blockchain [16], [17], acting as a universal digital ledger to store the security ... 9, 2015. [4] F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta, and P. Popovski,.
Trusted D2D-based Data Uploading in In-band Narrowband-IoT with Social Awareness L. Militano1 , A. Orsino1 , G. Araniti1 , M. Nitti2 , L. Atzori2 , A. Iera1 1
University Mediterranea of Reggio Calabria, DIIES Department, Italy e-mail: {leonardo.militano|antonino.orsino|araniti|antonio.iera}@unirc.it 2 University of Cagliari, DIEE Department, Italy, e-mail: {michele.nitti|l.atzori}@diee.unica.it
Abstract—Fifth generation (5G) systems are expected to introduce a revolution in the ICT domain with innovative networking features, such as device-to-device (D2D) communications. Accordingly, in-proximity devices directly communicate with each other, thus avoiding routing the data across the network infrastructure. This innovative technology is deemed to be also of high relevance to support effective heterogeneous objects interconnection within future IoT ecosystems. However, several open challenges shall be solved to achieve a seamless and reliable deployment of proximity-based communications. In this paper, we give a contribution to trust and security enhancements for opportunistic hop-by-hop forwarding schemes that rely on cellular D2D communications. To tackle the presence of malicious nodes in the network, reliability and reputation notions are introduced to model the level of trust among involved devices. To this aim, social-awareness of devices is accounted for, to better support D2D-based multihop content uploading. Our simulative results in small-scale IoT environments, demonstrate that data loss due to malicious nodes can be drastically reduced and gains in uploading time be reached with the proposed solution. Index Terms—Trustworthiness, Security, D2D communications, 5G, IoT, M2M.
I. I NTRODUCTION The Internet of Things (IoT) is a promising paradigm, which integrates heterogeneous and pervasive objects with different connecting and computing capabilities, from dummy entities capable of providing just their positions (through attached tags) to objects capable of sensing the status of an environment, processing the data and sending the results to the Cloud. Given the complexity and the challenges within the IoT, experts in the field share the opinion that the next-to-come fifth generation (5G) cellular systems will be a strong boost for the actual IoT deployment [1]. This vision is strongly sustained by the fervent activities, aimed at designing 5G wireless systems able to meet the requirements of IoT ecosystem, carried on worldwide by academic, industrial, and standardization bodies [2], [3]. Various types of interactions may coexist within an IoT ecosystem, including Machine-to-Machine (M2M), machine-to-human, human-to-machine, and machine-to-cloud, which all require ubiquitous connectivity. To this purpose, Device-to-Device (D2D) appears as a key communication paradigm to support heterogeneous objects interconnection [4]. Indeed, most of the IoT objects will have constrained resources in terms of energy, communication means, storage capabilities and computational power. In this context, short-range D2D cooperation among devices, may introduce benefits in terms of
improved spectrum utilization, higher throughput, and lower energy consumption. However, several open issues shall be solved to achieve a seamless, effective, and reliable deployment of proximity-based communications for IoT systems [5]. For an effective implementation of proximity communications, one of the most important challenges is to understand how the node-originated information shall be processed so as to build a reliable system on the basis of the objects’ behavior, namely the need of trustworthiness [6]. Indeed, in realistic scenarios, where also human interactions and human behavior are to be considered, the presence of malicious nodes in the network is a constant threat for a successful cooperation. Accordingly, without effective trust management foundations, attacks and malfunctions are likely to outweigh any possible cooperation benefits [6]. For the reference scenario considered in this paper, we consider that groups of devices in close proximity are willing to upload some data to the Cloud or to a central server and end-users may not be aware of whom they are going to forward the data to. Typical examples for these scenarios are small-scale crowded environments (e.g., stadiums, university campuses, music theatres, or fairs) where devices can exploit opportunistic data