A Review on Internet of Things for Defense and Public Safety
- Public protection (PP) radio communication: communications used by agencies and organizations responsible for dealing with the maintenance of law and order, protection of life and property, and emergency situations.
- Disaster relief (DR) radio communication: communications used by agencies and organizations dealing with a serious disruption in the functioning of society, posing a significant, widespread threat to human life, health, property or the environment, whether caused by accident, nature or human activity, and whether they happen suddenly or as a result of complex, long-term processes.
2. Compelling COTS IoT Applications
- Transportation: Telogis , which builds engine-monitoring systems for General Motors (GM) vehicles, estimates that its smart engine reduces fuel consumption by 25%, around 30% in idle time, increases fleet use by 25%, and workforce productivity by 15%.
- Energy efficiency: IoT-based energy management systems can reduce energy use in office by 20% . Smart thermostats and HVAC (Heating, Ventilation and Air Conditioning) save consumers as much as 10%–15% on heating and cooling. Additionally, smart appliances for home automation systems  are now being researched . IoT-based use cases for smart cities have been also identified, like traffic monitoring , surveillance  and pollution monitoring . Within this scenario, the content of the samples acquired may expose critical information. For example, in the case of noise pollution monitoring applications, the noise may also contain private conversations.
- Inventory management: USTRANSCOM’s Global Transportation Network (GTN)  and DLA (Defense Logistics Agency) developed a common information platform that enables the military to improve end-to-end supply visibility, service and logistics processes. The platform includes a single repository and universal access to logistics data so that any user or developer can easily access or manage supply chain information. Also, such a platform facilitates the development of new applications that run on the same backbone. Another contribution of USTRANSCOM is fleet management, which uses RFID trackers to monitor palletized shipments among major transit hubs.
- Mining: embedded sensors on equipment and vehicles offer a more precise picture of ground operations and enable real-time monitoring of equipment. This new technology, together with autonomous mining systems, is transforming daily operations. An IoT deployment reduces expenditure on infrastructure and machines by reducing outages and maintenance, energy consumption and environmental impact, while significantly improving productivity and mine safety by reducing injuries and fatalities. A renowned example is the autonomous drilling system of Rio Tinto (Perth, Australia), which includes tunneling machines, trains, autonomous haulage systems, and driverless trucks. This mission-control site manages operations of 15 mines, 31 pits, 4 port terminals and a 1600 km rail network .
3. Target Scenarios for Mission-Critical IoT
3.2. Fire-Control Systems
3.3.1. Fleet Monitoring and Management
3.3.2. Individual Supplies
3.4. Smart Cities Operations
3.5. Personal Sensing, Soldier Healthcare and Workforce Training
3.6. Collaborative and Crowd Sensing
3.7. Energy Management
4. Operational Requirements
4.1. Deployment Features
4.2. System Management and Planning
4.3. Supported Services and Applications
4.4. Network Capabilities
4.5. Supported Network Topologies
4.6. Mobility Capabilities
4.7. Security Capabilities
- Device and network security: the potential of IoT is derived to a large extent from the ubiquity of devices and applications, and the connections between them. This myriad of links creates a massive number of potential entry points for cyber-attackers. The systems also depend on backbone storage and processing functions, which can include other potential vulnerabilities. One of the ways to enhance the security of a complex network is to limit the number of nodes that an attacker can access from any given entry point. This approach conflicts with IoT, which generates much of its value from the integration of different systems. Securing a broad range of devices is also difficult. Many of them have limited capacity with no human interface and depend on real-time integration of data. This complicates traditional approaches to security, like multi-factor authentication or advanced encryption, which can hinder the exchange of data on the network, requiring more computing power on devices, or needing human interaction.
- Insider misuse: cyber risks and insider threats are a challenge for large organizations. A single mistake from a single user can allow an attacker to gain access to the system.
- Electronic warfare: most technologies communicate wirelessly on radio frequencies. Adversaries can use jamming techniques to block those signals making the devices unable to communicate with backbone infrastructure. Wireless connections also raise the risk of exposing the location through radio frequency emissions. Transmitters can serve as a beacon detectable by any radio receiver within range, and the triangulation of such emissions can compromise the mission.
