IoT-Cloud Service Optimization in Next Generation Smart Environments

Transcription

1 1 IoT-Clod Service Optimization in Next Generation Smart Environments Marc Barcelo, Alejandro Correa, Jaime Llorca, Antonia M. Tlino, Jose Lopez Vicario, Antoni Morell Universidad Atonoma de Barcelona, Spain, {marc.barcelo, alejandro.correa, jose.vicario, Nokia Bell Labs, NJ, USA, {jaime.llorca, Universita degli Stdi di Napoli Federico II, Italy, Abstract The impact of the Internet of Things (IoT) on the evoltion towards next generation smart environments (e.g., smart homes, bildings, cities) will largely depend on the efficient integration of IoT and clod compting technologies. With the predicted explosion in the nmber of connected devices and IoT services, crrent centralized clod architectres, which tend to consolidate compting and storage resorces into a few large data centers, will inevitably lead to excessive network load, end-toend service latencies, and overall power consmption. Thanks to recent advances in network virtalization and programmability, highly distribted clod networking architectres are a promising soltion to efficiently host, manage, and optimize next generation IoT services in smart environments. In this paper, we mathematically formlate the service distribtion problem (SDP) in IoT- Clod networks, referred to as the IoT-CSDP, as a minimm cost mixed-cast flow problem that can be efficiently solved via linear programming. We focs on energy consmption as the major driver of today s network and clod operational costs and characterize the heterogeneos set of IoT-Clod network resorces according to their associated sensing, compting, and transport capacity and energy efficiency. Or reslts show that, when properly optimized, the flexibility of IoT-Clod networks can be efficiently exploited to deliver a wide range of IoT services in the context of next generation smart environments, while significantly redcing overall power consmption. Index Terms Internet of Things, clod, edge compting, service optimization, energy efficiency, smart cities I. INTRODUCTION The Internet of Things (IoT) refers to the network of objects, devices, machines, vehicles, bildings, and other physical systems with embedded sensing, compting, and commnication capabilities, that sense and share real-time information abot the physical world [1], [2]. When connecting the IoT to the Clod, vast amonts of data collected from mltiple locations can be processed and analyzed to create meaningfl information for the end sers. At the same time, the intrinsic limitations of lightweight mobile devices (e.g., battery life, processing power, storage capacity) can be alleviated by taking advantage of the extensive resorces in the Clod [3], [4]. The reslting IoT-Clod paradigm enables a new breed of services and applications (e.g., health system monitoring, traffic control, energy management, vehiclar networking), expected to define the essence of next generation smart environments (e.g., smart homes, smart bildings, smart cities, smart grids) [5], [6]. However, with the predicted explosion in the nmber of IoT services and connected devices, traditional centralized clod architectres, in which compting and storage resorces are concentrated in a few large data centers, will inevitably lead to excessive network load, end-to-end service latencies, and nbearable energy costs [7], [8]. In order to meet the tight QoS reqirements associated with real-time IoT applications while maximizing overall efficiency, clod architectres are becoming increasingly distribted [7], with the presence of small clod nodes at the edge of the network, referred to as clodlets [9], micro-clods [10], fog nodes [11], or simply edge clod nodes [12]. The reslting clod infrastrctre may be organized into hierarchical layers, each with different compting and storage capabilities [13]. In addition, thanks to recent advances in mobile compting [14] and device layer virtalization [15], even end devices can become part of this highly distribted networked compte platform, creating what we refer to as IoT-Clod networks. These are highly virtalized heterogeneos platforms that offer compting, storage, and networking services with nprecedented distribtion and proximity to the end ser. Compared to traditional centralized clods, IoT-Clod networks provide increased flexibility in the allocation of resorces to IoT services, and a clear advantage in meeting their stringent latency, mobility, and locationawareness constraints. In the IoT-Clod network paradigm, services take the form of virtal network slices of the shared physical infrastrctre, which can flexibly and elastically consme compte, storage, and network resorces according to changing service reqirements. A key aspect driving both performance and efficiency is the actal placement of the service fnctions, as well as the roting of network flows throgh the appropriate fnction instances. In the context of clod networks, [16] introdced the clod service distribtion problem (CSDP), where the goal is to find the placement of virtal fnctions and the roting of network flows that meets QoS reqirements, satisfies resorce capacities and minimizes overall infrastrctre cost. The athors provide a network-flow based linear programming soltion that optimizes the distribtion of clod services with arbitrary fnction relationships (e.g., service chaining) over a distribted clod network. However, the work in [16] does not take into accont the increased flexibility that arises when introdcing the access network and the device layer into the virtalized infrastrctre, aspects that are critical for the efficient delivery of IoT services [17]. In this paper, we formalize the IoT-CSDP as a service placement and resorce allocation problem that goes beyond

2 2 traditional information services and clod architectres to inclde next generation IoT services and IoT-Clod infrastrctres. Or contribtions can be smmarized as follows: We introdce a flexible mathematical model for IoT- Clod networks that characterizes the capacity, efficiency, and reliability of sensing, compting, and transmission resorces across end device, access, and clod layers. Bilding on the clod service model introdced in [16], we characterize a generic IoT service via a directed rooted graph that encodes the relationship between the service fnctions that act on the sorce information flows to create the final agmented information that needs to be delivered to the end sers. We formally define the IoT service distribtion problem (IoT-CSDP) as the problem of finding the placement of IoT service fnctions and roting of network flows across the IoT-Clod infrastrctre that satisfy end ser demands while minimizing overall operational cost. We formlate the IoT-CSDP as a minimm cost mixed-cast network information flow problem on a properly agmented graph that allows captring nicast and mlticast flows, and admits optimal polynomial-time soltions. We evalate the soltion to the IoT-CSDP in an illstrative set of next generation smart environments that reqire the joint orchestration of device, access, and clod layers, and show the impact of the most relevant system parameters. Or reslts show that the proposed optimization model and associated flow-based soltion enables flly exploiting the flexibility of IoT-Clod networks to efficiently host, manage, and optimize a wide range of IoT services in the context of next generation smart environments. When compared to conventional centralized clod approaches, or soltion achieves