Olga León

Rodrigo Román
email: roman@lcc.uma.es

Juan Hernández-Serrano

Towards a Cooperative Intrusion Detection System for Cognitive Radio Networks

Keywords: CRNs, IDS, security, PUE

Cognitive Radio Networks (CRNs) arise as a promising solution to the scarcity of spectrum. By means of cooperation and smart decisions influenced by previous knowledge, CRNs are able to detect and profit from the best spectrum opportunities without interfering primary licensed users. However, besides the well-known attacks to wireless networks, new attacks threat this type of networks. In this paper we analyze these threats and propose a set of intrusion detection modules targeted to detect them. Provided method will allow a CRN to identify attack sources and types of attacks, and to properly react against them.

Introduction

Traditionally, spectrum allocation has followed a static policy so that specific bands have been assigned to particular services operating under license. This fact and the huge increase in new wireless applications during the last years has led to the lack of spectrum for emerging services. In addition, according to the Federal Communications Commission (FCC) most of the spectrum is vastly underutilized [1]. Cognitive Radio Networks (CRNs) [START_REF] Akyildiz | Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey[END_REF][START_REF] Cordeiro | Ieee 802.22: an introduction to the first wireless standard based on cognitive radios[END_REF] are regarded to be a possible solution to this problem by making use of the spectrum left unoccupied by licensed services or primary users. Thus, as secondary users of the spectrum, CRNs must be capable of identifying white spaces or vacant bands and select the best portion in order to operate while avoiding interferences to primary users. Therefore, when the presence of a primary is detected in the CRN operation channel, it must switch to another band, a process known as spectrum handoff.

Although there are a few proposals on CRNs [START_REF] León | Securing cognitive radio networks[END_REF], most research has focused on the on-going standard IEEE 802.22 [START_REF] Cordeiro | Ieee 802.22: an introduction to the first wireless standard based on cognitive radios[END_REF] for Wireless Regional Area Networks (WRANs). This standard defines a centralized CRN operating in a point-tomultipoint basis, formed by a a base station and a set of nodes attached to the base station via a wireless link. IEEE 802. [START_REF] Zhengyi | Fast Detection Method in Cooperative Cognitive Radio Networks[END_REF] WRANs are designed to operate in the TV broadcast bands while assuring that no harmful interference is caused to primary transmissions, i.e., TV broadcasting and low power licensed devices such as wireless microphones.

Research on CRNs has already been object of a big effort, but it is still a hot topic requiring further work and, particularly, with regard to network security. As for any other network scenario security is usually split into two lines of defense. The first one is focused on avoiding attacks and it is closely related to the use of cryptographic primitives. The second one should be more devoted to detect and identify the attacks that have passed over the first line. IDSs behave as a second line of defense, where these mechanisms can identify the existence of an intrusion and the (possible) source of the attack, and notify the network and/or the administrator so that appropriate preventive actions can take place [START_REF] Bace | Intrusion Detection[END_REF].

This paper provides an overview of the new vulnerabilities and attacks to CRNs and proposes guidelines to design mechanisms to efficiently detect and counteract them. Those mechanisms are described in the context of an Intrusion Detection System (IDS), since these new threats cannot be yet overcome by the first line of defense.

The structure of the paper is as follows: Sect. 2 describes the main threats to CRNs appeared in the literature. In Sect. 3 we identify the requirements and main concepts regarding to the implementation of an IDS for detecting such attacks. Next, in Sect. 4 we provide a high-level description of its structure and the tasks to be performed by each of its components. Finally, in Sect. 5 we present the conclusions and future lines of the work.

New Threats in CRN

Falling into the category of wireless networks, CRNs inherit most of the threats already studied by the research community, such as jamming attacks, selfish behaviors, eavesdropping, etc. However, due to the particular attributes of CRNs its impact on network performance may be different and also new security implications arise. Although this topic has received far less attention than other areas of cognitive radio, most of the work has focused (in decreasing order of importance) on four specific attacks: the Primary User Emulation (PUE) attack, specific attacks to cooperative sensing mechanisms, the Objective Function (OF) attack and the Lion attack targeted to disrupt TCP performance.

PUE

In a PUE attack, first coined in [START_REF] Chen | Ensuring trustworthy spectrum sensing in cognitive radio networks[END_REF], an attacker pretends to be a primary user or incumbent by transmitting a signal with similar characteristics to a primary signal or replying a real one, thus preventing the CRN form using a vacant band. The impact of this attack depends on several factors, such as the location of the attacker and the sensibility of CRs in their measurements. Moreover, based on previous knowledge of the CRN operation, an attacker can force PUE attacks whenever the CRN switches from one channel to another (frequency handoff) thus degrading the data throughput of the CRN or completely producing a Denial-of-Service (DoS) attack. To get this behavior, the attacker should estimate the next CRN operation channel in a limited time by sensing the media till find the new channel of operation and/or eavesdropping the common control data of the CRN (if exists). As per the former the attacker could discard some channels (e.g. channels already in use by primary transmissions) in order to minimize the channel search time or can even estimate the more probable new CRN channel based on its own local sensing.

Detecting PUE attacks poses two new main challenges for the detection mechanisms: 1) applying location algorithms to precisely pinpoint the position of the emitter; and 2) developing an anomaly or signature based scheme that, once the emitter is located helps the detection mechanism to detect abnormal emitter's behavior. The former can overcome any PUE attack based on impersonating a TV emitter, since position of legitimate TV towers is assumed to be known, and can at least localize a wireless microphone emitter. The latter would also allow the CRN to, once the wireless microphone emitter is located, identify the PUE attack by analyzing anomalous behavior patterns.

There are a few state-of-the-art proposals dealing with PUE attacks mainly based on the analysis of the received signal power [START_REF] Chen | Modeling primary user emulation attacks and defenses in cognitive radio networks[END_REF][START_REF] Jin | Mitigating primary user emulation attacks in dynamic spectrum access networks using hypothesis testing[END_REF]. However, these proposals assume that the attacker has a limited transmission power and/or the attacker is always located within the CRN. In [START_REF] Li | Dogfight in spectrum: Combating primary user emulation attacks in cognitive radio systems, part i: Known channel statistics[END_REF], an approach similar to random frequency hopping is presented where secondary users randomly select a channel to transmit among those available.

Attacks to Cooperative Sensing

Cooperative sensing in CRNs [START_REF] Mishra | Cooperative sensing among cognitive radios[END_REF] allows taking a decision about the presence of a primary user based on the reports provided by a set of CRs. Each secondary user senses the spectrum individually and shares its results with the others in order to improve the detection probability. As a consequence, malicious and selfish behaviors can arise, such as a malicious node which deliberately report false measurements leading to false positives or negatives or a selfish node, which do not cooperate in order to save energy, for instance. Often these attacks are aimed at improving the chances of a successful PUE attack.

OF Attacks

Objective Function (OF) attacks [START_REF] Clancy | Security in cognitive radio networks: Threats and mitigation[END_REF] are targeted to disrupt the learning algorithm of CR devices. Within a CRN, incumbents control several radio parameters in order to enhance the network performance. The parameters choice is often done by means of an artificial intelligence algorithm that makes slight modifications of several input factors to find their optimal values that maximize an objective or goal function. An attacker can alter the performance of the learning algorithm to its own profit by intentionally degrading (e.g. by jamming) the channel when some input factors are greater than a certain threshold.

The scientific community hasn't lately paid too much attention to OF attacks since they do not apply to WRAN 802.22 [START_REF] Cordeiro | Ieee 802.22: an introduction to the first wireless standard based on cognitive radios[END_REF], which is the only standard regarding such networks. The fact is that WRAN defines a centralized scenario where all the "cognitive" behavior falls under the base station responsibility.

However, the threat will affect a CRN actually made of cognitive radios and thus a complete IDS for CRN should take it into account.

Lion Attack

Finally, the Lion attack [START_REF] León | A new cross-layer attack to tcp in cognitive radio networks[END_REF][START_REF] Hernández-Serrano | Modeling the Lion Attack in Cognitive Radio Networks[END_REF] is a cross-layer attack targeted to disrupt TCP connections by performing a PUE attack in order to force a frequency handoff of the CRN. The interruption of communications at specific instants can considerably degrade TCP throughput, or, if the attacker can predict or know the new transmissions parameters to be used by the sender after the handoff, actually turn into a permanent Denial of Service (DoS).

3 Implementing an IDS for CRNs

Background on IDS

As shown by the previous section, there are multiple types of attacks that can affect the performance and integrity of CRNs. The development of a first line of defense, such as cryptographic primitives to protect the exchange of common control data, is actually compatible with the deployment of a second line of defense that detects an attack on the precise moment it is targeting the network; this is the role of Intrusion Detection Systems (IDSs).

In a CRN there will be one or several detection entities that monitor the data and its surroundings. If the evidence is inconclusive or there is a need to have a holistic point of view of the situation, the detection entity can make use of the distributed nature of the network and use a collaborative mechanism (with mechanisms to manage uncooperative CRs) to take a global intrusion detection action. Note that even in centralized CRNs (such as WRANs), the existence of distributed detection entities can help to develop a more accurate IDS.