- Automation: the full automation of equipment and vehicles extends the reach of cyber threats to the physical domain.
4.8. Robustness Capabilities
4.9. Coverage Capabilities
4.12. Interoperability Capabilities
4.13. Target Platforms
5. Building IoT for Tactical and Emergency Environments
- Perception layer: this first layer represents the physical elements aimed at collecting and processing information. Most COTS IoT devices are designed for benign environments and currently focus on home automation, personal services and multimedia content delivery. Miniaturized devices such as transducers (sensors and actuators), smartphones, System on Chips (SoCs) and embedded computers are getting more powerful and energy efficient. The next generation of processors includes new hardware features aimed at providing highly trusted computing platforms. For example, Intel includes an implementation of the Trusted Platform Module (TPM) designed to secure hardware through cryptography. Technologies such as ARM TrustZone, Freescale Trust Architecture, and Intel Trusted Execution enable the integration of both software and hardware security features . Plug-and-play mechanisms are needed by this layer to configure heterogeneous networks. Big data processes are initiated at this perception layer. This layer transfers data to the Object Abstraction layer through secure channels.
- Object Abstraction Layer: it transfers data to the Service Management layer through secure channels. To transfer the data, the protocols used in the COTS IoT nodes either use existing wireless standards, or an adaptation of previous wireless protocols in the target sector. Typically, IoT devices should operate using low power under lossy and noisy conditions. Other functions like cloud computing and data management processes are handled at this layer .
- Service Management Layer or Middleware: this layer enables the abstraction of specific hardware platforms. It processes the data received, takes decisions and delivers the services over network protocols .
- Application Layer: it provides the services requested to meet users’ demands.
- Business Management Layer: this layer designs, analyzes, develops and evaluates elements related to IoT systems, supporting decision-making processes based on Big Data. The control mechanisms for accessing data in the Applications layer are also handled by this layer. It builds a business model based on the data received from the Application layer. Moreover, this layer monitors and manages the underlying four layers, comparing the output of each one with the output expected to enhance services and maintain users’ privacy . This layer is hosted on powerful devices due to its complexity and computational needs.
- Identity-related services: these services are employed to identify objects, but are also used in other types of services.
- Information Aggregation services: these services collect and summarize raw measurements.
- Collaborative-Aware services: these services act on top the Information Aggregation services and use the obtained data to make decisions.
- Ubiquitous services: these collaborative-aware services function anytime to anyone, anywhere.
5.1. IoT Standardized Protocols
5.1.1. Application Layer Protocols
5.1.2. Service Discovery Protocols
5.2. Enabling Technologies
5.3. Enabling Protocols
5.4.1. Hardware and Software Platforms
5.4.2. Cloud Platforms
5.4.3. Fog Computing
- Location: fog resources provide less delay because they are positioned between smart objects and the cloud data-centers.
- Distribution: it is possible to deploy many of such micro centers close to the end-users as their cost is a small fraction of a cloud data-center.
- Scalability: the number of micro fog centers can be increased to cope with the increasing load and the increased number of end-users.
- Density of devices: fog helps to provide resilient and replicated services.
- Mobility support: fog resources act as a mobile cloud.
- Real-time: it provides better performance for real-time interactive services.
- Standardization: fog resources can interoperate with various cloud providers.
- On-the-fly analysis: fog resources can perform data aggregation to send partially processed data as opposed to raw data to the cloud data center for further processing.
5.5. Digital Analytics
- Open Integration standards: they facilitate interoperability among devices with different capabilities and ownership through supporting ontologies. IoT ontologies should be integrated with existing community standards.
- Reasoning support: Ontology-based reasoning has been applied towards military sensor management systems, including those tasked with pairing sensors to mission tasks. Gomez et al.  present an ontology based on Military Missions and Means Framework that formalizes sensor specifications as well as expressing corresponding task specifications. When there is limited network connectivity, such reasoning capabilities could be applied tocontinually assess how available IoT resources can be utilized.