p to 80% overall power consmption redctions while garanteeing mch more stringent end-to-end latency constraints. The remainder of this paper is organized as follows: Section II reviews related work. Section III presents the IoT- Clod network paradigm. Section IV describes the system model. Section V introdces the IoT-CSDP and its flowbased formlation. Section VI presents simlation reslts from the soltion to the IoT-CSDP in the context of illstrative smart environments: smart cities, smart bildings, and smart transportation systems. Finally, Section VII smmarizes the paper and presents the main conclsions. II. RELATED WORK In the context of integrating end devices into the Clod, special attention has been given to wireless sensors networks (WSNs) de to their relevance in the IoT [18]. In [19], the athors present a theoretical model for -Clod architectres and stdy the performance improvements that can be obtained over traditional WSN architectres. In [20], the athors describe a -Clod architectre that makes se of the Contiki Operating System to provide end sers with access to the data acqired by different heterogeneos sensing infrastrctres. In [21], the athors propose WSN-SOrA, a service oriented architectre that orchestrates service provisioning for embedded networked systems in large scale WSNs. In [22], the athors propose a sensory data processing framework that redces the amont of data forwarded to the Clod by processing data at the gateway layer sing monitoring, filtering, prediction, compression, and recommendation techniqes. In [23], the athors address sensor data seflness and WSN reliability by proposing a WSN-Clod integration scheme in which WSN gateways selectively transmit information to the Clod based on the time and priority featres of the sers reqested data, and wireless sensors execte a priority-based sleep schedling algorithm to redce their power consmption. In [24], a trst and reptation calclation and management system with athentication is proposed to address some of the secrity concerns that arise when integrating WSNs into the Clod. In [25], the athors propose a position-based sleepschedling mechanism that redces the active time of dtycycled sensors in order to enhance the lifetime of wireless sensors connected to the Clod. This problem is also addressed in [26], where the athors propose a management framework to control the dty cycle of sensors in IoT environments that may be performing mltiple tasks with different qality-ofinformation reqirements. In addition to wireless sensors, the efficient integration of smart devices, sch as smartphones and connected vehicles, has also attracted the attention of the research commnity. The athors in [27] analyze the se of smartphones as shared edge compting devices, taking advantage of their increasing processing capabilities. In [28], software defined networking (SDN) and edge compting technologies are combined to enable programmable vehiclar ad hoc networks (VANETs). The work in [29] presents a model for fog compting architectres with edge or fog nodes forming an intermediate layer between device and clod layers. The athors show how the fog layer enables relevant energy savings when spporting IoT applications, bt do not provide any optimization strategy to distribte the service fnctions over the entire infrastrctre. In [30], the athors develop a federation strategy among IoT devices and clods so they can share their compting resorces in order to maximize the total nmber of exected tasks. In [31], the athors present an IoT resorce management system in which micro data centers co-located with network gateways at the edge of the network perform billing, pricing, and resorce reservation for IoT services. They also propose a probabilistic resorce estimation model that takes into accont the flctating connectivity behavior of IoT devices [32]. While existent literatre provides a significant amont of stdies describing alternative models and architectres that illstrate the advantages, limitations, and challenges associated with IoT-Clod networks, to the best of or knowledge, this is the first work that mathematically formalizes the problem of optimal distribtion of generic IoT services over IoT-Clod networks, taking into accont the heterogeneos natre of sensing, transmission, and compting resorces across the physical infrastrctre, as well as the niqe fnction interrelationships, mixed-cast flow natre, and tight QoS reqirements of IoT services.

3 3 III. IOT-CLOUD NETWORKS IoT-Clod networks reslt from the convergence of distribted clod networks and the IoT. These can take advantage of the biqitos sensing capabilities of the IoT and the virtally nlimited compting resorces of the Clod to offer a new class of services that create agmented information from the clod-based analysis of IoT-based data. In IoT-Clod networks, sensors, smartphones, connected vehicles, are not simple endpoints, bt smart sensing, storing, and compting resorces that can be jointly orchestrated with the rest of the clod infrastrctre [33]. In this new paradigm, and thanks to recent advances in network fnctions virtalization (NFV) and software defined networking (SDN) technologies [34], service fnctionality can be dynamically allocated across the reslting highly distribted platform and flows can be roted throgh the appropriate service fnctions in order to maximize end devices battery life, optimize service performance, and minimize overall operational cost. The main advantages of IoT-Clod networks with respect to traditional centralized clod approaches are: Low latency: service fnctions can be placed at the edge of the network in close proximity to the end sers to spport real-time services. High reliability: service fnctions can be replicated across a highly distribted platform for increased falt tolerance and disaster recovery. Redced operational cost: service fnctions that reqire large inpts or prodce large otpts can be placed close to their respective sorces and/or destinations for redced network load and associated operational cost. High flexibility: virtalization technologies allows sharing the heterogeneos physical infrastrctre among mltiple services that can elastically tap into a rich pool of resorces withot the need of dedicated deployments. Location awareness and mobility spport: the crrent location of mobile sers can be leveraged to provide personalized contextal services as well as service mobility. Scalability: the distribtion of compting and storage resorces close to the sorces of information allows scaling the nmber of connected devices and services withot satrating the transport network. IoT-Clod networks hence emerge as an ideal platform for the implementation of IoT services in the context of a wide range of smart environments, sch as smart grids, smart mobility, smart bildings, and smart cities. For instance, in smart grids, the data collected by smart meters can be preanalyzed at the edge of the network redcing the amont of data sent to clod data centers, saving both compting and transport resorces, and significantly redcing service latency. In smart mobility services, the analysis of data collected at the edge of the network can significantly increase responsiveness to sdden events, sch as vehicle collisions, improving the efficiency and safety of transportation networks. In smart bildings, while centralized clod resorces can be sed for complex data analysis, edge clod nodes are ideally sited for low-latency real-time physical system control and actation. Health-Care, Critical