forwarding over other devices in proximity. In these contexts, malicious nodes may decide to drop the data packets they are expected to forward without informing the interested users. To cope with these threats, reliability and reputation notions will be considered to model the level of trust among the involved entities. By taking inspiration from recent Social Internet of Things (SIoT) models, in this paper we consider the sociality level of the devices to model the reliability of the communication [7]. The historical reputation of the cooperative users will also be considered to offer rational users the possibility to filter out untrusted users and avoid unsuccessful opportunistic hop-by-hop D2D interactions. More specifically, we consider LTE-A (Long Term Evolution-Advanced) scenarios where multihop cooperative uploading is implemented over cellular D2D resources [8]. The trust constraints for a successful D2D-based content uploading are modeled, where sociality among devices, as a measure of reliability, and historical reputation are included. The objective is to define multihop D2D topologies that meet the constraints of reciprocal UEs proximity for the direct links activation and of a adequate trust level among the cooperating devices. Through simulationbased performance evaluations, we show that it is possible to
obtain a significant reduction in data losses due to malicious behavior of the involved devices. Moreover, the trust-based and social-aware solution proposed also guarantees higher gains in terms of uploading time experienced by the devices taking part in the cooperative D2D-based content uploading. The remainder of the paper is organized as follows: Section II introduces the research background and motivation; the algorithmic solution for the definition of trusted D2D cooperative topologies is given in Section III; a detailed description of the proposed trust model and the sociality concepts is given in Section IV; numerical results and conclusions are provided in Section V and Section VI. II. BACKGROUND AND M OTIVATION As recently discussed in [1], cellular networks are being considered as promising candidates to allow effective internetworking of IoT devices, thanks to the benefits these offer in terms of enhanced coverage, high data rate, low latency, low cost per bit, high spectrum efficiency. In this context, the Third Generation Partnership Project (3GPP) has introduced novel features to support machine-type communications (MTC) [9] by accounting for the intrinsic battery-constrained capabilities of IoT devices and the related traffic patterns (e.g., small data packets). More recently, in September 2015, 3GPP decided to standardize narrowband IoT (NB-IOT), a new narrowband radio technology to address the requirements of the Internet of Things (IoT). This new technology will provide improved indoor coverage, support of massive number of low throughput devices, low delay sensitivity, ultra-low device cost, low device power consumption and optimized network architecture. In particular, the technology can be deployed “in-band” using the resource blocks within a normal LTE carrier, or in the unused resource blocks within a LTE carriers guard-band, or “standalone” for deployments in dedicated spectrum [10]. At the same time, the efforts of academic, industrial and standardization bodies are pushing towards the fulfillment of IoT requirements through the next-to-come fifth generation (5G) wireless systems [11]. In the evolution towards 5G cellular systems, D2D communications will play an undoubted key role in the IoT/5G integration [5]. D2D communications refers to the paradigm that foresees devices communicating directly with each other without routing the data paths across a network infrastructure. In wireless scenarios this means bypassing the base station (BS) or access point (AP) and relaying on direct inter-device connections established over either cellular resources or alternative over Wi-Fi/Bluetooth technologies. This approach has recently gained momentum as a means to extend the coverage and overcome the limitations of conventional cellular systems. The main benefits it can introduce are [12]: (i) high data rate transmissions supported also by devices remotely located from the BS/AP; (ii) reliable communications also in case of network failure, as may be the case of disaster scenarios; (iii) energy saving since devices in close proximity can interact at a lower transmission power level; (iv) traffic offloading that reduces the overall number of
Fig. 1.
D2D communications in 5G IoT networks.