There is a "de-facto" agreement on the basic elements [START_REF] Giannetsos | Intrusion Detection in Wireless Sensor Networks[END_REF] for an IDS for distributed systems: a local packet monitoring module that receives the packets from the neighborhood, a statistics module that stores the information derived from the packets and information regarding the neighborhood, a local detection module that detects the existence of the different attacks, an alert database that stores information about possible attacks, a cooperative detection module that collaborate with other detection entities located within the neighborhood, and a local response module that take decisions according to the output of the detection modules.

Focusing on the detection modules used in these IDS for CRNs, they must make use of first-hand information, second-hand information, statistical data, and the data acquired by the CRs during its normal operation. These modules can then use this data to distinguish between normal and abnormal activities, thus discovering the existence of intrusions. There are actually three main techniques that an IDS can use to classify actions [START_REF] Axelsson | Intrusion detection systems: A survey and taxonomy[END_REF]: misuse detection, anomaly detection, and specification-based detection. The former compares the collected information with predefined "signatures" of well-known attacks. The second technique store patterns of what can be considered as "normal" behaviour, and react against any significant deviation of those patterns. Finally, the latter is also based on deviations from normal behavior, although the concept of normal behavior is based on manually defined specifications instead of on machine learning techniques and training. All these three techniques can be used in the context of a CRN, such as a signature-based scheme to detect Lion Attacks, or an anomaly-based technique to detect OF attacks.

IDS Requirements and Attacker Model

When designing the blueprint of the IDS and the functionality of its detection modules (see Sect. 4), it is necessary to consider both certain requirements that the elements of the IDS must fulfill and the attacker model that specifies the capabilities of the adversaries that target the services of the CRNs. Regarding IDS requirements, these are the most relevant [START_REF] Mishra | Intrusion detection in wireless ad hoc networks[END_REF]: 1) the IDS must not introduce new weaknesses into the system and thus, for example, it must consider the existence of malicious/faulty nodes and must prevent DoS attacks targeting the IDS message management systems; 2) the IDS must be fault-tolerant, able to run continuously and recover from problematic situations thus forcing to design mechanisms that store the current and previous state of the IDS; 3) the IDS must provide adequate mechanisms that allow users or the network itself to know about the existence of a certain attack an react against it; and 4) the design of the IDS must allow the addition of new detection modules and a seamless interaction with existing detection mechanisms.

As for the attacker model, we assume that the attacker capabilities are quite diverse: 1) the attacker can have no, partial or complete knowledge of the CRN operation; 2) most attackers will make use of small radios with a limited action range, but we will not discard the existence of powerful emitters with the capacity of faithfully emulating a primary TV signal; 3) the number of devices owned by the attacker can range from one till many cooperating radio devices, which could difficult the operation of the IDS detection mechanisms; and 4) the attacker can both move within a given area or remain static.

A Blueprint for an IDS Suited to CRNs

In this section we will define the blueprint of an IDS for CRN, which will contain the different detection mechanisms for the attacks described in Sect. 2. Such blueprint can be used as a foundation for the creation of a functional and usable IDS. The architecture of the IDS is shown in Fig. 1, and includes the following modules: input, memory, output, and detection. The input module is in charge of managing the first-hand information, the second-hand information, and the cooperative processes. The memory module is used to store the statistical information derived from the input and to provide an interface to the specific network information managed by the CR. The output module takes decisions according to the output of the detection modules (e.g. it informs the user or a central system) and stores other information such as the alert database. Finally, the detection module detects the existence of the different attacks, using as an input the data provided by the input and memory modules. From now on we will focus on the modules or sub-modules composing the detection module. Fig. 1 sketches the different modules with their relationships. Note that this IDS blueprint is not exclusive, as it can be possible to add new detection mechanisms that will take advantage on the existence of the input, memory, and output modules.

Module of Cooperative Location of Primary Emitters

As afore-mentioned, locating a source of real or fake primary transmissions may lead to mitigate or at least effectively react against PUE attacks. Physical location of RF transmission sources has been a hot topic for many years in wireless applications, but most of the proposals in the literature rely on measures of certain distance dependent parameters performed at the BS or at nodes whose position is well known. Typically, these parameters are [START_REF] Patwari | Locating the nodes: cooperative localization in wireless sensor networks[END_REF]: 1) received signal strength (RSS), based on the fact that signal strength varies inversely with the square of the distance in free space; 2) the time taken by the signal to travel between two nodes, which allows to estimate the angle of arrival (AOA) or the time of arrival (TOA) but which requires cooperation of the locating node; and 3) the difference time of arrival (TDOA), which utilizes cross-correlation processes to calculate the difference in time of arrival of the emitter signal at two or higher pairs of nodes.

From the above commented techniques, we consider TDOA to best suit the IDS requirements since RSS is susceptible of high errors due to the dynamics of outdoor environments (multipath signals and shadowing) and TOA/AOA requires cooperation of the node to be located and, since we aim to determine the position of potential attackers, any cooperation from the node we wish to locate cannot be expected.

TDOA techniques require at least two TDOA measures (cooperation of 3 nodes) to locate an emitter on a surface and three measures (at least four nodes) to locate the emitter on the 3-dimensional space. These measures lead to a linear system of equations that can be easily solved [START_REF] Bucher | A synthesizable vhdl model of the exact solution for threedimensional hyperbolic positioning system[END_REF]. However, in practice measurements are subjected to errors and then a solution of the system can be rarely found. In this case, the location problem can be posed as an optimization problem and solved using, for example, a least squares (LS) estimations such as Taylor [START_REF] Foy | Position-location solutions by Taylor-series estimation[END_REF] or extended Kalman-Bucy filter [START_REF] Kalman | New results in linear filtering and prediction theory[END_REF], which can be a better choice for mobile sources. Consequently, the accuracy of the estimation will highly depend on the number of TDOA measurements and thus on the number of cooperating nodes.

We represent the cooperative location module as a box, see Fig. 1, with the following inputs and outputs: From input module:

-N -1 TDOA measurements obtained as the differences between the primary signal measure obtained by the node implementing the module and the same measure at other N -1 cooperating nodes. -Necessary parameters for the chosen optimization method. As stated before, the optimization can be based, for example, on least squares (LS) methods or on extended Kalman-Bucy filters and both require at least an initial estimation of the position of the emitter, a covariance matrix and, in the latter case, a mobility pattern. From memory module:

-An indicator of the reliability of the measurements based on previous results and computed by the reliability system module (section 4.2). -Read access to the previously stored emitter's position and its associated estimation error, especially when estimating mobile emitters' positions. To memory module:

-The estimated position of the primary emitter. The estimation position should follow the format f (t) = (x, y, z) being f (t) constant in time if the primary source is static and a mobility prediction otherwise. -A guess of the error performed in the estimation of the position.

Module of Reliability System

This module is in charge of measuring the reliability of the TDOA measurements provided by a given CRN node. It computes the TDOA measurement that a given node should have taken according to the emitter's position estimated by the cooperative location module. This value is then compared with the TDOA measurement provided by such node. The greater the divergence is, the less reliability is assigned to that node's measurements. Obviously, the module should take into account past measurements of the same node. From input module:

-N -1 TDOA measurements obtained as the differences between the primary signal measure obtained by the node implementing the module and the same measure at other N -1 cooperating nodes. From memory module:

-The previously computed reliability indicators.

-The estimated position of the primary emitter made by the cooperative location method and its associated interval of error. To memory module:

-Updated reliability indicators for any of the cooperating nodes reporting TDOA measurements.

Module for Detecting Jamming and PUE Attacks

Jamming attacks interfere with the CRN operation channel forcing the network to switch to another channel with better conditions. If the attack is repeated whenever the CRN switches, the throughput can be degraded or even starved at all. PUE attacks have the same purpose of jamming ones but differ in that they emulate primary transmissions instead of just producing noise. In 802.22, PUE attacks can be classified depending on the type of the primary signal into TV signal-based and wireless microphone-based attacks. Attacks based on jamming or wireless microphone-based PUE may be detected with an anomaly detection IDS module: jamming/PUE appearing whenever the CRN switches from one channel to another. As a result this module should be able to identify and attacker "following" the CRN. This can be achieved by estimation of the attacker's current and future position (with a given mobility pattern) and/or its Radio Frequency Fingerprint (RFF).

TV signal-based PUE attacks can be more easily overcome since legitimate TV primary emitters' positions are assumed to be fixed and known. As a result comparing the estimated position given by the cooperative location module with the database of TV emitters will identify whether it is a PUE attack or a legitimate transmission. In order to reduce the rate of false positives/negatives, RFF techniques [START_REF] Ureten | Wireless security through RF fingerprinting[END_REF] can be used to probabilistically recognize a predefined source of transmissions.