Infrastrctres Monitoring Clod Network Pblic Transport, Polltion, Traffic Monitoring Smart Devices Connected Vehicles Fig. 1: IoT-Clod network reslting from the integration of IoT devices into the clod network infrastrctre. IV. SYSTEM MODEL After describing the sitability of IoT-Clod networks for the delivery of IoT services in ftre smart environments, we now introdce the mathematical network and service models that will be sed to optimize the distribtion of IoT services over IoT-Clod networks. A. Network model We refer to an IoT-Clod network as the converged platform that reslts from the integration of programmable IoT devices into the clod infrastrctre (Fig. 1). We model an IoT- Clod network as a directed graph G = (V, E) with V vertices and E edges representing the set of network nodes and links, respectively. A node in V may represent an end device, an access point, or a clod node (at possibly different hierarchical layers). Each node is characterized by its energy resorces (e.g., power grid, battery), processing resorces (e.g., processor, microprocessor), and data acqisition or sensing resorces (e.g., camera, sensors, I/O interfaces). We denote by c pr and c sn the data processing and sensing capacities (in bits per second or bps) at node 2V, and by e pr and e sn the data processing and sensing nit energy costs (in Watts per bps) at node 2V, respectively. Nodes are interconnected via wireless or wireline links, each characterized by their transmission capacity and nit energy cost. We se c tr v and e tr v to denote the capacity (in bps), and the nit energy cost (in Watts per bps) of link (v, ) 2E, respectively. B. Service model We denote by O the set of information objects or flows that can be captred, processed or transported over the IoT- Clod network, and by P the set of virtal fnctions that can process information objects as part of an offered service. A

4 4 o1 p1 o2 p2 o3 p3 o5 r, d,, z f w f sn, d, o, z q d,o, z tr, d,o, z f v o4 Fig. 2: An example of a service graph, T =(A, O ), with O =5 objects, A =4edges, and P =3virtal fnctions. f pr o, d,o, z f pr i, d,o, z pr given information object o 2Ois, in general, the otpt of a fnction p o 2Pthat reqires the set of objects Z(o) as inpt. For example, in a mobility assistance service, the final object delivered to the end ser may be personalized mobility information reslting from the analysis of data collected by mltiple sensing devices (e.g., GPS devices, wireless sensors, cameras). We represent a generic IoT-Clod service by a directed rooted graph T =(A, O ). For any node o 2O, there is a set of incoming edges {(z,o) 2A : z 2Z(o)}, as shown in Fig. 2. Hence, the set of objects Z(o) Oreqired to generate object o via fnction p o are represented as the children of o in the service graph T. In particlar, the root of the service graph r 2Orepresents the final information object that needs to be delivered to the end ser(s). The service graph hence encodes the relationship between the virtal fnctions that act on the sorce objects to create the final object that needs to be delivered to the end sers. When a ser reqests service, the ser is, in essence, reqesting the final information object or flow represented by the root of the service graph r. In terms of QoS, we se H d,o to denote the maximm delay allowed for the delivery of content object o 2Oat destination d 2V, where we denote by h v and r v the transport delay (in seconds) and reliability (in terms of packet delivery ratio) associated with link (v, ) 2E, respectively, and by h the processing delay at clod node 2V. In addition, we se E (in Watts) to denote the maximm power consmption allowed at battery-powered node 2V in order to garantee its minimm lifetime reqirements. C. Flow model We model the sensing, processing, and transmission of information across the IoT-Clod network in terms of the following ser-object and global flows: User-object flows: are characterized by a triplet (d, o, z), which indicates that the given flow is carrying information of object z 2Osed to deliver final prodct o 2O at destination d 2 V. In particlar, fv tr,d,o,z, fv sn,d,o,z, f pri,d,o,z and f pro,d,o,z indicate the fraction of object z carried/captred/processed by edge (v, ) 2Eor node 2Vfor final prodct o 2Oat destination d 2V, respectively. Note that we differentiate between f pri,d,o,z and f pro,d,o,z, which denote the inpt and otpt flows of the processing nit at node 2V associated with triplet (d, o, z). Fig. 3 illstrates the network flows associated Fig. 3: Inpt/otpt ser-object flows at node 2V. with a given triplet (d, o, z) at node 2V, where pr represents the processing nit that hosts virtal fnctions and q d,o,z is a binary demand parameter that indicates if node 2Vreqests object z 2O. Note that q d,o,z =0if 6= d or z 6= o, since sers only reqest final information objects for themselves. Global flows: fv, tr f sn and f pr determine the total amont of information flow carried, captred, and processed at a given physical link or node, respectively. V. THE INTERNET OF THINGS -CLOUD SERVICE DISTRIBUTION PROBLEM (IOT-CSDP) The IoT-CSDP bilds on the linear CSDP [16] and extends it via the characterization of the access network and the device layer. In particlar, the IoT-CSDP complements the CSDP as follows: The sensing or data acqisition capabilities of end devices, sch as sensors, video cameras or RFID tags, are considered. The capacities and energy costs of processing and transmission resorces are modeled across the entire heterogeneos IoT-Clod network, inclding core, metro, access, and end devices. The limited energy resorces of battery powered devices are taken into accont in order to garantee their minimm lifetime reqirements. The reliability of links is considered in order to characterize the possible packet losses and associated retransmissions, particlarly relevant in low power wireless links. The end-to-end latency is modeled by considering the delay contribtions along the entire service path, from the nodes that generate the sorce data to the delivery of the final agmented information to the end sers. The IoT-CSDP captres the niqe natre of IoT services, typically characterized by a mlticast pstream phase in which sensed data that can be sed for mltiple services and end sers is ploaded to edge clod nodes, and a typically nicast downstream phase in which specific information reslting from the processing of sensed data is delivered in a personalized manner to the end sers. Given that the clod network infrastrctre is shared among mltiple services, the IoT-CSDP assmes a load-

