMT backbones rapidly. It will also change the commercial landscape, as subsystem manufacturers will remain at the forefront of the market, closing the gap with platform manufacturers.
Communications and Networking for IoT
One of the main pillars of IoT is its connectivity. It consists of a huge network of elements, which are connected to gather and share information. In general, the information is gathered and used to automate or help make decisions. Due to the variety of data types and applications, different communication and network protocols are needed.
Bluetooth: This protocol works within the frequency of 2.4 GHz, and can be used for short-range (<100 m) applications. One step further into Its evolution is Bluetooth Low Energy (BLE), which presents a significant reduction in the power needed for this protocol. This type can be beneficial for the transmission of small amounts of data from sensors or wearables.
Cellular: Current cellular infrastructure can be also used to extend the communication capabilities of IoT nodes. Depending upon the chosen band and the specific technology, it can be adequate for low power applications (e.g., 2G) as well as for high data rates applications (e.g., LTE). Additionally, there are subtypes of cellular communications, such as the LTE-M and NB-IoT, which were born to provide more data bandwidth or lower power use, respectively.
LoRaWAN: it is a low-power, wide-area (LPWA) protocol designed for battery-powered systems. It operates in the sub GHz 433/868/915 MHz and within the 2.4 GHz. LoRaWAN networks generally follow star topologies, where the elements are: end nodes, gateways, and a set of servers.
Near field communications (NFC): NFC works in the frequency band of 13.56 MHz and the range is a few centimeters. This type of communication is used to extend close-contact communications. In NFC there is an active node (such as a smartphone) generating an RF field that energizes a tag. It works in the frequency band of 13.56 MHz and the range is a few centimeters.
Sigfox: Sigfox uses a technology-based ultra-narrow band (UNB) and it works in the ISM bands, requiring a dedicated infrastructure. It means that it can be globally used but a local operator is needed.
Wi-FI: Working in the frequency of 2.4 GHz and 5 GHz, Wi-Fi connectivity is widely chosen because of its pervasiveness and high data rates. Its main drawback is its high power consumption, so it is not frequently used in battery-powered applications.
Wi-Sun: Wi-Sun is a field area network (FAN) protocol created by the Wi-Sun Alliance and designed to have a low power consumption and latency. It operates in the sub GHz frequency bands as well as in the 2.4 GHz band through a mesh topology.
ZigBee: This communication protocol works in the 2.4 GHz band, for short-range (<100 m) in restricted areas. ZigBee is made for transmitting small amounts of information, namely where really low latency is needed and is widely used in the industry and consumer applications. The ZigBee RF4CE was made to replace IR remote controls (e.g., TVs and DVD systems) and remove the need of having a line of sight between the remote control and the device.
Z-wave: intended for home automation applications, working in ISM frequency bands and with a rate up to 100 Kbps. Its applications follow a mesh network topology performing up to 4 hopes.
MIOT requires Robust Communication and Network technologies
In order to make effective use of IoT, the devices must be able to connect to global networks to transmit sensor data and receive actionable analytics. One significant challenge when adopting commercial IoT in military operations is that military networks, especially tactical ones, usually do not connect to the Internet or have restricted, limited, and expensive (e.g., using SATCOM) Internet access. There is also an unavailability of network services in remote terrains, deserts, oceans, and mountains. Therefore military should invest in resilient, flexible capabilities to extend Internet connectivity in denied areas utilizing technologies like high-altitude communications relay platforms, and Microsatellites.
Communication of data between devices is a power consuming task, specially, wireless communication. Therefore, new communications and routing protocols are required that facilitates communication with low power consumption and with low memory. Communication technologies are required that are robust to signal interference and/or loss of network operation.
Most battlefield military IoT networks shall operate over tactical radios. There is need to develop next generation of high-bandwidth radios that could make these integrated networks a reality. Cognitive radio and dynamic spectrum management techniques are required to automatically overcome bad conditions in the communications environment. Systems should be robust to jamming, supporting techniques to actively track jamming signals and applying automatic jamming avoidance measures.
Processing and Information Management
In order to make sense of the massive amount of data our IoT sensors collect, we need to process it. IoT/ MIOT things collect huge amount of sensor data, they require large compute and storage resources to analyze, store and process the data.
The current most common compute and storage resources are cloud based because the cloud offers massive data handling, scalability, and flexibility. However this may not be sufficient to meet the requirements of many MIOT applications, due to resource-constrained military networks, issues of latency, reliability and the security. In military applications, particularly those oriented toward real-time battlefield understanding, synthesis of actionable information from diverse data sources in near real-time becomes an important requirement.
New techniques and emerging concepts like fog computing that can bring some compute and storage resources to the edge of the network instead of relying everything on the cloud. Both the fog and cloud computing may be required for optimal performance of MIOT applications.
For military systems, an additional challenge emerges from the management of cognitive load for soldiers. Should distracting or irrelevant (or worse, deceptive) information be transmitted to them, it may adversely impact soldier performance or lead to incorrect actions being taken.
As the data generated by IoT infrastructures grows exponentially, methods for limiting the amount of raw data to process and to prioritize the transmission of the most information produced by analytics become increasingly necessary. Military network usage requires methods to prioritize content transmission, based on both intrinsic information quality and the needs of soldiers.