From input module:

-Type of signal: pure jamming, primary signal (e.g. TV or wireless microphone signals). Jamming would be any signal that is not a primary emission, e.g. TV or wireless microphone transmissions. Mechanisms for detecting primary signals have been widely studied and some proposals have appeared in the literature [START_REF] Zhengyi | Fast Detection Method in Cooperative Cognitive Radio Networks[END_REF][START_REF] Nieminen | Primary User Detection in Distributed Cognitive Radio Networks under Timing Inaccuracy[END_REF]. -RFF of the primary emitter.

From memory module:

-The estimated position of the primary emitter made by the cooperative location method and its associated error. -Previously computed probability of jamming/PUE attack for the current emitter (stored by it current position estimation or its RFF) To memory module:

-Updated probability of jamming/PUE attack for the current emitter. To output module:

-If the probability of being under a jamming attack is above a certain threshold γ jam , the module outputs an alert of jamming attack by the current emitter.

-If the probability of being under a PUE attack exceeds a certain threshold γ P UE , the module outputs an alert of PUE attack. -If any of the previous is true, the module outputs the estimated position the emitter and its associated error.

Module for Detecting Lion Attacks

This module relies on a signature-based scheme which looks for matches between instants of retransmission attempts for a given TCP connection and the beginning of a frequency handoff caused by the detection of a potential primary user. Whenever there is a match, the probability of being under a Lion attack is increased. Therefore, this module should take as input cross cross layer data (TCP retransmission instants and physical handoffs) provided by the input module, as well as past probabilities of being under attack provided by the memory module. For example, let us consider a TCP connection with an initial retransmission timer of τ seconds. If a segment is lost due to a frequency handoff forced by an attack (jamming or PUE), it will be retransmitted after τ seconds. Since TCP's backoff algorithm doubles the retransmission timer with each unsuccessful attempt, next retransmissions will occur after t = τ, 3τ, 7τ, 15τ, . . . , (2 i -1)τ For common values τ = 200ms and handoff intervals of 1.5s, retransmission instants are 200, 600, 1400, 3000, 6200, . . .ms. The first three retransmission attempts will obviously fail because they match the first handoff period, so we do not take them into account to compute the probability of attack. However, at t = 1.5s, the handoff has ended and the CRN is operating in a new channel, so new retransmissions should now succeed. If a malicious user is performing a Lion attack, it may predict the time of the next retransmission and force a new handoff, leading again to the failure of the next retransmission attempt. The attacker may repeat this process each time the CRN performs a frequency handoff, completely starving the TCP source. In a naïve implementation, if we define a module threshold of 4 retransmission failures out of the first handoff in order to have a probability of 100% of being under a Lion attack, with the fourth retransmission failure at t = 3s (first one after the first handoff) the module output will be a probability of 100% 4 = 25%, with the fifth (t = 6.2s) of 50%, etc. And so on until, with the sixth retransmission (the fourth out of the first handoff), we get a probability of 100% and an alert for this attack is reported.

From input module:

-The physical/MAC layer reports when the CRN is performing a handoff.

-The transport layer provides the current TCP retransmission instants. From memory module:

-Past TCP retransmission instants/attempts and handoff intervals. To memory module:

-Current probability of being under a Lion attack for a given threshold for a given emitter. -The module stores a log of TCP retransmission instants/attempts and physical handoff intervals. To output module:

-If the probability of being under a Lion reaches 100% for a given threshold, the module outputs an alert of Lion attack by the current emitter, the estimated position of the emitter and its associated error.

Module for Detecting OF Attacks

The basis of this module is to detect abnormal environment conditions related to the use of some transmission parameters as security, modulation, codification, etc. Consequently it should store some statistics about the environment characteristics related to configuration profiles and check whether with some profiles it can be found a long deviation from the expected values. Correlation between agreement time intervals and abnormally bad environment conditions give us a probability of being under an OF attack. If this probability becomes greater than a certain threshold γ OF then an alert is generated.

From input module: -Boolean indicating if there is an on-going parameter agreement and, being the case, the parameters in negotiation. -Current environment conditions. From memory module:

-Historic of environment conditions (normal values, variance, predictions, etc) and agreement time intervals. To memory module:

-New environment data and agreement time interval. To output module:

-If the probability of being under a OF attack is above a certain threshold γ OF , the module outputs an alert of OF attack, the parameters under attack and the current estimated emitter's position.

Conclusions and Future Work

CRNs can improve the current inefficiency in spectrum usage by detecting which frequency bands are not being in use by licensed services. However, the specific mechanisms for CRNs, such as spectrum sensing or cooperation among CRs, pose new security challenges that need to be properly addressed. In this paper we have first presented an overview of the main new threats to CRNs appeared in the literature: the PUE attack, attacks to cooperative sensing, OF attacks and the Lion attack.

Traditional protection against attacks relies on a first line of defense based on proactive measures, such as confidentiality and authentication, and a second line that actually detects each attack and consequently get the chances to react against it. CRNs inherit the first line of defense from the wireless networks approach, however the design of a second line of defense suited to CRNs, such as an IDS, is still challenging. Consequently. the target of this paper has been to provide future researchers with the guidelines to implement a valuable IDS for the new threats to CRNs. The proposed high-level scheme fulfills the standard requirements for an in-network IDS [START_REF] Mishra | Intrusion detection in wireless ad hoc networks[END_REF] and inherits the "cognitive" behavior of CRNs, which implies learning from the past, making intelligent decisions and positively evolving. We have focused on defining the necessary inputs (input module), the storage requirements (memory module) and the attack alerts (output module) generated based on "cognitive" decisions (detection module) from present and past data.

More research is required to provide optimal detection mechanisms that will guarantee a safe change of paradigm. With security being a global issue spanning through all protocol layers and across all network elements, a chain is as strong as its weakest link. Therefore, no matter the efforts, if one layer is vulnerable, the whole network is. A promising future line of research is to propose standardized cross-layer interfaces for CRNs which allow to get the security to all its extent.

Fig. 1 .

 1 Fig. 1. Modules of a cooperative IDS for CRN

Acknowledgements This work has been partially supported by the Spanish Comisión Interministerial de Ciencia y Tecnología (CICYT) with the project P2PSEC (TEC2008-06663-C03-01); the Spanish Ministerio de Ciencia e Innovación with the CONSOLIDER project ARES (CSD2007-00004) and the FEDER co-funded project SPRINT (TIN2009-09237); and the Generalitat de Catalunya with the grant 2007 GRC 01015 to consolidated research groups awarded to the Information Security Group of the Universitat Politècnica de Catalunya (UPC).


Andrea Bartoli

Mischa Dohler

Juan Hernández-Serrano

Apostolos Kountouris

Dominique Barthel

Low-Power Low-Rate Goes Long-Range: The Case for Secure and Cooperative Machine-to-Machine Communications

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Low-Power Low-Rate Goes Long-Range:

The Case for Secure & Cooperative Machine-to-Machine Communications

Introductory Remarks

The world is becoming increasingly connected. Information and communications technologies (ICT) are a true facilitator of this connectivity. The revolution, which a few decades ago began to connect people by means of mobile phones, is slowly ebbing down as virtually every human being is "cellphoned" today. Similarly, the revolution, which a few decades ago began to connect computers via the (today) Internet, is also ebbing down as virtually every computer is essentially "interneted" today. Another revolution, however, has slowly began to take shape: the one of interconnecting objects around us and thereby allowing to create an Internet of Things (IoT) [START_REF] Iera | The internet of things[END_REF]. Not all objects will be connected, however; and the majority not even in short term. Objects important to the well being and safety of humans will likely be connected first, leading to the vision of Internet of Important Things (IoIT) and acting as physical extension of the current Internet. The myriad of applications is huge and the benefit well understood [START_REF] Kortuem | Smart objects as building blocks for the internet of things[END_REF].

Connecting objects, devices, things is clearly an opportunity but also poses serious challenges. The opportunity is in instrumenting and interconnecting the physical world around us and thus allowing it to act intelligently; this is essentially the vision of IBM's Smarter Planet initiative [3]. The main challenges remain in viably networking this large amount of objects given their obvious constraints in power, processing capabilities, memory and size.

Projections on the number of connected objects differ wildly [START_REF] Dohler | Machine-to-machine: An emerging communication paradigm[END_REF]. The WWRF predicts that by 2017 there will be around 7 Trillion devices connected; Market Study estimated in 2009 that there will be 50 Billion devices by 2010; and ABI Research estimated in 2010 that there will be 225 Million objects connected by means of a cellular link by 2014. These visions differ by orders of magnitude. Only time will tell how many objects will get eventually connected. This paper however addresses the important issue of how these objects get connected.

Related to "how" connectivity is facilitated, the major design driver is the need to draw low power during the sensing, communication and actuation process. This is due to fact that batteries cannot be changed too often and perpetual energy scavenging typically only yields low energy volumes. Since the wireless communication module is typically the one drawing most current, most efforts in the past concentrated in designing suitable wireless communication mechanisms operating over short distances and yielding fairly low rates.

The era of wireless sensor networks (WSNs) had been born and occupied mainly academic circles for more than a decade. Having gathered a great expertise in the area and published hundreds of articles, the academic community has essentially proven that, from a technology point of view, WSNs can be used to viably connect objects over short distances.