5 5 proportional cost (e.g., energy) model, in which the cost of a given service is proportional to its se of the physical infrastrctre. This reslts in a significantly redced complexity linear program that enables faster reactions to variations of sers service demands. A. Mathematical Formlation The IoT-CSDP is formlated as a minimm cost mixed-cast flow problem as follows: 1) Objective Fnction: We define a generic linear cost fnction characterized by the energy costs of the IoT-Clod network resorces: minimize f tr,f sn,f pr X (v,)2e e tr vfv tr + X (e sn f sn + e pr f pr ) (1) 2V 2) Generalized Flow Conservation Constraints: Userobject flows mst satisfy demand and flow conservation. By modeling the demand q d,o,z as part of the otgoing flow of node, we can se the following generalized flow conservation constraints: f sn,d,o,z q d,o,z + f pri,d,o,z + X + f pro,d,o,z + X v2n () w2n + () f tr,d,o,z w = f tr,d,o,z v 8, d, o, z. (2) Flow conservation constraints state that the otgoing flow associated with a given triplet (d, o, z) mst be eqal to the incoming flow for that same triplet, for any node 2V. As illstrated in Fig. 3, the otgoing flow is composed of the otgoing transport flows, the processing flow leaving node towards the processing nit, and the demand flow; while the incoming flow is composed of the incoming transport flows, the captring flow, and the processing flow going ot of the processing nit. In addition, each processing nit mst satisfy the following flow conservation constraints: f pro,d,o,z apple f pri,d,o,y 8, d, o, z, y 2Z(z). (3) These make sre that in order to have a processed flow z for demand (d, o), a flow associated with each of the inpt objects reqired to generate z, y 2Z(z), for demand (d, o), mst be present at the inpt of the processing nit. 3) Fnction Availability Constraints: These constraints allow restricting the set of virtal fnctions that can be implemented at a given node: f pro,d,o,z =0 8, d, o, z, p z /2P, (4) where P P is the set of virtal fnctions available at node 2V. 4) Sorce Constraints: These constraints initialize the availability of sorce/sensed/captred information objects at their respective sorces: f sn,d,o,z =0 8, d, o, z /2 O, (5) where O S denotes the set of objects that are sorced/captred at node, with S O denoting the set of available sorce information objects. In addition, we need to make sre that sorce objects S Oare not created in the network: f pro,d,o,z =0 8, d, o, z 2S. (6) 5) Mixed-cast Constraints: Mixed-cast constraints allow modeling the nicast or mlticast natre of flows via the corresponding relationship between ser-object and global flows. Since a single captred object can be sed to satisfy mltiple demands, captred flows are said to be mlticast. Hence, the ser-object that captre flows for the same object, bt for different destinations, are allowed to overlap: f sn,d,o,z X z2o apple f sn,z 8, d, o, z, (7) f sn,z B z = f sn 8, (8) where B z denotes the bitrate of object z, and therefore f sn is defined in bps. On the other hand, transport and processing flows can either be nicast or mlticast. In particlar, for IoT services, the transport of captred objects that may be sefl to create mltiple final objects for mltiple sers are modeled as mlticast flows. The mlticast or nicast natre of transport flows that carry processed objects may, however, be modeled as nicast flows if the given processed object is personalized for a specific destination. Processing flows follow the same reasoning and are hence modeled as nicast if the processed flow is destined for a niqe destination, and as mlticast, otherwise. Accordingly, we se the following constraints for mlticast transport and processing flows: fv tr,d,o,z X z2o f pro,d,o,z X z2o apple f tr,z v 8, d, o, z, (9) fv tr,z B z = fv tr0 8, (10) apple f pr,z 8, d, o, z, (11) f pr,z B z z = f pr 8. (12) Note that ser-object flows are sized by the rate in bps of the object, B z. In addition, otpt processing flows are scaled by a factor z that captres possible relative flow size changes associated with the generation of object z from its inpt Z(z) via fnction p z. As a reslt, fv tr0 and f pr are defined in bps. When transporting or processing personalized objects destined for a specific destination, the corresponding transport and processing flows are modeled as nicast. In this case, serobject flows cannot overlap and mst be added across both objects and destinations: X X X fv tr,d,o,z B z = fv tr0 8(v, ), (13) d2v o2o z2o X X X d2v o2o z2o f pro,d,o,z B z z = f pr 8. (14) Note that at this point, transport global flows are denoted by fv tr0, since the actal total transport flows need to accont for possible retransmissions in the presence of nreliable links, as shown in eqation (19) in the following sbsection.

6 6 6) QoS Constraints: Latency: We se d,o,z to denote the (local) delay associated with a particlar ser-object flow (d, o, z). This is compted as the weighted sm of the delay associated with the transport and processing of (d, o, z) flows. That is, 8d, o, z: d,o,z = X (v,)2e f tr,d,o,z v h v + X 2V f pro,d,o,z h, (15) where h v and h (seconds) are the average transport delay at link (v, ) and processing delay at node, respectively. The h v vales inclde the qeing time that each object spends at node v before being forwarded, as well as the propagation time of link (v, ). The h vales depend on the processing capacity of node. Note that in (15), withot loss of generality, we assme inpt processing flows to have zero delay and captre all processing delay with the otpt processing flows, ag as described in [16]. We then se d,o,z to denote the aggregate delay associated with ser-object flow (d, o, z), compted as the sm of the local delay, d,o,z, pls the maximm across the aggregate delays of all of its inpt flows, 8y 2Z(z), as: ag d,o,y ag d,o,z = d,o,z 8d, o, z 2S, (16) d,o,z + ag d,o,y apple ag d,o,z 8d, o, z, y 2Z(z). (17) Finally, the aggregate delay associated with the delivery of final object o at destination d is constrained according to the specified maximm latency reqirements H d,o (in seconds): ag d,o,o apple H d,o 8d, o. (18) Reliability: The reliability of links has a relevant impact on the traffic flow de to the retransmissions cased by packet losses, particlarly in low power wireless links. The average nmber of retransmissions reqired in link (v, ) 2 E is modeled as the reciprocal of its packet delivery ratio: (,w)2e f tr v = f tr0 v /r v 8(v, ). (19) The vale of the packet delivery ratio, r v, is between zero (i.e., disconnected nodes) and 1 (i.e., ideal link), and therefore it increases the actal transport flow. Battery Lifetime: In order to garantee battery-powered devices lifetime reqirements, the total power consmption at node 2 V, considering transport, processing and sensing, mst be lower than its maximm power consmption E (in Watts). Then: X fwe tr tx + X fve tr rx + (v,)2e f sn e sn + f pr e pr apple E 8, (20) where e tx and e rx denote the transmission and reception cost (in Watts per bps), respectively. 7) Capacity Constraints: Global flows mst satisfy capacity constraints: fv tr apple c tr v 8(v, ), (21) apple c sn f sn f pr 8, (22) apple c pr 8. (23) 8) Integer/Fractional Flow Constraints: User-object flows can either be binary or fractional. That is, 8(v, ),,d,o,z: f tr,d,o,z v or f tr,d,o,z v,f pri,d,o,z,f pri,d,o,z,f pro,d,o,z,f sn,d,o,z 2{0, 1}, (24),f pro,d,o,z,f sn,d,o,z 2 [0, 1]. (25) The se of fractional flow variables allows splitting service flows over mltiple paths, providing added flexibility to optimize the se of IoT-Clod resorces. In addition, the reslting linear program admits optimal polynomial time soltions. However, the se of fractional flows challenges the precise comptation of end-to-end latencies in mesh network topologies [35]. In the next section, we choose to solve the IoT-CSDP with binary flow variables in order to precisely model end-to-end delay in mesh network topologies. While the complexity of the reslting integer linear program increases exponentially with the nmber of binary variables, the IoT- CSDP can be efficiently solved to within a few mintes in all tested scenarios. We remark that the soltion to the IoT-CSDP is centralized, i.e., it reqires collecting global information abot service demands and network resorces at a centralized network controller, and disseminating the soltion to all network nodes. In this work, as in [35], we assme that service demands and network