| Network Working Group | L. Feeney |
| Internet-Draft | Uppsala University |
| Intended status: Informational | V. Fodor |
| Expires: May 3, 2018 | KTH |
| October 30, 2017 |
Inter-network Coexistence in the Internet of Things
draft-feeney-t2trg-inter-network-01
The breadth of IoT applications implies that there will be many diverse, administratively independent networks operating in the same physical location. In many cases, these networks will use unlicensed spectrum, due to its low cost and ease of deployment. However, this spectrum is becoming increasingly crowded. IoT networks will therefore be subject to wireless interference, both from similar networks and from networks that use the wireless channel in very different ways.
High-density, heterogeneous wireless environments present formidable challenges for network coexistence. The PHY and MAC layers are primarily responsible for defining how radios use the channel. But higher layer protocols are also a party to adverse interactions between networks. To date, there have been few performance studies that fully reflect this aspect of the future IoT operating environment, particularly with respect to protocol behavior and network-scale interactions.
This document describes key challenges for coexistence and highlights some recent research results showing the impact of protocol level interactions on network performance. It identifies two opportunities for the IRTF T2TRG community. The first is to define best practices for performance evaluation and protocol design in the context of network coexistence. The second is to investigate the use of higher layer protocols to actively participate in managing network coexistence.
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An IoT application is a set of wireless devices that act together to perform some sensing and control function. Most applications also provide a user interface, such as a mobile app or cloud-based service. In general, each application is deployed independently of any other applications that may be operating in the area and forms its own separate, cryptographically isolated network.
An enormous range of IoT applications are expected to become pervasive in daily life. Networks will be installed in public spaces, businesses, and residences by a wide range of individual, commercial, and government actors. As a result, there will be many diverse, administratively independent networks operating in the same physical location. For example, a future home environment may include IoT applications for security, heating and cooling, elder care, air quality monitoring, personal health and fitness, smart home appliances, structural monitoring, lighting, utilities, and entertainment.
Many of these networks will use unlicensed spectrum due to low cost and simplicity of deployment for both the user and developer. In unlicensed spectrum, there is no authority that has a management relationship with (or even knows about) all of the potentially interfering networks that can be present in some location. This means that there is no entity that can coordinate networks’ use of the shared wireless channel. Networks will therefore experience interference caused by transmissions from devices belonging to other networks.
The PHY and MAC layers have primary responsibility for ensuring that devices share the channel efficiently, while spectrum regulations limit devices’ output power and overall channel utilization. But the MAC layer can only explicitly coordinate devices within a single network. It provides only limited protection from other networks, which may have very different transmission footprints over time, spectrum or physical space.
Network coexistence is mainly evaluated in terms of PHY layer and radio hardware resilience to interference. This is generally based on analytic modeling of the probability of successful packet reception for varying SNIR conditions and on carefully controlled measurements of interacting RF waveforms. (See [NIST] for a discussion of relevant issues and [SUM11] for an example of such an analysis for IEEE 802.15.4g.)
Analytic modeling of interactions between MAC layers is much harder, because the MAC itself plays an active role in transmission timing and parameters. Interfering networks are therefore usually modeled as increasing the intensity of some statistical process representing noise or loss. Other studies are based on simulation or testbed measurements of simple scenarios. The research literature contains a number of such studies, especially for IEEE 802.11 and IEEE 802.15.4. (See [SURVEY] and [SURVEY2] for an overview.)
But there are very few studies that evaluate complex, heterogeneous interference scenarios, particularly with regard to protocol behavior and network-scale interactions. Most existing solutions rely on low IoT traffic loads and careful channel selection to achieve adequate performance. But recent work [WETZ17] demonstrates that real world instances of IoT interference require considerable effort to diagnose.
This document explores key challenges for network coexistence in future IoT environments and presents some recent research results. These suggest that protocol level interactions can significantly affect network performance, even when the channel is not heavily loaded. Higher layer protocols will therefore need to be aware of the potential impact of inter-network interference and avoid contributing to adverse interactions. We argue that network coexistence in future IoT environments is not yet well understood and identify possible T2TRG roles in addressing this issue.
Widespread deployment of diverse IoT applications presents four main challenges: 1) scale 2) lack of a trust relationship between independently deployed networks 3) resource limitations, especially battery capacity 4) diversity of application requirements and channel utilization behavior.