Naturally, various pioneering companies dedicated to WSNs emerged trying to capitalize on the commercial value of the emerging technology. Among the pioneers, were companies like Crossbow, Dust Networks, Arch Rock and Coronis. They played a central role in the development of the Internet of Things and acted as a first bridge between academic findings and industrial needs. An example of a pertinent academic finding is that cooperation and relaying are great tools to save energy when covering larger geographical areas; an example of industrial need is that security is a must for any of the real-world deployments.

With more and more companies emerging, and thus proving the viability of an IoT, the community realized quickly that a plethora of propitiatory technologies is counterproductive to the vision of a quickly scaling IoT. The emergence of standards developing organizations (SDOs) in the area of short-range low-power low-rate wireless systems has hence been a natural development. The various standardization bodies aimed to creating a common understanding of the architecture, protocols and functionality of the IoT. Developments in SDOs typically reflected industrial needs whilst incorporating findings of academia. Examples of said bodies are the IEEE, IETF, HART, ISA, DASH7, among others.

On the longer term, however, we will likely experience another shift in designing the IoT and/or IoIT. Notably, to be able to truly cover large geographic areas (with often heterogenous devices) is either not possible with known shortrange technologies or would simply require too much investment in multihop infrastructure. In addition, applications with mobility, roaming and alike cannot be supported, thus short-cutting large markets, such as car and logistics telemetry. The vision of machine-to-machine (M2M) communication enabled by cellular network connectivity has hence been taking shape in past years, ignited by pioneering developments of Swedish company Maingate in 1998 as well as European manufacturing giants Ericsson and Nokia shortly after. The SDOs dominant in this area are ETSI M2M and 3GPP LTE.

Both short range systems, in M2M language referred to as Capillary M2M, and long range system, in M2M referred to as Cellular M2M, will likely coexist until (almost) full migration to cellular system will have been achieved. Independent however of whether the system is short or long range, there will be two issues which need to be considered from the moment of conception, i.e. 1) a proper security design meeting the industrial requirements of the 21st century; and 2) a proper cooperation and networking design meeting the requirements of a functional IoT. These aspects, w.r.t. systems discussed above, are the focus of this overview and positioning paper.

The paper is structured as follows. In Section 2, we discuss in some more details the impact and importance of security and cooperation in general; we then apply these insights to specific technologies. Notably, in Section 3, we elaborate on current developments which will shape the near future of the IoT. In Section 4, we will elaborate on likely future developments which will shape the IoT of the long-term future. Section 5 concludes this position paper.

Security and Cooperation

Security refers to the process of protecting assets. In the context of ICT, these assets traditionally refer to data contents (syntax) and the network itself (protocols, routers, etc). However, latest trends indicate that the data meaning (semantics) and data ownership (privacy) commence playing a central role in asset management. The trend thus extends the need of providing security by means of confidentiality, integrity and authentication (CIA) to more advanced issues of trust and privacy. All these issues are a necessity but their incorporation is complicated by the following facts:

1. Devices are unattended. This generally requires a higher level of security and also trust mechanisms since devices are easier to comprise and not supervised; however, the increase in complexity is often not justifiable requiring other measures, such as cooperation, to be taken. 2. Devices are of low complexity. This prevents the use of sophisticated and powerful security schemes. Recent research however has shown that asymmetric cryptography [START_REF] Roman | Applicability of public key infrastructures in wireless sensor networks[END_REF] could efficiently be implemented in these networks, easing computing requirements (as well as key management). Yet, only a very limited number standards provide the possibility of using it. 3. Devices are large in numbers. This complicates key distribution and management as well as fast authentication approaches. In fact, most cryptographic schemes are very secure but serious security leaks occur due to poor key management. Yet, very few standards issue recommendations of how to properly manage keys for specific cryptographic algorithms. 4. Data ownership is not always clear. This requires privacy issues to be potentially respected from the beginning of design, where of importance is to clearly separate personal from technical data. Different approaches are gaining in popularity, among them the use of escrow-type system architectures [START_REF] Efthymiou | Smart grid privacy via anonymization of smart metering data[END_REF].

In summary, whilst security in terms of cryptographic mechanisms is fairly well understood, the joint consideration and inclusion of trust, privacy and key management are largely unexplored.

Cooperation refers to the process of devices helping each other in one form or another to jointly achieve a goal more efficiently than each device could do on its own. Whilst notions of cooperation through routing have been core to the networking community since its beginnings, cooperation has also been found beneficial from a capacity point of view [START_REF] Sirkeci-Mergen | On the broadcast capacity of wireless networks with cooperative relays[END_REF], at physical layer [START_REF] Dohler | Cooperative Communications: Hardware, Channel & PHY. Corporate Headquarters 111[END_REF], medium access control layer [START_REF] Alonso-Zarate | Cooperative arq: A medium access control (mac) layer perspective[END_REF] and application layer [START_REF] Fitzek | Cooperation in Wireless Networks: Principles and Applications[END_REF]. Cooperation within the context of low-power system is a necessity as per below reasons:

1. Range is limited. This requires multihop, one form of cooperation, to be used. Long distances towards gateways can hence be covered by means of multiple short hops. 2. Complexity is limited. Cooperation allows counteracting the limited perdevice complexity to achieve a more powerful system-wide complexity. In the context of security, for instance, whilst each device in the network might be fairly vulnerable to security threats, a good system design ought to ensure that the ensemble of cooperating devices exhibits a significantly higher degree of resistance to threats.

In summary, whilst cooperation has been well explored in the past by different communities, it offers enormous potentials in the context of designing a more secure system. Both security as well as cooperation, however, are only part of a larger design exercise for a viable future-proof IoT architecture. Notably, a strong requirement of such an architecture is to be fully IP(v6) compliant. Therefore, all security and cooperation mechanisms ought to fit this framework. In addition, most IoT networks today require devices to do some form of data aggregation along the data collection path, which further complicates design if IP's end-to-end paradigm is not to be violated.

Capillary M2M Solutions -IoT of the Present

The short range capillary M2M solutions are being standardized by various SDOs, notably the IEEE, IETF and interest groups relying thereupon.

IEEE Standards Solutions

The IEEE is standardizing the physical (PHY) and medium access control (MAC) layers. There are three families facilitating low-power short-range IoT operation, i.e. IEEE 802. [START_REF] Shelby | Embedded web services[END_REF] -IEEE 802.11 Low Power. With the growing market for smart objects and wireless sensors, several companies have developed application specific integrated circuits that are optimized for sensing applications. These products achieve a similar power profile as above low power architectures whilst leveraging the huge installed base of over 2 billion Wifi certified devices; a vibrant standard and industry alliance of close to 300 members; well proven encryption, authentication and end to end network security; mature network management systems; etc. Among one of the first companies promoting the concept of low power Wifi was Ozmo Devices. They tune the .11 protocol stack as well as introduce aggressive power saving operations. -Security Issues. The Wifi Protected Access (WPA) security protocol has become the industry standard for securing .11 networks. Using a pre-shared encryption key (PSK) or digital certificates, the WPA algorithm Temporal Key Integrity Protocol (TKIP) securely encrypts data and provides authentication to said networks. TKIP was designed to be a transition between old hardware and new encryption models. The IEEE 802.11i protocol improved upon the WPA algorithm (TKIP) to the new WPA2 [START_REF] Chen | Wireless lan security and ieee 802.11i[END_REF] that uses a better encryption algorithm: Advanced Encryption Standard (AES). As a major step forward, the protocol also specifies more advanced key distribution techniques, which result in better session security to prevent eavesdropping.

IETF Standards Solutions

The Internet Engineering Task Force (IETF) is actually not an SDO since not approved by the US government. It is composed of individuals, not companies. It meets about three times a year, and gathers an average of 1,300 individuals. It enjoys more than 120 active working groups organized into various areas.

The general scope of the IETF is above the wire/link and below the application. However, layers are getting fuzzy (MAC & APL influence routing) and we lately hence experience a constant exploration of edges. There are three working groups pertinent to capillary M2M where we will concentrate on two, i.e. IETF 6LoWPAN (establishing gateway to Internet); IETF ROLL (facilitating routing in low-power network); IETF CoRE (defining application transfer protocol). We subsequently briefly discuss their role in the capillary M2M ecosystem.

IETF 6LoWPAN. IPv6 over Low power WPAN (6LoWPAN) acts as a simplified gateway between the low power embedded network and the Internet. It facilitates neighborhood discovery, header compression with up to 80% compression rate, packet fragmentation (1260 byte IPv6 frames → 127 byte IEEE 802.15.4 frames), and thus direct end-to-end Internet integration. However, it does not provide routing. Security is also catered for [START_REF] Barker | Security aspects in 6lowpan networks[END_REF].