cellular connections; (v) heterogeneous connectivity accounting that direct communications among devices does not only rely on cellular radio interface, but can be established through alternative radio technologies; (vi) instantaneous communications between a set of devices in the same way that walkietalkies are used for emergency services. These features make D2D a very appealing solution to satisfy also the exacting requirements put by IoT in emerging 5G network scenarios where either local, uplink or downlink transmissions may occur, see Fig. 1. Proximity communications enabled by D2D communications represent a fertile ground for use cases where devices detect their mutual proximity and subsequently trigger different services, such as social interactions and gaming, advertisements, local information exchange, etc. By means of D2D discovery and communication functions, for instance, a user can find other near users to share data (multimedia content, environmental sensing, traffic condition, etc.), play interactive games, and so on. In applications for public safety support and emergency handling, devices can provide at least local connectivity in case of damage to the network infrastructure. Similarly, D2D communications may contribute to solve problems in emerging wireless communication scenarios, such as vehicle-to-vehicle (V2V) communication in Intelligent Traffic Systems (ITS) for traffic control/safety applications, or indirect indoor localization. In this paper, the potential benefits of D2D communications are exploited for cooperative content uploading from a set of devices to the cellular base station, through shortrange multihop relaying. A necessary condition for such a “cooperative” relaying solution to bring benefits compared to the ”non-cooperative” case, is that the link quality of the multihop D2D channels is higher than the one of the separate links to the Internet. This condition is more likely to occur in non-isotropic propagation environments with obstacles where non line of sight (NLOS) conditions may cause partial and temporary out-of-coverage conditions, as it is the case of the Internet of Vehicles, an instance of the IoT, where objects are represented by cars [13]. III. C OOPERATIVE M ULTIHOP D2D- BASED DATA U PLOADING We consider a single LTE-A cell with multiple devices interested in uploading their content to the Internet, adopting
an in-band NB-IOT solution. Data uploading according to the traditional cellular-mode is performed through the activation of separate links from each device to the eNodeB. The alternative solution we propose in this paper is a cooperative content uploading under control of the eNodeB (i.e., networkassisted D2D), where the UEs organize themselves to form a “logical multihop D2D topology” and cooperate in uploading the content generated by all the involved devices [8]. In the formed cooperative topology, the UE (User Equipment) located farther from the base station relay their content to a nearby UE and only the UE playing the head-end role in the topology, the so-called gateway, is in charge of uploading all the contents received from the other UEs to the eNodeB. The UE with the best link quality in the coalition is chosen as gateway and may receive (if needed) all the radio resources that would have been separately allocated by the eNodeB to the UEs in the coalition. All intermediate UEs in the topology also act as relays for the contents received from the upstream UEs, thus benefiting of the higher quality of the short D2D links w.r.t. the direct cellular link. In the most general configuration, each relay has one or more links active to receive data from the preceding sources in the tree topology, and one single link active to relay data (its own generated traffic and the traffic from the incoming D2D links) to the subsequent UE in the topology. Each UE operates in half-duplex mode; thus, it either receives or transmits in a given transmission time interval. We consider a reasonable assumption for rational self-interested devices, that each UE uploads its own generated content first and then the content received by the preceding UEs in the topology. In particular, the transmission starts only after that the generic UE has received the whole content (in other words, UEs use the decode-and-forward relaying protocol). In realistic scenarios, end-users may not be aware of whom they are going to be connected to and malicious devices may decide to drop data packets they are expected to forward without informing the interested users. To cope with these threats, when defining the cooperative topologies, countermeasures must be considered to offer rational users the possibility to filter out untrusted users and avoid unsuccessful opportunistic hop-by-hop D2D interactions. The solution we propose for an effective and trusted D2Dbased data uploading can be summarized as follows: CQI collection: the eNodeB collects the CQI values from each UE, relevant to the direct links with all its neighbors and to the uplink toward the eNodeB. Virtual resource allocation: the eNodeB computes the radio resources allocated to the UEs as if they were transmitting separately on the uplink according to the adopted scheduling policy. These resources are “virtually” allocated since UEs may form a coalition (i.e., a cooperative multihop topology) and can be used as the pool of resources allocated to the gateway. Cooperative coalition formation: the eNodeB implements the coalition formation algorithm to determine the set of stable cooperative topologies for D2D-based multihop data uploading, the roles of each node in the coalition, and the
