Cognitive Radio Inspired M2M Communications Invited Paper

Elias Z. Tragos Institute of Computer Science

Foundation for Research and Technology Hellas, Greece etragos@ics.forth.gr

Abstract - The Internet of Things (loT) presents itself as one of the basic pillars of the Future Internet. Such an extended network of heterogeneous "things" forms a global network infrastructure supported by several communication protocols where physical and virtual objects are interconnected. It is envisaged that billions of machines will be connected to the Internet, pushing the current communication technologies to their limits in terms of connectivity and performance. The wireless interconnectivity of a large number of devices in the ISM bands is a fundamental issue due to the resulting high interference environment, which degrades severely the performance and the connectivity of these devices. The Cognitive Radio technology can help mitigate the interference effect in such environments, employing smart techniques for accessing the wireless spectrum in an opportunistic manner. This paper discusses the adaptation of Cognitive Radio technology for enhancing the machine-to-machine communications, presenting the challenges and open issues for future research.

Keywortis-cognitive radio, m2m communications, smart objects, spectrum management, energy efficiency, internet of things

I. INTRODUCTION

The Internet technology has undergone enormous changes since its early stages and has become an important communication infrastructure targeting to provide anywhere, anytime connectivity. The wide deployment of mobile and wireless technologies has brought the Internet to a vast number of users (expected to jump to almost 4 billion in a few years), since it has enabled the access from almost everywhere. Human-to-human (H2H) communications have always been the center of importance and the communication technologies have been developed for supporting human-based voice or data applications. Thus, current mobile networks are optimized for human-oriented traffic characteristics communication [I ].

Lately, an entirely different type of communication has emerged with the inclusion of "machines" in the communication landscape. Several types of devices (laptops, smartphones, tablets, sensors, actuators etc.) are able to communicate with each other exchanging information and data without human intervention. This type of "machine-tomachine" (M2M) communication is expected to be a key part of the future communication landscape, since a large number of future smart applications are based on machinedata [2][3]. As mentioned by the World Wireless Research Forums, until 2020 it is expected to have 7 trillion wireless devices for 7 billion of people [4]. This explosion in the number of wireless devices raises various requirements for the new communication technologies that will be developed to support the M2M interconnectivity. The increased network usage, the new

Vangelis Angelakis Department of Science and Technology

Linkoping University, Sweden vangelis.angelakis@liu.se

connectivity requirements, the heterogeneous communication technologies involved and the huge amounts of traffic that these devices will flow through the Internet are key issues to be addressed by future M2M technologies, since current Internet is not fully capable of supporting this type of communication.

For addressing some of these issues, intelligence is being introduced into the devices, creating the concept of "smart objects" or "things". The requirement for the interconnection of "things" resulted in the development of the "Internet of Things" (loT) which has gained significant research attention the latest years [5]. Although initially the term loT was very much related with the Radio Frequency Identification (RFID) devices, recently it has been broadened to include all types of smart objects and machines. Basically anything can be considered as an object connected to a global network (the Internet). According to the EC's Directorate-General Information Society and Media, "the Internet of Things must be seen as a vision where "things", especially everyday objects, such as nearly all home appliances but also furniture, clothes, vehicles, roads and smart materials, and more, are readable, recognisable, locatable, addressable and/or controllable via the Internet" [6]. Sensors, actuators, mobile phones, Radio Frequency IDentification (RFID) tags, cameras, thermometers, microphones, speakers, refrigerators, TVs are just a few of the objects the loT consists of For the realization of the loT paradigm, M2M communications are the key enabler. Furthermore, since most of these "things" are not standalone devices, but can be accessed or controlled by humans, the concept of Machine-to-Machine-to-Human (M2M2H) communications has emerged [3]. The machines will have to be able to communicate not only with other machines but also with humans, either with the same or with different communication technologies and protocols.

In such a complex networking landscape, the requirement for anywhere, anytime and anyplace connectivity seems to be valid not only for the humans but also for the objects, since many M2M applications can be of high importance, i.e. medical applications or emergency/alarms. Current wireless networking technologies, which are also used for M2M communications, are severely impacted by interference, which can degrade significantly the performance and the connectivity of the devices. This paper discusses the adaptability of the recently emerged concept of Cognitive Radio (CR) technology on the M2M communications. In section II the fundamental properties and effects of interference in the wireless M2M devices connectivity in discussed. In Section III the model for CR-inspired smart objects is proposed, while in section IV the applications of CR-inspired M2M communications are presented.