As IoT becomes pervasive, there will be many independent networks operating in any given location. Devices will experience high levels of both homogeneous and heterogeneous radio interference.
Since networks use different kinds of radios and have different wireless coverage areas, their topologies will overlap with each other in complex ways. Interference will therefore affect not only individual wireless links, but also protocols operating at network scale.
Interaction scenarios will also be highly dynamic, with mobility and user activity leading to frequent changes in the set of interfering devices.
In unlicensed spectrum, there is not necessarily any basis for a trust relationship between networks. Networks with overlapping wireless coverage may well have been deployed by unrelated actors. There is no single authority that has an administrative relationship with all of the potentially interfering networks in any given location. This means that there is no entity that all networks can trust to coordinate their access to the shared channel.
Devices within any one network will be able to authenticate themselves to each other and their own administrator (usually a non-expert user). But the network itself may not have any meaningful external identity. Even if two networks can exchange information, there is no obvious way for each network to determine whether the other is trustworthy with respect to some coexistence mechanism.
IoT networks are severely resource constrained in many respects, including channel capacity, energy, hardware capabilities and cost.
The large number of devices sharing the wireless channel naturally limits the capacity available to each device. In addition, many devices use low bit-rate radios, which further reduces the communication capacity. (Note, however, that adverse interactions between networks can occur even in cases where the channel is only lightly used.)
For energy-harvesting and battery-powered devices, maximizing lifetime is essential. Protocol design is dominated by the need to minimize device activity and especially by the need to keep the energy-hungry radio turned off as much as possible, while still ensuring needed connectivity. Even sensing the channel conditions is an expensive operation. This limits networks’ ability to observe and adapt to the behavior of their neighbors.
Finally, for many IoT applications, devices must be low cost and easily deployed and managed by non-expert users. They often have very limited memory and CPU resources. These factors constrain the design space and limit the complexity of proposed solutions.
Even networks that use the same radio hardware and protocols will interfere with each other. But the diversity of IoT radios, protocols and applications creates additional challenges. Even characterizing the space of possible interactions may be challenging: Protocols can be anything from freely available, to consortia-driven standards such as ZigBee or WirelessHART, to completely proprietary.
This diversity is driven by the diversity of IoT applications. Applications will differ significantly in their devices’ communication range and the overall network coverage area. They will vary in the number of devices and traffic load. They will have different requirements for latency and reliability. And they will use different energy sources and have different requirements on energy efficiency and lifetime. To meet these requirements, applications will use a wide variety of radios, protocols and network structures.
Different radio technologies divide the spectrum into channels differently: In the 2.4GHz unlicensed band, for example, IEEE 802.11 has up to 14 overlapping channels, while IEEE 802.15.4 and BluetoothLE have 16 and 40 non-overlapping ones. Radios also use a variety of modulation techniques at the PHY layer to define how data is encoded on the channel as RF energy.
This means that there are many different ways that RF energy is distributed over time and spectrum. As a result, it may not be possible for channel sensing mechanisms to reliably detect the presence of potentially interfering transmissions or identify the source of interference and packet loss.
Each PHY layer makes different tradeoffs between transmit power, communication range, bit-rate, bandwidth, energy consumption and resilience. In sub-GHz spectrum, for example, IEEE 802.15.4g/Wi-SUN (smart utility network) provides 50-200 kbps bit-rates with ranges of > 1000m. Very low power EnOcean devices provide similar bit-rates, but ranges of < 100m. By contrast, LoRa provides bit-rates of at most a few kbps, but can obtain 10km of range. Differences in bit-rate and frame size mean that packet transmit times can range from < 10 ms to > 200 ms. Radios operating in 2.4GHz, such as IEEE 802.15.4, IEEE 802.11 and Bluetooth, show similar diversity.
Along with different kinds of radios, different kinds of network structures can be used to meet application requirements for density and coverage area. The most common structure is the star topology, where all devices communicate directly with a controller. Networks can also cover larger areas or achieve higher reliability by using multi-hop forwarding over various topologies, such as trees or directed acyclic graphs, cluster trees, and meshes.