Edge computing is critical to IoMT/IoBT
The key to a sound edge architecture is split-second timing. The number of connected sensors and the huge amount of data that must be processed can quickly overload the system. That’s why the researchers recommend an architecture equipped with intelligent data filters, edge device regulation, and network infrastructure upgrades to increase maximum bandwidth.
Processors and transmitters
Considering that IoMT is based on the existence of a safe, secure, and capable network, powerful processors will remain a core component for processing big data at a fast pace. Moreover, with data being transmitted wirelessly through radio communication systems, transmitters need to be capable of transmitting larger volumes of data further and faster.
The defence industry is working on a variety of solutions aimed at tackling the technical issues related to the storage of large volumes of data. Many private companies, including Amazon, are offering storage solutions to government users, including the US DoD. However, a data storage capability for combat operations will have to comply with many technical specifications and would probably have to be separate from purely COTS solutions.
In the civilian market, IoT is becoming the next cloud battleground. Amazon, Google, Microsoft, Alibaba and IBM are vying with each other to provide the cloud infrastructure that will connect and run the world’s connected things.
Various IoT-specific cloud services have been launched to enable fast and efficient data storage and processing in the cloud, mainly on infrastructure as a service (IaaS), but also on platform as a service (PaaS) solutions. Vendors are increasingly looking to verticalise these to attract industry-specific workloads.
GIS based visualization
Military IoT deployed for situation awareness requires visualization that displays the environment and conditions of smart things. There is need new 3D display technologies for visualization of smart things that provides more information about their situation.
The network management of military devices and systems with diverse capabilities are challenging. Software solutions spanning security and device management that allow IoT devices to seamlessly discover each other, dynamically communicate and interact with nearby devices is required
AI and analytics
The amount of data that is expected to generate by billions of IoT devices have to handle by the Big Data. Advances in data analytics have allowed for the efficient analysis of the rapidly increasing amounts of data created by IoT devices. AI is a key element for the optimal use of IoMT, as it allows for more efficient analysis of the vast amounts of data that flow at a high rate from an increasingly large number of edge devices.
New advanced analytical tools and algorithms are required that can be used to examine large amounts of battlefield data to uncover subtle or hidden threats and threat activities, correlations, and other insights.
Defence/security-related intelligence mainly comes in the form of open-source intelligence (OSINT), logistics, support and maintenance, and battlefield intelligence. With around 80% of the information available on the internet, other media sources, and social networks, analysis has relied on expert systems.
Big data analytics can scan through a larger volume of data and at the same time reduce the associated noise using AI technologies, such as machine learning. Logistics, support and maintenance hugely benefit from big data analytics.
Predictive or condition-based maintenance can reduce costs and increase the availability of platforms. Depending on the customer and their security concerns, as well as the available industrial capabilities IoMT, in conjunction with big data analytics and performance-based logistics (PBL), is a highly-promising combination for the defence industry.
Finally, battlefield intelligence IoMT is expected to maintain a human-centric or man-in-the-loop approach. Due to its nature, which involves firing against targets, especially when it comes to operations in civilian areas, human identification and clearance for firing will always be necessary. There are many ethical dilemmas that arise from this necessity, which are expected to act as barriers to the rapid expansion of IoMT in the field of armed unmanned systems. For this specific market segment, it is important for a user to invest in the quality and quantity of its sensors, so as to be able to recognise and identify targets.
AI still experiences issues related to causality. For example, machines still cannot always tell the difference between a man holding a baseball bat and a weapon, and, if it does come up with an answer, it cannot always explain why. That is an extremely important aspect especially for the security domain, where unmanned systems with AI technology, especially when operating in swarms, could eventually carry out their missions near civilians and civilian assets.
In terms of the moral dilemmas posed, people are very reluctant to have in their vicinity an unmanned system that could decide for itself what or who consists a threat, even if the accuracy rate of the algorithm is the highest possible. Many defence contractors already offer their solutions for OSINT analysis and systems’ health monitoring, which are also available to the civilian market as well. Examples of such companies are Northrop Grumman, Lockheed Martin, Boeing, ESRI, and Palantir Technologies.
For Military to exploit the potential of MIOT while mitigating the risks associated with MIOT deployments, the first step is to take a comprehensive look at securing the MIOT technology ecosystem. Security concerns are the main issue holding back the military’s use of the Internet of Things.
MIOT requires security of everything from distributed sensors and devices, distributed computing environment to cloud, to end users, information and operational technologies. The basic pillar of any security-related communications is security, confidentiality, integrity and authentication services. The network also needs to be safe guarded against malicious intrusions and was of disruptions. The data residing at the sensor nodes is of paramount importance. The sensor a node needs to physically safeguard as-well-as the data needs to be stored in an encrypted form.
However, in providing security solutions, suppliers have had trouble going beyond their traditional domains. For example, operators’ IoT security offers have mostly been about device authentication and network reliability. Clearly, breaches can occur at the device level, network level, app-level, storage level, and data level. There is some work in progress to help vendors and operators come together.
For example, AT&T has joined the IoT Cybersecurity Alliance, working with IBM, Nokia, Palo Alto Networks, Symantec, and Trustonic to offer end-to-end solutions. End-to-end security is a must for widespread IoT adoption. Leaders in unified threat management are Check Point Software, FireEye, Fortinet, Mimecast, and Palo Alto Networks. Major IT vendors such as Cisco, IBM, Dell and HPE also offer compelling IoT security solutions.
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