ISSN:1882-5621/13/ 2013 NICT 1

II. INTERFERENCE IN M2M COMMUNICATIONS

Interference is one of the main performance limiting factors in M2M, ad hoc and sensor networks. Interference depends on the locations of the communicating nodes, the multiple access (MAC) scheme, the transmit powers and the fading distribution. For a given set of communicating nodes, at any instant the spatial distribution of the transmitting nodes is decided by the MAC mechanism in place.

The characteristics of interference in the M2M case differ from those of the interference in infrastructure-based wireless systems (such as the cellular systems). Moreover, the distributed nature of several network functions in ad M2M and the minimization of central control for core functions (such as in topology control, medium access, and information routing) lead to the need for interference models that can describe the effects of interference on the overall network penetrating all the communication layers. Finally, the communication devices in such M2M networks can have severe restrictions on their transmission/reception and processing capabilities and hard requirements for low energy consumption to preserve network lifetime (e.g. in remote sensors). Therefore, being able to characterize interference is extremely helpful in designing efficient M2M networks.

In characterizing interference, the Protocol Model and the Physical Model [7] establish conditions for successful communication on a given link in the presence of interference. While the Protocol Model accounts for the effects of interference based on pairwise device relations (distance), the Physical Model takes into account the total interference. Therefore, the Protocol Interference Model can heavily abstract several aspects of communication, and its simpl icity has motivated its use in the design and evaluation of several communication protocols. The Physical Model though, due to its more accurate and realistic formulation, is more appropriate for capacity evaluation and study of other physical layer related issues. M2M network connectivity and interference modeling using graphs is facilitated by the ease with which graphs can be used to model interference as a pairwise relationship between two devices or two links. Therefore, graph-based interference models are closely related to the Protocol Interference Model. Nevertheless, several authors have proposed interference graphs based on the aggregate interference, i.e., according to the Physical Interference Model. Interference graphs are particularly useful in the context of resource allocation and topology control problems, where elements and tools of Graph Theory are readily applicable.

Overall in modeling interference in M2M as in ad-hoc networks, there are four aspects that need to be taken into account [8]. (I) The radio propagation model, that describes the effects of path losses, fading and shadowing. (2) the transmitters spatial distribution, that describes how nodes are distributed. (3) The medium access control mechanism adopted in the network, which can be based on random access (e.g., CSMA) or deterministic access (e.g., TDMAlCDMA), and (4) the traffic model, that describes the transmitter's goal (e.g. random queue arrivals or transmitting a known buffer).

Keeping interference low is of key importance. In addition to enhancing throughput, minimizing interference may help in

lowering node energy spendings by reducing the number of collisions (or the amount of energy spent in an effort of avoiding them) and consequently retransmissions on the media access layer. Interference can be reduced by reducing transmIssIon power. The area covered by the smaller transmission range will contain fewer nodes, yielding less interference. On the other hand, reducing the transmission range has the consequence of limiting the number of one-hop neighbors, bringing routing into more central role. However, there is a limit to how much the transmission power can be decreased, both technically (at the implementation of the transmission electronics), but also from a conceptual point of view. As in ad hoc networks, if a node's transmission range becomes too small it may become disconnected fTOm the network. Hence, transmission ranges must be assigned to nodes in such a way that the desired global network properties are maintained.

III. CR-INSPIRED SMART OBJECTS

The radio spectrum is a natural resource regulated by governmental and international agencies. It is assigned to license holders on a long term basis using fixed assignment policy, which affects the spectrum usage as recent measurements have shown [9]. This shows that for large portions of spectrum the utilization is quite low, while for the ISM bands the utilization is quite high leading to significant interference as discussed in Section II. Cognitive Radio (CR), proposed by 1. Mitola in [10], has emerged the last decade as a promising technology able to exploit the unused portions of spectrum in an opportunistic manner. CR was first introduced for opportunistic radio access in niche applications, such as wireless microphones, later adopted on traditional ad-hoc networks [11] for improving spectrum access, lately the interest in being moved to sensor networks and the smart grid as presented in [12].