These structures affect how transmissions within a network are correlated with each other in time and space, such as forwarding a frame across a mesh. It can also affect interactions between networks, particularly networks whose radios have very different coverage areas. For example, a long-range device belonging to one network may be located in the midst of a mesh of short-range devices belonging to another network.
The MAC layer defines how senders coordinate their transmissions within a network. Like the PHY layer, different MAC layers create different distributions of RF energy in time and (for channel hopping protocols) spectrum.
CSMA-based (channel sensing and backoff) protocols can provide some protection from external transmissions, since they defer to any ongoing transmission that they detect. Conflicts due to hidden terminals can occur even within a single network, but differences between radio technologies and network structures may exacerbate the problem. In addition, MAC timing parameters, such as backoff times, are generally proportional to bit-rate and frame transmit times. Timing incompatibilities between interfering senders can reduce the effectiveness of backoff and retransmissions in heterogeneous environments.
TDMA-based (transmission schedule) protocols can be more efficient in their use of the channel and energy than CSMA protocols. But because networks construct their transmission schedules independently, they may allocate transmission slots that conflict with each other. Since senders rely on their assigned schedule, such conflicts can be costly.
Minimizing energy consumption is often the absolute priority for IoT design. It is necessary to keep the radio turned off as much as possible, while still ensuring connectivity. As with MAC protocols (with which they are sometimes integrated), there are a variety of approaches to power saving. With synchronous methods, devices wake up according to a schedule that ensures that senders and receivers are awake at the same time (as in IEEE 802.15.4 PANs or TSCH). Asynchronous methods allow devices to coordinate their wake up schedules on-demand (as in ContikiMAC).
Coordinating the duty cycles of a sender and receiver imposes strict timing constraints on the radio operations. As with the PHY and MAC, each power save protocol creates its own distribution of RF energy over time. Depending on application requirements and tradeoffs for latency and battery lifetime, this could be on timescale of < 1s to > 1000s.
IoT networks usually use IP(v6), but there is also considerable diversity in higher layer protocols, both open and proprietary. Routing protocols make different tradeoffs between latency, reliability, energy efficiency and overhead, depending on the application requirements. The operation of the routing protocol also affects the distribution of RF energy in physical space, as frames are forwarded toward a root or across a mesh. The routing protocol may also react to the presence of interference by attempting to re-route its traffic.
Higher layer protocols are largely abstracted away from the behavior of individual wireless links. They use a variety of mechanisms to maintain communication performance under conditions of loss and delay, including retransmissions, multi-path communication, and application-specific adaptations.
Finally, the variety of transport, transfer and application protocols used in IoT networks reflects the diversity of use cases: The RESTful model is central for IoT applications based on web services [I-D.keranen-t2trg-rest-iot]. Wireless sensing applications often use in-network data processing and aggregation. Industrial IoT applications emphasize low latency and reliability. Wide-area IoT networks collect small amounts of data from a very large number of devices.
Radios using the same spectrum can differ by two orders of magnitude in terms of communication range, bit-rate, bandwidth and transmit power. Different PHY and MAC layers create different distributions of RF energy over time, spectrum, and physical space. The variety of possible interactions between them may lead to patterns of loss and delay that are difficult for higher layer protocols to predict or manage. In addition, higher layer protocols for power saving, routing and data communication create their own distributions of network activity over time and space. Interactions between networks therefore occur at multiple scales. This suggests that inter-network interactions are a potential issue at all layers of the protocol stack.
How will sub-GHz LPWAN networks such as LoRa and SigFox, whose base stations cover wide areas, interact with possibly large numbers of smaller networks using IEEE 802.15.4g/WiSUN or EnOCean radios? What happens if two or more independent networks using CoAP over RPL over 6LowPAN (or 6TiSCH) are operating in the same room? What happens if a beacon-enabled PAN (or a ZigBee or ContikiMAC network) is thrown into the mix? And some BluetoothLE? Especially in a WiFi heavy environment, the value of channel hopping for interference mitigation in IEEE 802.15.4 networks may be limited.
To date, there have been very few studies that examine network performance under realistic – dense, heterogeneous – interference scenarios. Some existing observations and results are noted here.
Interference between WiFi networks is widely observed, especially in 2.4GHz spectrum in dense residential and urban areas, where there are many independently deployed networks and large amounts of traffic.