relative routing path. For details on the adopted game theoretic formulation and the merge and split algorithm for convergence to stable coalitions please refer to [8], details are not reported here due to length constraints. In particular, the preference for a device to join a coalition is defined in terms of data uploading time. A detailed modeling of the uploading time for all UEs in a multihop D2D coalition has been derived in [8]. The constraints set for the possible coalitions to form include: (i) the D2D coverage constraints for the devices involved in a routing path in each coalition, and (ii) trustworthiness constraints for the devices in a coalition. We consider a feasibility threshold F T which is the minimum value of trust for each D2D link in a coalition. We say that coalition is not feasible if the resulting topology foresees at least one link i → j that does not meet the following constraint: pti,j · di,j ≥ F T
(1)
where pti,j → [0, 1] is the player trust that player i associates to player j as defined in Section (IV), where a player is a device being part of a given coalition, whereas the second term di,j is a binary function taking the value of 0 if the users i and j are not in proximity, and the value of 1 otherwise. Data transmission configuration: Once the wished coalitions are formed in the cell, the eNodeB determines the radio resources assigned to the gateway and to each D2D link and transmits all the information to the UEs so that the transmissions can start. To avoid interferences between transmissions, the devices in different cooperative topologies are always allocated to orthogonal frequency resources by the scheduler at the eNodeB. In particular, we consider a Proportional Fairness scheduler at the eNodeB, in charge of assigning the adequate number of RBs1 to each scheduled user and selecting the Modulation and Coding Scheme (MCS) for each RB based on the Channel Quality Indicator (CQI) feedback from the UEs. As for the D2D communications, we assume that the UEs that simultaneously transmit within the same cooperative group will adopt different RBs to avoid any mutual interference (this leads to a worst case analysis and better results can be obtained with improved interference management solutions). IV. T HE S OCIAL - AWARE T RUST M ODEL The trust model we propose exploits social relationships among the involved devices (to improve device reliability) and recommendation exchange (to the purpose of reputation definition). In particular, we consider the potential of the SIoT model to embrace the social networking concepts and build trustworthy relationships among the devices [7]. Mobility patterns and relevant context can be considered to configure the appropriate forms of socialization among the UEs. Specifically, the so-called co-location object relationships (C-LOR) and co-work object relationships (C-WOR) are established between devices in a similar manner as among humans, when 1 The RB corresponds to the smallest time frequency resource that can be allocated to a user (12 sub-carriers) in LTE. For example, a channel bandwidth of 20Mhz corresponds to 100 RB.
they share personal (e.g., cohabitation) or public (e.g., work) experiences. Another type of relationship may be defined for the objects owned by a single user, which is named ownership object relationship (OOR). The parental object relationship (POR) is defined among similar devices built in the same period by the same manufacturer, where the production batch is considered a family. Finally, the social object relationship (SOR) is established when objects come into contact, sporadically or continuously, for reasons related to relations among their owners. The eNodeB serves as a trusted third party supporting the coalition formation among the interested players. To this aim, the eNodeB will store up-to-date information about the reliability, reputation and trust parameters relative to the users in the cell. In particular, we assume that the eNodeB will store a so-called player trust matrix (PTM) with information relevant to every couple of devices in the network. The information stored in the PTM will be used whenever a new trustbased coalition formation step is considered for cooperative content uploading by eNodeB. After each cooperative content uploading, the eNodeB will send an acknowledgment to the respective source nodes. Moreover, it will detect malicious behaviors in the coalitions based on data loss levels and update the reliability level of the interested players. We assume the data amount for the information exchange between source nodes and the eNodeB to be small and the data to be sent over control channels. Compared to the main content size to be uploaded by the source nodes, control messages are very small and the corresponding transmission time and energy consumption are assumed to be negligible. Let i → j be a generic D2D link in the coalition being considered at time t during the coalition formation algorithm, where node j is expected to act as relay/gateway for the data he receives from node i (its own and the preceding nodes in the topology); we consider i, j as the truster and the trustee respectively. The parameters the eNodeB uses to determine the level of trust for the link, and contained in the PTM, are: Social player reliability (spri,j ): is the reliability that node i assigns to player j only based on the social relationship that links the two players and is a value in [0, 1]; t Player reliability (pri,j ): is the reliability that node i assigns to player j at time instant t. This is a subjective evaluation of the players which we consider to be influenced by the social player reliability and the outcome of past interactions where player j was expected to act as relay/gateway for player i. Specifically, the player reliability value is a real number ranging in [0, 1] with the 0/1 values meaning that i judges player j as completely unreliable/reliable. Recommendation reliability (rri,j ): is the reliability that node i assigns to the recommendations provided by player j about other players in the network. Also this parameter is based on a subjective evaluation of the interested player and in our model it is influenced by the social relationship between the interested UEs. It is a real number ranging in [0, 1] with the 0/1 values meaning that i judges the recommendation received from j as completely unreliable/reliable.