With the explosion of the Internet of things, sensors and smart objects in general have received much attention, aiming to optimize their performance for enabling efficient M2M communications. This attention will continue to increase in the near future, since M2M communications will become an essential part of our everyday lives. For efficiently interconnecting wirelessly millions or even billions of smart objects several issues have been identified [5]:

Hardware heterogeneity, Different communication protocols Different communication technologies Interference in WSN frequencies Single-radio devices Limited energy resources No M2M-tailored QoS requirements Low trustworthiness of M2M communications Very large number of devices in small areas

When M2M communications are based on CR-inspired Smart Objects (CRSO), most of the above mentioned issues can be adequately addressed. This is achieved due to the intelligence that is induced in the smart objects and their ability to adapt to environmental conditions. The basic idea of a CRSO is that it can exploit the unused spectrum bands

opportunistically, but taking only in an energy efficient way (since as mentioned before one of the key issues of M2M communications is the limited energy resources of the involved machines). In this respect, the cognitive cycle introduced by Mitola [10] can be changed according to Figure 1, in order to include the "energy efficiency" aspect. Here we include two new modules in the cycle: (i) battery information module and (ii) energy efficiency module. The battery information module is responsible for maintaining information regarding the remaining capacity of the battery and to keep track record of the average energy consumption at each spectrum band. The energy efficiency module is in direct communication with all other modules of the cognitive cycle to consult them (and command when in critical battery state) for taking decisions and acting with regards to: (i) consume less energy when sensing the spectrum (i.e. sense the spectrum less frequently), (ii) analyse the spectrum with regards to the required energy to transmit in the band under examination and (ii) decide to use a spectrum band with low required energy consumption.

Receive QoS requirements f Transmitted

Signal

Radio Environment

Spectrum Holes Information

Spect urn Holes Inf rrnation

RFStimuli

Figure 1. Energy efficiency-based cognitive cycle

Taking into account the energy efficiency-based cognitive cycle, the internal structure of a CRSO is depicted in Figure 2. Biased by the name, one would probably assume that "Cognitive Radio" is only related to the PHY layer, but this is not entirely true. CR affects multiple layers as it is related to all access related functionalities, so it also affects functionalities on the Link and Network layers (L2 and L3).

Figure 2. CR-inspired Smart Object (CRSO)

Traditional smart objects have only a default Medium Access Control (MAC) module able to sense the channels and transmit when the channel is idle (and there are data to transmit). In a CRSO the MAC module comprises several modules: (i) spectrum sensing module, (ii) spectrum analysis module, (iii) history and prediction module, and (iv) spectrum decision module. In addition to those, the Reconfiguration module is responsible for reconfiguring the parameters of the Software-Defined-Radio (SDR)-based Radio Frequency (RF) front-end according to the selected communication technology/protocol. The parameters that are reconfigured are: (i) frequency, (ii) bandwidth, (iii) modulation, (iv) channel coding, (v) output power and (vi) other operational parameters, i.e. receiver sensitivity, noise threshold, BER, etc.

Below we summarize the basic functionality and the interconnections of each one of the main CRSO modules:

(i) Spectrum Sensing Module (SSM). This module is responsible for monitoring the spectrum and identitying which spectrum frequencies are used by either Primary Users (PUs) or other Secondary Users (SUs). In order to do so, a number of mechanisms can be used, i.e. energy detection, matched filter detection, cyclostationary feature detection, etc. [11]. Aiming to avoid misdetections, this module implements cooperative sensing, exchanging the sensing results with other CRSOs for taking joint decisions regarding the existence of PUs in the examined frequencies. In a CRSO, the SSM takes direct input from the Radio Interface in order to perform the identification of the spectrum usage. This information is given to the Spectrum Analysis Module (SAM) for analyzing the sensing results. The SSM also controls the sensing parameters (spectrum frequencies to examine, sensing duration, how often to sense, etc.) of the Radio Interface according to the input it gets from the History and Prediction Module.

(ii) Spectrum Analysis Module (SAM). This module is responsible for analyzing the sensing results in order to identifY the characteristics of the examined spectrum fTequencies in terms of capacity, condition, interference, occupancy, PU presence and other link-layer related parameters. For estimating the capacity the Shannon formula can be used:

5 C = Blog(l+--),

N+I where B is the width of the spectrum that will be used, S is the signal power, N is the noise power and 1 is the additive interference caused by all other sources on those spectrum frequencies. Since in CR there is no standard definition for a "channel", the SCRO can select its own central operating frequency and bandwidth, according to its transmission requirements, without being limited by the channel width of i.e. 2MHz for IEEE 802.15.4. In this respect, the CRSOs can use as much bandwidth as it is available for high resourcedemanding applications. The output of the SAM goes both to the Spectrum Decision Module for selecting the spectrum band to use and to the History and Prediction Module.