WiFi presents a strongly homogeneous interference environment. WiFi networks consist of an AP and associated devices that communicate directly with their AP. WiFi also uses a CSMA-based MAC, which means that senders to defer to any ongoing WiFi transmission, regardless of its source. Traffic is dominated by media streaming, which is supported by adaptive mechanisms at the client and server, as well as throughout the protocol stack.
Despite these simplifying factors, poor WiFi performance is a problem in dense urban and residential areas, even noted by the general public. This does not bode well for the much large number of heterogeneous networks deployed in the future IoT environment.
A common scenario in 2.4GHz spectrum will involve high-power, high-traffic WiFi networks impacting networks based on low power, low bit-rate radios, such as IEEE 802.15.4. The research literature includes a range of mitigation strategies directed to specific cases. (See [SURVEY] and [SURVEY2] for an overview.)
Practical existing solutions are mostly based on IEEE 802.15.4 devices identifying and using the least interfered channels, either statically or by channel hopping. But in areas where there is a lot of WiFi traffic, there may be very few such channels. WiFi conventionally uses non-overlapping WiFi channels 1, 6, and 11, leaving just three minimally interfered IEEE 802.15.4 channels. As a result, low power IoT networks operating in these areas may be crowded into a very small number of “good” channels.
Recent research suggests that protocol level interactions can lead to severe performance degradation, even when the channel is not heavily loaded. While these studies focus on various IEEE 802.15.4 MAC layers, the results suggest broader implications for protocol design.
[F3G15] and [FF16] show that IEEE 802.15.4 beacon-enabled PANs can experience severe disruptions due to protocol level interactions. This includes behaviors such as short-term oscillations in throughput and extended periods of disconnectivity – even when the channel itself is only lightly loaded. Similarly, [YTB17] shows that interfering IEEE 802.15.4 6TiSCH-based networks experience packet loss and extended blackout periods, as well as increased energy consumption. Such outages are likely to affect the operation of higher layer protocols.
These behaviors appear to be due to a combination of timing rigidities in the MAC protocol, periodicity in the radio duty cycle, and clock drift between networks. More generally, battery constraints force devices to spend most of their time with their radios turned off. Senders and receivers therefore need some way to coordinate their radio wake up times so that they can exchange packets. These mechanisms often depend heavily on careful timing of radio operations, instead of (or in addition to) explicit control traffic. This timing dependence makes networks more sensitive to disruption than might be expected from just considering overall channel utilization and collision probabilities. In addition, clock drift results in networks’ synchronizing and desynchronizing with each other, which creates dynamic effects.
Network coexistence remains an open research problem, particularly with respect to protocol behavior and network-scale interactions. We identify several areas of relevance for IETF/IRTF activities. In particular, we highlight a role for T2TRG in 1) developing and advocating best practices for protocol design and performance evaluation and 2) speculative research into the possibility of higher layer protocols actively contributing to network coexistence.
To date, there have been few studies that fully reflect the complexity of the future IoT operating environment. In practice, there is also a lack of testbed and simulation tools that support the necessary scale and diversity of protocols and hardware to facilitate such work. The community therefore does not yet have a good understanding of performance and reliability of IoT protocols.
Performance evaluation of IoT protocols should consider whether they will perform acceptably in the presence of diverse networks operating in the same spectrum. In the IETF/IRTF context, this includes adaptation done by 6lo, 6TiSCH and LPWAN groups, as well as routing, transfer and application protocols, such as RPL and CoAP. Moreover, networks using IETF protocols will share spectrum with networks using protocols from a variety of other open and proprietary sources. It will be important to develop methodologies for investigating these interactions as well.
T2TRG can contribute to the development of and advocacy for best practices for protocol design and performance evaluation.
Network coexistence is likely to rely primarily on improving resilience to interference in the PHY and MAC layer, as well as higher layer protocols. More speculatively, there may be opportunities for higher layer protocols to actively participate in interference mitigation, by sharing information about their operation and even by explicit coordination between networks.
When two networks use the same PHY layer, it is possible for frames transmitted by devices in one network to be successfully received by devices in other networks. These frames cannot be authenticated and are usually discarded at the MAC layer. But they might provide a way for networks to exchange signaling information that can be handled as IP packets at the network layer, even if they have very different protocol stacks otherwise. Such a mechanism could allow devices to describe their expected channel utilization patterns, for example.