Player reputation (ppti,j ): is the reputation that player i assigns to player j for the specific service based on the recommendations provided by other players in the network at time instant t. Similar to the previous parameters, it is a real number ranging in [0, 1] with the 0/1 values meaning that the reputation assigned by i to player j based on the recommendation of the other devices is minimum/maximum. Player trust (ptti,j ): is the trust level that player i associates to player j at time t, which is the final parameter that determines whether player i is willing to entrust player j as relay/gateway node in a D2D-based cooperative coalition. t This is a weighted combination of the reliability pri,j and the t reputation ppi,j for the player. The final player trust value is a real number in [0, 1] with the 0/1 values meaning that player j is considered as completely untrusted/trusted by i. a) Player reliability: The player reliability is updated over time based on the past experience related to cooperative interactions where a player was expected to act as relay/gateway for the data sent by a precedent player in the formed D2D topology. To consider the past experience, we assume that for each cooperative interaction the eNodeB sends an acknowledgment to the source nodes in the coalition with information about the data being successfully received. However, based on this simple information, it is not possible for the eNodeB to determine which node in the cooperative topology has actually dropped the data. In our proposal we assume that the eNodeB will associate the outcome value δd to the node j that was entrusted by node i as relay/gateway forming a D2D link i → j. Thus, at time t = 0 when no cooperation history exists, the only information the interested devices can exploit for judging the player reliability is social player reliability (spri,j ). This is set according to predefined values as reported in Table I2 . At subsequent time instants t t > 0 the player reliability pri,j is computed by taking into consideration the past interactions between i and j, with j acting as relay/gateway for data sent by i. We define with ∆ti,j = {δ1 , . . . , δd . . . δD } the set of past interactions registered until time t, where the generic δd ∈ [0, 1] ∈ R is a value measuring the outcome of the cooperative interaction. This is equal to the total percentage of data that has been successfully forwarded by node j and reached the eNodeB. t Thus, the player reliability pri,j is computed as follows:
t pri,j =
spri,j α · spri,j + (1 − α) ·
t=0 P
d∈∆t i,j |∆ti,j |
δd
t>0
(2)
where α ∈ [0, 1] is a real number used as weighting factor to give more or less importance to the initial sociality relationship between the involved nodes. b) Player reputation: The player reputation is based on the opinions of the community in the network. This information stored in the PTM at the eNodeB is updated after each cooperative interaction. Let us consider a player i asking 2 If two communicating entities are tied by two or more types of relationships, the strongest tie with the highest factor has to be considered [7].
TABLE I P LAYER AND RECOMMENDATION RELIABILITY VALUES ASSOCIATED TO THE SOCIAL RELATIONSHIP BETWEEN DEVICES . Relationship Ownership object relationship (OOR) Co-location object relationship (C-LOR) Co-work object relationship (C-WOR) Social object relationship (SOR) Parental object relationship (POR) No relationship
Description Objects owned by the same person Objects sharing personal experiences Objects sharing public experiences Objects in contact for owner’s relations Objects with production relations
an opinion about player j and let K ⊆ N \{i} be the set of players which provide an opinion about player j to player i, where N is the total set of devices in the network. The opinion player k will provide is its own measure of trust about player j at time instant t, i.e., pttk,j . The opinion received from the other players in the network is weighted by a confidence factor the requesting player has about the received recommendation. This weighting factor is the so-called recommendation reliability (rri,k ). In our model the recommendation reliability is set according to the social relationship between the involved devices and its value is reported in Table I. Noteworthy, we assume that the recommendation reliability has a lower value w.r.t. social player reliability in general. The reason for this choice is that the recommendation received by a socially related device may be influenced by the outcome of past cooperative iterations with other devices which affected the ability to provide an objective recommendation. Thus, the player reputation at time t is computed as follows: P ppti,j
=
k∈K
rri,k · pttk,j P rri,k
Social player reliability (spri,j ) 1 0.8 0.7 0.6 0.5 0.1
Recomm. reliability (rri,j ) 0.9 0.6 0.5 0.5 0.4 0.1