(iii) History and Prediction Module (HPM). This module is an optional module in CR nodes, but it is included in the CRSOs in order to save energy in the spectrum sensing process. The HPM receives input from the SAM regarding the occupancy in the examined frequencies and keeps a history of

the occupancy in each band. Then, it uses prediction models to identify the access trends in each frequency in the near future. That way, the HPM can identify the frequencies that are utilized by other transmissions most of the time and gives this info to the SSM in order to avoid sensing those frequencies. This will result in a shorter sensing duration and less sensing calculations, due to the less frequencies that will be sensed, decreasing at the same time the energy consumed in the sensing process. Several prediction mechanisms can be applied for estimating the future occupancy of the frequencies, i.e. Markov models [13], or probability-based schemes [14].

(iv) Spectrum Decision Module (SDM). This module is responsible for selecting the spectrum that will be used and its operating parameters, i.e. central frequency, bandwidth, modulation, power, etc. This decision is taken according to the input that the module receives by the SAM and the HPM regarding the unoccupied frequencies and the probability that the frequencies will be free in the near future. The SDM takes also into account the remaining energy in the battery, the traffic QoS requirements from the upper layers according to the application that the CRSO is running and the estimated energy consumption at each frequency for this application. The signal attenuation is directly proportional to the distance and the operating frequency, thus for a specific distance higher selected frequency results in greater attenuation and higher required transmission power. Thus, for long distances and in cases of low remaining energy it is better to select low operating frequencies for decreasing the required transmission power ..

(v) Spectrum-aware Routing Module (SRM). This module belongs to the network layer and aims at combining the routing algorithms with a spectrum-aware metric. In CR M2M communications, the network topology depends heavily on the selected frequency band at each smart object. A spectrumaware routing metric considers the spectrum availability at each machine and exploits paths that have higher availability and can support higher traffic. Of course this metric should also take into account the remaining battery of each CRSO in order to avoid over-utilizing machines that have low battery. A joint framework including spectrum-aware routing, spectrum sensing and spectrum selection is an optimal solution for achieving high spectrum utilization in M2M communications.

(vi) Spectrum Mobility Module (SMM). This is a cross-layer module that aims to perform spectrum handovers. Spectrum handover (or handoff) is the process of changing spectrum band when: (i) a PU activity is sensed in the current band, or (ii) the current spectrum cannot meet the requirements of the CRSO's application. This module receives information from several modules, i.e. (i) from the SSM regarding PU activity, (ii) from the SRM regarding routing information (i.e. no available route), and (iii) from the application layer regarding the application's QoS parameters.

Another cross-layer module is the Security and Privacy Module (SPM), which ensures the secure and privacy preserving operation of the CRSOs. With smart objects continuously gaining more access in the everyday life of people, the information they gather must be secured and not disclosed to any third parties. Secure object configuration and management, information security, privacy enhancing

technologies, trust modeling, CR-based security mechanisms (for addressing i.e. PU emulation attacks) are a sample of some key technologies that should be employed within a CRSO. This will increase the trustworthiness of M2M communications, giving incentives to both users and application providers for utilizing this emerging technology.

The CR-inspired functions enable the CRSOs to be aware of their environment and autonomously optimize network performance by cognitively selecting the optimum resource management techniques. In this respect, the CRSOs are able to employ intelligent cross-layer interference mitigation mechanisms and negotiation protocols for resource bargaining for jointly achieving more fair usage of the wireless resources. The cognition can also ensure seamless connectivity in M2M communications under any network conditions, since even in high load situations, the CRSOs will be able to identify unoccupied frequencies in order to transmit the required data.

IV. APPLICATIONS OF CR-INSPIRED M2M COMMUNlCA TIONS

Due to the inherent intelligence of CR technology there are many potential areas in M2M communications that can benefit greatly from the use of CRSOs. Especially smart city applications can utilize the CR technology in order to interconnect seamlessly a large number of heterogeneous devices without the need for complicated middleware technologies. Below we summarize the most important application areas of CR-inspired M2M communications.