- Data Provenance: the steps taken to generate data have been commonly acknowledged as important towards assessment of data quality and trustworthiness. In a military context, issues of provenance will be a dominant concern because the state, ownership, and reliability of devices will be uncertain. The capability will be critical when automated or semi-automated content assessment becomes desirable. New architectures will need to incorporate provenance and trust management tightly integrated in IoT technologies. The W3 PROV specification  is a primary standard for digital provenance representation, which is now being extended for IoT.
6. Main Challenges and Technical Limitations
From COTS to Mission-Critical IoT: Further Recommendations
- Introduce rapid field testing: the military should consider creating a dedicated technology comprising military personnel in a live training environment to experiment with technologies and get real end-user feedback early in the development process. This testbed could change the way the military accomplishes its mission, or introduces creative new ways to use IoT devices and applications. Its goal would be twofold: to recognize devices and systems with potential applications and, second, to identify completely new strategies, tactics, and methods for accomplishing missions using COTS.
- The military can to a certain extent, take advantage of civilian mobile waveforms such as 4G/5G LTE . Nevertheless, those advances will need to be paired with military-specific communications architectures (e.g., multiband radios with scarce bandwidth, MANET topologies and defensive countermeasures).
- Use Platform as a Service (PaaS) to deliver web-based services without building and maintaining the infrastructure, thereby creating a more flexible and scalable framework to adjust and update the systems. Adopting PaaS also carries risks for the military, and requires private contractors to implement additional security procedures.
- Realize a comprehensive trust framework that can support all the requirements of IoT for the military. Many state-of-the-art approaches that address issues such as trust and value depend on inter-domain policies and control. In military environments, policies will likely be contextual and transient, conflated by inter-organizational and adversarial interactions.
- Information theories will need to focus on decision making and cognitive layers of information management and assimilation. Further, methods for eliciting causal relationships from sparse and extensive heterogeneously-sourced data will require additional theoretical research.
Conflicts of Interest
|DoD||Department of Defense|
|ACV||Armored Combat Vehicles|
|BFT||Blue Force Tracking|
|C2||Command and Control|
|C4ISR||Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance|
|COP||Common Operational Picture|
|EPC||Electronic Product Code|
|EPM||Electronic Protection Measures|
|IaaS||Infrastructure as a Service|
|IoT||Internet of Things|
|ISR||Intelligence Surveillance and Reconnaissance|
|JIE||Joint Information Environment|
|LPD||Low Probability of Detection|
|LPI||Low Probability of Interception|
|MANET||Mobile ad-hoc networks|
|MPE||Mission Partner Environment|
|NCW||Network Centric Warfare|
|NFC||Near Field Communication|
|NFV||Network Function Virtualization|
|PaaS||Platform as a Service|
|PPDR||Public Protection Disaster Relief|
|QoI||Quality of Information|
|SaaS||Software as a Service|
|SDR||Software Defined Radio|
|TLS||Transport Layer Security|
|UAV||Unmanned Aerial Vehicle|
|VoI||Value of Information|
|WSN||Wireless Sensor Networks|
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Fraga-Lamas, P.; Fernández-Caramés, T.M.; Suárez-Albela, M.; Castedo, L.; González-López, M. A Review on Internet of Things for Defense and Public Safety. Sensors 2016, 16, 1644. https://doi.org/10.3390/s16101644
Fraga-Lamas P, Fernández-Caramés TM, Suárez-Albela M, Castedo L, González-López M. A Review on Internet of Things for Defense and Public Safety. Sensors. 2016; 16(10):1644. https://doi.org/10.3390/s16101644Chicago/Turabian Style
Fraga-Lamas, Paula, Tiago M. Fernández-Caramés, Manuel Suárez-Albela, Luis Castedo, and Miguel González-López. 2016. "A Review on Internet of Things for Defense and Public Safety" Sensors 16, no. 10: 1644. https://doi.org/10.3390/s16101644