IETF ROLL. Routing Over Low power and Lossy networks (ROLL) deals with the design of a routing protocol for wireless low power mesh networks. It is in its final stage of standardization. It is based on a gradient routing protocol where nodes acquire a rank based on the distance to the collecting node and the messages follow the gradient of ranks to reach the destination. Again, security is currently being catered for [START_REF]A Security Framework for Routing over Low Power and Lossy Networks[END_REF].

IETF CoRE. Constrained RESTful Environments (CoRE) aims to extend the web architecture using constrained networks and devices [START_REF] Shelby | Embedded web services[END_REF]. Two items are dealt with, i.e. definition of the application transfer protocol Constrained Application Protocol (CoAP) that realizes a minimal subset of the known protocols REST along with resource discovery, subscription/ notification, and the use of appropriate security measures; and define a set of security bootstrapping methods for use in constrained environments in order to associate devices and set up keying material for secure operation.

WirlessHart Standard Solution

WirelessHART -Security Provisioning. The WirelessHART Security Manager is responsible for the generation, storage, and management of the keys that are used for device authentication and encryption of data. In order to provide authentication WirelessHart provides the MIC that is generated with CCM* (counter with CBC-MAC) using the AES-128 algorithm. For its generation it is necessary to include a 128-bit key, a nonce of 13 bytes and the message header without encryption. Public, Join, Network and Session Keys must be provided from the WirelessHART Network Manager:

• Network key: it is shared by all network devices and is used to generate the MIC on the MAC layer. • Public key: it is pre-configured in every node and is used to provide authentication during joining process; in this case network key cannot be used because it is delivered from the security manager after the first authentication.

• Join key: it is pre-configured in every new node and whenever a new node joins in the network it will be authenticated by the network manager that will send to it the network and the session keys. • Session key: it is the unique key between two network devices. It is used to provide confidentiality and integrity to any interchanged messages in order to ensure privacy to end-to-end communication. The delivery of this key is managed by the security manager. Providing secure links core to the WirelessHART design.

ISA 100.11a Standard Solution

ISA100.11a is a wireless communication standard aiming to provide reliable and secure operation for non-critical monitoring, alerting, and control applications specifically focused to meet the needs of industrial users. ISA100.11a defines a subset of the OSI stack and an organization structure of permitted networks, system management, gateway, and security specifications for low-data-rate wireless connectivity with fixed, portable, and moving devices, including support for very limited power consumption. ISA100.11a utilizes the 802.15.4 PHY layer, provides extensions to the 802.15.4 MAC and defines network layer through application layer functions and services. The medium and part of the data link layer is based on IEEE 802.15.4 2.4GHz DSS PHY and extends the 802.15.4 MAC layer including methods for channel hopping, TDM based bandwidth management, mesh networking (forming, routing and discovery support). The network layer is based on IETF RFC 4944 [16] (transport of IPv6 packets over IEEE 802.15.4) with constraints to focus on security and low power; network layer services include address translation (and compression), fragmentation and routing. The Transport layer is based on UDP per RFC4944, and includes security services. The Application layer provides and object model and object-to-object communication services. Key goals are robustness in harsh industrial applications, coexistence in the presence of other wireless services, and low cost/low complexity deployment. Security services are extended throughout the entire stack and are based on the security offered by IEEE 802.15.4-2006 with symmetrical and asymmetrical keys, configuration, operation and maintenance.

ZigBee Alliance

ZigBee, created by the ZigBee Alliance, is a set of recommendations to facilitate interoperability between wireless low power devices. The relationship between IEEE 802.15.4 and ZigBee is similar to that between IEEE 802.11 and the Wifi Alliance. ZigBee relies today on the PHY and MAC layers of IEEE 802.15.4, will shortly rely on the networking layer of the IETF 6LoWPAN (and likely ROLL), and then builds its industrial profiles on top. We shall briefly focus on the security services offered for its most popular modes, i.e. standard security mode for ZigBee stack 1; and high security mode for ZigBee stack 2 (ZigBee PRO). However, no matter how secure the system is, the reliance on the insecure IEEE 802.15.4 MAC layer makes it a vulnerable design choice.

DASH7 Alliance

DASH7 is the name of a technology promoted by the DASH7 Alliance. It is an emerging embedded low power networking technology using the ISO/IEC 18000-7 standard for active RFID, operating at in the 433MHz unlicensed spectrum. DASH7 provides multi-year battery life, range of up to 2km, low latency for tracking moving objects, small protocol stack, sensor and security support, and data transfer of up to 200 kbit/s. It has found interest in military circles too where the US DoD awarded a $429 million contract for DASH7 devices, making it one of the largest wireless sensor networking deployments in the world.

Wavenis Open Standards Alliance

Wavenis technology relies on ultra-low power RF components allowing for decade long battery-driven operation with applications. Wavenis-OSA is an independent standards alliance whose participants work together to define the Wavenis technology roadmap and to deliver new Wavenis features and capabilities Based on Wavenis features and capabilities, similar to the ZigBee profiles, all new Wavenis adopters can define their own Wavenis profiles to meet specific application requirements: frequency bands, data rate, output power, channel bandwidth, network topology, self-routing and self-healing options, etc. The work is driven by the Technical Committee, composed of the four PHY/MAC, IP, Application and security working groups.

4 Cellular M2M Solutions -IoT of the Future Cellular M2M technology developments are commencing to take momentum, with many companies and various SDOs envisioning future IoT applications to run over such networks. From a rate and range point of view, current cellular systems already meet the M2M requirements; however, from a power consumption point of view, many issues remain open. We will thus briefly discuss various cellular M2M initiatives. Not all MTC applications have the same characteristics and not every optimization is suitable to all applications; therefore, features are defined to provide some structure to the customer and the network is then tuned accordingly to needs. These features are offered on a per subscription basis and include items such as Low Mobility, Time Controlled, Time Tolerant, Packet Switched only, Small Data Transmissions, Mobile originated only, Infrequent Mobile Terminated, MTC Monitoring, Priority Alarm Message (PAM), Secure Connection, Location Specific Trigger, Network Provided destination for Uplink Data, Infrequent transmission, Group Based Policing, Group Based Addressing, etc.

ETSI M2M

Whilst the potential and market value are clear, technical problems -mainly in the area of low-power consumption, support of large amount of nodes and low delays -still remain. These and other problems are currently being addressed by the 3GPP and the EU integrated projected EXALTED [START_REF]The ICT EXALTED project[END_REF].

Conclusions

The same way as highways have changed the way people travel, mobile phones have transformed the way people communicate and the Internet the way computers and, by extension, people connect. This paved the way for an emerging trend which calls for connecting objects around us, thus forming an Internet of Things. This position paper has discussed the history behind developments in this area as well as current and future technologies used to facilitate the needed connectivity breakthrough. Driven by the low power requirements, cooperative techniques will be core to these systems; driven by the unsupervised operation, security will play a pivotal role in the system design, which is complicated by the fact that complexity has to be kept low whilst handling a large amount of objects and devices.

To summarize, pioneering academic work in the early 90s by people like Prof Kris Pister, Berkeley University, US, has ignited a two-decade long research era on low power embedded networks which later became to be known as wireless sensor networks. Spurned by these advances, proprietary commercial solutions have appeared by pioneering companies like Crossbow and Dust Networks.

The success of these companies has been a turning point in that various standardization activities have kicked in to ensure that the plethora of emerging technologies are interoperable to ensure the needed scalability of the emerging IoT. Key standards are those of the IEEE (802.15.4-2006/e/f/g/k) and IETF (6LoW-PAN/ROLL), as well as all the interest groups which have formed around it (Zig-Bee/WirelesHART/ISA100.11) or developed independently (DASH7/ANT+/ WOSA).

Recognizing the disadvantages of these low-power systems, i.e. lack of true ubiquitous coverage and inability to support mobility/roaming, the concept of machine-to-machine (M2M) was born. It facilitates low-power low-rate connectivity between objects over large distances by relying on existing cellular infrastructures. To make this a viable technology, however, much work is needed, notably to reduce the power consumption of the cellular modules. The driving standards dedicated to making this reality are ETSI M2M and 3GPP LTE-M.

Connecting objects around us is hence becoming reality, the more so with the plethora of available short-range and long-range communication technologies.