resorces change at a longer time-scale than the time needed to implement a new IoT-Clod configration, leaving aspects related to distribted implementations of or soltion to handle faster dynamics as part of ftre work. VI. EVALUATION In this section, we present reslts from the soltion to the IoT-CSDP for illstrative IoT services in smart environments, obtained via the linear programming solver Xpress-MP. We analyze and compare the efficiency of the IoT-Clod soltion, which finds the optimal location of IoT service fnctions exploiting the fll flexibility of the IoT-Clod infrastrctre with: i) a conventional clod approach, in which all service fnctions are centralized at the highest clod layer, ii) the recently proposed clodlet or fog approach [36], in which the processing of IoT services is handled by micro clods located one hop away from the end devices, and iii) a flly distribted approach, in which all service fnctions are exected at the end devices. A. Simlation Details We consider a hierarchical IoT-Clod network architectre composed of three main layers: i) a clod layer, in which clod nodes are organized into 3 tiers, i.e., a head office (HO) node representing the largest centralized data center, intermediate offices (IOs), and end offices (EOs), ii) an

7 7 access layer, composed of base station (BS) nodes hosting micro-clods (MCs) or clodlets [9], and iii) a device layer, containing wireless sensors, smart devices (e.g., smartphones, tablets, smart glasses), and connected vehicles. The specific configration of the IoT-Clod network will be described for each of the simlated scenarios. Precisely modeling the processing capacity (in MIPS) and energy efficiency (in MIPS/W) of the IoT-Clod nodes is difficlt de to the limited information disclosed by clod and network operators. In this paper, we compte approximate vales sing information extracted from [37] (for clod eqipment) and [38] (for wireless sensors/actators), which are presented in Table I. Note that de to resorce consolidation and mltiplexing gains, the efficiency of clod nodes increases at higher layers. Regarding the efficiency of smart devices, given their heterogeneity, we consider a representative range of vales that go from 50 to 1000 MIPS/W [39]. We assme that connected vehicles are eqipped with similar processors [40]. Finally, note that the relatively high processing efficiency of sensors/actators is de to the se of low-power microprocessors. However, they are very limited in terms of battery and compting capacity. In terms of commnication technologies, clod nodes and base stations are connected via optical links, smart devices se 4G or WiFi, and wireless sensors commnicate sing the ZigBee protocol. In order to model ZigBee links, which are particlarly lossy de to their low transmission power, we assme that these are time-varying and follow the logdistance path loss model described in [41] for rban micro-cell scenarios. Then, the path losses (PL) are modeled as follows: PL(dB) =PL 0 (db) log(d) + 3 log(f c /5) + X, (26) where PL 0 is the path loss at the reference distance d 0 (1m), is the path loss exponent, d is the commnication distance, f c is the system freqency (in GHz) and X N 0, 2 is zero mean Gassian noise that models the shadowing effects. This model assmes =2.8 and =4 db [41]. Moreover, a collisionfree MAC layer is assmed to obtain reslts independent from the specific MAC mechanism [42]. Taking into accont the general characteristics of ZigBee transceivers, we assme f c =2.4 GHz, PL 0 (db)=35 db [43], a transmit power of 3 dbm, a sensitivity vale of -91 dbm [44] and omnidirectional antennas. We also consider that the packets generated at wireless sensors have a length of 127 bytes (i.e., standard packet size in IEEE ). On the other hand, we assme that the channel losses in optical, 4G, and WiFi links do not have a relevant impact on their reliability, compared to ZigBee links [41]. The average transport delay vales considered in the simlations inclde the effect of both qeing and propagation delays. While the first is generally higher for the wireless links at the device and access layers de to their limited transmission capacities, the second is typically higher for the optical links connecting clod nodes de to the longer distances among them. Taking this into accont, we assme an average delay of 5 ms for wireless links, while the delay of optical links TABLE I: Typical capacities and efficiencies of IoT-Clod network resorces Capacity Efficiency Clod Node (HO) 53.5 Million MIPS 500 MIPS/W Clod Node (IO) 26 Million MIPS 200 MIPS/W Clod Node (EO) 13 Million MIPS 133 MIPS/W Micro Clod Node (MC) 6.5 Million MIPS 100 MIPS/W Wireless /Actator 1 MIPS 480 MIPS/W Smart Device 2000 MIPS MIPS/W Connected Vehicle 2000 MIPS 1000 MIPS/W Optical Link 4480 Gbps 12.6 nj/bit 4G Link (Down/Up) 72/12 Mbps 76.2/19 µj/bit WiFi Link 150 Mbps 300 nj/bit ZigBee Link 250 kbps 100 nj/bit depends on the hierarchy level of the clod node. In particlar, we assme that a packet needs an additional 2 ms to reach an end office, 7 ms to reach an intermediate office, and 15 ms to reach a head office. The processing delay mainly depends on the particlar processing capabilities of each node. In the simlation reslts, we assme that wireless nodes have an average processing delay of 4 ms, while clod nodes, which can process larger amonts of bits simltaneosly, have a processing delay of 1 ms. In the following, we compte the soltion to the IoT-CSDP in a nmber of representative smart environments (i.e., smart city, smart bilding, and smart mobility) to evalate the impact of different system parameters in the IoT-Clod soltion, sch as transmission and processing efficiencies, nmber of sers, and latency reqirements. B. Smart City We consider the smart city architectre illstrated in Fig. 4a, in which end sers reqest information sing their smart devices. Since smart cities may provide different types of IoT services, we solve the IoT-CSDP for two relevant services: agmented reality and city monitoring. 1) Agmented Reality Service: We consider an agmented reality service in which sers consme video streams that reslt from the combination of the real time video stream captred by the camera of their own smart device, contextal information downloaded from the Internet, and data from the city collected by the WSNs. In particlar, we assme that the agmented video reslts from the combination of 200 kbps of sorce video from the ser device, 50 kbps of city information, and 1 packet/s from each wireless sensor. In terms of processing complexity, we assme that 2000 instrctions per bit are reqired to generate the agmented video. In order to take into accont crrent network access technologies, we assme that half of the sers access the network sing 4G and the other half se a WiFi connexion. Note that both captred and agmented video mst be sent via nicast, bt the contextal information coming from the internet and the wireless sensors can be sent via mlticast, as the same data can be sed for mltiple destinations. The service graph for this application is shown in Fig. 4b. In Fig. 5, we show the average power consmption for different smart device processing efficiencies. The simlation reslts show that the IoT-Clod soltion adapts the offloading