(i) Smart Buildings: In the smart city domain, buildings will be equipped with intelligence embracing a wide range of technologies to improve the everyday life of the inhabitants. Two basic smart building applications are: (a) building automation and (b) building energy management. With the wide adoption of wireless networks, a large number of Wi-Fi access points are installed within buildings, creating severe interference in the ISM bands. For implementing the smart building applications, a large number of smart objects have to be installed within building for monitoring the energy consumption of devices (i.e. TVs, air conditioners, lights) and for home automation (sensors and actuators in doors, windows). Equipping these devices with cognition can allow for a distributed self-configuration in non-ISM frequencies. Since normally these types of objects do not require transmitting large amounts of data, opportunistic usage of short ti meslots of the ISM bands (even when they are congested by the Wi-Fi users) will be sufficient for the applications. When more resource-demanding applications are requested (i.e. transferring video for surveillance) then free spectrum in other bands can be utilized without disturbing the Wi-Fi users. Furthermore, CRSOs would be ideal for in-house multimedia distribution and sharing between devices such as TVs, PCs and Hi-Fi systems. For this type of applications high data rates are required (9-30Mbps) which cannot be achieved in congested Wi-Fi environment, but CRSOs can perform spectrum aggregation in unused frequencies to utilize wider bandwidth i.e. in TV bands for achieving high data rates.

(ii) Vehicular Communications: Intelligent Transportation Systems (ITS) utilize road-based and vehicle-based sensors for

several applications, like electronic tolling and traffic monitoring. In the 5.8 - 5.9 GHz band, 75MHz and 30MHz have been assigned in the US and in Europe respectively for Dedicated Short Range Communication (DSRC) communications with a channel width of lOMHz. This is only sufficient for delivering short amount of data in short distances to vehicles for alarms and emergencies. This requires though a wide deployment of many Road Side Units (RSUs) in order to provide full coverage. When smart objects installed on vehicles are also included in the network of road-side ITS sensors, then the current DSRC technology won't be able to support the huge amount of data that will be needed to be exchanged for vehicular applications like traffic monitoring and management and smart parking in congested roads. CR technology can greatly assist in this direction, by allowing the opportunistic utilization of more spectrum frequencies to enable higher data rates or longer range. That way, a minimum deployment (or even no deployment) of RSUs would be feasible and vehicles will be able to communicate in long distances (in lower unused frequencies) so that they will know the traffic long ahead either by short messages or even by video multicast.

(iii) Smart Grid: Many interconnected heterogeneous communicating systems form a smart grid aiming to provide and make use of information to save energy and reduce costs in electricity networks. The smart grid networks continuously expand including more metering devices and it is estimated that the amount of exchanged data will be several thousands of terabytes in the near future [15]. Current M2M wireless networking technologies are not able to support this amount of data efficiently due to the limited spectrum bandwidth. With CRSOs as the main nodes of the future smart grid the spectrum utilization will be optimized enabling efficient large scale data transmissions, with concurrent lower energy consumption.

(iv) Environmental Monitoring: For monitoring the environmental conditions in a smart city, a large number of heterogeneous devices have to be installed in several key locations (i.e. squares) monitoring humidity, air quality, noise, temperature, CO and C02 emissions. Another option would be to have dedicated vehicles (Le. buses or garbage collector vehicles) moving on the city roads with sensors devices performing measurements in many areas. For the second approach, location determination devices (i.e. GPS) should also exist on the vehicle. To ensure the reliable and energy-efficient operation of the large number of devices, the CR technology should be adopted to exploit dynamic spectrum management techniques in an overcrowded spectrum that is sensed in current large cities as identified in [16][ 13]. Furthermore, the heterogeneity of the devices will be concealed due to the reconfiguration capabilities of the CRSOs, so that their interconnectivity of the devices will be easier and seamless, allowing the transferring of data in long distances and in distant areas with no network coverage.

V. CONCLUSIONS

In this paper we have proposed a new paradigm of M2M communications inspired by the Cognitive Radio technology. Current M2M solutions are susceptible to interference by various sources as they mainly utilize the free ISM bands, which are also used by a number of wireless technologies.

Thus, current M2M solutions have limited performance and will not be able to support efficiently the communication of a very large number of machines in dense areas, as it is envisaged for the future loT networks. CR technology enables the CRSOs to have knowledge of their operating environment and to be able to sense and opportunistically use unoccupied spectrum frequencies, according to their application requirements. Furthermore, the proposed CRSO model enables energy efficiency by design, with specialized modules that consider the energy consumption for all spectrum-related decisions. However, current SDR solutions have significant processing and computational requirements, which make the real-world development of CRSOs rather difficult. As CR is an emerging technology, it is expected that in the near future the SDRs will be optimized and this issue will be resolved.

ACKNOWLEDGMENT

This work has been supported in part by the EC Marie Curie projects MESH-WISE (FP7-PEOPLE-20I 2-IAPP: 324515) and WiNDOW (FP7-PEOPLE-2012-IRSES: 318992).

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