  .4 (as used by ZigBee); IEEE 802.[START_REF] Shelby | Embedded web services[END_REF].1 (as used by Bluetooth); and IEEE 802.[START_REF] Shelby | Embedded web services[END_REF].11 (as used by Wifi). We subsequently briefly discuss their role in the capillary M2M ecosystem. IEEE 802.15.4. It is maintained by the IEEE 802.15 working group. IEEE 802.15.4-2006 intends to offer the fundamental lower network layers of a type of wireless personal area network (WPAN) which focuses on low-cost, low-speed ubiquitous communication between devices. The link layer is generally very secure, accept that the acknowledgements are sent in clear thus constituting a very serious security hole which has been greatly underestimated by many real-world deployments, including those using ZigBee. The following list summarizes the currently evolving versions:-IEEE 802.[START_REF] Shelby | Embedded web services[END_REF].4e. The IEEE 802.15.4e task group is in charge to modify the MAC sub-layer of IEEE 802.15.4 to meet the requirements of various industrial applications overcoming limitations of the current MACs. The application includes factory automation, process automation, intelligent building, asset tracking, and smart grid. This task group has emphasized three major elements: media management to minimize listening costs, improved security mechanisms, and increased link level reliability through the use of multiple channels, especially in the narrow, lower frequency bands. Now, with the 4e standard approaching ratification, IP networks will be able to improve their performance. Security has been taken very seriously, where the loophole of the unsecured acknowledgement has been rectified. -IEEE 802.15.4k. It addresses applications such as critical infrastructure monitoring. It defines an alternate PHY and only those MAC modifications needed to support its implementation. It is fully concentrated on ultra-low power operation, thus allowing for connectivity where no permanent energy sources are available. IEEE 802.15.4 is the basis for the ZigBee, WirelessHART, and ISA 100.11a specification, each of which further attempts to offer a complete networking solution by developing the upper layers which are not covered by the standard. IEEE 802.15.1. Bluetooth has originally been a proprietary wireless technology developed by Ericsson in 1994 as a wireless alternative to RS-232 data cables. Today Bluetooth is managed by the Bluetooth Special Interest Group with the aim to guarantee true interoperability between Bluetooth-enabled devices; a goal it has not fully lived up to. Bluetooth has however been the forerunner of the IoT with many devices being Bluetooth enabled today. Whilst current realizations of Bluetooth will be part of the IoT arena, latest developments into low-power designs are likely going to be some further steps forward. -IEEE 802.15.1 Bluetooth Low Energy. Bluetooth low energy is an alternative to the Bluetooth standard that was introduced in Bluetooth v4.0, and is aimed at very low power applications running off a coin cell. It has a communication range of a few dozen meters, also operates in the 2.4GHz ISM band, supports data rates of around 200kbps, and draws less than 15mA in transmission. First chips have appeared in late 2010, such as the TI CC2540. -Security Issues. Bluetooth has some serious security concerns not all of which have been addressed in recent standards revisions. Some of these issues are summarized in [11]. IEEE 802.11. In 1997 the IEEE adopted IEEE Standard 802.11-1997, the first wireless LAN (WLAN) standard. This technology is promoted from WiFi Alliance that is a trade association in charge of certifies products if they conform to certain standards of interpretability. Wifi has had a tremendous success in recent years and has also technically been advanced through various amendments. As such, IEEE 802.11 networks are not suitable to low-power networking designs; however, latest developments into low-power solutions may yield some surprises. Notably, if low-power Wifi really takes off, the problem of coverage which IEEE 802.15.4 networks try to overcome by means of multihop will automatically be reduced.

-IEEE 802.15.4f. It has been chartered to define new wireless PHYs and MAC enhancements required to support active RFID system for bi-directional and location determination applications. An active RFID tag is a device which is typically attached to an asset or person with a unique identification and the ability to produce its own radio signal not derived from an external radio signal. Currently, three PHY layers are under discussion.

-IEEE 802.

[START_REF] Shelby | Embedded web services[END_REF]

.4g. The role of IEEE 802.15 Smart Utility Networks (SUN) Task Group 4g is to create a PHY amendment to 802.15.4 to provide a global standard that facilitates very large scale process control applications such as the utility smart-grid network capable of supporting large, geographically diverse networks with minimal infrastructure, with potentially millions of fixed endpoints. It is currently under development.

  (Highway Addressable Remote Transducer)is an open-standard wireless networking technology developed by HART Communication Foundation. It is the wireless version of the HART protocol, which is the most used in the automation and industrial applications which require real time responses. The protocol utilizes a time synchronized, self-organizing, and self-healing mesh architecture. The protocol currently supports operation in the 2.4 GHz ISM Band using IEEE 802.15.4 standard radios. With respect to the stack of Wire-lessHart, the PHY layer is based on the IEEE 802.15.4-2006 whereas the MAC layer has been modified to meet the industrial needs. Its MAC layer is based on TSMP and similar to IEEE 802.15.4e with the only difference that a set of time/frequency hopping patterns are fixed. The frequency hopping approach allows to mitigate fading and interferences in the communication channel.

  ETSI M2M is composed by various manufacturers, operators and service providers, among others. ETSI typically provides the framework, requirements and architecture, whereupon technologies such as 3GPP or IEEE can be used to populate the developed architecture. The work is organized in stages: -Stage 0: Use cases documents. Several use case documents have been developed in parallel, such as M2M requirements for smart metering, health applications, etc. -Stage 1: Services requirements. The thus resulting service requirements have then been developed which aims to unify the requirements of the different use case documents. -Stage 2: Architecture. Here, capabilities and interfaces are developed, as well as message flows, etc. -Stage 3: Refinement. In this stage, the architecture is refined to meet the prior outlined user requirements.

	ETSI M2M currently (Q1 2011) also works on security requirements which in-
	fluence the entire M2M architectural design.
	4.2 3GPP LTE-M
	The concept of M2M has been born out from 2G cellular systems and, early
	adopters of GSM/GPRS data plans, clearly demonstrated the its value. 3GPP
	thus naturally issued in January 2007 a technical report TR 22.868 "Study on
	Facilitating Machine to Machine Communication in 3GPP Systems" which iden-
	tified that a huge market potential for M2M beyond the current market segment.
	However, due to CDMA-based 3G systems not being suitable to low power op-
	erations, there have been little developments until recently. With OFDM-based
	LTE on the horizon, cellular M2M has suddenly become of interest again and
	a set of further documents has been issued lately, e.g. TS 22.368 "Service Re-
	quirements for Machine-Type Communications (MTC)" and TR 23.888 "System
	Improvements for MTC".

Acknowledgements

This work has in part been supported by a France Telecom research contract on M2M security as well as the EU project ICT-258512 EXALTED.

Security and cooperative paradigms have already been playing a pivotal role in their design, and will continue doing so in the years to come.


Sergio Ricciardi
email: sergior@ac.upc.edu

Davide Careglio
email: careglio@ac.upc.edu

Ugo Fiore
email: ufiore@unina.it

Francesco Palmieri
email: fpalmier@unina.it

Germán Santos-Boada

Josep Solé-Pareta

Analyzing Local Strategies f or Energy-Efficient Networking

Keywords: sleep mode, energy-efficiency, power consumption, low power idle, adaptive link rate

Power management strategies that allow network infrastructures to achieve advanced functionalities with limited energy budget are expected to induce significant cost savings and positive effects on the environment, reducing Green House Gases (GHG) emissions. Power consumption can be drastically reduced on individual network elements by temporarily switching off or downclocking unloaded interfaces and line cards. At the state-of-the-art, Adaptive Link Rate (ALR) and Low Power Idle (LPI) are the most effective local-level techniques for lowering power demands during low utilization periods. In this paper, by modeling and analyzing in detail the aforementioned local strategies, we point out that the energy consumption does not depend on the data being transmitted but only depends on the interface link rate, and hence is throughput-independent. In particular, faster interfaces require lower energy per bit than slower interfaces, although, with ALR, slower interfaces require less energy per throughput than faster interfaces. We also note that for current technologies the energy/bit is the same both at 1 Gbps and 10 Gbps, meaning that the increase in the link rate has not been compensated at the same pace by a decrease in the energy consumption.

Introduction

Most of the currently known non-renewable primary energy sources are becoming scarcer and will get exhausted in only some decades. On the other hand, they are highly polluting as their burning process emits large quantity of GHGs causing climate changes and global warming phenomena. The current growth scenario is not sustainable, and international init iatives are trying to decrease the energy consumption and the GHG emissions by 20% for 2020 [1]. In order to achieve such drastic reductions, it is necessary to adopt a radical change in the current lifestyle and business as usual model. For such a transformat ion, the key factor is the use of energy-efficient processes together with energy-aware solutions and policies that increasingly explo it renewable energy sources in providing all the public utility services. In the networking scenario, min iaturizat ion and ICT growing dynamics, effectively described by the Moore's and Gilder's laws [START_REF] Oore | Cramming more components onto integrated circuits[END_REF] [START_REF] Gilder | Telecosm: How Infinite Bandwidth Will Revolutionize Our World[END_REF], have not had the expected counterpart in power consumption reduction. Miniaturization has reduced unit-power consumption but has allowed mo re logic ports to be put into the same space, thus increasing performances and, concomitantly, power utilizat ion (a phenomenon called rebound effect already known as Jevons paradox [START_REF] Jevons | The Coal Question; An Inquiry concerning the Progress of the Nation, and the Probable Exhaustion of our Coalmines[END_REF]). As a consequence, the total power required per node is growing faster and faster. Nevertheless, traffic dynamics often result in a significantly different network usage which presents peaks alternated by low load periods, making roo m for power management techniques that, while satisfying the users' demand, exp loit the low load periods for saving as much energy (and, thus, money) as possible. Accordingly, adaptive power management strategies that can be implemented independently and at different levels of granularity on each network device can be introduced at the local equipment-level to decrease power consumption in the operational phase and bring positive effects for the environment and significant cost savings. Power consumption can be drastically reduced by temporarily switching off or downclocking unloaded interfaces. In this work, such local energy containment strategies have been properly modeled and their behavior analy zed through simulations with the goal of better understanding their operating dynamics, strengths and weaknesses.