8 8 25 Device Layer Access Layer Clod Layer Smart Devices HO IO IO EO EO EO WSN 1 WSN 2 WSN 3 Virtalized Sensing Platform Average Power Consmption per User (W) Flly Distribted Clodlet Centralized IoT-Clod Processing Efficiency (MIPS/W) Fig. 5: Average power consmption per ser of the agmented reality application for different smart device processing efficiencies. (a) Smart city architectre. The device layer is composed of smart devices, which provide live video streams and reqest agmented videos, and 3 WSNs collecting environmental information arond the city. Measrements Real-Time Video City Information Agmented Video (b) Service graph of the agmented reality application. This combines measrements from wireless sensors, live video streams from the smart devices and city information coming from the HO. Solid and dashed arrows indicate mlticast and nicast flows, respectively. Measrements (WSN1) Measrements (WSN2) Measrements (WSN3) Aggregated Measrements City Information A City Information B City Information C (c) Service graph of the city monitoring application. The city information is obtained throgh the analysis of the aggregated measrements collected by 3 different WSNs. readings are allowed to be sent via mlticast, bt the city information can only be sent via mlticast for mltiple sers reqesting the same information simltaneosly. Fig. 4: Smart city environment decisions according to the processing efficiency of end devices. Specifically, note that if the device processing efficiency is sfficiently high, i.e., 500 MIPS/W, the IoT-Clod soltion allocates the agmented video processing fnction at the end devices. Otherwise, some processing tasks have to be offloaded to the clod in order to redce the overall processing cost. We show that for efficiencies between 50 and 300 MIPS/W the IoT-Clod ses hybrid soltions, in which the placement strategy is not niform for all sers. In these cases, the processing tasks of WiFi sers are offloaded to the Clod, while the network access cost of 4G devices keeps their video processing fnctionality at the end devices. We can also observe that the clodlet soltion yields an overall power consmption that is even higher than that of the centralized approach. This is becase the micro clod nodes have lower processing efficiencies than the larger clod data centers, and this does not get compensated by the savings in terms of transport cost. Note that we consider the se of highly efficient optical fiber links throghot the clod layer. 2) City Monitoring Service: Thanks to the deployment of large scale wireless sensor networks, smart cities can provide real-time information abot the city and its infrastrctres. In this section, we consider a city monitoring service in which end sers reqest city information that is generated from the analysis of data collected by wireless sensors, sch as in [45]. The service graph of this application is shown in Fig. 4c. We evalate the impact of the city information bitrate and the nmber of simltaneos sers reqesting the same information. In Fig. 6, we compare the power consmption for different ser demands in terms of bitrate. A high bitrate means that the ser reqests detailed information, while low bitrates indicate that the service only provides the most relevant information. We assme that end sers reqest City Information B (See Fig. 4c), which receives the aggregated measrements of WSN 1, WSN 2 and WSN 3 as inpt. We assme smart devices have a processing efficiency of 1000 MIPS/W. The processing complexity of analyzing the data from each WSN is 500 instrctions per bit, while the complexity of generating

9 9 Average Power Consmption per User (W) Flly Distribted Clodlet Centralized IoT-Clod Personalized Information Rate (kbps) Fig. 6: Average power consmption per ser of a city monitoring application for different personalized information rates. Average Power Consmption (W) Flly Distribted Clodlet Centralized IoT-Clod Simltaneos Wifi Users Flly Distribted Clodlet Centralized IoT-Clod (a) Simltaneos WiFi sers the personalized information reqested by the ser is 5000 instrctions per bit. An average rate of 1 packet per second is generated by each wireless sensor. We consider the same nmber of WiFi and 4G sers, and an average nmber of 4 sers simltaneosly reqesting the same information in total. Note that the sensor readings can be sent via mlticast, since they can be sed to generate different final information objects. However, personalized information can be sent via mlticast only when mltiple sers reqest the same information at the same time. Otherwise, these mst be sent via nicast. Nevertheless, we assme that the transmission from the base stations to the end sers is always nicast, as neither WiFi nor 4G mlticast technologies are yet commercially available. As we can observe, if sers reqest a low bitrate the IoT-Clod soltion tends to consolidate the processing fnctionalities at the HO in order to redce the total processing cost. As a reslt, the overall power consmption is redced by more than 84% compared to the flly distribted soltion. However, the centralized clod approach becomes less efficient at high bitrates de to the transport cost from the clod nodes to the smart devices, particlarly to the 4G devices. Then, the IoT- Clod soltion pshes some processing fnctionalities closer to the sers in order to redce transport costs, at the expense of redcing the consolidation gain. In particlar, the IoT-Clod soltion moves the fnction that generates the personalized data to the end devices for bitrates above 20 kbps, while keeping the sensor data analysis at the clod layer. We can observe that the overall power consmption of the centralized and clodlet soltions are severely affected by the reqested bitrate, being their cost more than 78% higher than the cost of the IoT-Clod soltion if the bitrate exceeds 200 kbps. In Fig. 7, we compare the power consmption for different average nmber of sers reqesting simltaneosly the same information. In this case, we separate the power consmption of WiFi and 4G sers to observe the impact of simltaneos sers in each case. The bitrate of the city information is assmed to be 200 kbps and the processing efficiency of the smart devices is 1000 MIPS/W. In Fig. 7a, we observe that if there are more than 2 simltaneos WiFi sers, it is Average Power Consmption (W) Simltaneos 4G Users (b) Simltaneos 4G sers Fig. 7: Average power consmption per ser of a city monitoring service for different nmber of simltaneos sers. more energy efficient to consolidate the processing tasks at the HO than to process them locally, even if this increases the total transport cost. Note that if the information reqested is generated at the HO, the network can take more advantage of mlticast delivery. However, for sers accessing the network sing 4G (see Fig. 7b), the additional transport cost of 4G networks cannot be compensated even with 4 simltaneos 4G sers, being the overall cost of the centralized soltion 80% higher than that of the IoT-Clod soltion. Nevertheless, it is important to note that if wireless mlticast streaming schemes were considered, the impact of processing consolidation wold be more relevant in both cases. We again observe the higher power consmption of the clodlet soltion de to its low processing efficiency. This is particlarly relevant in Fig. 7a de to the higher significance of processing costs compared to transport costs in WiFi networks. We remark that the effect of the nmber of sers is specially relevant nder the presence of mlticast flows. Note that if all flows are nicast, then, nder the adopted linear cost model, the total power consmption wold be proportional to the total nmber of sers, since each transport/processing fnction needs to be condcted individally for each end ser.