Alternatively, many applications have some form of connectivity to the network infrastructure, often as part of the user interface. This might be a way to provide access to additional resources or to establish a trust relationship. A coordination mechanism could be based on information exchange via some trusted cloud-based service, for example. The inspiration here is from cognitive radio solutions where secondary users obtain information about activity of primary users from trusted sources.
However, this is very much an open research area and there are substantial challenges in developing such mechanisms:
1) There is an enormous diversity of radios, channel access methods and utilization patterns that might need to be described. It is not clear what information should be signaled or what actions a receiver should take in response.
2) Battery lifetime, channel capacity, and device CPU and memory resources continue to be significant limitations. In particular, the radio duty cycle is highly constrained, limiting both sensing and communication.
3) Any cooperative mechanism must operate effectively in the absence of any administrative or trust relationship between networks. Alternatively, there must be some way to establish an appropriate level of trust. This presents a significant challenge to the practical implementation of cooperative mechanisms proposed in the literature. (See Security Considerations below.)
4) The privacy implications of networks sharing information about their activity must be carefully considered. (See Security Considerations.)
Despite the challenges, this topic seems particularly amenable to standards and interoperability-oriented approaches enabled by IRTF T2TRG. There may be synergy with IRTF T2TRG work in IoT semantic interoperability: Can IoT networks describe not only the ‘things’ they connect, but also themselves? There may also be synergy with IETF activities (PLUS) in making signaling information available within encrypted flows.
An overview of security challenges in IoT environments is given in [I-D.irtf-t2trg-iot-seccons]. The current document focuses on coexistence between independently administrated networks operating in the same location. The biggest security challenge for managing network interactions is that such networks do not necessarily have any basis for a trust relationship.
Regulations concerning unlicensed spectrum only control radio behaviors such as transmit power and channel utilization. They do not mandate the use of any specific protocol, nor is it possible to enforce that a network participates correctly in some particular coexistence mechanism.
Most wireless protocols adapt their behavior to channel conditions to some extent, such as CSMA backoff or channel blacklisting. But the more a network changes its behavior in response to small amounts of information from an untrusted source, the more leverage an attacker has to disrupt it. Similarly, the more information about its future behavior a network provides to an untrusted destination, the easier it is for an attacker to disrupt it. The risk is further exacerbated by the high energy cost of listening to the channel to directly observe the behavior of other networks.
Any proposed solution will therefore need to be resilient to the possibility of incompatible, oblivious, selfish, or even hostile networks when designing a coexistence mechanism. This is especially true for methods in which two networks actively coordinate their use of the shared channel. At a minimum, participating in information exchange should not substantially increase vulnerability to disruption in the case of a malicious (or merely incompatible) actor.
In addition, networks that try to be friendly toward each other may disclose substantial information about their operation. There are privacy issues associated with IoT networks making such information visible, because of their close coupling with human activity. Particularly for health-related applications, even being able to identify the type of application or its level of activity may reveal sensitive data. Ideally, it should be possible for a network to both obfuscate its communication patterns (if needed) and act cooperatively.
One maxim that may be useful in designing the set of information that a network discloses as a matter of course with the intention of facilitating coexistence is that the information disclosed should not provide more insight than that information an attacker might have gained by simply observing the network for a while. But note that simply disclosing that information in an accessible way still changes the economy of surveillance — the objective is that it also changes the economy of coexistence, and these effects need to be carefully weighed against each other.
The future IoT operating environment will contain many diverse, administratively independent networks sharing unlicensed wireless spectrum. Ensuring network coexistence is essential for avoiding the “tragedy of the commons” and enabling practical deployment of IoT solutions.
Network coexistence is and will continue to be largely the domain of spectrum regulation and of the PHY and MAC layers. But the operation of protocols throughout the network stack affects the distribution of RF energy over time, spectrum and physical space at many different scales. This suggests that higher layer protocols are also a party to adverse interactions between networks.
The community currently lacks a good understanding of the impact of inter-network interference, particularly with regard to protocol behavior and network-scale interactions. Recent results suggest that inter-network interactions can significantly affect performance, even when the channel itself is not overloaded. Periodic behaviors and timing-sensitive energy saving mechanisms appear to be key factors, though more research is needed.