Active local strategies for network energy efficiency

Experimental measurements collected fro m several network devices [START_REF] Chabarek | Power awareness in network design and routing[END_REF] show that in current architectures half of the energy consumption is associated to the base system and the other half to the number of installed line interface cards (even if idle). Furthermore, the power consumption of the actual electronic routing/switching matrix and line cards is, quite surprisingly, almost independent from the network load , so that the energy demand of heavily loaded devices is only about 3% greater than tha t of idle ones. These results suggest that it is necessary to develop energy -efficient architectures exploit ing the ability of temporarily switching off or putting into energy saving mode devices or subsystems (e.g. switching fabrics, line cards, I/O ports, etc.) in order to minimize energy consumption whenever possible. Putting entire nodes into sleep mode (per node sleep mode) may be unpractical, especially for large and highly connected ones, since many very expensive transmission links become unused, he nce negating significant capital investments (CAPEX) for the entire duration of the sleep interval. Furthermore, per node sleep mode drastically reduces the overall meshing degree, by limit ing the network reliab ility and partially negates the possibility o f balancing the load on multiple available links/paths. On the other hand, putting into sleep mode only single interfaces (per interface sleep mode) may introduce considerable energy savings in particular when operating at high speeds, since, for example, in a co mmercial off-the-shelf (COTS) Ethernet switch (Catalyst 2970 24port LAN switch) a 1000baseT interface adds about 1.8 W to the overall consumption [START_REF] Christensen | Reducing the Energy Consumption of Networked Devices[END_REF] (Table 1). Per-interface sleeping mechanis ms (ALR and LPI) have been identified [6][7] as viable and effective solutions . In ALR, the ability to dynamically modify the lin k rate according to the real traffic needs is used as a technique to reduce the power consumption. Operating a device at a lower frequency can enable reductions in the energy consumption and also allows the use of dynamic voltage scaling (DVS) for reducing the operating voltage. This allows power to scale cubically and hence energy consumption quadratically with operating frequency [START_REF] Zhai | Theoretical and Practical Limits of Dynamic Voltage Scaling[END_REF]. [START_REF] Tzannes | ADSL2 Helps Slash Power in Broadband Designs[END_REF]. In LPI, transmission on single interface is stopped when there is no data to send and quickly resumed when new packets arrive, in contrast with the continuous IDLE signal used in legacy systems. LPI defines large periods over which no signal is transmitted and small periods during which a signal is transmitted to synchronize the receiver. When operating in low-power mode, the elements in the receiver can be frozen, and then awakened within a few microseconds, as reported in Table 2 [START_REF] Reviriego | Performance Evaluation of Energy Efficient Ethernet[END_REF]. Significant energy savings can be obtained when the involved devices spend a considerable fraction of their time in the low power mode. Although the savings vary fro m device to device, the energy consumption, when the device is in low power mode, can be as low as 10% that the one in active mode. During the transitions back and forth from low power mode there is a considerable increase in energy consumption as many elements in the transceiver have to be active. The actual value will depend on the implementation and possibly ranges fro m 50% to 100% o f the active mode energy consumption. In network environments where packet arrival rates can be highly non-uniform, allowing interface transitions between different operating rates or sleep/active modes can introduce additional packet delay, or even loss, due to the associated transition times. The main issues to be add ressed are the coordination among nodes during the transitions from and to a lo w power consumption state or fro m a transmission rate to another one. In line of principle, these transitions should be kept as transparent as possible to upper layer protocols and applications. Several solutions can usefully exp loit the tradeoff between potential energy savings, performance and transparency. For example, buffering , packet coalescing and coordinated Ethernet strategies may be introduced, to collects packets into small bursts and thereby creating gaps long enough to profitably sleep [START_REF] Christensen | Reducing the Energy Consumption of Networked Devices[END_REF][12] [START_REF] Nedevschi | Reducing network energy consumption via sleeping and rate adaptation[END_REF]. Potential concerns are that buffering will add too much delay across the network and that traffic burstiness will exacerbate the loss.

Modeling and analyzing local energy containme nt methods

In this section, we present a model of the aforementioned local techniques built by interpolating realistic data obtained from the available literature and experimental measurement on available state-of-the-art hardware. We exp loited and analyzed through simu lation some of the most interesting properties and operational features of these techniques when applied to individual non-cooperating network devices. Let G(V,E) be a directed graph representing the physical network topology; V the set of vertices that represent the network nodes and E the set of edges that represent the network links. Note that, as a (unidirectional) lin k is attached to each interface, the set of links E actually coincides with the set of interfaces. Each interface has its own native speed: i E, v i R={10 Mbps, 100 Mbps, 1000 Mbps, 10000 Mbps} represents the native link rate of interface i. The energy/power consumption of interfaces working at their native link rates [START_REF] Christensen | Reducing the Energy Consumption of Networked Devices[END_REF][14] are illustrated in Table 3. Scaling the energy consumption per bit (ESI metric) reveals that the energy consumption for forwarding one bit is not the same fo r every interface but depends on its native link rate. In particu lar, the energy per bit is lower for faster interfaces, mean ing that forwarding one bit on a slower interface requires more energy than on a faster one (besides occupying the link resource for a longer time). We also note how the energy/bit ratio is the same both at 1 Gbps and 10 Gbps, that is, there is no gain in the energy/bit at 10 Gbps (as instead occurs when switching between 10/100 Mbps and between 100/ 1000 Mbps). This behavior is due to the current 10 Gbps technology, whose increase in the link rate (achieved through advanced modulation techniques [START_REF] Nortel | A comparison of next-generation 40-Gbps technologies[END_REF]) has not been compensated at the same pace by a decrease in the energy consumption. As a result, 10 Gbps interfaces consume 10 times more energy than 1 Gbps ones, i.e. the power consumption scales linearly fro m 1 to 10 Gbps. Consequently, the best balance between power consumption and bit rate is reached at 1 Gbps (see Fig. 1). This situation is further stressed when the throughput is not equal to the link rate, which corresponds to an underutilized channel. Our observations confirm that an interface consumes the same power whatever its current throughput is: power consumption is throughput-independent. For this reason, the link rate can be adapted to the current throughput by using ALR with consequent energy savings. However, the IEEE Energy Efficient Ethernet working group, when analyzing the opportunity to adopt ALR or LPI in the 802.3az standard, decided in favor of LPI [START_REF]Energy Efficient Ethernet Task Force[END_REF] since the two strategies have been considered as alternative to be included in the standard. Instead, we evaluate the advantages offered by a co mbination of them and advocate the complementary use of the two strategies. To model the ALR, let us consider the set R={r 1 , …, r m } set of available link rates; i E, r i R represents the working link rate, that is the link rate at which the interface i is currently operating; obviously, it holds that i E, r i v i . In the actual standard an interface may switch only to the set of existing lin k rates , thus R={10 Mbps, 100 Mbps, 1000 Mbps, 10000 Mbps}. According to current technologies, we consider three possible operating modes for interfaces: (1) Off, occurs when the interface is down; (2) LPI, interface up in low power mode and there is no data to transmit; (3) ALR, there is data to transmit and the interface is up at working rate r i R. Let us consider an interface i with native link rate v i and a constant data throughput t d ; then, with A LR, the interface will switch its current link rate to r i : r i-1 < t d r i . In our simu lations we found that, when using the ALR, the power consumption of an interface i depends not only on the working link rate r i but also on the native link rate v i . In other words, transmitting a fixed data throughput t d has different power consumption depending on the interface native link rate v i : in this case, slower interfaces consume less power than faster ones for the same throughput t d , even if they work at the same rate r i . Th is result, quite surprising if we consider that slower interfaces consume more energy per bit than faster ones, may be explained considering that the different technologies adopted for reaching higher link rates [START_REF] Nortel | A comparison of next-generation 40-Gbps technologies[END_REF] to greater fixed power consumption for faster interfaces. In fact, as route rs, also the interfaces have fixed and variable power consumption. The fixed part is always present just for the interface to stay up and accounts for the control circuits, while the variable traffic-proportional power consumption is due to the transceivers. In the following we model such energy consumptions and show a breakdown of the different energy components in a 10 Gbps interface.