10 10 C. Smart Bilding In this section, we consider an IoT service that controls a bilding atomation system. A WSN that collects 5 different types of measrements (e.g., hmidity, temperatre, light, smoke, occpancy) is assisted by the Clod to manage 5 actators distribted across the bilding (See Fig. 8a). According to the sensor readings, and after a simple data analysis, the actators manage different atonomos systems. In terms of processing complexity, both data aggregation and data analysis reqire 50 instrctions per bit. We also assme that the measrements from wireless sensors can be combined sing data aggregation techniqes as long as they contain the same type of information. This redces traffic by a compression rate that depends on the spatial and temporal correlation of data. A realistic compression rate of 50% [46] is assmed in these simlations. The sensor readings may be sent via mlticast, bt the actator decisions are sent via nicast. However, note that in this case the distribtion mode does not have any impact in the total traffic flow, since every object has only one possible destination, and hence there are no mlticast opportnities. The service graph associated with this service is shown in Fig.8b. In order to observe the impact of the limited battery capacity of wireless actators, we constrain their average power consmption according to their expected battery lifetime. Note that battery replacement is a costly operation and therefore nodes shold not deplete their batteries before their schedled maintenance. With this in mind, we only allow soltions that assre a minimm lifetime of one year for the wireless actators. Assming that each actator is eqipped with a battery storing 27 kj [47], this translates into a maximm power of E = 856µW. In Fig. 9, we compare the power consmption for different packet generation rates at the wireless sensors. A high bitrate increases the accracy of the service, bt it also increases the power consmption of the wireless sensors and actators. Note that when the average power consmption of a given soltion is not shown, it indicates that sch soltion is infeasible, i.e., it violates QoS or capacity constraints. In scenarios that reqire more than 2 packets/s from each sensor, the processing tasks are flly offloaded to the Clod to redce the power consmption of the wireless actators. However, if the packet rate can be redced, the limited processing capacity of actators can be sed to redce the traffic sent to the Clod. As we can observe, the device layer can control the actators withot the assistance of the clod infrastrctre as long as the packet rate is lower than 1.5 packets/s. As a reslt, the total transport consmption is redced. However, the processing tasks have to be offloaded to the Clod at higher rates in order to satisfy the minimm lifetime reqirements of the wireless actators. Therefore, in this case, the IoT-Clod soltion corresponds to the centralized soltion. It is worth noting that the optimized IoT-Clod soltion only ses the clod resorces when they are strictly necessary. Device Layer Access Layer Clod Layer IO HO EO EO EO IO Virtalized Network + Actator (a) Smart bilding architectre. The device layer is composed of wireless sensors and actators. The sensors collect the environmental measrements, while the actators control the bilding atomation system. Average Power Consmption (W) Measrements A Measrements B Measrements E Aggregated Measrements Actator A Decision Actator B Decision Actator E Decision (b) Service graph of the smart bilding application. The sensor readings are first aggregated, to redce the transport flow, and then analyzed to control the bilding atomation system sing the wireless actators. Solid and dashed arrows state for mlticast and nicast flows, respectively Fig. 8: Smart bilding environment Flly Distribted Clodlet Centralized IoT-Clod Packets/s Fig. 9: Average power consmption of an atonomos sensing actation service for different packet generation rates. Note that missing vales indicate that the soltion is infeasible.

11 Device Layer Access Layer Clod Layer IO HO EO EO EO (a) Smart traffic architectre. The device layer is composed by the connected vehicles, which provide life video streams to the traffic management system. IO Average Power Consmption (W) Flly Distribted Clodlet Centralized IoT-Clod Maximm Latency (ms) Fig. 11: Average power consmption of a traffic management service for different latency reqirements. Note that missing vales indicate that the soltion is infeasible. Video Streams from Vehicles Traffic Lights Measrements Combined Video Analyzed Measrements Traffic Decisions (b) Service graph of the smart mobility application. The measrements collected at the traffic lights are combined with the video streams of vehicles. Solid and dashed arrows indicate mlticast and nicast flows, respectively. D. Smart Mobility Fig. 10: Smart mobility environment In this section, we consider a service that interconnects city vehicles among each other, as well as with city traffic lights. This enables optimizing city traffic, redcing congestion, and increasing road safety. We assme the scenario in Fig. 10a, in which vehicles are connected to other nearby vehicles and to their closest base stations. In addition, each base station has 5 traffic lights connected to it, each generating 127 bytes per second of traffic information. The vehicles generate video streams of 200 kbps. The streams from nearby vehicles are combined and compressed sing a scaling factor of 0.5. The measrements from the traffic lights and the video streams are then jointly analyzed to predict traffic jams and manage traffic lights accordingly. In particlar, we consider the service graph shown in Fig. 10b. In terms of processing complexity, we assme that processing the traffic measrements reqires 50 instrctions per bit, the combination of the video streams 2000 instrctions per bit, and the generation of the traffic decisions reqires 5000 instrctions per bit. We frther assme that vehicles embedded compting resorces have a processing efficiency of 1000 MIPS/W. In Fig. 11, we show the average power consmption for different latency reqirements. Low end-to-end service latencies are specially relevant for smart mobility services in which processed information is sed to coordinate city traffic in real- time. It is important to remark that there exists a minimm end-to-end service latency dictated by the network topology and the adopted soltion approach. As shown in Fig. 10a, sorce streams need to be sent at least to the base stations (5 ms), which control the traffic lights, then forwarded to be jointly processed (i.e., at least 4 ms), and finally otpt decisions mst be sent back to the base stations (i.e., at least 2 ms) that control the traffic lights. Hence, the minimm achievable transport delay is 11 ms. In addition, we need to consider the processing delay, which is at least 2 ms if the processing tasks are placed at a clod location (i.e., 1 ms for each processing fnction). Note that this minimm endto-end latency can only be achieved with the IoT-Clod and clodlet soltions. A flly distribted soltion can only achieve a minimm latency of 28 ms (i.e., 20 ms for transport and 8 ms for processing). Finally, the centralized clod soltion has a minimm latency of 55 ms (i.e., 53 ms for transport and 2 ms for processing). This can be observed in Fig. 11, where maximm latency reqirements below 28 ms can only be satisfied by the optimized IoT-Clod soltion. It is worth noting that the IoT-Clod optimal soltion does not need to place the virtal fnctions at the access layer to achieve its minimm latency. Since the information processed at the micro clod nodes needs to go throgh the clod layer for sharing prposes, placing the virtal fnctions at the edge of the clod layer (EOs) redces the processing cost withot increasing the end-to-end latency. The reslts presented in this paper clearly show the benefits that arise from the flexibility of an optimized IoT- Clod soltion and the limitations of crrent centralized, flly distribted, and clodlet soltions for the efficient delivery of next generation IoT services. On one hand, centralizing all processing tasks at a single core-level data center is shown to be efficient in terms of processing costs, while incrring transport costs and end-to-end latencies that become nsstainable for increasingly bandwidth-intensive lowlatency IoT services. On the other hand, placing all processing