These issues are relevant to IETF/IRTF, most obviously with respect to the performance of protocols such as 6LowPAN, 6TiSCH, RPL, and CoAP. We also identify two topics as especially relevant to T2TRG:
1) Performance evaluation and protocol design should reflect the complexity and heterogeneity of future IoT environments. The community could benefit substantially from the development and documentation of best practices in this regard.
2) There may be a role for network coexistence mechanisms based on information sharing or explicit coordination between networks. This topic seems particularly amenable to standards and interoperability oriented approaches. However, there are substantial research challenges.
| [F3G15] | Feeney, L., Frey, M., Fodor, V. and M. Gunes, "Modes of inter-network interaction in beacon-enabled IEEE 802.15.4 networks", 2015 14th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), DOI 10.1109/medhocnet.2015.7173294, June 2015. |
| [FF16] | Feeney, L. and V. Fodor, "Reliability in co-located 802.15.4 personal area networks", Proceedings of the 6th ACM International Workshop on Pervasive Wireless Healthcare - MobiHealth '16, DOI 10.1145/2944921.2944923, 2016. |
| [I-D.irtf-t2trg-iot-seccons] | Garcia-Morchon, O., Kumar, S. and M. Sethi, "State-of-the-Art and Challenges for the Internet of Things Security", Internet-Draft draft-irtf-t2trg-iot-seccons-08, October 2017. |
| [I-D.keranen-t2trg-rest-iot] | Keranen, A., Kovatsch, M. and K. Hartke, "RESTful Design for Internet of Things Systems", Internet-Draft draft-keranen-t2trg-rest-iot-05, September 2017. |
| [NIST] | Koepke, G., Young, W., Ladbury, J. and J. Coder, "Interference and Coexistence of Wireless Systems in Critical Infrastructure", National Institute of Standards and Technology report, DOI 10.6028/nist.tn.1885, July 2015. |
| [SUM11] | Sum, C., Kojima, F. and H. Harada, "Coexistence of homogeneous and heterogeneous systems for IEEE 802.15.4g smart utility networks", 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), DOI 10.1109/dyspan.2011.5936241, May 2011. |
| [SURVEY] | Han, Y., Ekici, E., Kremo, H. and O. Altintas, "Spectrum sharing methods for the coexistence of multiple RF systems: A survey", Ad Hoc Networks Vol. 53, pp. 53-78, DOI 10.1016/j.adhoc.2016.09.009, December 2016. |
| [SURVEY2] | Baccour, N., Puccinelli, D., Voigt, T., Koubaa, A., Noda, C., Fotouhi, H., Alves, M., Youssef, H., Zuniga, M., Boano, C. and K. Römer, "External Radio Interference", SpringerBriefs in Electrical and Computer Engineering pp. 21-63, DOI 10.1007/978-3-319-00774-8_2, 2013. |
| [TCG316] | Tinnirello, I., Croce, D., Galioto, N., Garlisi, D. and F. Giuliano, "Cross-Technology WiFi/ZigBee Communications: Dealing With Channel Insertions and Deletions", IEEE Communications Letters Vol. 20, pp. 2300-2303, DOI 10.1109/lcomm.2016.2603978, November 2016. |
| [WETZ17] | Wetzker, U., Splitt, I., Zimmerling, M., Boano, C. and K. Romer, "Troubleshooting Wireless Coexistence Problems in the Industrial Internet of Things", 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES), DOI 10.1109/cse-euc-dcabes.2016.167, August 2016. |
| [YTB17] | Ben Yaala, S., Théoleyre, F. and R. Bouallegue, "Cooperative resynchronization to improve the reliability of colocated IEEE 802.15.4 -TSCH networks in dense deployments", Ad Hoc Networks Vol. 64, pp. 112-126, DOI 10.1016/j.adhoc.2017.07.002, September 2017. |
The authors would like to thank Michael Frey, Charalampos Orfanidis, Martin Jacobsson, and Per Gunningberg for their valuable collaboration in simulation and measurement studies of inter-network interference. We would also like to thank Carsten Bormann for his support and encouragement in preparing this document, particularly the discussion of security considerations.