In general, to model the fixed and the variable power consumption, we define { (v i ,r j ) | j = 1,2,…,m} where (v i , r j ) is the power consumption of the interface i E with native speed v i R operating at link rate r j R and (v i , r j )< (v i , r k ) j<k. Also, we define n as the fixed power consumption of node n V accounting for its base system, switching matrix, control circuits, etc. Note that n does not include the power consumption of the node interfaces , which is given by the term. Let's see how the term models the power consumption of the interfaces. Let i, j E be two interfaces with respectively native and working link rates (v i , r i ) and (v j , r j ). We can observe that can be characterized fro m the following properties:

) , ( ) , ( : ) , ( i i i i i i r r R R r  (1) E i r i i i , ) , ( (2)
E i r r i i i , ) , ( (3)
Where (1) is the functional defin ition of Ψ, ( 2) and ( 3) state the proportionality of Ψ to the native and working link rates respectively ; 0 ) , ( ) ( :

0 i i i i r r r off r E i (4)
the energy consumption of an interface Off or in LPI is nearly 0;

) , ( ) , ( ) ( : ,

j j i i j i j i r r r r E j i ( 5
)
two interfaces with the same working link rate but different native link rate s have different power consumptions ; ) , ( ) , ( ) ( : ,

j j i i j i j i r r r r E j i ( 6
two interfaces with the same r/v ratio, but with different native/working lin k rates, have different energy consumptions . In order to the interfaces power consumption in a realistic case, we first consider a two-level system, that is a system in which interfaces may work only at two link rates: high and low power modes. The total energy consumption is therefore given by the sum of the energy cost spent in low power mode plus the cost in high mode. That is, the sum of the lo w-power mode instantaneous cost times the total time spent in low-power mode plus the high-power mode instantaneous cost times the total time spent in high-power mode. In the general case, when more than one link rate is available, we div ide the time in intervals so that the link rate stays constant during each interval and record the duration of each interval. We indicate as N the number of time intervals with unchanging state so that there are N-1 link rate transitions with changing states; if t i is the duration of the i-th time interval (in seconds) then the total time considered is given by: . 1

N i i t T (8)
Let r i be the lin k rate in the i-th time interval; the time needed for the link rate transition (assume that every transition requires the same time); c j the instantaneous power consumption at link rate j; a proportionality constant between the instantaneous power demand c j and the corresponding link rate j so that c j = Θ + ζ • j; X hk the power consumption when transitioning fro m lin k rate h to link rate k. Then, for a single interface:

. ) 1 ( ) ( 2 2 1 1 2 1 1 1 1 X N T r X T r T X t r t X t c N i r r N i r r N i i i N i i N i r r N i i r i i i i i i i (9)
For sake of simplicity, we consider all interfaces to behave in the same way. In the realistic hypothesis [START_REF] Christensen | Reducing the Energy Consumption of Networked Devices[END_REF][17] that X hk c max{h,k} = ζ • max{h,k}, r X and eq. ( 9) becomes:

) )( (

N T r . ( 10
) Starting fro m the above energy model, co mb ined with s everal real energy consumption observations available in literature [START_REF] Christensen | Reducing the Energy Consumption of Networked Devices[END_REF][9][14] [START_REF] Christensen | IEEE 802.3az: The Road to Energy Efficient Ethernet[END_REF][18], we simu lated some native speed interfaces working at different link rates. The associated per bit energy consumption values are shown in the chart of Fig. 2. We can see how the energy per bit depends both from the native link rate of the interfaces and on their actual working link rate. Furthermore, we can notice how the energy consumption of native high-speed interfaces does not vary much when switching to lower link rates, whilst the energy consumption of native low-speed interfaces is highly variable, especially when working at lo w rates. In Fig. 3 we p lotted the power consumption breakdown for a s imu lated routing device modeled with interfaces at 10 Gbps. The base systems accounts for approximately 50% of the total energy consumption while the interfaces (fixed and variable parts) accounts for the other half. As we can see, the larger interfaces energy consumption is due to the fixed part that is independent from the link rate, while only the remain ing 15% of energy is due to the rate-proportional energy consumption. This scenario suggests that between LPI and ALR it is preferab le to use LPI: when there is no data to transmit, A LR sends the continuous idle signal (which is everything but idle), whilst LPI enters the lower power consumption mode, thus consuming lo wer energy; furthermore, the auto-negotiation mechanism of ALR may be in the order of ms, while the sleep/wake-up transitions of LPI only requires some s for 10 Gbps interfaces [START_REF] Christensen | IEEE 802.3az: The Road to Energy Efficient Ethernet[END_REF][19] and, consequently, lower delay; finally, t ransmitting the same amount of data in A LR takes longer time than LPI, since in LPI the transmission is realized at the maximu m rate, instead in ALR the transmissions is lowered to best fit the throughput , thus occupying the resources for longer time. The Energy-Efficient Ethernet working group has recently adopted the LPI as power management solution for IEEE 802.3az [START_REF]Energy Efficient Ethernet Task Force[END_REF]. Furthermore, the difference in energy consumption between interfaces with different native link rates suggests also the possibility for and advanced ALR with circuit overprovisioning, i.e. a network interface may be provisioned with different circuits, say a low and a h igh speed one, and may switch between one or other according to the required data throughput. This solution, at the expense of increased capital expenditures (CAPEX) for the additional hardware, may lead to decreased operational costs (OPEX) due to the lower fixed power consumption of slow circuits. Finally, also another possibility is given by the heterogeneity of the equipment in a network. In fact, such heterogeneity may be explo ited by a global load-balancing schema, implemented as part of a routing and wavelength assignment algorithm, which tries to distribute the connection requests in such a way that the overall network energy consumption is minimized. A best-fit allocation scheme may be implemented in order to close match the bandwidth demands with the interfaces native link rates, so that the fixed power consumption cost is amort ized and the maximu m efficiency is reached.

Conclusions

By modeling and analyzing the available local energy efficiency strategies, we observed that faster interfaces consume less energy per bit than slower ones, but also that, lowering the interfaces working lin k rates during low utilizat ion periods, does not lead to the same power savings for all interfaces but dep ends on the interface native link rate. Nat ive slower interfaces (e.g., 100 Mbps) consume less power than native faster interfaces (e.g., 1,000 Mbps) for transmitting the same throughput (e.g., 80 Mbps) due to the higher fixed power consumption that comes with faster interfaces. In other words, while the energy-per-bit is lower for faster interfaces, with the ALR the energy-per-throughput is lower for slower interfaces. Furthermore, we observed that the energy consumption of native high -speed interfaces does not vary much when switching to lower link rates, whilst the energy consumption of native low-speed interfaces is highly variable, especially when working at low rates. Finally, we point out that the different fixed and variable power consumptions of int erfaces may be exp loited by circuit over-provisioning techniques as well as load balancing schemes for minimizing the overall energy consumption and, thus, network operational costs.

Fig. 1 .

 1 Fig. 1. Energy and power consumption of different interfaces working at their native speeds.

  ) two interfaces with the same native link rate but different working link rates have different power consumptions ;

Fig. 2 .

 2 Fig. 2. Energy per bit for native interfaces operating at different link rates (interpolation obtained by putting not defined values to 0).

Fig. 3 .

 3 Fig. 3. Power consumption breakdown for a router with interfaces at native link rate v = 10 Gbps and working link rates r i = 10 i M bps.

Table 1 .

 1 COTS switch power consumption with varying number of interfaces.

	# Active Interfaces	10BaseT	100baseTX	1000baseT
	0	69.1 W	69.1 W	69.1 W
	2	70.2 W	70.1 W	72.9 W
	4	71.1 W	70.0 W	76.7 W
	6	71.6 W	71.1 W	80.2 W
	8	71.9 W	71.9W	83.7 W
	For example, the Intel 82541PI Gigabit Ethernet Controller draining about 1 W at 1
	Gbps full operation is able to support a smart power down feature by turning off PHY
	if no signal is present on link and drops the link rate to 10 Mbps when a reduction of
	energy consumption is required [9]. A lso in the last mile, the ADSL2 standard (ITU
	G.992.3, G.922.4, G.992.5) is able to support multiple data rates corresponding to
	different link states (L0: full rate, L2: reduced rate, L3: lin k off) for power
	management sake

Table 2 .

 2 Common wake-on-arrival strategy parameters for different interfaces technologies.

	Technology	Wakeup Time	Sleep Time	Average Power savings
	100baseTX	30 s	100 s	90%
	1000baseT	16 s	182 s	90%
	10GbaseT	4.16 s	2.88 s	90%

Table 3 .

 3 Energy and power consumption of interfaces working at native speeds.

	Native link	Power per	Energy S caling Index	Energy Consumption Rate
	rate v i	interface	(ES I) -Energy per bit	(ECR) -Power per Gbps
	10 M bps	0.1 W	10 nJ/bit	10 W/Gbps
	100 M bps	0.2 W	2 nJ/bit	2 W/Gbps
	1,000 M bps	0.5 W	0.5 nJ/bit	0.5 W/Gbps
	10,000 M bps	5.0 W	0.5 nJ/bit	0.5 W/Gbps

ESI and ECR are different energy/power consumption metrics that may be reduced to equivalent values, in fact it holds that: W/Gbps = (J/s) / (Gbit/s) = J/Gbit = nJ/bit.

Acknowledgme nts

This work was supported in part by the COST Action IC0804 on Energy Efficiency in Large Scale Distributed Systems, the Spanish Ministry of Science and Innovation under the DOMINO project (TEC2010-18522), the Catalan Govern ment under the contract SGR 1140 and the DIUE/ ESF under the grant FI-201000740.