12 12 fnctionality at the device layer, while appropriate for low complexity operations, can seriosly compromise the desirable lightweight, long battery life properties of end devices. An intermediate soltion, sch as placing the processing tasks at the access layer, improves service latencies compared to a centralized soltion, bt it also redces overall processing efficiency de to the lower compting capabilities of micro clod nodes. In contrast, we have shown that an optimized IoT- Clod soltion overcomes these limitations by placing service fnctions at different layers of the IoT-Clod infrastrctre as a fnction of the inpt parameters, striking the right balance between processing efficiency, transport efficiency, and endto-end latency. VII. CONCLUSIONS The conflence of distribted clod networking and the Internet of Things (IoT) enables a new class of services that create agmented information from the clod analysis of IoT data. IoT-Clod networks redce the distance between end sers and clod resorces sing edge clod nodes distribted across the network, in order to spport the key low latency, mobility, and location-awareness reqirements of IoT services. We proposed a comprehensive optimization framework to enhance the efficiency of IoT services in next generation smart environments. We formlated the service distribtion problem (SDP) in IoT-Clod networks (IoT-CSDP) as a mincost mixed-cast flow problem. The soltion to the IoT-CSDP determines the optimal placement of service fnctions and the roting of network flows, taking into accont the heterogeneos capacities and efficiencies of sensing, compting, and transport resorces across the distribted IoT-Clod infrastrctre. We solved the IoT-CSDP for the optimization of IoT services in mltiple smart environments. Reslts show that the IoT-CSDP soltion captres the critical tradeoffs that appear in IoT-Clod platforms de to the heterogeneity of IoT services, clod network technologies, and end ser devices. When compared to crrent soltions, smart IoT services optimized over a flly virtalized IoT-Clod platform are shown to garantee stringent QoS reqirements in terms of reliability, battery lifetime, and end-to-end latency, while redcing overall power consmption by more than 80%. Motivated by these promising reslts, interesting directions for ftre work inclde implementing the proposed soltion in a clod compting testbed, designing approximation algorithms of improved comptational complexity, and stdying the benefit of distribted optimization techniqes to enable local reactions to fast system dynamics. VIII. ACKNOWLEDGEMENTS This work was spported in part by the Spanish Government nder project TEC R. REFERENCES [1] L. Atzori, A. Iera, and G. 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SatyaMrty, Estimating the energy consmption of wireless sensor node: Iris, International Jornal on Recent Trends in Engineering and Technology, vol. 3, no. 4, pp , May Marc Barcelo received his M.Sc. degree in electrical engineering (Hons.) and Ph.D. degree (cm lade) from Universitat Atonoma de Barcelona (UAB) in 2010 and 2015, respectively. From 2009 to 2015 he was with the UAB s Telecommnications and Systems Engineering Department participating in several international R&D projects, sch as ADIBEAM and SINTONIA. Dring 2014, he was an International Research Intern at Nokia Bell Labs, New Jersey, USA. He joined Ikerlan, Mondragon-Arrasate, Spain, in 2016, where he is crrently a Research Scientist in the Cybersecre IoT department. He has pblished over 20 papers in recognized international jornals and conferences. Crrently, his research efforts are focsed on the design of efficient commnication architectres for the Internet of Things. In 2011, he received the COIT/AEIT prize for his M.Sc. thesis. Alejandro Correa received his B.Sc and M.Sc. in Electrical Engineering (with honors) from Universitat Atonoma de Barcelona (UAB), in 2008 and 2010, respectively. Since 2011 he is a PhD candidate at the Telecommnications and Systems Engineering Department of UAB where he has been involved in several R&D projects, sch as ATLANTIDA and 3SENS. He has pblished in 8 international jornals and 7 international conferences. His main research interests inclde pedestrian indoor positioning systems and wireless sensor networks. Jaime Llorca received the B.E. degree in Electrical Engineering from the Universitat Politecnica de Catalnya (UPC), Barcelona, Spain, in 2001, and the M.S. and Ph.D. degrees in Electrical and Compter Engineering from the University of Maryland, College Park, MD, USA, in 2003 and 2008, respectively. He held a post-doctoral position at the Center for Networking of Infrastrctre s (CNIS), College Park, MD, USA, from 2008 to He joined Nokia Bell Labs at Holmdel, NJ, USA, in 2010, where he is crrently a Research Scientist in the Network Algorithms Department. His research interests inclde energy efficient networks, distribted clod networking, content distribtion, resorce allocation, network information theory, and network optimization. He is a recipient of the 2007 Best Paper Award at the IEEE International Conference on s, Networks and Information Processing (ISSNIP), the 2016 Best Paper Award at the IEEE International Conference on Commnications (ICC), and the 2015 Jimmy H.C. Lin Award for Innovation.

14 14 Antonia M. Tlino (S 00 M 03 SM 05 F 13) received the Ph.D. degree in Electrical Engineering from Seconda Universita degli Stdi di Napoli, Italy, in She held research positions at Princeton University, at the Center for Wireless Commnications, Ol, Finland and at Universita degli Stdi del Sannio, Benevento, Italy. From 2002 she has joined the Faclty of the Universita degli Stdi di Napoli Federico II, and in 2009 she joined Bell Labs. From 2011, Dr. Tlino is Member of the Editorial Board of the IEEE Transactions on Information Theory and in 2013, she was elevated to IEEE Fellow. She has received several paper awards and among the others the 2009 Stephen O. Rice Prize in the Field of Commnications Theory for the best paper pblished in the IEEE TRANSACTION ON COMMUNICATION in She has been principal investigator of several research projects sponsored by the Eropean Union and the Italian National Concil, and was selected by the National Academy of Engineering for the Frontiers of Engineering program in Her research interests lie in the area of commnication systems approached with the complementary tools provided by signal processing, information theory and random matrix theory. Prof. J.L. Vicario, received both the degree in electrical engineering and the Ph.D. degree (cm lade) from the Universitat Politecnica de Catalnya (UPC), Barcelona, in 2002 and 2006, respectively. He also holds an MBA from the IESE Bsiness School, Universidad de Navarra. Since September 2006, he is an Associate Professor at the Universitat Atonoma de Barcelona teaching corses in digital commnications and signal processing and participating in several R&D Projects sch as IST FP6 SATNEX, ESA DINGPOS, ESA ADIBEAM, CENIT ATLANTIDA, CENIT SINTONIA (as PI), PROFIT INTERRURAL (as PI), INFOREGIO XALOC (as PI), AVANZA DESSO (as PI) and RECERCAIXA 3SENS. He has a wide expertise on statistical signal processing, pblishing 24 papers in recognized international jornals and arond 50 papers in conferences. Crrently, his research efforts are focsed on the development of data mining algorithms covering different areas sch as wireless sensor networks, twitter analysis, financial applications and air traffic management. Antoni Morell received the M.Sc. degree in electrical and electronic engineering and the Ph.D. degree from Universitat Politecnica de Catalnya (UPC), Barcelona, in 2002 and 2008, respectively. He was with the Signal Theory and Commnications Department, UPC, from 2002 to He joined Universitat Atonoma de Barcelona in 2005, where he crrently holds an Associate Professor position. He teaches corses on commnications, signals and systems. He has been involved in over 10 R&D projects, national and international, being the Principal Investigator in two of them. He has expertise in optimization techniqes applied to commnications, also in the field of wireless sensor networks. He has pblished over 40 papers in recognized international jornals and conference proceedings and his crrent research interests inclde smart data applications of machine learning as well as inertial-aided indoor positioning and roting/access strategies for WSNs.