TABLE II M AIN S IMULATION PARAMETERS Parameter Cell radius Maximum D2D link coverage TTI TDD configuration Carrier Frequency Cellular transmission power consumption D2D power consumption CQI-MCS mapping for D2D links Noise power Cellular link model D2D link model Content size Weighting factors α = β # Malicious nodes Simulation time # of Runs
Value 500 m 100 m 1 ms 0 2.1 GHz 23 dBm -19 dBm [14] -174 dBm/Hz Rayleigh fading channel Rician fading channel 10-100 MB 0.5 30% of UEs 100 s 500
(3)
k∈K
c) Player trust: Based on the notions introduced above, player i can finally determine the player trust value ptti,j it associates to player j at time instant t. This is a combination t of the player reliability value at time t, (pri,j ), and the player t reputation (ppi,j ), suitably weighted by a real coefficient β ranging in [0, 1] ∈ R: ( ptti,j =
0.5 t β · pri,j + (1 − β) · ppti,j
t=0 t>0
(4)
Note that for new coming nodes at time t = 0, the initial trust is set to 0.5 to contrast whitewashing strategies where a dishonest adviser is able to whitewash its low trustworthiness by starting a new account with the initial trustworthiness value. V. P ERFORMANCE E VALUATION In this section we evaluate the performance of the proposed solution and its ability to cope with malicious nodes. The assessment campaign is conducted by following the system model guidelines in [15]. The main simulation parameters are listed in Table II. A single cell with available radio resources RB = 50 is considered, wherein up to 20 UEs are uniformly distributed. We consider that 30% of the UEs are malicious, which means they drop all the incoming data from
(a) Data loss. Fig. 2.
(b) Uploading time gain.
Impact of F T , with 20 UEs and content size per UE set to 50MB.
the upstreams in the cooperating topology. We compare our trust-based proposal with a so-called basic solution where the coalition formation completely disregards the trustworthiness analysis. The performance parameters we focus on are: data losses and average data uploading time gain achieved by the cooperative upload w.r.t. a pure cellular upload modality. The first analysis we focus the attention on is the impact of the feasibility threshold (F T ) on the coalition formation process. In particular, we consider three different values for the F T parameter, which, we recall, gives the minimum level of trust required for each D2D link within a coalition (in a study case with 20 UEs and content size for each UE set to 50MB). As we can observe from Fig. 2 (a), the total data loss strongly depends on this value since for higher F T values the proposed solution is able to better filter out the malicious nodes in the network coalition. Noteworthy, in all cases our solution
(a) Data loss (50MB).
(b) Time gain (50MB). Fig. 3.
(c) Data loss (20 UEs).
(d) Time gain (20 UEs).
Impact of the number of UEs and the content size (F T = 0.6).
outperforms the basic solution. In particular, we achieve a reduction between 6% and 91% w.r.t. the basic approach, for a F T value set respectively to 0.4 and 0.8. When considering the uploading time gain for the cooperating devices, in Fig. 2 (b) we observe that, the proposed trustbased solution always guarantees better results. In particular, with F T = 0.8 the trust-based solution guarantees 37% uploading time gain, whereas the basic solution reaches a lower gain, i.e., a 23% gain. The motivation of this result is that given the coalitions that are being formed, when malicious nodes are involved in one of these coalitions all the users within the coalition achieve an uploading time gain that is equal to zero because no data are uploaded to the eNodeB. Considering our trust-based approach, instead, users tend to form coalition only among trusted users increasing the probability for the content to successfully reach the eNodeB. Finally, in Fig. 3 we show the impact of the content size for the UEs in the range of [10 − 100]M B and of the number of UEs in the range of [2 − 20], when setting the F T parameter to a sample value of 0.6. Looking at the plots, we observe that the gap between the basic and the trust-based solution increases linearly with the number of UEs and the content size. VI. C ONCLUSION We proposed a trust-based solutions for effective D2Denhanced cooperative content uploading in narrowband-IoT cellular environments. To limit the impact of the malicious nodes, social-awareness has been modeled to evaluate the reliability for the nodes and to suitably weight the recommendations exchange for the reputation definition. A simulative analysis validated the proposed solution in a wide range of settings for small-scale IoT scenarios. The results showed how the social based trusted solution guarantees higher gains in the content uploading time and has the ability to increase the amount of successful cooperative interactions by filtering out the malicious nodes. In our future work we will investigate the proposed solution in other networking contexts, where D2D communications in unlicensed bands are used. The heterogeneous and large-scale IoT ecosystem may require enhanced solutions for distributed
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